Scaling Performance Comparison: ScyllaDB Tablets vs Cassandra vNodes
Benchmarks show ScyllaDB tablet-based scaling 7.2× faster than Cassandra’s vNode-based scaling (9× with cleanup), sustaining ~3.5X higher throughput with fewer errors Real-world database deployments rarely experience steady traffic. Systems need sufficient headroom to absorb short bursts, perform maintenance safely, and survive unexpected spikes. At the same time, permanently sizing for peak load is wasteful. Elasticity lets you handle fluctuations without running an overprovisioned cluster. Increase capacity just-in-time when needed, then scale back as soon as the peak passes. When we built ScyllaDB just over a decade ago, it scaled fast enough for user needs at the time. However, deployments grew larger and nodes stored far more data per vCPU. Streaming took longer, especially on complex schemas that required heavy CPU work to serialize and deserialize data. The leaderless design forced operators to serialize topology changes, preventing parallel bootstraps or decommissions. And static (vNode-based) token assignments also meant data couldn’t be moved dynamically once a node was added. ScyllaDB’s recent move to tablet-based data distribution was designed to address those elasticity constraints. ScyllaDB now organizes data into independent tablets that dynamically split or merge as data grows or shrinks. Instead of being fixed to static ranges, tablets are load balanced transparently in the background to maintain optimal distribution. Clusters scale quickly with demand, so teams don’t need to overprovision ahead of time. If load increases, multiple nodes can be bootstrapped in parallel and start serving traffic almost immediately. Tablets rebalance in small increments, letting teams safely use up to ~90% of available storage. This means less wasted storage. The goal of this design is to make data movement more granular and reduce the serialized steps that constrained vNode-based scaling. To understand the impact of this design shift, we evaluated how both ScyllaDB (now using tablets) and Cassandra (still using vNodes) compare when they must increase capacity under active traffic. The goal was to observe scale-out under realistic conditions: workloads running, caches warm, and topology changes occurring mid-operation. By expanding both clusters step by step, we captured how quickly capacity came online, how much the running workload was affected, and how each system performed after each expansion. Before we go deeper into the details, here are the key findings from the tests: Bootstrap operations: ScyllaDB completed capacity expansion 7.2X faster than Cassandra Total scaling time: When including Cassandra’s required cleanup operations (which can be performed during maintenance windows), the time difference reaches 9X Throughput while scaling: ScyllaDB sustained ~3.5X more traffic during these scaling operations Stability under load: ScyllaDB had far fewer errors and timeouts during scaling, even at higher traffic levels Why Fast Scaling Matters Most real-world database deployments are overprovisioned to some extent. The extra capacity helps sustain traffic fluctuations and short-lived bursts. It also supports routine maintenance tasks, like applying security patches, rolling out infrastructure maintenance, or recovering from replica failures. Another important consideration in real-world deployments is that benchmark reports often overlook traffic variability over time. In practice, only a subset of workloads consistently demand high baseline throughput, with low variability from their peak needs. Most workloads follow a cyclical pattern, with daily peaks during active hours and significantly lower baseline traffic during off-hours. A diurnal workload example, ranging between 50K to 250K operations per second in a day Fast scaling is also critical for handling unexpected events, such as viral traffic spikes, flash loads, backlog drains after cascading failures, or sudden pressure from upstream systems. It’s especially valuable when traffic has large peak-to-baseline swings, capacity needs to shift often, responses to load must be quick, or costs depend on scaling back down immediately after a surge. Comparing Tablets vs vNodes Fast scaling is ultimately a data distribution problem, and Cassandra’s vNodes and ScyllaDB’s tablets handle that distribution in distinctly different ways. Here’s more detail on the differences we previewed earlier. Apache Cassandra Apache Cassandra follows a token ring architecture. When a node joins the cluster, it is assigned a number of tokens (the default is 16), each representing a portion of the token ring. The node becomes responsible for the data whose partition keys fall within its assigned token ranges. During node bootstrap, existing replicas stream the relevant data to the new replica based on its token ownership. Conversely, when a node is removed, the process is reversed. Cassandra generally recommends avoiding concurrent topology changes; in practice, many operators add/remove nodes serially to reduce risk during range movements. Digression: In reality, topology changes in an Apache Cassandra cluster are plain unsafe. We explained the reasons in a previous blog, and pointed out that even its community acknowledged some of its design flaws. In addition to the administrative overhead involved in scaling a Cassandra cluster, there are other considerations. Adding nodes with higher CPU and memory is not straightforward. It typically requires a new tuning round and manually assigning a higher weight (increasing the number of tokens) to better match capacity. After bootstrap operations, Cassandra requires an intermediary step (cleanup) for older replicas in order to free up disk space and eliminate the risk of data resurrection. Lastly, multiple scaling rounds introduce significant streaming overhead since data is continuously shuffled across the cluster. Cassandra Token Ring ScyllaDB ScyllaDB introduced tablets starting with the 2024.2 release. Tablets are the smallest unit of replication in ScyllaDB and can be migrated independently across the cluster. Each table is dynamically split into tablets based on its size, with each tablet being assigned to a subset of replicas. In effect, tablets are smaller, manageable fragments of a table. As the topology evolves, tablet state transitions are triggered. A global load balancer balances tablets across the cluster, accounting for heterogeneity in node capacity (e.g., assigning more tablets to replicas with greater resources). Under the hood, Raft provides the underlying consensus mechanism that serializes tablet transitions in a way that avoids conflicting topology changes and ensures correctness. The load balancer is hosted on a single node, but not a designated node. If that node crashes or goes down for maintenance, the load balancer will start on another node. Raft and tablets effectively decouple topology changes from streaming operations. Users can orchestrate topology changes in parallel with minimal administrative overhead. ScyllaDB does not require a post-bootstrap cleanup phase. That allows for immediate request serving and more efficient data movement across the network. Visual representation of tablets state transitions Adding Nodes Starting with a 3-node cluster, we ran our “real-life” mixed workload targeting 70% of each database’s inferred total capacity. Before any scaling activity, both ScyllaDB and Cassandra were warmed up to ensure disk and cache activity were in effect. Note: Configuration details are provided in the Appendix. We then started the mixed workload and let it run for another 30 minutes to establish a performance baseline. At this point, we bootstrapped 3 additional nodes, expanding the cluster to 6 nodes. We then allowed the workload to run for an additional 30 minutes to observe the effects of this first scaling step. We increased traffic proportionally. After sustaining it for another 30 minutes, we bootstrapped 3 more nodes, bringing each cluster to a total of 9 nodes. Finally, we increased traffic one last time to ensure each database could sustain its anticipated traffic. Note: See the Appendix for details on the test setup and our Cassandra tuning work. The following table shows the target throughput used during and after each scaling step along with each cluster’s inferred maximum capacity: Nodes ScyllaDB Cassandra 3 (baseline) 196K ops/sec (Max 280K) 56K ops/sec (Max 80K) 6 392K ops/sec (Max 560K) 112K ops/sec (Max 160K) 9 672K ops/sec (Max 840K) 168K ops/sec (Max 240K) We conducted this scaling exercise twice for each database, introducing a minor variation in each run. For ScyllaDB, we bootstrapped all 6 additional nodes in parallel. For Cassandra, we enabled both the Key Cache and Row Cache, as we observed it performed better overall under our initial performance results. Comparison of different scaling approaches At first glance, it might look like ScyllaDB offers only a modest improvement over Cassandra (somewhere between 1.25X and 3.6X faster). But there are deeper nuances to consider. Resiliency In both of our Cassandra benchmarks, we observed a high rate of errors, including frequent timeouts and OverloadedExceptions reported by the server. Notably, our client was configured with an exponential backoff, allowing up to 10 retries per operation. In this environment, both Cassandra configurations showed elevated error rates under sustained load during scaling. The following table summarizes the number of errors observed by the client during the tests: Kind Step Throughput Retries Cassandra 5.0 – Page Cache 3 → 6 nodes 56K ops/sec 2010 Cassandra 5.0 – Page Cache 6 → 9 nodes 112K ops/sec 0 Cassandra 5.0 – Row & Key Cache 3 → 6 nodes 56K ops/sec 5004 Cassandra 5.0 – Row & Key Cache 6 → 9 nodes 112K ops/sec 8779 With the sole exception of scaling from 6 to 9 nodes in the Page Cache scenario, all other Cassandra scaling exercises resulted in noticeable traffic disruption, even while handling 3.5X less traffic than ScyllaDB. In particular, the “Row & Key Cache” configuration proved itself unable to sustain prolonged traffic, ultimately forcing us to terminate that test prematurely. Performance The earlier comparison chart also highlights the cost of repeated streaming across incremental expansion steps. Although bootstrap duration is governed by the volume of data being streamed and decreases as more nodes are added, each scaling operation redundantly re-streams data that was already redistributed in prior steps. This introduces significant overhead, compounding both the time and performance of scaling operations. As demonstrated, scaling directly from 3 to 9 nodes using ScyllaDB tablets eliminates the intermediary incremental redistribution overhead. By avoiding redundant streaming at each intermediate step, the system performs a single, targeted redistribution of tablets, resulting in a significantly faster and more efficient bootstrap process. ScyllaDB tablet streaming from 3 to 9 nodes After the scale out operations completed, we ran the following load tests to assess each database’s ability to withstand increased traffic: For ScyllaDB, we increased traffic to 80% of its peak capacity (280 * 3 * 0.8 = 672 Kops) For Cassandra, we increased traffic to 100% (240 Kops) and 125% (300 Kops) of its peak capacity to validate our starting assumptions ScyllaDB sustains 672 Kops/sec with load (per vCPU) around 80% utilization, as expected. Apache Cassandra latency variability under different throughput rates (240K vs 300K ops/sec) Cassandra maintained its expected 240K peak traffic. However, it failed to sustain 300K over time – leading to increased pauses and errors. This outcome was anticipated since the test was designed to validate our initial baseline assumptions, not to achieve or demonstrate superlinear scaling. Expectations In our tests, ScyllaDB scaled faster and delivered greater improvements in latency and throughput at each step. That reduces the number of scaling operations required. The compounded benefits translate to significantly faster capacity expansion. In contrast, Cassandra’s scaling behavior is more incremental. The initial scale-out from 3 to 6 nodes took 24 minutes. The subsequent step from 6 to 9 nodes introduced additional overhead, requiring 16 minutes. From this observation, we empirically derived a formula to model the scaling factor per step: 16 = 24 × (0.5/1.0)^overhead Solving for the exponent, we approximated the streaming overhead factor as 0.6. Using this, we constructed a practical formula to estimate Cassandra’s bootstrap duration at each scale step: Bootstrap_time ≈ Base_time × (data_to_stream / data_per_node)^0.6 With these formulas, we can project the bootstrap times for subsequent scaling steps. Based on our earlier performance results (where Cassandra sustained approximately 80K ops/sec for every 3-node increase), 27 total nodes of Cassandra would be required to match the throughput achieved by ScyllaDB. The following table presents the estimated cumulative bootstrap times needed for Cassandra to reach ScyllaDB performance, using the previously derived formula and applying the 0.6 streaming overhead factor at each step: Nodes Data to Stream Bootstrap Time Cumulative Time Peak Capacity 3 2.0TB – 0 min 80K 3 → 6 1.0TB 24.0 min 24.0 min 160K 6 → 9 0.67TB 15.8 min 39.8 min 240K 9 → 12 0.50TB 12.4 min 52.2 min 320K 12 → 15 0.40TB 10.4 min 62.6 min 400K 15 → 18 0.33TB 9.0 min 71.6 min 480K 18 → 21 0.29TB 8.1 min 79.7 min 560K 21 → 24 0.25TB 7.3 min 87.0 min 640K 24 → 27 0.22TB 6.7 min 93.7 min 720K Time to reach throughput capacity for bootstrap operations As the table and chart visually show, ScyllaDB responds to capacity needs 7.2X faster than Cassandra. That’s before accounting for the added operational and maintenance overhead associated with the process. Cleanup Cleanup is a process to reclaim disk space after a scale-out operation takes place in Cassandra. As the Cassandra documentation states: As a safety measure, Cassandra does not automatically remove data from nodes that “lose” part of their token range due to a range movement operation (bootstrap, move, replace). (…) If you do not do this, the old data will still be counted against the load on that node. We estimated the following cleanup times after scaling to 9 nodes with unthrottled compactions: Unlike topology changes, Cassandra cleanup operations can be executed in parallel across multiple replicas, rather than being serialized. The trade-off, however, is a temporary increase in compaction activity – something that may impact system performance through its execution. In practice, many users choose to run cleanup serially or per rack to minimize disruption to user-facing traffic. Despite its parallelizability, careful coordination is often preferred in production environments to minimize latency impact. The following table outlines the total time required under various cleanup strategies: In conclusion, ScyllaDB scaled faster and sustained higher throughput during scale-out, and it removes cleanup as part of the scaling cycle. Even for users willing to accept the risk of running cleanup in parallel across all Cassandra nodes, ScyllaDB still offers 9X faster capacity response time, once the minimum required cleanup time is factored into Cassandra’s previously estimated bootstrap durations. These results reflect how both databases behave under one specific scaling pattern. Teams should benchmark against their own workload shapes and operational constraints to see how these architectural differences play out in their particular environment. Parting Thoughts We know readers are (rightfully) skeptical of vendor benchmarks. As discussed earlier, Cassandra and ScyllaDB rely on fundamentally different scaling models, which makes designing a perfect comparison inherently difficult. The scaling exercises demonstrated here were not designed to fully maximize ScyllaDB tablets’ potential. The test design actually favors Cassandra by focusing on symmetrical scaling. Asymmetrical scaling scenarios would better highlight the advantage of tablets vs vNodes. Even with a design that favored Cassandra’s vNodes model, the results show the impact of tablets. ScyllaDB sustained 4X the throughput of Apache Cassandra while maintaining consistently lower P99 latencies under similar infrastructure. Interpreted differently, ScyllaDB delivers comparable performance to Cassandra using significantly smaller instances, which could then be scaled further by introducing larger, asymmetric nodes as needed. This approach (scaling from 3 small nodes to another 3 [much larger] nodes) optimizes infrastructure TCO and aligns naturally with ScyllaDB Tablets architecture. However, this would be far more difficult to achieve (and test) in Cassandra in practice. Also, the tests intentionally did not use large instances to avoid favoring ScyllaDB. ScyllaDB’s shard-per-core architecture is designed to linearly scale across large instances without requiring extensive tuning cycles, which are often encountered with Apache Cassandra. For example, a 3-node cluster running on the largest AWS Graviton4 instances can sustain over 4M operations per second. When combined with Tablets, ScyllaDB deployments can scale from tens of thousands to millions of operations per second within minutes. Finally, remember that performance should be just one component in a team’s database evaluation. ScyllaDB offers numerous features beyond Cassandra (local and global indexes, materialized views, workload prioritization, per query timeouts, internal cache, and advanced dictionary-based compression, for example). Appendix: How We Ran the Tests Both ScyllaDB and Cassandra tests were carried out in AWS EC2 in an apples-to-apples scenario. We ran our tests on a 3-node cluster running on top of i4i.4xlarge instances placed under the same Cluster Placement Group to further reduce networking round-trips. Consequently, each node was placed on an artificial rack using the GossipingPropertyFileSnitch. As usual, all tests used LOCAL_QUORUM as the consistency level, a replication factor of 3. They used NetworkTopologyStrategy as the replication strategy. To assess scalability under real-world traffic patterns, like Gaussian and other similar bell curve shapes, we measured the time required to bootstrap new replicas to a live cluster without disrupting active traffic. Based on these results, we derived a mathematical model to quantify and compare the scalability gaps between both systems. Methodology To assess scalability under realistic conditions, we ran performance tests to simulate typical production traffic fluctuations. The actual benchmarking is a series of invocations of ScyllaDB’s fork of latte with a consistency level of LOCAL_QUORUM. To test scalability, we used a “real-life” mixed distribution, with the majority (80%) of operations distributed over a hot set, and the remaining 20% iterating over a cold set. latte is the Lightweight Benchmarking Tool for Apache Cassandra as developed by Piotr Kołaczkowski, a DataStax Software Engineer. Under the hood, latte relies on ScyllaDB’s Rust driver, compatible with Apache Cassandra. It outperforms other widely used benchmarking tools, provides better scalability and has no GC pauses, resulting in less latency variability on the results. Unlike other benchmarking tools, latte (thanks to its use of Rune) also provides a flexible syntax for defining workloads closely tied on how developers actually interact with their databases. Lastly, we can always brag we did it in Rust, just because… 🙂 We set baseline traffic at 70% of its observed peak before P99 latency crossed a 10ms threshold. This was to ensure both databases retained sufficient CPU and I/O headroom to handle sudden traffic and concurrency spikes, as well as the overhead of scaling operations. Setup The following table shows the infrastructure we used for our tests: Cassandra/ScyllaDB Loaders EC2 Instance type i4i.4xlarge c6in.8xlarge Cluster size 3 1 vCPUs (total) 16 (48) 32 RAM (total) 128 (384) GiB 64 GiB Storage (total) 1 x 3.750 AWS Nitro SSD EBS-only Network Up to 25 Gbps 50 Gbps ScyllaDB and Cassandra nodes, as well as their respective loaders, were placed under their own exclusive AWS Cluster Placement Group for low-latency networking. Given the side-effect of all replicas being placed under the same availability zone, we placed each node under an artificial rack using the GossipingPropertyFileSnitch. The schema used through all testing suites resembles the same schema as the default cassandra-stress, whereas the keyspace relies on NetworkTopologyStrategy with a replication factor of 3: CREATE TABLE IF NOT EXISTS keyspace1.standard1 ( key blob PRIMARY KEY, c0 blob, c1 blob, c2 blob, c3 blob, c4 blob ) ; We used a payload of 1010 bytes, where: 10-bytes represent the keysize, and; Each of the 5 columns is a distinct 200-byte blob Both databases were pre-populated with 2 billion partitions for an approximate (replicated) storage utilization of ~2.02TB. That’s about 60% disk utilization, considering the metadata overhead. Tuning Apache Cassandra Cassandra was originally designed to be run on commodity hardware. As such, one of its features is shipping with numerous different tuning options suitable for various use cases. However, this flexibility comes with a cost: tuning Cassandra is entirely up to its administrators, with limited guidance from online resources. Unlike ScyllaDB, an Apache Cassandra deployment requires users to manually tune kernel settings, set user limits, configure the JVM, set disks’ read-ahead, decide upon compaction strategies, and figure out the best approach for pushing metrics to external monitoring systems. To make things worse, some configuration file comments are outdated or ambiguous across versions. For example, CASSANDRA-16315 and CASSANDRA-7139 describe problems involving the default setting for concurrent compactors and offer advice on how to tune that parameter. Along those lines, it’s worth mentioning Amy Tobey’s Cassandra tuning guide (perhaps the most relevant Cassandra tuning resource available to date), where it says: “The inaccuracy of some comments in Cassandra configs is an old tradition, dating back to 2010 or 2011. (…) What you need to know is that a lot of the advice in the config commentary is misleading. Whenever it says “number of cores” or “number of disks” is a good time to be suspicious. (…)” – Excerpt from Amy’s Cassandra tuning guide, cassandra.yaml section Tuning the JVM is a journey of its own. Cassandra 5.0 production recommendations don’t mention it, and the jvm-* files page only deals with the file-based structure as shipped with the database. Although DataStax’s Tuning Java resources does a better job on providing recommendations, it warns to adjust “settings gradually and test each incremental change.” Further, we didn’t find any references to ZGC (available as of JDK17) on either the Apache Cassandra or DataStax websites. That made us wonder whether this garbage collector was even recommended. Eventually, we settled on using settings similar to those that TheLastPickle used in their Apache Cassandra 4.0 Benchmarks. During our scaling tests, we hit another inconsistency: we noticed Cassandra’s streaming operations had a default cap of 24MiB/s per node, resulting in suboptimal transfer times. Upon raising those thresholds, we noticed that: Cassandra 4.0 docs mentioned tuning the stream_throughput_outbound_megabits_per_sec option Both Cassandra 4.1 and Cassandra 5.0 docs referenced the stream_throughput_outbound option This Instaclustr article (or carefully interpreting cassandra_latest.yaml) seem like the best resource for understanding the correct entire_sstable_stream_throughput_outbound option. In other words, 3 distinct settings exist for tuning the previous 3 major releases of Cassandra. If your organization is looking to upgrade, we strongly encourage you to conduct a careful review and full round of testing on your own. This is not an edge case; others noted similar upgrade problems under the Apache Cassandra Mailing List. CASSANDRA-20692 demonstrates that Apache Cassandra 5 failed to notice a potential WAL corruption under its newer Direct IO implementation, as issuing I/O requests without O_DSYNC could manifest as data loss during abrupt restarts. This, in turn, gives users a false sense of improved write performance. Configuring Apache Cassandra is not intuitive. We used cassandra_latest.yaml as a starting point, and ran multiple iterations of the same workload under a variety of settings and different GC settings. The results are shown below and demonstrate how little tuning can have a dramatic impact on Cassandra’s performance (for better or for worse). We started by evaluating the performance of G1GC and observed that tail latencies were severely affected beyond a throughput of 40K/s. Simply switching to ZGC gave a nice performance boost, so we decided to stick with it for the remainder of our testing. The following table shows the performance variability of Cassandra 5.0 while using different tuning settings (it’s ordered from best to worst case): Test Kind Garbage Collector Read-ahead Compaction Throughput P99 Latency Throughput Cassandra RA4 Compaction256 ZGC 4KB 256MB/s 6.662ms 120K/s Cassandra RA4 Compaction0 ZGC 4KB Unthrottled 8.159ms 120K/s Cassandra RA8 Compaction256 ZGC 8KB 256MB/s 4.657ms 100K/s Cassandra RA8 Compaction0 ZGC 8KB Unthrottled 4.903ms 100K/s Cassandra G1GC G1GC 4KB 256MB/s 5.521ms 40K/s Although we spent a considerable amount of time tuning Cassandra to provide an unbiased and neutral comparison, we eventually found ourselves in a feedback loop. That is, the reported performance levels are only applicable for the workload being stressed running under the infrastructure in question. If we were to switch to different instance types or run different workload profiles, then additional tuning cycles would be necessary. We anticipate that the majority of Cassandra deployments do not undergo the level of testing we carried out on a per-workload basis. We hope that our experience may prevent other users from running into the same mistakes and gotchas that we did. We’re not claiming that our settings are the absolute best, but we don’t expect that further iterations will yield large performance improvements beyond what we observed. Tuning ScyllaDB We carried out very little tuning for ScyllaDB beyond what is described in the Configure ScyllaDB documentation. Unlike Apache Cassandra, the
scylla_setup script takes care of most of the
nitty-gritty details related to optimal OS tuning. ScyllaDB also
used tablets for data distribution. We targeted a minimum of 100
tablets/shard with the following CREATE KEYSPACE
statement: CREATE KEYSPACE IF NOT EXISTS keyspace1 WITH
REPLICATION = { 'class': 'NetworkTopologyStrategy', 'datacenter1':
3 } AND tablets = {'enabled': true, 'initial': 2048};
Limitations of our Testing Performance testing often fails to
capture real-world performance metrics tied to the semantics and
access patterns of applications. Aspects such as variable
concurrency, the impact of DELETEs (tombstones), hotspots,
and large partitions were beyond the scope of our testing. Our work
also did not aim to provide a feature-specific comparison. While
Apache Cassandra 5.0 ships with newer (and less battle-tested)
features like
Storage-attached Indexes (SAI), ScyllaDB also ships with
Workload Prioritization,
Local Secondary Indexes, and
Synchronous Materialized Views, all with no equivalent
counterpart. However, we ensured both databases’ transparent and
newer features were used, such as Cassandra’s Trie Memtables,
Trie-indexed SSTables and its newer Unified Compaction Strategy, as
well as ScyllaDB’s features like Tablets,
Shard-awareness, SSTable Index Caching, and so forth. Future
tests will use ScyllaDB’s Trie-indexed SSTables. Also note that
both databases now offer Vector Search, which was not in scope for
this project. Finally, this benchmark focuses specifically on
scaling operations, not steady-state performance. ScyllaDB has
historically demonstrated higher throughput and lower latency than
Cassandra in multiple performance benchmarks. Cassandra 5
introduces architectural improvements, but our preliminary testing
shows that ScyllaDB maintains its performance advantage. Producing
a full apples-to-apples benchmark suite for Cassandra 5 is a
sizable project that’s outside the scope of this study. For teams
evaluating a migration, the best insights will come from testing
your real-life workload profile, data models, and SLAs directly on
ScyllaDB. If you are running your own evaluations (tip: ScyllaDB
Cloud is the easiest way), our technical
team can review your setup and share tips for accurately
measuring ScyllaDB’s performance in your specific environment. Announcing ScyllaDB 2025.4, with Extended Tablets Support, DynamoDB Alternator Updates & Trie-Based Indexes
An overview of recent ScyllaDB changes, including extended tablets support, native vector search, Alternator enhancements, a new SSTable index format, and new instance support The ScyllaDB team is pleased to announce the release of ScyllaDB 2025.4, a production-ready ScyllaDB Short Term Support (STS) Minor Feature Release. More information on ScyllaDB’s Long Term Support (LTS) policy is available here. Highlights of the 2025.4 release include: Tablets now support Materialized Views (MV), Secondary Indexes (SI), Change Data Capture (CDC), and Lightweight Transactions (LWT). This fully bridges the previous feature gap between Tablets and vNodes. ScyllaDB Vector Search is now available (in GA), introducing native low-latency Approximate Nearest Neighbor (ANN) similarity search through CQL. See the Getting Started Guide and try it out. Alternator (ScyllaDB’s DynamoDB-compatible API) fully supports Tablets by following tablets_mode_for_new_keyspaces configuration flag, except for the still-experimental Streams. The new Trie-based index format improves indexing efficiency. New deployment options with i8g and i8ge show significant performance advantages over i4i, i3en as well as i7i and i7ie. For full details on how to use these features — as well as additional changes — see the release notes. Read Release Notes Vector Search Vector Search Support ScyllaDB 2025.4 introduces native Vector Search to power AI-driven applications. By integrating vector indexing directly into the ScyllaDB ecosystem, teams can now perform similarity searches without moving data to a separate vector database. CQL Integration: Store and query embeddings using standard CQL syntax. ANN Queries: Support for Approximate Nearest Neighbor (ANN) search for RAG and personalization. Dedicated Service: Managed vector indexing service ensures high performance without impacting core database operations. Availability: Initially launched on ScyllaDB Cloud. For more information: ScyllaDB Vector Search: 1B Vectors with 2ms P99s and 250K QPS Throughput Building a Low-Latency Vector Search Engine for ScyllaDB Quick Start Guide to Vector Search Extended Tablets Support The new release extends ScyllaDB’s tablet-based elasticity to use cases that involve advanced ScyllaDB capabilities such as Change Data Capture, Materialized Views, and Secondary Indexes. It also extends tablets to ScyllaDB’s DynamoDB-compatible API (Alternator). Alternator Improvements Alternator, ScyllaDB’s DynamoDB-compatible API, now more closely matches DynamoDB’s GetRecords behavior. Event metadata is fully populated, including EventSource=aws:dynamodb, awsRegion set to the receiving node’s datacenter, an updated eventVersion, and the sizeBytes subfield in DynamoDB. Performance was improved by caching parsed expressions in requests. That caching reduces overhead for complex expressions and provides ~7–15% higher single-node throughput in tested workloads. Alternator also adds support for per-table metrics (for additional insight into Alternator usage). Trie-Based SSTable Index Format A new trie-based SSTable index format is designed to improve lookup performance and reduce memory overhead. The default SSTable format remains “me,” but a new “ms” format is available, which uses trie-based indexes. The new format is disabled by default and can be enabled by settingsstable_format: ms in
scylla.yaml. When enabled, only newly created SSTables
use the trie-based index; existing SSTables keep their current
format until rewritten with nodetool upgradesstables. New
Deployment Options This release expands support to all I7i and I7ie
instance types (beyond the previously supported i7i.large,
i7i.xlarge, i7i.2xlarge). These instances offer improved
price-to-performance compared to previous-generation instances.
Support was also added for the i8g and i8ge
families, which provide better price-to-performance than
x86-based instances. Read
the Release Notes for More Details The Taming of Collection Scans
Explore several collection layouts for efficient scanning, including a split-list structure that avoids extra memory Here’s a puzzle that I came across when trying to make tasks wake up in Seastar be a no-exception-throwing operation (related issue): come up with a collection of objects optimized for “scanning” usage. That is, when iterating over all elements of the collection, maximize the hardware utilization to process a single element as fast as possible. And, as always, we’re expected to minimize the amount of memory needed to maintain it. This seemingly simple puzzle will demonstrate some hidden effects of a CPU’s data processing. Looking ahead, such a collection can be used, for example, as a queue of running tasks. New tasks are added at the back of the queue; when processed, the queue is scanned in front-to-back order, and all tasks are usually processed. Throughout this article, we’ll refer to this use case of a collection being the queue of tasks to execute. There will be occasional side notes using this scenario to demonstrate various concerns. We will explore different ways to solve this puzzle of organizing collections for efficient scanning. First, we compare three collections: array, intrusive list, and array of pointers. You will see that the scanning performance of those collections differs greatly, and heavily depends on the way adjacent elements are referenced by the collection. After analyzing the way the processor executes the scanning code instructions, we suggest a new collection called a “split list.” Although this new collection seems awkward and bulky, it ultimately provides excellent scanning performance and memory efficiency. Classical solutions First consider two collections that usually come to mind: a plain sequential array of elements and a linked list of elements. The latter collection is sometimes unavoidable, particularly when the elements need to be created and destroyed independently and cannot be freely moved across memory. As a test, we’ll use elements that contain random, pre-populated integers and a loop that walks all elements in the collection and calculates the sum of those integers. Every programmer knows that in this case, an array of integers will win because of cache efficiency. To exclude the obvious advantage of one collection over another, we’ll penalize the array and prioritize the list. First, each element will occupy a 64-byte slot even when placed in an array, so walking a plain array doesn’t benefit from caching several adjacent elements. Second, we will use an intrusive list, which means that the “next” pointer will be stored next to the element value itself. The processor can then read both the pointer and the value with a single fetch from memory to cache. The expectation here is that both collections will behave the same. However, a scanning test shows that’s not true, especially on a large scale. The plot above shows the time to process a single entry (vertical axis) versus the number of elements in the list (horizontal axis). Both axes use a logarithmic scale because the collection size was increased ten times at each new test step. Plus, the vertical axis just looks better this way. So now we have two collections – an array and a list – and the list’s performance is worse than the array’s. However, as mentioned above, the list has an undeniable advantage: elements in the list are independent of each other (in the sense that they can be allocated and destroyed independently). A less obvious advantage is that the data type stored in a list collection can be an abstract class, while the actual elements stored in the list can be specific classes that inherit from that base class. The ability to collect objects of different types can be crucial in the task processing scenario described above, where a task is described as an abstract base class and specific task implementations inherit from it and implement their own execution methods. Is it possible to build a collection that can maintain its elements independently, as a list of elements does, yet still provide scanning performance that’s the same (or close to) that of the array? Not so classical solution Let’s make an array of elements be “dispersed,” like a list, in a straightforward manner by turning each array element into a pointer to that element, and allocating the element itself elsewhere, as if it were prepared to be inserted into a list. In this array, pointers will no longer be aligned to a cache-line, thus letting the processor benefit from reading several pointers from memory at once. Elements are still 64-bytes in size, to be consistent with previous tests. The memory for pointers is allocated contiguously, with a single large allocation. This is not ideal for dynamic collection, where the number of elements is not known beforehand: the larger the collection grows, the more re-allocations are needed. It’s possible to overcome this by maintaining a list of sub-arrays. Looking ahead, just note that this chunked array of pointers will indeed behave slightly worse than a contiguous one. All further measurements and analysis refer to the contiguous collection. This approach actually looks worse than the linked list because it occupies more memory than the list. Also, when walking the list, the code touches one cache line per element – but when walking this collection, it additionally populates the cache with the contents of that array of pointers. Running the same scanning test shows that this cost is imaginary and the collection beats the list several times, approaching the plain array in its per-element efficiency. The processor’s inner parallelism To get an idea of why an array of pointers works better than the intrusive list, let’s drill down to the assembly level and analyze how the instructions are executed by the processor. Here’s what the array scanning main loop looks like in assembly:
x: mov (%rdi,%rax,8),%rax mov 0x10(%rax),%eax
add %rax,%rcx lea 0x1(%rdx),%eax mov %rax,%rdx cmp %rsi,%rax jb
x We can see two memory accesses – the first moves the
pointer to the array element to the ‘rax’ register,
and the second fetches the value from the element into its
‘eax’ 32-bit sub-part. Then there comes in-register
math and conditional jumping back to the start of the loop to
process the next element. The main loop of the list scanning code
looks much shorter: x: mov 0x10(%rdi),%edx mov (%rdi),%rdi
add %rdx,%rax test %rdi,%rdi jne x Again, there are two
memory accesses – the first fetches the value pointer into the
‘edx’ register and the next one fetches the pointer to
the next element to the ‘rdi’ register. Instructions
that involve fetching data from memory can be split into four
stages: i-fetch – The processor fetches the instruction itself from
memory. In our case, the instruction is likely in the instruction
cache, so the fetch goes very fast. decode – The processor decides
what the instruction should do and what operands are needed for
this. m-fetch – The processor reads the data it needs from memory.
In our case, elements are always read from memory because they are
“large enough” not to be fetched into cache with anything else,
while array pointers are likely to sit in cache. exec – The
processor executes the instruction. Let’s illustrate this sequence
with a color bar:
Also, we know that modern processors can run multiple
instructions in parallel, by executing parts of different
instructions at the same time in different parts of the conveyor,
as well as running instructions fully in parallel. One
example of this parallel execution can be seen in the
array-scanning example above, namely the 
add %rax,%rcx
lea 0x1(%rdx),%eax part. Here, the second instruction is
the increment of the index that’s used to scan through the array of
pointers. The compiler rendered this as lea
instruction instead of the inc (or add)
one because inc and lea are executed in
different parts of the pipeline. When placed back-to-back,
they will truly run in parallel. If the inc was used,
the second instruction would have to spend some time in the same
pipeline stage as the add. Here’s what executing
the above array scan can look like:
Here, fetching the element pointer from the array is short because
it likely happens from cache. Fetching the element’s value is long
(and painted darker) because the element is most certainly not in
cache (and thus requires a memory read). Also, fetching the value
from the element happens after the element pointer is fetched into
the register. Similarly, the instruction that adds value to the
result cannot execute before the value itself is fetched from
memory, so it waits after being decoded. And here’s what scanning
the list can look like:
At first glance, almost nothing changed. The difference is that the
next pointer is fetched from memory and takes a long time, but the
value is fetched from cache (and is faster). Also, fetching the
value can start before the next pointer is saved into the register.
Considering that during an array scan, the “read element pointer
from array” is long at times (e.g., when it needs to read the next
cache line from memory), it’s still not clear why list scanning
doesn’t win at all. In order to see why the array of pointers wins,
we need to combine two consecutive loop iterations. First comes the
array scan:
It’s not obvious, but two loop iterations can run like that.
Fetching the pointer for the next element is pretty much
independent from fetching the pointer of the previous element; it’s
just the next element of an array that’s already in the cache. Just
like predicting the next branches, processors can “predict” that
the next memory fetch will come from the pointer sitting next to
the one being processed and start loading it ahead of time. List
scanning cannot afford that parallelism even if the processor
“foresees” that the fetched pointer will be dereferenced. As a
consequence, its two loop iterations end up being serialized:
Note that the processor can only start fetching the next element
after it finishes fetching the next pointer itself, so the
parallelism of accessing elements is greatly penalized here. Also
note that despite how it seems in the above images, scanning the
list can be many times slower than scanning the array,
because blue bars (memory fetches) are in reality many times longer
than the others (e.g., those fetching the instruction, decoding it,
and storing the result in the register). A compromise solution The
array of pointers turned out to be a much better solution than the
list of elements, but it still has an inefficiency: extra memory
that can grow large. Here we can say that this algorithm has O(N)
memory complexity, meaning that it requires extra memory that’s
proportional to the number of elements in the collection.
Allocating it can be troublesome for many reasons – for example,
because of memory fragmentation and because, at large scale,
growing the array would require copying all the pointers from one
place to another. There are ways to mitigate the problem of
maintaining this extra memory, but is it possible to eliminate it
completely? Or at least make it “constant complexity” (i.e.,
independent from the number of elements in it)? The requirement
to not allocate extra memory can be crucial in task processing
scenarios. In it, the auxiliary memory is allocated when an element
is appended to the existing collection. And a new task is appended
to the run-queue when it’s being woken up. If the allocation fails,
the appending also fails as well as the wake-up call. And having
non-failing wake-ups can be critical. It looks like letting
the processor fetch independent data in different
consecutive loop iterations is beneficial. With a list, it would be
good if adjacent elements were accessed independently. That can be
achieved by splitting the list into several sub-lists, and – when
iterating the whole collection – processing it in a round-robin
manner. Specifically, take an element from the first list, then
from the second, … then from the Nth, then advancing on the first,
then advancing on the second, and so on.
The scanning code is made with the assumption that the collection
only grows by appending elements to one of its ends – the front or
the back end. This perfectly suits the task-processing usage
scenario and allows making the scanning code break condition to be
very simple: once a null element is met in either of the lists, all
lists after it are empty as well, so scanning can stop.
Below is the simplistic implementation of the scanning loop. A full
implementation that handles appends is a bit more hairy and is
based on the C++ “iterator” concept. But overall, it has the same
efficiency and resulting assembly code. First, checking this list
with 
N=2
OK, scanning two lists “in parallel” definitely helps. Since the
number of splits is compile-time constant, we now need to run
several tests to see which value is the most efficient one.
The more we split the list, the worse it seems to behave at
small scales, but the better at large scale. Splits at 16 and 32
lanes seem to “saturate” the processor’s parallelism ability.
Here’s how the results look at a different angle:
Here, the horizontal axis shows 
N (the number of lists
in the collection), and individual lines on the plot correspond to
different collection sizes starting from 10 elements and ending at
one million. And both axes are at logarithmic scale too. At a low
scale with 10 and 100 elements, adding more lists doesn’t improve
the scanning speed. But at larger scales, 16 parallel lists are
indeed the saturation point. Interestingly, the assembly code of
the split-list main loop part contains two times more instructions
than the plain list scan. x: mov %eax,%edx add $0x1,%eax and
$0xf,%edx mov -0x78(%rsp,%rdx,8),%rcx mov 0x10(%rcx),%edi mov
0x8(%rcx),%rcx add %rdi,%rsi mov %rcx,-0x78(%rsp,%rdx,8) cmp
%r8d,%eax jne x It also has two times more memory access
than the plain list scanning code. Nonetheless, since the memory is
better organized, prefetching it in a parallel manner makes this
code win in terms of timing. Comparing different processors (and
compilers) The above measurements were done on an
AMD Threadripper processor and the binary was compiled with a
GCC-15
compiler. It’s interesting to check what code different
compilers render and, more importantly, how different processors
behave. First, let’s look at it with the instructions set. No big
surprises here; plain list is the shortest code, split list is the
longest:
Running the tests on different processors, however, renders very
different results. Below are the number of cycles a processor needs
to process a single element. Since the plain list is the outlier,
it will be shown on its own plot. Here are the top performers –
array, array of pointers, and split list:
The split list is, as we’ve seen, the slowest one. But it’s not
drastically different. More interesting is the way the Xeon
processor beats the other competitors. A similar ratio was measured
for plain list processing by different processors:
But, again, even on the Xeon processor, it’s an order of magnitude
slower than the split list. Summing things up In this article, we
explored ways to organize a collection of objects to allow for
efficient scanning. We compared four collections – array, intrusive
list, array of pointers, and split-list. Since plain arrays have
problems maintaining objects independently, we used them as a base
reference and mainly compared three other collections with each
other to find out which one behaved the best. From the experiments,
we discovered that an array of pointers provided the best timing
for single-element access, but required a lot of extra memory. This
cost can be mitigated to some extent, but the memory itself doesn’t
go away. The split-list approach showed comparable (almost as good)
performance. And the advantage of the split-list solution is that
it doesn’t require extra memory to work.
Top Blogs of 2025: Rust, Elasticity, and Real-Time DB Workloads
Let’s look back at the top 10 ScyllaDB blog posts published in 2025, as well as 10 “classics” that are still resonating with readers. But first: thank you to all the community members who contributed to our blogs in various ways…from users sharing best practices at Monster SCALE Summit and P99 CONF, to engineers explaining how they raised the bar for database performance, to anyone who has initiated or contributed to the discussion on HackerNews, Reddit, and the like. And if you have suggestions for additional blog topics, please share them with us on our socials. With no further ado, here are the most-read blog posts that we published in 2025… Inside ScyllaDB Rust Driver 1.0: A Fully Async Shard-Aware CQL Driver Using Tokio By Wojciech Przytuła The engineering challenges and design decisions that led to the 1.0 release of ScyllaDB Rust Driver. Read: Inside ScyllaDB Rust Driver 1.0: A Fully Async Shard-Aware CQL Driver Using Tokio Related: P99 CONF on-demand Introducing ScyllaDB X Cloud: A (Mostly) Technical Overview By Tzach Livyatan ScyllaDB X Cloud just landed! It’s a truly elastic database that supports variable/unpredictable workloads with consistent low latency, plus low costs. Read: Introducing ScyllaDB X Cloud: A (Mostly) Technical Overview Related: ScyllaDB X Cloud: An Inside Look with Avi Kivity Inside Tripadvisor’s Real-Time Personalization with ScyllaDB + AWS By Dean Poulin See the engineering behind real-time personalization at Tripadvisor’s massive (and rapidly growing) scale Read: Inside Tripadvisor’s Real-Time Personalization with ScyllaDB + AWS Related: How ShareChat Scaled their ML Feature Store 1000X without Scaling the Database Why We Changed Our Data Streaming Approach By Asias He How moving from mutation-based streaming to file-based streaming resulted in 25X faster streaming time. Read: Why We Changed Our Data Streaming Approach Related: More engineering blog posts How Supercell Handles Real-Time Persisted Events with ScyllaDB By Cynthia Dunlop How a team of just two engineers tackled real-time persisted events for hundreds of millions of players Read: How Supercell Handles Real-Time Persisted Events with ScyllaDB Related: Rust Rewrite, Postgres Exit: Blitz Revamps Its “League of Legends” Backend Why Teams Are Ditching DynamoDB By Guilherme da Silva Nogueira, Felipe Cardeneti Mendes Teams sometimes need lower latency, lower costs (especially as they scale) or the ability to run their applications somewhere other than AWS Read: Why Teams Are Ditching DynamoDB Related: ScyllaDB vs DynamoDB: 5-Minute Demo A New Way to Estimate DynamoDB Costs By Tim Koopmans We built a new DynamoDB cost analyzer that helps developers understand what their workloads will really cost Read: A New Way to Estimate DynamoDB Costs Related: Understanding The True Cost of DynamoDB Efficient Full Table Scans with ScyllaDB Tablets By Felipe Cardeneti Mendes How “tablets” data distribution optimizes the perfromance of full table scans on ScyllaDB. Read: Efficient Full Table Scans with ScyllaDB Tablets Related: Fast and Deterministic Full Table Scans at Scale How We Simulate Real-World Production Workloads with “latte” By Valerii Ponomarov Learn why and how we adopted latte, a Rust-based lightweight benchmarking tool, for ScyllaDB’s specialized testing needs. Read: How We Simulate Real-World Production Workloads with “latte” Related: Database Benchmarking for Performance Masterclass How JioCinema Uses ScyllaDB Bloom Filters for Personalization By Cynthia Dunlop JioCinema (now Disney+ Hotstar) was operating at a scale that required creative solutions beyond typical Redis Bloom filters. This post explains why and how they used ScyllaDB’s built-in Bloom filters for real-time watch status checks. Read: How JioCinema Uses ScyllaDB Bloom Filters for Personalization Related: More user perspectives Bonus: Top NoSQL Database Blogs From Years Past Many of the blogs published in previous years continued to resonate with the community. Here’s a rundown of 10 enduring favorites: How io_uring and eBPF Will Revolutionize Programming in Linux (Glauber Costa): How io_uring and eBPF will change the way programmers develop asynchronous interfaces and execute arbitrary code, such as tracepoints, more securely. [2020] Database Internals: Working with IO (Pavel Emelyanov): Explore the tradeoffs of different Linux I/O methods and learn how databases can take advantage of a modern SSD’s unique characteristics. [2024] On Coordinated Omission (Ivan Prisyazhynyy): Your benchmark may be lying to you. Learn why coordinated omissions are a concern and how they are handled in ScyllaDB benchmarking. [2021] ScyllaDB vs MongoDB vs PostgreSQL: Tractian’s Benchmarking & Migration (João Pedro Voltani): TRACTIAN compares ScyllaDB, MongoDB, and PostgreSQL and walks through their MongoDB-to-ScyllaDB migration, including challenges and results. [2023] Introducing “Database Performance at Scale”: A Free, Open Source Book (Dor Laor): A practical guide to understanding the tradeoffs and pitfalls of optimizing data-intensive applications for high throughput and low latency. [2023] ScyllaDB vs. DynamoDB Benchmark: Comparing Price Performance Across Workloads (Eliran Sinvani): A comparison of cost and latency across DynamoDB pricing models and ScyllaDB under varied workloads and read/write ratios. [2023] Benchmarking MongoDB vs ScyllaDB: Performance, Scalability & Cost (Dr. Daniel Seybold): A third-party benchmark comparing MongoDB and ScyllaDB on throughput, latency, scalability, and price-performance. [2023] Apache Cassandra 4.0 vs. ScyllaDB 4.4: Comparing Performance (Juliusz Stasiewicz, Piotr Grabowski, Karol Baryla): Benchmarks showing 2×–5× higher throughput and significantly better latency with ScyllaDB versus Cassandra. [2022] DynamoDB: When to Move Out (Felipe Cardeneti Mendes): Why teams leave DynamoDB, including throttling, latency, item size limits, flexibility constraints, and cost. [2023] Rust vs. Zig in Reality: A (Somewhat) Friendly Debate(Cynthia Dunlop): A recap of a P99 CONF debate on systems programming languages with participants from Bun.js, Turso, and ScyllaDB. [2024]
Lessons Learned Leading High-Stakes Data Migrations
“No one ever said ‘meh, it’s just our database'” Every data migration is high stakes to the person leading it. Whether you’re upgrading an internal app’s database or moving 362 PB of Twitter’s data from bare metal to GCP, a lot can go awry — and you don’t want to be blamed for downtime or data loss. But a migration done right will not only optimize your project’s infrastructure. It will also leave you with a deeper understanding of your system and maybe even yield some fun “war stories” to share with your peers. To cheat a bit, why not learn from others’ experiences first? Enter Miles Ward (CTO at SADA and former Google and AWS cloud lead) and Tim Koopmans (Senior Director at ScyllaDB, performance geek and SaaS startup founder). Miles and Tim recently got together to chat about lessons they’ve personally learned from leading real-world data migrations. You can watch the complete discussion here: Let’s look at three key takeaways from the chat. 1. Start with the Hardest, Ugliest Part First It’s always tempting to start a project with some quick wins. But tackling the worst part first will yield better results overall. Miles explains, “Start with the hardest, ugliest part first because you’re going to be wrong in terms of estimating timelines and noodling through who has the correct skills for each step and what are all of the edge conditions that drive complexity.” For example, he saw this approach in action during Google’s seven-year migration of the Gmail backend (handling trillions of transactions per day) from its internal Gmail data system to Spanner. First, Google built Spanner specifically for this purpose. Then, the migration team ran roll-forwards and roll-backs of individual mailbox migrations for over two years before deciding that the performance, reliability and consistency in the new environment met their expectations. Miles added, “You also get an emotional benefit in your teams. Once that scariest part is done, everything else is easier. I think that tends to work well both interpersonally and technically.” 2. Map the Minefield You can’t safely migrate until you’ve fully mapped out every little dependency. Both Tim and Miles stress the importance of exhaustive discovery: cataloging every upstream caller, every downstream consumer, every health check and contractual downtime window before a single byte shifts. Miles warns, “If you don’t have an idea of what the consequences of your change are…you’ll design a migration that’s ignorant of those needs.” Miles then offered a cautionary anecdote from his time at Twitter, as part of a team that migrated 362 petabytes of active data from bare-metal data centers into Google Cloud. They used an 800 Gbps interconnect (about the total internet throughput at the time) and transferred everything in 43 days. To be fair, this was a data warehouse migration, so it didn’t involve hundreds of thousands of transactional queries per second. Still, Twitter’s ad systems and revenue depended entirely on that warehouse, making the migration mission-critical. Miles shared: “They brought incredible engineers and those folks worked with us for months to lay out the plan before we moved any bytes. Compare that to something done a little more slapdash. I think there are plenty of places where businesses go too slow, where they overinvest in risk management because they haven’t modeled the cost-benefit of a faster migration. But if you don’t have that modeling done, you should probably take the slow boat and do it carefully.” 3. Engineer a “Blissfully Boring” Cutover “If you’re not feeling sleepy on cut-over day,” Miles quipped, “you’ve done something terribly wrong.” But how do you get to that point? Tim shared that he’s always found dual writes with single reads useful: you can switch over once both systems are up to speed. If the database doesn’t support dual writes, replicating writes via Change Data Capture (CDC) or something similar works well. Those strategies provide confidence that the source and target behave the same under load before you start serving real traffic. Then Tim asked Miles, “Would you say those are generally good approaches, or does it just depend?” Miles’ response: “I think the biggest driver of ‘It depends’ is that those concepts are generally sound, but real‐world migrations are more complex. You always want split writes when feasible, so you build operational experience under write load in the new environment. But sample architecture diagrams and Terraform examples make migrations look simpler than they usually are.” Another complicating factor: most companies don’t have one application on one database. They have dozens of applications talking across multiple databases, data warehouses, cache layers and so on. All of this matters when you start routing read traffic from various sources. Some systems use scheduled database-to-warehouse extractions, while others avoid streaming replication costs. Load patterns shift throughout the day as different workloads come online. That’s why you should test beyond the immediate reads after migration or when initial writes move to the new environment. So codify every step, version it and test it all multiple times – exactly the same way. And if you need to justify extra preparation or planning for migration, frame it as improving your overall high-availability design. Those practices will carry forward even after the cutover. Also, be aware that new platforms will inevitably have different operational characteristics…that’s why you’re adopting them. But these changes can break hard-coded alerts or automation. For example, maybe you had alerts set to trigger at 10,000 transactions per second, but the new system easily handles 100,000. Ensure that your previous automation still works and systematically evaluate all upstream and downstream dependencies. Follow these tips and the big day could resemble Digital Turbine’s stellar example. Miles shared, “If Digital Turbine’s database went down, its business went down. But the company’s DynamoDB to ScyllaDB migration was totally drama free. It took two and a half weeks, all buttoned up, done. It was going so well that everybody had a beer in the middle of the cutover.” Closing Thoughts Data migrations are always “high stakes.” As Miles bluntly put it, “I know that if I screw this up, I’ll piss off customers, drive them to competitors, or miss out on joint growth opportunities. It all comes down to trust. There are countless ways you can screw up an application in a way that breaches stakeholder trust. But doing careful planning, being thoughtful about the migration process, and making the right design decisions sets the team up to grow trust instead of eroding it.” Data migration projects are also great opportunities to strengthen your team’s architecture and build your own engineering expertise. Tim left us with this thought: “My advice for anyone who’s scared of running a data migration: Just have a crack at it. Do it carefully, and you’ll learn a lot about distributed systems in general – and gain all sorts of weird new insights into your own systems in particular. ” Watch the complete video (at the start of this article) for more details on these topics – as well as some fun “war stories.” Bonus: Access our free NoSQL Migration Masterclass for a deeper dive into migration strategy, missteps, and logistics.ScyllaDB Operator 1.19.0 Release: Multi-Tenant Monitoring with Prometheus and OpenShift Support
Multi-tenant monitoring with Prometheus/OpenShift, improved sysctl config, and a new opt-in synchronization for safer topology changes The ScyllaDB team is pleased to announce the release of ScyllaDB Operator 1.19.0. ScyllaDB Operator is an open-source project that helps you run ScyllaDB on Kubernetes. It manages ScyllaDB clusters deployed to Kubernetes and automates tasks related to operating a ScyllaDB cluster, like installation, vertical and horizontal scaling, as well as rolling upgrades. The latest release introduces the “External mode,” which enables multi-tenant monitoring with Prometheus and OpenShift support. It also adds a new guardrail in the must-gather debugging tool preventing accidental inclusion of sensitive information, optimizes kernel parameter (sysctl) configuration, and introduces an opt-in synchronization feature for safer topology changes – plus several other updates. Multi-tenant monitoring with Prometheus and OpenShift support ScyllaDB Operator monitoring uses Prometheus (an industry-standard cloud-native monitoring system) for metric collection and aggregation. Up until now, you had to run a fresh, clean instance of Prometheus for every ScyllaDB cluster. We coined the term “Managed mode” for this architecture (because, in that
case, ScyllaDB Operator would manage the Prometheus deployment):
ScyllaDB Operator 1.19 introduces the
“
External mode” – an option to connect (one or
more) ScyllaDB clusters with a shared Prometheus deployment that
may already be present in your production environment:
The 
External mode provides a very important
capability for users who run ScyllaDB on Red Hat OpenShift. The
User Workload Monitoring (UWM) capability of OpenShift becomes
available as a backend for ScyllaDB Monitoring:
Under the hood, ScyllaDB Operator 1.19 implements the new
monitoring architectures by extending the
ScyllaDBMonitoring CRD with a new field
.spec.components.prometheus.mode that can now be set
to Managed or External.
Managed is the preexisting behavior (to deploy a
clean Prometheus instance), while
External deploys just the Grafana dashboard using
your existing Prometheus as a data source instead, and puts
ServiceMonitors and PrometheusRules
in place to get all the ScyllaDB metrics there. See the new
ScyllaDB Monitoring overview and
Setting up ScyllaDB Monitoring documents to learn more about
the new mode and how to set up ScyllaDB Monitoring
with an existing Prometheus instance. The
Setting up ScyllaDB Monitoring on OpenShift guide offers
guidance on how to set up
User Workload Monitoring (UWM) for ScyllaDB in OpenShift. That
being said, our experience shows that cluster administrators prefer
closer control over the monitoring stack than what the
Managed mode offered. For this reason, we intend to
standardize on using External in the long run.
So, we’re still supporting the Managed mode, but it’s
being deprecated and will be removed in a future Operator version.
If you are an existing user, please consider deploying your own
Prometheus using the Prometheus
Operator platform guide and switching from Managed
to External. Sensitive information excluded from
must-gather ScyllaDB Operator comes with an embedded tool (called
must-gather) that helps preserve the configuration
(Kubernetes objects) and runtime state (ScyllaDB node logs, gossip
information, nodetool status, etc.) in a convenient
archive. This allows comparative analysis and troubleshooting with
a holistic, reproducible view. As of ScyllaDB Operator 1.19,
must-gather comes with a new setting
--exclude-resource that serves as an additional
guardrail preventing accidental inclusion of sensitive information
– covering Secrets and SealedSecrets by default. Users can specify
additional types to be restricted from capturing, or override the
defaults by setting the --include-sensitive-resources
flag. See the
Gathering data with must-gather guide for more information.
Configuration of kernel parameters (sysctl) ScyllaDB nodes require
kernel parameter (sysctl) configuration for optimal performance and
stability – ScyllaDB Operator 1.19 improves the API to do that.
Before 1.19, it was possible to configure these parameters through
v1.ScyllaCluster‘s .spec.sysctls.
However, we learned that this wasn’t the optimal place in the API
for a setting that affects entire Kubernetes nodes. So, ScyllaDB
Operator 1.19 lets you configure sysctls through
v1alpha1.NodeConfig for a range of Kubernetes nodes at
once by matching the specified placement rules using a label-based
selector. See the
Configuring kernel parameters (sysctls) section of the
documentation to learn how to configure the sysctl values
recommended for production-grade ScyllaDB deployments. With the
introduction of sysctl to NodeConfig, the legacy way of configuring
sysctl values through v1.ScyllaCluster‘s
.spec.sysctls is now deprecated. Topology change
operations synchronisation ScyllaDB
requires that no existing nodes are down when a new node is added
to a cluster. ScyllaDB Operator 1.19 addresses this by
extending ScyllaDB Pods for newly joining nodes with a barrier
blocking the ScyllaDB container from starting until the
preconditions for bootstrapping a new node are met. This feature is
opt-in in ScyllaDB Operator 1.19. You can enable it by setting the
--feature-gates=BootstrapSynchronisation=true
command-line argument to ScyllaDB Operator. This feature supports
ScyllaDB 2025.2 and newer. If you are running a multi-datacenter
ScyllaDB cluster (multiple ScyllaCluster objects bound
together with external seeds), you are still required to verify the
preconditions yourself before initiating any topology changes. This
is because the synchronisation only occurs on the level of an
individual ScyllaCluster. See
Synchronising bootstrap operations in ScyllaDB for more
information. Other notable changes Deprecation of
ScyllaDBMonitoring components’ exposeOptions By adding support for
external Prometheus instances, ScyllaDB Operator 1.19 makes a step
towards reducing ScyllaDBMonitoring‘s complexity
by deprecating exposeOptions in both
ScyllaDBMonitoring‘s Prometheus and Grafana
components. The use of exposeOptions is limited
because it provides no way to configure an Ingress that will
terminate TLS, which is likely the most common approach in
production. As an alternative, this release introduces a more
pragmatic and flexible approach: You can simply document how the
components’ corresponding Services can be exposed. This gives you
the flexibility to do exactly what your use case requires. See the
Exposing Grafana documentation to learn how to expose Grafana
deployed by ScyllaDBMonitoring using a
self-managed Ingress resource. The deprecated
ScyllaDBMonitoring‘s exposeOptions will
be removed in a future Operator version. Dependency updates This
release also includes regular updates of ScyllaDB Monitoring and
the packaged dashboards to support the latest ScyllaDB releases
(4.11.1->4.12.1, #3031),
as well as its dependencies: Grafana (12.0.2->12.2.0) and
Prometheus (v3.5.0->v3.6.0). For more changes and details, check
out the GitHub
release notes. Upgrade instructions For instructions on
upgrading ScyllaDB Operator to 1.19, see the
Upgrading Scylla Operator documentation. Supported versions
ScyllaDB 2024.1, 2025.1 – 2025.3 Kubernetes 1.31 – 1.34 Container
Runtime Interface API v1 ScyllaDB Manager 3.5, 3.7 Getting started
with ScyllaDB Operator ScyllaDB Operator
Documentation Learn
how to deploy ScyllaDB on Google Kubernetes Engine (GKE)
Learn
how to deploy ScyllaDB on Amazon Elastic Kubernetes Engine
(EKS) Learn how
to deploy ScyllaDB on a Kubernetes Cluster Related links
ScyllaDB Operator source (on
GitHub) ScyllaDB Operator image on
DockerHub ScyllaDB Operator
Helm Chart repository ScyllaDB Operator documentation
ScyllaDB Operator for Kubernetes
lesson in ScyllaDB University Report a
problem Your feedback is always welcome! Feel free to open an
issue or reach out on the #scylla-operator channel in ScyllaDB User Slack. Instaclustr product update: December 2025
Instaclustr product update: December 2025Here’s a roundup of the latest features and updates that we’ve recently released.
If you have any particular feature requests or enhancement ideas that you would like to see, please get in touch with us.
Major announcements OpenSearch®AI Search for OpenSearch®: Unlocking next-generation search
AI Search for OpenSearch, which is now available in Public Preview on the NetApp Instaclustr Managed Platform, is designed to bring semantic, hybrid, and multimodal search capabilities to OpenSearch deployments—turning them into an end-to-end AI-powered search solution within minutes. With built-in ML models, vector indexing, and streamlined ingestion pipelines, next-generation search can be enabled in minutes without adding operational complexity. This feature powers smarter, more relevant discovery experiences backed by AI—securely deployed across any cloud or on-premises environment.
ClickHouse®
FSx for NetApp ONTAP and Managed ClickHouse® integration is now
available
We’re excited to announce that NetApp has introduced seamless
integration between Amazon FSx for NetApp ONTAP and Instaclustr
Managed ClickHouse, to enable customers to build a truly hybrid
lakehouse architecture on AWS. This integration is designed to
deliver lightning-fast analytics without the need for complex data
movement, while leveraging FSx for ONTAP’s unified file and object
storage, tiered performance, and cost optimization. Customers can
now run zero-copy lakehouse analytics with ClickHouse directly on
FSx for ONTAP data—to simplify operations, accelerate
time-to-insight, and reduce total cost of ownership.
Instaclustr for PostgreSQL® on Amazon FSx for ONTAP: A new
era
We’re excited to announce the public preview of Instaclustr Managed
PostgreSQL integrated with Amazon FSx for NetApp ONTAP—combining
enterprise-grade storage with world-class open source database
management. This integration is designed to deliver higher IOPS,
lower latency, and advanced data management without increasing
instance size or adding costly hardware. Customers can now run
PostgreSQL clusters backed by FSx for ONTAP storage, leveraging
on-disk compression for cost savings and paving the way for
ONTAP-powered features, such as instant snapshot backups, instant
restores, and fast forking. These ONTAP-enabled features are
planned to unlock huge operational benefits and will be launched
with our GA release.
- Released Apache Cassandra 5.0.5 into general availability on the NetApp Instaclustr Managed Platform.
- Transitioned Apache Cassandra v4.1.8 to CLOSED lifecycle state; scheduled to reach End of Life (EOL) on December 20, 2025.
- Kafka on Azure now supports v5 generation nodes, available in General Availability.
- Instaclustr Managed Apache ZooKeeper has moved from General Availability to closed status.
- Kafka and Kafka Connect 3.1.2 and 3.5.1 are retired; 3.6.2, 3.7.1, 3.8.1 are in legacy support. Next set of lifecycle state changes for Kafka and Kafka Connect in end March 2026 will see all supported versions 3.8.1 and below marked End of Life.
- Karapace Rest Proxy and Schema Registry 3.15.0 are closed. Customers are advised to move to version 5.x.
- Kafka Rest Proxy 5.0.0 and Kafka Schema Registry 5.0.0, 5.0.4 have been moved to end of life. Affected customers have been contacted by Support to schedule a migration to a supported version as soon as possible.
- ClickHouse 25.3.6 has been added to our managed platform in General Availability.
- Kafka Table Engine integration with ClickHouse has added support to enable real-time data ingestion, streamline streaming analytics, and accelerate insights.
- New ClickHouse node sizes, powered by AWS m7g, r7i, and r7g instances, are now in Limited Availability for cluster creation.
- Cadence is now available to be provisioned with Cassandra 5.x, designed to deliver improved performance, enhanced scalability, and stronger security for mission-critical workflows.
- OpenSearch 2.19.3 and 3.2.0 have been released to General Availability.
- PostgreSQL AWS PrivateLink support has been added, enabling connectivity between VPCs using AWS PrivateLink.
- PostgreSQL version 18.0 has now been released to General Availability, alongside PostgreSQL version 16.10, 17.6.
- Added new PostgreSQL metrics for connect states and wait event types.
- PostgreSQL Load Balancer add-on is now available, providing a unified endpoint for cluster access, simplifying failover handling, and ensuring node health through regular checks.
- We’re working on enabling multi-datacenter (multi-DC) cluster provisioning via API and console, designed to make it easier to deploy clusters across regions with secure networking and reduced manual steps.
- We’re working on adding Kafka Tiered Storage for clusters running in GCP— designed to bring affordable, scalable retention, and instant access to historical data, to ensure flexibility and performance across clouds for enterprise Kafka users.
- We’re planning to extend our Managed ClickHouse to allow it to work with on-prem deployments.
- Following the success of our public preview, we’re preparing to launch PostgreSQL integrated with FSx for NetApp ONTAP (FSxN) into General Availability. This enhancement is designed to combine enterprise-grade PostgreSQL with FSxN’s scalable, cost-efficient storage, enabling customers to optimize infrastructure costs while improving performance and flexibility.
- As part of our ongoing advancements in AI for OpenSearch, we are planning to enable adding GPU nodes into OpenSearch clusters, aiming to enhance the performance and efficiency of machine learning and AI workloads.
- Self-service Tags Management feature—allowing users to add, edit, or delete tags for their clusters directly through the Instaclustr console, APIs, or Terraform provider for RIYOA deployments.
- Cadence Workflow, the open source orchestration engine created by Uber, has officially joined the Cloud Native Computing Foundation (CNCF) as a Sandbox project. This milestone ensures transparent governance, community-driven innovation, and a sustainable future for one of the most trusted workflow technologies in modern microservices and agentic AI architectures. Uber donates Cadence Workflow to CNCF: The next big leap for the open source project—read the full story and discover what’s next for Cadence.
- Upgrading ClickHouse® isn’t just about new features—it’s essential for security, performance, and long-term stability. In ClickHouse upgrade: Why staying updated matters, you’ll learn why skipping upgrades can lead to technical debt, missed optimizations, and security risks. Then, explore A guide to ClickHouse® upgrades and best practices for practical strategies, including when to choose LTS releases for mission-critical workloads and when stable releases make sense for fast-moving environments.
- Our latest blog, AI Search for OpenSearch®: Unlocking next-generation search, explains how this new solution enables smarter discovery experiences using built-in ML models, vector embeddings, and advanced search techniques—all fully managed on the NetApp Instaclustr Platform. Ready to explore the future of search? Read the full article and see how AI can transform your OpenSearch deployments.
If you have any questions or need further assistance with these enhancements to the Instaclustr Managed Platform, please contact us.
SAFE HARBOR STATEMENT: Any unreleased services or features referenced in this blog are not currently available and may not be made generally available on time or at all, as may be determined in NetApp’s sole discretion. Any such referenced services or features do not represent promises to deliver, commitments, or obligations of NetApp and may not be incorporated into any contract. Customers should make their purchase decisions based upon services and features that are currently generally available.
The post Instaclustr product update: December 2025 appeared first on Instaclustr.
Consuming CDC with Java, Go… and Rust!
A quick look at how to use ScyllaDB Change Data Caputure with the Rust connector In 2021, we published a guide for using Java and Go with ScyllaDB CDC. Today, we are happy to share a new version of that post, including how to use ScyllaDB CDC with the Rust connector! Note: We will skip some of the sections in the original post, like “Why Use a Library?” and challenges in using CDC. If you are planning to use CDC in production, you should absolutely go back and read them. But if you’re just looking to get a demo up and running, this post will get you there. Getting Started with Rust scylla-cdc-rust is a library for consuming the ScyllaDB CDC Log in Rust applications. It automatically and transparently handles errors and topology changes of the underlying ScyllaDB cluster. As a result, the API allows the user to read the CDC log without having to deeply understand the internal structure of CDC. The library was written in pure Rust, using ScyllaDB Rust Driver and Tokio. Let’s see how to use the Rust library. We will build an application that prints changes happening to a table in real-time. You can see the final code here. Installing the library The scylla-cdc library is available on crates.io. Setting up the CDC consumer The most important part of using the library is to define a callback that will be executed after reading a CDC log from the database. Such a callback is defined by implementing the Consumer trait located in scylla-cdc::consumer. For now, we will define a struct with no member variables for this purpose: Since the callback will be executed asynchronously, we have to use the async-trait crate to implement the Consumer trait. We also use the anyhow crate for error handling. The library is going to create one instance of TutorialConsumer per CDC stream, so we also need to define a ConsumerFactory for them: Adding shared state to the consumer Different instances of Consumer are being used in separate Tokio tasks. Due to that, the runtime might schedule them on separate threads. In response, a struct implementing the Consumer trait should also implement the Send trait and a struct implementing the ConsumerFactory trait should implement Send and Sync traits. Luckily, Rust implements these traits by default if all member variables of a struct implement them. If the consumers need to share some state, like a reference to an object, they can be wrapped in an Arc. An example of that might be a Consumer that counts rows read by all its instances: Note: In general, keeping shared mutable state in the Consumer is not recommended. That’s because it requires synchronization (i.e. a mutex or an atomic like AtomicUsize), which reduces the speedup granted by Tokio by running the Consumer logic on multiple cores. Fortunately, keeping exclusive (not shared) mutable state in the Consumer comes with no additional overhead. Starting the application Now we’re ready to create our main function: As we can see, we have to configure a few things in order to start the log reader: We have to create a connection to the database, using the Session struct from ScyllaDB Rust Driver. Specify the keyspace and the table name. We create time bounds for our reader. This step is not compulsory – by default, the reader will start reading from now and will continue reading forever. In our case, we are going to read all logs added during the last 6 minutes. We create the factory. We can build the log reader. After creating the log reader, we can await the handle it returns so that our application will terminate as soon as the reader finishes. Now, let’s insert some rows into the table. After inserting 3 rows and running the application, you should see the output:Hello, scylla-cdc!
Hello, scylla-cdc! Hello, scylla-cdc! The application
printed one line for each CDC log consumed. To see how to use
CDCRow and save progress, see the full example below. Full Example
Follow
this detailed cdc-rust tutorial or git clone
https://github.com/scylladb/scylla-cdc-rust cd scylla-cdc-rust
cargo run --release --bin scylla-cdc-printer -- --keyspace KEYSPACE
--table TABLE --hostname HOSTNAME Where HOSTNAME is the IP
address of the cluster. Getting Started with Java and Go For a
detailed walk through of with Java and Go examples, see our
previous blog,
Consuming CDC with Java and Go. Further reading In this blog,
we have explained what problems the scylla-cdc-rust,
scylla-cdc-java, and scylla-cdc-go libraries solve and how to write
a simple application with each. If you would like to learn more,
check out the links below:
Replicator example application in the scylla-cdc-java
repository. It is an advanced application that replicates a table
from one Scylla cluster to another one using the CDC log and
scylla-cdc-java library. Example
applications in scylla-cdc-go repository. The repository
currently contains two examples: “simple-printer”, which prints
changes from a particular schema, “printer”, which is the same as
the example presented in the blog, and “replicator”, which is a
relatively complex application which replicates changes from one
cluster to another. API
reference for scylla-cdc-go. Includes slightly more
sophisticated examples which, unlike the example in this blog,
cover saving progress. CDC
documentation. Knowledge about the design of Scylla’s CDC can
be helpful in understanding the concepts in the documentation for
both the Java and Go libraries. The parts about the CDC log schema
and representation of data in the log is especially useful.
ScyllaDB users slack. We
will be happy to answer your questions about the CDC on the #cdc
channel. We hope all that talk about consuming data has managed to
whet your appetite for CDC! Happy and fruitful coding! Freezing streaming data into Apache Iceberg™—Part 1: Using Apache Kafka®Connect Iceberg Sink Connector
IntroductionEver since the first distributed system—i.e. 2 or more computers networked together (in 1969)—there has been the problem of distributed data consistency: How can you ensure that data from one computer is available and consistent with the second (and more) computers? This problem can be uni-directional (one computer is considered the source of truth, others are just copies), or bi-directional (data must be synchronized in both directions across multiple computers).
Some approaches to this problem I’ve come across in the last 8 years include Kafka Connect (for elegantly solving the heterogeneous many-to-many integration problem by streaming data from source systems to Kafka and from Kafka to sink systems, some earlier blogs on Apache Camel Kafka Connectors and a blog series on zero-code data pipelines), MirrorMaker2 (MM2, for replicating Kafka clusters, a 2 part blog series), and Debezium (Change Data Capture/CDC, for capturing changes from databases as streams and making them available in downstream systems, e.g. for Apache Cassandra and PostgreSQL)—MM2 and Debezium are actually both built on Kafka Connect.
Recently, some “sink” systems have been taking over responsibility for streaming data from Kafka into themselves, e.g. OpenSearch pull-based ingestion (c.f. OpenSearch Sink Connector), and the ClickHouse Kafka Table Engine (c.f. ClickHouse Sink Connector). These “pull-based” approaches are potentially easier to configure and don’t require running a separate Kafka Connect cluster and sink connectors, but some downsides may be that they are not as reliable or independently scalable, and you will need to carefully monitor and scale them to ensure they perform adequately.
And then there’s “zero-copy” approaches—these rely on the well-known computer science trick of sharing a single copy of data using references (or pointers), rather than duplicating the data. This idea has been around for almost as long as computers, and is still widely applicable, as we’ll see in part 2 of the blog.
The distributed data use case we’re going to explore in this 2-part blog series is streaming Apache Kafka data into Apache Iceberg, or “Freezing streaming Apache Kafka data into an (Apache) Iceberg”! In part 1 we’ll introduce Apache Iceberg and look at the first approach for “freezing” streaming data using the Kafka Connect Iceberg Sink Connector.
What is Apache Iceberg?Apache Iceberg is an open source specification open table format optimized for column-oriented workloads, supporting huge analytic datasets. It supports multiple different concurrent engines that can insert and query table data using SQL—and Iceberg is organized like, well, an iceberg!
The tip of the Iceberg is the Catalog. An Iceberg Catalog acts as a central metadata repository, tracking the current state of Iceberg tables, including their names, schemas, and metadata file locations. It serves as the “single source of truth” for a data Lakehouse, enabling query engines to find the correct metadata file for a table to ensure consistent and atomic read/write operations.
Just under the water, the next layer is the metadata layer. The Iceberg metadata layer tracks the structure and content of data tables in a data lake, enabling features like efficient query planning, versioning, and schema evolution. It does this by maintaining a layered structure of metadata files, manifest lists, and manifest files that store information about table schemas, partitions, and data files, allowing query engines to prune unnecessary files and perform operations atomically.
The data layer is at the bottom. The Iceberg data layer is the storage component where the actual data files are stored. It supports different storage backends, including cloud-based object storage like Amazon S3 or Google Cloud Storage, or HDFS. It uses file formats like Parquet or Avro. Its main purpose is to work in conjunction with Iceberg’s metadata layer to manage table snapshots and provide a more reliable and performant table format for data lakes, bringing data warehouse features to large datasets.
As shown in the above diagram, Iceberg supports multiple different engines, including Apache Spark and ClickHouse. Engines provide the “database” features you would expect, including:
- Data Management
- ACID Transactions
- Query Planning and Optimization
- Schema Evolution
- And more!
I’ve recently been reading an excellent book on Apache Iceberg (“Apache Iceberg: The Definitive Guide”), which explains the philosophy, architecture and design, including operation, of Iceberg. For example, it says that it’s best practice to treat data lake storage as immutable—data should only be added to a Data Lake, not deleted. So, in theory at least, writing infinite, immutable Kafka streams to Iceberg should be straightforward!
But because it’s a complex distributed system (which looks like a database from above water but is really a bunch of files below water!), there is some operational complexity. For example, it handles change and consistency by creating new snapshots for every modification, enabling time travel, isolating readers from writes, and supporting optimistic concurrency control for multiple writers. But you need to manage snapshots (e.g. expiring old snapshots). And chapter 4 (performance optimisation) explains that you may need to worry about compaction (reducing too many small files), partitioning approaches (which can impact read performance), and handling row-level updates. The first two issues may be relevant for Kafka, but probably not the last one. So, it looks like it’s good fit for the streaming Kafka use cases, but we may need to watch out for Iceberg management issues.
“Freezing” streaming data with the Kafka Iceberg Sink ConnectorBut Apache Iceberg is “frozen”—what’s the connection to fast-moving streaming data? You certainly don’t want to collide with an iceberg from your speedy streaming “ship”—but you may want to freeze your streaming data for long-term analytical queries in the future. How can you do that without sinking? Actually, a “sink” is the first answer: A Kafka Connect Iceberg Sink Connector is the most common way of “freezing” your streaming data in Iceberg!
Kafka Connect is the standard framework provided by Apache Kafka to move data from multiple heterogeneous source systems to multiple heterogeneous sink systems, using:
- A Kafka cluster
- A Kafka Connect cluster (running connectors)
- Kafka Connect source connectors
- Kafka topics and
- Kafka Connect Sink Connectors
That is, a highly decoupled approach. It provides real-time data movement with high scalability, reliability, error handling and simple transformations.
Here’s the Kafka Connect Iceberg Sink Connector official documentation.
It appears to be reasonably complicated to configure this sink connector; you will need to know something about Iceberg. For example, what is a “control topic”? It’s apparently used to coordinate commits for exactly-once semantics (EOS).
The connector supports fan-out (writing to multiple Iceberg tables from one topic), fan-in (writing to one Iceberg table from multiple topics), static and dynamic routing, and filtering.
In common with many technologies that you may want to use as Kafka Connect sinks, they may not all have good support for Kafka metadata. The KafkaMetadata Transform (which injects topic, partition, offset and timestamp properties) is only experimental at present.
How are Iceberg tables created with the correct metadata? If you have JSON record values, then schemas are inferred by default (but may not be correct or optimal). Alternatively, explicit schemas can be included in-line or referenced from a Kafka Schema Registry (e.g. Karapace), and, as an added bonus, schema evolution is supported. Also note that Iceberg tables may have to be manually created prior to use if your Catalog doesn’t support table auto-creation.
From what I understood about Iceberg, to use it (e.g. for writes), you need support from an engine (e.g. to add raw data to the Iceberg warehouse, create the metadata files, and update the catalog). How does this work for Kafka Connect? From this blog I discovered that the Kafka Connect Iceberg Sink connector is functioning as an Iceberg engine for writes, so there really is an engine, but it’s built into the connector.
As is the case with all Kafka Connect Sink Connectors, records are available immediately they are written to Kafka topics by Kafka producers and Kafka Connect Source Connectors, i.e. records in active segments can be copied immediately to sink systems. But is the Iceberg Sink Connector real-time? Not really! The default time to write to Iceberg is every 5 minutes (iceberg.control.commit.interval-ms) to prevent multiplication of small files—something that Iceberg(s) doesn’t/don’t like (“melting”?). In practice, it’s because every data file must be tracked in the metadata layer, which impacts performance in many ways—proliferation of small files is typically addressed by optimization and compaction (e.g. Apache Spark supports Iceberg management, including these operations).
So, unlike most Kafka Connect sink connectors, which write as quickly as possible, there will be lag before records appear in Iceberg tables (“time to freeze” perhaps)!
The systems are separate (Kafka and Iceberg are independent), records are copied to Iceberg, and that’s it! This is a clean separation of concerns and ownership. Kafka owns the source data (with Kafka controlling data lifecycles, including record expiry), Kafka Connect Iceberg Sink Connector performs the reading from Kafka and writing to Iceberg, and is independently scalable to Kafka. Kafka doesn’t handle any of the Iceberg management. Once the data has landed in Iceberg, Kafka has no further visibility or interest in it. And the pipeline is purely one way, write only – reads or deletes are not supported.
Here’s a summary of this approach to freezing streams:
- Kafka Connect Iceberg Sink Connector shares all the benefits of the Kafka Connect framework, including scalability, reliability, error handling, routing, and transformations.
- At least, JSON values are required, ideally full schemas and referenced in Karapace—but not all schemas are guaranteed to work.
- Kafka Connect doesn’t “manage” Iceberg (e.g. automatically aggregate small files, remove snapshots, etc.)
- You may have to tune the commit interval – 5 minutes is the default.
- But it does have a built-in engine that supports writing to Iceberg.
- You may need to use an external tool (e.g. Apache Spark) for Iceberg management procedures.
- It’s write-only to Iceberg. Reads or deletes are not supported
But what’s the best thing about the Kafka Connect Iceberg Sink Connector? It’s available now (as part of the Apache Iceberg build) and works on the NetApp Instaclustr Kafka Connect platform as a “bring your own connector” (instructions here).
In part 2, we’ll look at Kafka Tiered Storage and Iceberg Topics!
The post Freezing streaming data into Apache Iceberg™—Part 1: Using Apache Kafka®Connect Iceberg Sink Connector appeared first on Instaclustr.
Netflix Live Origin
Xiaomei Liu, Joseph Lynch, Chris Newton
Introduction
Behind the Streams: Building a Reliable Cloud Live Streaming Pipeline for Netflix introduced the architecture of the streaming pipeline. This blog post looks at the custom Origin Server we built for Live — the Netflix Live Origin. It sits at the demarcation point between the cloud live streaming pipelines on its upstream side and the distribution system, Open Connect, Netflix’s in-house Content Delivery Network (CDN), on its downstream side, and acts as a broker managing what content makes it out to Open Connect and ultimately to the client devices.
Netflix Live Origin is a multi-tenant microservice operating on EC2 instances within the AWS cloud. We lean on standard HTTP protocol features to communicate with the Live Origin. The Packager pushes segments to it using PUT requests, which place a file into storage at the particular location named in the URL. The storage location corresponds to the URL that is used when the Open Connect side issues the corresponding GET request.
Live Origin architecture is influenced by key technical decisions of the live streaming architecture. First, resilience is achieved through redundant regional live streaming pipelines, with failover orchestrated at the server-side to reduce client complexity. The implementation of epoch locking at the cloud encoder enables the origin to select a segment from either encoding pipeline. Second, Netflix adopted a manifest design with segment templates and constant segment duration to avoid frequent manifest refresh. The constant duration templates enable Origin to predict the segment publishing schedule.
Multi-pipeline and multi-region aware origin
Live streams inevitably contain defects due to the non-deterministic nature of live contribution feeds and strict real-time segment publishing timelines. Common defects include:
- Short segments: Missing video frames and audio samples.
- Missing segments: Entire segments are absent.
- Segment timing discontinuity: Issues with the Track Fragment Decode Time.
Communicating segment discontinuity from the server to the client via a segment template-based manifest is impractical, and these defective segments can disrupt client streaming.
The redundant cloud streaming pipelines operate independently, encompassing distinct cloud regions, contribution feeds, encoder, and packager deployments. This independence substantially mitigates the probability of simultaneous defective segments across the dual pipelines. Owing to its strategic placement within the distribution path, the live origin naturally emerges as a component capable of intelligent candidate selection.
The Netflix Live Origin features multi-pipeline and multi-region awareness. When a segment is requested, the live origin checks candidates from each pipeline in a deterministic order, selecting the first valid one. Segment defects are detected via lightweight media inspection at the packager. This defect information is provided as metadata when the segment is published to the live origin. In the rare case of concurrent defects at the dual pipeline, the segment defects can be communicated downstream for intelligent client-side error concealment.
Open Connect streaming optimization
When the Live project started, Open Connect had become highly optimised for VOD content delivery — nginx had been chosen many years ago as the Web Server since it is highly capable in this role, and a number of enhancements had been added to it and to the underlying operating system (BSD). Unlike traditional CDNs, Open Connect is more of a distributed origin server — VOD assets are pre-positioned onto carefully selected server machines (OCAs, or Open Connect Appliances) rather than being filled on demand.
Alongside the VOD delivery, an on-demand fill system has been used for non-VOD assets — this includes artwork and the downloadable portions of the clients, etc. These are also served out of the same nginx workers, albeit under a distinct server block, using a distinct set of hostnames.
Live didn’t fit neatly into this ‘small object delivery’ model, so we extended the proxy-caching functionality of nginx to address Live-specific needs. We will touch on some of these here related to optimized interactions with the Origin Server. Look for a future blog post that will go into more details on the Open Connect side.
The segment templates provided to clients are also provided to the OCAs as part of the Live Event Configuration data. Using the Availability Start Time and Initial Segment number, the OCA is able to determine the legitimate range of segments for each event at any point in time — requests for objects outside this range can be rejected, preventing unnecessary requests going up through the fill hierarchy to the origin. If a request makes it through to the origin, and the segment isn’t available yet, the origin server will return a 404 Status Code (indicating File Not Found) with the expiration policy of that error so that it can be cached within Open Connect until just before that segment is expected to be published.
If the Live Origin knows when segments are being pushed to it, and knows what the live edge is — when a request is received for the immediately next object, rather than handing back another 404 error (which would go all the way back through Open Connect to the client), the Live Origin can ‘hold open’ the request, and service it once the segment has been published to it. By doing this, the degree of chatter within the network handling requests that arrive early has been significantly reduced. As part of this, millisecond grain caching was added to nginx to enhance the standard HTTP Cache Control, which only works at second granularity, a long time when segments are generated every 2 seconds.
Streaming metadata enhancement
The HTTP standard allows for the addition of request and response headers that can be used to provide additional information as files move between clients and servers. The HTTP headers provide notifications of events within the stream in a highly scalable way that is independently conveyed to client devices, regardless of their playback position within the stream.
These notifications are provided to the origin by the live streaming pipeline and are inserted by the origin in the form of headers, appearing on the segments generated at that point in time (and persist to future segments — they are cumulative). Whenever a segment is received at an OCA, this notification information is extracted from the response headers and used to update an in-memory data structure, keyed by event ID; and whenever a segment is served from the OCA, the latest such notification data is attached to the response. This means that, given any flow of segments into an OCA, it will always have the most recent notification data, even if all clients requesting it are behind the live edge. In fact, the notification information can be conveyed on any response, not just those supplying new segments.
Cache invalidation and origin mask
An invalidation system has been available since the early days of the project. It can be used to “flush” all content associated with an event by altering the key used when looking up objects in cache — this is done by incorporating a version number into the cache key that can then be bumped on demand. This is used during pre-event testing so that the network can be returned to a pristine state for the test with minimal fuss.
Each segment published by the Live Origin conveys the encoding pipeline it was generated by, as well as the region it was requested from. Any issues that are found after segments make their way into the network can be remedied by an enhanced invalidation system that takes such variants into account. It is possible to invalidate (that is, cause to be considered expired) segments in a range of segment numbers, but only if they were sourced from encoder A, or from Encoder A, but only if retrieved from region X.
In combination with Open Connect’s enhanced cache invalidation, the Netflix Live Origin allows selective encoding pipeline masking to exclude a range of segments from a particular pipeline when serving segments to Open Connect. The enhanced cache invalidation and origin masking enable live streaming operations to hide known problematic segments (e.g., segments causing client playback errors) from streaming clients once the bad segments are detected, protecting millions of streaming clients during the DVR playback window.
Origin storage architecture
Our original storage architecture for the Live Origin was simple: just use AWS S3 like we do for SVOD. This served us well initially for our low-traffic events, but as we scaled up we discovered that Live streaming has unique latency and workload requirements that differ significantly from on-demand where we have significant time ahead-of-time to pre-position content. While S3 met its stated uptime guarantees, our strict 2-second retry budget inherent to Live events (where every write is critical) led us to explore optimizations specifically tailored for real-time delivery at scale. AWS S3 is an amazing object store, but our Live streaming requirements were closer to those of a global low-latency highly-available database. So, we went back to the drawing board and started from the requirements. The Origin required:
- [HA Writes] Extremely high write availability, ideally as close to full write availability within a single AWS region, with low second replication delay to other regions. Any failed write operation within 500ms is considered a bug that must be triaged and prevented from re-occurring.
- [Throughput] High write throughput, with hundreds of MiB replicating across regions
- [Large Partitions] Efficiently support O(MiB) writes that accumulate to O(10k) keys per partition with O(GiB) total size per event.
- [Strong Consistency] Within the same region, we needed read-your-write semantics to hit our <1s read delay requirements (must be able to read published segments)
- [Origin Storm] During worst-case load involving Open Connect edge cases, we may need to handle O(GiB) of read throughput without affecting writes.
Fortunately, Netflix had previously invested in building a KeyValue Storage Abstraction that cleverly leveraged Apache Cassandra to provide chunked storage of MiB or even GiB values. This abstraction was initially built to support cloud saves of Game state. The Live use case would push the boundaries of this solution, however, in terms of availability for writes (#1), cumulative partition size (#3), and read throughput during Origin Storm (#5).
High Availability for Writes of Large Payloads
The KeyValue Payload Chunking and Compression Algorithm breaks O(MiB) work down so each part can be idempotently retried and hedged to maintain strict latency service level objectives, as well as spreading the data across the full cluster. When we combine this algorithm with Apache Cassandra’s local-quorum consistency model, which allows write availability even with an entire Availability Zone outage, plus a write-optimized Log-Structured Merge Tree (LSM) storage engine, we could meet the first four requirements. After iterating on the performance and availability of this solution, we were not only able to achieve the write availability required, but did so with a P99 tail latency that was similar to the status quo’s P50 average latency while also handling cross-region replication behind the scenes for the Origin. This new solution was significantly more expensive (as expected, databases backed by SSD cost more), but minimizing cost was not a key objective and low latency with high availability was:
High Availability Reads at Gbps Throughputs
Now that we solved the write reliability problem, we had to handle the Origin Storm failure case, where potentially dozens of Open Connect top-tier caches could be requesting multiple O(MiB) video segments at once. Our back-of-the-envelope calculations showed worst-case read throughput in the O(100Gbps) range, which would normally be extremely expensive for a strongly-consistent storage engine like Apache Cassandra. With careful tuning of chunk access, we were able to respond to reads at network line rate (100Gbps) from Apache Cassandra, but we observed unacceptable performance and availability degradation on concurrent writes. To resolve this issue, we introduced write-through caching of chunks using our distributed caching system EVCache, which is based on Memcached. This allows almost all reads to be served from a highly scalable cache, allowing us to easily hit 200Gbps and beyond without affecting the write path, achieving read-write separation.
Final Storage Architecture
In the final storage architecture, the Live Origin writes and reads to KeyValue, which manages a write-through cache to EVCache (memcached) and implements a safe chunking protocol that spreads large values and partitions them out across the storage cluster (Apache Cassandra). This allows almost all read load to be handled from cache, with only misses hitting the storage. This combination of cache and highly available storage has met the demanding needs of our Live Origin for over a year now.
Delivering this consistent low latency for large writes with cross-region replication and consistent write-through caching to a distributed cache required solving numerous hard problems with novel techniques, which we plan to share in detail during a future post.
Scalability and scalable architecture
Netflix’s live streaming platform must handle a high volume of diverse stream renditions for each live event. This complexity stems from supporting various video encoding formats (each with multiple encoder ladders), numerous audio options (across languages, formats, and bitrates), and different content versions (e.g., with or without advertisements). The combination of these elements, alongside concurrent event support, leads to a significant number of unique stream renditions per live event. This, in turn, necessitates a high Requests Per Second (RPS) capacity from the multi-tenant live origin service to ensure publishing-side scalability.
In addition, Netflix’s global reach presents distinct challenges to the live origin on the retrieval side. During the Tyson vs. Paul fight event in 2024, a historic peak of 65 million concurrent streams was observed. Consequently, a scalable architecture for live origin is essential for the success of large-scale live streaming.
Scaling architecture
We chose to build a highly scalable origin instead of relying on the traditional origin shields approach for better end-to-end cache consistency control and simpler system architecture. The live origin in this architecture directly connects with top-tier Open Connect nodes, which are geographically distributed across several sites. To minimize the load on the origin, only designated nodes per stream rendition at each site are permitted to directly fill from the origin.
While the origin service can autoscale horizontally using EC2 instances, there are other system resources that are not autoscalable, such as storage platform capacity and AWS to Open Connect backbone bandwidth capacity. Since in live streaming, not all requests to the live origin are of the same importance, the origin is designed to prioritize more critical requests over less critical requests when system resources are limited. The table below outlines the request categories, their identification, and protection methods.
Publishing isolation
Publishing traffic, unlike potentially surging CDN retrieval traffic, is predictable, making path isolation a highly effective solution. As shown in the scalability architecture diagram, the origin utilizes separate EC2 publishing and CDN stacks to protect the latency and failure-sensitive origin writes. In addition, the storage abstraction layer features distinct clusters for key-value (KV) read and KV write operations. Finally, the storage layer itself separates read (EVCache) and write (Cassandra) paths. This comprehensive path isolation facilitates independent cloud scaling of publishing and retrieval, and also prevents CDN-facing traffic surges from impacting the performance and reliability of origin publishing.
Priority rate limiting
Given Netflix’s scale, managing incoming requests during a traffic storm is challenging, especially considering non-autoscalable system resources. The Netflix Live Origin implemented priority-based rate limiting when the underlying system is under stress. This approach ensures that requests with greater user impact are prioritized to succeed, while requests with lower user impact are allowed to fail during times of stress in order to protect the streaming infrastructure and are permitted to retry later to succeed.
Leveraging Netflix’s microservice platform priority rate limiting feature, the origin prioritizes live edge traffic over DVR traffic during periods of high load on the storage platform. The live edge vs. DVR traffic detection is based on the predictable segment template. The template is further cached in memory on the origin node to enable priority rate limiting without access to the datastore, which is valuable especially during periods of high datastore stress.
To mitigate traffic surges, TTL cache control is used alongside priority rate limiting. When the low-priority traffic is impacted, the origin instructs Open Connect to slow down and cache identical requests for 5 seconds by setting a max-age = 5s and returns an HTTP 503 error code. This strategy effectively dampens traffic surges by preventing repeated requests to the origin within that 5-second window.
The following diagrams illustrate origin priority rate limiting with simulated traffic. The nliveorigin_mp41 traffic is the low-priority traffic and is mixed with other high-priority traffic. In the first row: the 1st diagram shows the request RPS, the 2nd diagram shows the percentage of request failure. In the second row, the 1st diagram shows datastore resource utilization, and the 2nd diagram shows the origin retrieval P99 latency. The results clearly show that only the low-priority traffic (nliveorigin_mp41) is impacted at datastore high utilization, and the origin request latency is under control.
404 storm and cache optimization
Publishing isolation and priority rate limiting successfully protect the live origin from DVR traffic storms. However, the traffic storm generated by requests for non-existent segments presents further challenges and opportunities for optimization.
The live origin structures metadata hierarchically as event > stream rendition > segment, and the segment publishing template is maintained at the stream rendition level. This hierarchical organization allows the origin to preemptively reject requests with an HTTP 404(not found)/410(Gone) error, leveraging highly cacheable event and stream rendition level metadata, avoiding unnecessary queries to the segment level metadata:
- If the event is unknown, reject the request with 404
- If the event is known, but the segment request timing does not match the expected publishing timing, reject the request with 404 and cache control TTL matching the expected publishing time
- If the event is known, the requested segment is never generated or misses the retry deadline, reject the request with a 410 error, preventing the client from repeatedly requesting
At the storage layer, metadata is stored separately from media data in the control plane datastore. Unlike the media datastore, the control plane datastore does not use a distributed cache to avoid cache inconsistency. Event and rendition level metadata benefits from a high cache hit ratio when in-memory caching is utilized at the live origin instance. During traffic storms involving non-existent segments, the cache hit ratio for control plane access easily exceeds 90%.
The use of in-memory caching for metadata effectively handles 404 storms at the live origin without causing datastore stress. This metadata caching complements the storage system’s distributed media cache, providing a complete solution for traffic surge protection.
Summary
The Netflix Live Origin, built upon an optimized storage platform, is specifically designed for live streaming. It incorporates advanced media and segment publishing scheduling awareness and leverages enhanced intelligence to improve streaming quality, optimize scalability, and improve Open Connect live streaming operations.
Acknowledgement
Many teams and stunning colleagues contributed to the Netflix live origin. Special thanks to Flavio Ribeiro for advocacy and sponsorship of the live origin project; to Raj Ummadisetty, Prudhviraj Karumanchi for the storage platform; to Rosanna Lee, Hunter Ford, and Thiago Pontes for storage lifecycle management; to Ameya Vasani for e2e test framework; Thomas Symborski for orchestrator integration; to James Schek for Open Connect integration; to Kevin Wang for platform priority rate limit; to Di Li, Nathan Hubbard for origin scalability testing.
Netflix Live Origin was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.
Stay ahead with Apache Cassandra®: 2025 CEP highlights
Apache Cassandra® committers are working hard, building new features to help you more seamlessly ease operational challenges of a distributed database. Let’s dive into some recently approved CEPs and explain how these upcoming features will improve your workflow and efficiency.
What is a CEP?CEP stands for Cassandra Enhancement Proposal. They are the process for outlining, discussing, and gathering endorsements for a new feature in Cassandra. They’re more than a feature request; those who put forth a CEP have intent to build the feature, and the proposal encourages a high amount of collaboration with the Cassandra contributors.
The CEPs discussed here were recently approved for implementation or have had significant progress in their implementation. As with all open-source development, inclusion in a future release is contingent upon successful implementation, community consensus, testing, and approval by project committers.
CEP-42: Constraints frameworkWith collaboration from NetApp Instaclustr, CEP-42, and subsequent iterations, delivers schema level constraints giving Cassandra users and operators more control over their data. Adding constraints on the schema level means that data can be validated at write time and send the appropriate error when data is invalid.
Constraints are defined in-line or as a separate definition. The inline style allows for only one constraint while a definition allows users to define multiple constraints with different expressions.
The scope of this CEP-42 initially supported a few constraints that covered the majority of cases, but in follow up efforts the expanded list of support includes scalar (>, <, >=, <=), LENGTH(), OCTET_LENGTH(), NOT NULL, JSON(), REGEX(). A user is also able to define their own constraints if they implement it and put them on Cassandra’s class path.
A simple example of an in-line constraint:
CREATE TABLE users (
username text PRIMARY KEY,
age int CHECK age >= 0 and age < 120
);
Constraints are not supported for UDTs (User-Defined Types) nor collections (except for using NOT NULL for frozen collections).
Enabling constraints closer to the data is a subtle but mighty way for operators to ensure that data goes into the database correctly. By defining rules just once, application code is simplified, more robust, and prevents validation from being bypassed. Those who have worked with MySQL, Postgres, or MongoDB will enjoy this addition to Cassandra.
CEP-51: Support “Include” Semantics for cassandra.yamlThe cassandra.yaml file holds important settings for storage,
memory, replication, compaction, and more. It’s no surprise that
the average size of the file around 1,000 lines (though, yes—most
are comments). CEP-51 enables splitting the cassandra.yaml
configuration into multiple files using includes
semantics. From the outside, this feels like a small change, but
the implications are huge if a user chooses to opt-in.
In general, the size of the configuration file makes it difficult to manage and coordinate changes. It’s often the case that multiple teams manage various aspects of the single file. In addition, cassandra.yaml permissions are readable for those with access to this file, meaning private information like credentials are comingled with all other settings. There is risk from an operational and security standpoint.
Enabling the new semantics and therefore modularity for the configuration file eases management, deployment, complexity around environment-specific settings, and security in one shot. The configuration file follows the principle of least privilege once the cassandra.yaml is broken up into smaller, well-defined files; sensitive configuration settings are separated out from general settings with fine-grained access for the individual files. With the feature enabled, different development teams are better equipped to deploy safely and independently.
If you’ve deployed your Cassandra cluster on the NetApp Instaclustr platform, the cassandra.yaml file is already configured and managed for you. We pride ourselves on making it easy for you to get up and running fast.
CEP-52: Schema annotations for Apache CassandraWith extensive review by the NetApp Instaclustr team and Stefan Miklosovic, CEP-52 introduces schema annotations in CQL allowing in-line comments and labels of schema elements such as keyspaces, tables, columns, and User Defined Types (UDT). Users can easily define and alter comments and labels on these elements. They can be copied over when desired using CREATE TABLE LIKE syntax. Comments are stored as plain text while labels are stored as structured metadata.
Comments and labels serve different annotation purposes: Comments document what a schema object is for, whereas labels describe how sensitive or controlled it is meant to be. For example, labels can be used to identify columns as “PII” or “confidential”, while the comment on that column explains usage, e.g. “Last login timestamp.”
Users can query these annotations. CEP-52 defines two new read-only tables (system_views.schema_comments and system_views.schema_security_labels) to store comments and security labels so objects with comments can be returned as a list or a user/machine process can query for specific labels, beneficial for auditing and classification. Note that adding security labels are descriptive metadata and do not enforce access control to the data.
CEP-53: Cassandra rolling restarts via SidecarSidecar is an auxiliary component in the Cassandra ecosystem that exposes cluster management and streaming capabilities through APIs. Introducing rolling restarts through Sidecar, this feature is designed to provide operators with more efficient and safer restarts without cluster-wide downtime. More specifically, operators can monitor, pause, resume, and abort restarts all through an API with configurable options if restarts fail.
Rolling restarts brings operators a step closer to cluster-wide operations and lifecycle management via Sidecar. Operators will be able to configure the number of nodes to restart concurrently with minimal risk as this CEP unleashes clear states as a node progresses through a restart. Accounting for a variety of edge cases, an operator can feel assured that, for example, a non-functioning sidecar won’t derail operations.
The current process for restarting a node is a multi-step, manual process, which does not scale for large cluster sizes (and is also tedious for small clusters). Restarting clusters previously lacked a streamlined approach as each node needed to be restarted one at a time, making the process time-intensive and error-prone.
Though Sidecar is still considered WIP, it’s got big plans to improve operating large clusters!
The NetApp Instaclustr Platform, in conjunction with our expert TechOps team already orchestrates these laborious tasks for our Cassandra customers with a high level of care to ensure their cluster stays online. Restarting or upgrading your Cassandra nodes is a huge pain-point for operators, but it doesn’t have to be when using our managed platform (with round-the-clock support!)
CEP-54: Zstd with dictionary SSTable compressionCEP-54, with NetApp Instaclustr’s collaboration, aims to add support Zstd with dictionary compression for SSTables. Zstd, or Zstandard, is a fast, lossless data compression algorithm that boasts impressive ratio and speed and has been supported in Cassandra since 4.0. Certain workloads can benefit from significantly faster read/write performance, reduced storage footprint, and increased storage device lifetime when using dictionary compression.
At a high level, operators choose a table they want to compress with a dictionary. A dictionary must be trained first on a small amount of already present data (recommended no more than 10MiB). The result of a training is a dictionary, which is stored cluster-wide for all other nodes to use, and this dictionary is used for all subsequent writes of SSTables to a disk.
Workloads with structured data of similar rows benefit most from Zstd with dictionary compression. Some examples of ideal workloads include event logs, telemetry data, metadata tables with templated messages. Think: repeated row data. If the table data is too unstructured or random, this feature likely won’t be optimal for dictionary compression, however plain Zstd will still be an excellent option.
New SSTables with dictionaries are readable across nodes and can stream, repair, and backup. Existing tables are unaffected if dictionary compression is not enabled. Too many unique dictionaries hurt decompression; use minimal dictionaries (recommended dictionary size is about 100KiB and one dictionary per table) and only adopt new ones when they’re noticeably better.
CEP-55: Generated role namesCEP-55 adds support to create users/roles without supplying a name, simplifying
user management, especially when generating users and roles in bulk. With an example syntax, CREATE GENERATED ROLE WITH GENERATED PASSWORD, new keys are placed in a newly introduced configuration section in cassandra.yaml under “role_name_policy.”
Stefan Miklosovic, our Cassandra engineer at NetApp Instaclustr, created this CEP as a logical follow up to CEP-24 (password validation/generation), which he authored as well. These quality-of-life improvements let operators spend less time doing trivial tasks with high-risk potential and more time on truly complex matters.
Manual name selection seems trivial until a hundred role names need to be generated; now there is a security risk if the new usernames—or worse passwords—are easily guessable. With CEP-55, the generated role name will be UUID-like, with optional prefix/suffix and size hints, however a pluggable policy is available to generate and validate names as well. This is an opt-in feature with no effect to the existing method of generating role names.
The future of Apache Cassandra is brightThese Cassandra Enhancement Proposals demonstrate a strong commitment to making Apache Cassandra more powerful, secure, and easier to operate. By staying on top of these updates, we ensure our managed platform seamlessly supports future releases that accelerate your business needs.
At NetApp Instaclustr, our expert TechOps team already orchestrates laborious tasks like restarts and upgrades for our Apache Cassandra customers, ensuring their clusters stay online. Our platform handles the complexity so you can get up and running fast.
Learn more about our fully managed and hosted Apache Cassandra offering and try it for free today!
The post Stay ahead with Apache Cassandra®: 2025 CEP highlights appeared first on Instaclustr.
Vector search benchmarking: Embeddings, insertion, and searching documents with ClickHouse® and Apache Cassandra®
Welcome back to our series on vector search benchmarking. In part 1, we dove into setting up a benchmarking project and explored how to implement vector search in PostgreSQL from the example code in GitHub. We saw how a hands-on project with students from Northeastern University provided a real-world testing ground for Retrieval-Augmented Generation (RAG) pipelines.
Now, we’re continuing our journey by exploring two more powerful open source technologies: ClickHouse and Apache Cassandra. Both handle vector data differently and understanding their methods is key to effective vector search benchmarking. Using the same student project as our guide, this post will examine the code for embedding, inserting, and retrieving data to see how these technologies stack up.
Let’s get started.
Vector search benchmarking with ClickHouseClickHouse is a column-oriented database management system known for its incredible speed in analytical queries. It’s no surprise that it has also embraced vector search. Let’s see how the student project team implemented and benchmarked the core components.
Step 1: Embedding and inserting data
scripts/vectorize_and_upload.py
This is the file that handles Step 1 of the pipeline for
ClickHouse. Embeddings in this file
(scripts/vectorize_and_upload.py) are used as vector
representations of Guardian news articles for the purpose of
storing them in a database and performing semantic search. Here’s
how embeddings are handled step-by-step (the steps look similar to
PostgreSQL).
First up, is the generation of embeddings. The same
SentenceTransformer model used in part 1
(all-MiniLM-L6-v2) is loaded in the class constructor.
In the method generate_embeddings(self, articles), for
each article:
- The article’s title and body are concatenated into a text string.
- The model generates an embedding vector
(
self.model.encode(text_for_embedding)), which is a numerical representation of the article’s semantic content. - The embedding is added to the article’s dictionary under the
key
embedding.
Then the embeddings are stored in ClickHouse as follows.
- The database table
guardian_articlesis created with an embeddingArray(Float64) NOT NULLcolumn specifically to store these vectors. - In
upload_to_clickhouse_debug(self, articles_with_embeddings), the script inserts articles into ClickHouse, including the embedding vector as part of each row.
services/clickhouse/clickhouse_dao.py
The steps to search are the same as for PostgreSQL in part 1.
Here’s part of the related_articles method for
ClickHouse:
def related_articles(self, query: str, limit: int =
5):
"""Search for similar articles using vector similarity""" ... query_embedding = self.model.encode(query).tolist() search_query = f""" SELECT url, title, body, publication_date, cosineDistance(embedding, {query_embedding}) as distance FROM guardian_articles ORDER BY distance ASC LIMIT {limit} """ ...
When searching for related articles, it encodes the query into an embedding, then performs a vector similarity search in ClickHouse using cosineDistance between stored embeddings and the query embedding, and results are ordered by similarity, returning the most relevant articles.
Vector search benchmarking with Apache CassandraNext, let’s turn our attention to Apache Cassandra. As a distributed NoSQL database, Cassandra is designed for high availability and scalability, making it an intriguing option for large-scale RAG applications.
Step 1: Embedding and inserting datascripts/pull_docs_cassandra.py
As in the above examples, embeddings in this file are used to
convert article text (body) into numerical vector
representations for storage and later retrieval in Cassandra.
For each article, the code extracts the body and
computes the embeddings:
embedding = model.encode(body) embedding_list = [float(x) for x in embedding]
model.encode(body)converts the text to aNumPyarray of 384 floats.- The array is converted to a standard Python list of floats for Cassandra storage.
Next, the embedding is stored in the vector column of the
articles table using a CQL INSERT:
insert_cql = SimpleStatement(""" INSERT INTO articles (url, title, body, publication_date, vector) VALUES (%s, %s, %s, %s, %s) IF NOT EXISTS; """) result = session.execute(insert_cql, (url, title, body, publication_date, embedding_list))
The schema for the table specifies: vector
vector<float, 384>, meaning each article has a
corresponding 384-dimensional embedding. The code also creates a
custom index for the vector column:
session.execute(""" CREATE CUSTOM INDEX IF NOT EXISTS ann_index ON articles(vector) USING 'StorageAttachedIndex'; """)
This enables efficient vector (ANN: Approximate Nearest Neighbor) search capabilities, allowing similarity queries on stored embeddings.
A key part of the setup is the schema and indexing. The
Cassandra schema in
services/cassandra/init/01-schema.cql defines the
vector column.
Being a NoSQL database, Cassandra schemas are a bit different to normal SQL databases, so it’s worth taking a closer look. This Cassandra schema is designed to support Retrieval-Augmented Generation (RAG) architectures, which combine information retrieval with generative models to answer queries using both stored data and generative AI. Here’s how the schema supports RAG:
- Keyspace and table structure
- Keyspace (
vectorembeds): Analogous to a database, this isolates all RAG-related tables and data. - Table (
articles): Stores retrievable knowledge sources (e.g., articles) for use in generation.
- Keyspace (
- Table columns
url TEXT PRIMARY KEY: Uniquely identifies each article/document, useful for referencing and deduplication.title TEXTandbody TEXT: Store the actual content and metadata, which may be retrieved and passed to the generative model during RAG.publication_date TIMESTAMP: Enables filtering or ranking based on recency.vector VECTOR<FLOAT, 384>: Stores the embedding representation of the article. The new Cassandra vector data type is documented here.
- Indexing
- Sets up an Approximate Nearest Neighbor (ANN) index using Cassandra’s Storage Attached Index.
More information about Cassandra vector support is in the documentation.
Step 2: Vector search and retrievalThe retrieval logic in
services/cassandra/cassandra_dao.py showcases the
elegance of Cassandra’s vector search capabilities.
The code to create the query embeddings and perform the query is similar to the previous examples, but the CQL query to retrieve similar documents looks like this:
query_cql = """ SELECT url, title, body, publication_date FROM articles ORDER BY vector ANN OF ? LIMIT ? """ prepared = self.client.prepare(query_cql) rows = self.client.execute(prepared, (emb, limit))What have we learned?
By exploring the code from this RAG benchmarking project we’ve seen distinct approaches to vector search. Here’s a summary of key takeaways:
- Critical steps in the process:
- Step 1: Embedding articles and inserting them into the vector databases.
- Step 2: Embedding queries and retrieving relevant articles from the database.
- Key design pattern:
- The DAO (Data Access Object) design pattern provides a clean, scalable way to support multiple databases.
- This approach could extend to other databases, such as OpenSearch, in the future.
- Additional insights:
- It’s possible to perform vector searches over the latest documents, pre-empting queries, and potentially speeding up the pipeline.
So far, we have only scratched the surface. The students built a complete benchmarking application with a GUI (using Steamlit), used multiple other interesting components (e.g. LangChain, LangGraph, FastAPI and uvicorn), Grafana and LangSmith for metrics, and Claude to use the retrieved articles to answer questions, and Docker support for the components. They also revealed some preliminary performance results! Here’s what the final system looked like (this and the previous blog focused on the bottom boxes only).
In a future article, we will examine the rest of the application code, look at the preliminary performance results the students uncovered, and discuss what they tell us about the trade-offs between these different databases.
Ready to learn more right now? We have a wealth of resources on vector search. You can explore our blogs on ClickHouse vector search and Apache Cassandra Vector Search (here, here, and here) to deepen your understanding.
The post Vector search benchmarking: Embeddings, insertion, and searching documents with ClickHouse® and Apache Cassandra® appeared first on Instaclustr.
Optimizing Cassandra Repair for Higher Node Density
This is the fourth post in my series on improving the cost efficiency of Apache Cassandra through increased node density. In the last post, we explored compaction strategies, specifically the new UnifiedCompactionStrategy (UCS) which appeared in Cassandra 5.
- Streaming Throughput
- Compaction Throughput and Strategies
- Repair (you are here)
- Query Throughput
- Garbage Collection and Memory Management
- Efficient Disk Access
- Compression Performance and Ratio
- Linearly Scaling Subsystems with CPU Core Count and Memory
Now, we’ll tackle another aspect of Cassandra operations that directly impacts how much data you can efficiently store per node: repair. Having worked with repairs across hundreds of clusters since 2012, I’ve developed strong opinions on what works and what doesn’t when you’re pushing the limits of node density.
Building easy-cass-mcp: An MCP Server for Cassandra Operations
I’ve started working on a new project that I’d like to share, easy-cass-mcp, an MCP (Model Context Protocol) server specifically designed to assist Apache Cassandra operators.
After spending over a decade optimizing Cassandra clusters in production environments, I’ve seen teams consistently struggle with how to interpret system metrics, configuration settings, schema design, and system configuration, and most importantly, how to understand how they all impact each other. While many teams have solid monitoring through JMX-based collectors, extracting and contextualizing specific operational metrics for troubleshooting or optimization can still be cumbersome. The good news is that we now have the infrastructure to make all this operational knowledge accessible through conversational AI.
easy-cass-stress Joins the Apache Cassandra Project
I’m taking a quick break from my series on Cassandra node density to share some news with the Cassandra community: easy-cass-stress has officially been donated to the Apache Software Foundation and is now part of the Apache Cassandra project ecosystem as cassandra-easy-stress.
Why This Matters
Over the past decade, I’ve worked with countless teams struggling with Cassandra performance testing and benchmarking. The reality is that stress testing distributed systems requires tools that can accurately simulate real-world workloads. Many tools make this difficult by requiring the end user to learn complex configurations and nuance. While consulting at The Last Pickle, I set out to create an easy to use tool that lets people get up and running in just a few minutes
Azure fault domains vs availability zones: Achieving zero downtime migrations
The challenges of operating production-ready enterprise systems in the cloud are ensuring applications remain up to date, secure and benefit from the latest features. This can include operating system or application version upgrades, but it is not limited to advancements in cloud provider offerings or the retirement of older ones. Recently, NetApp Instaclustr undertook a migration activity for (almost) all our Azure fault domain customers to availability zones and Basic SKU IP addresses.
Understanding Azure fault domains vs availability zones“Azure fault domain vs availability zone” reflects a critical distinction in ensuring high availability and fault tolerance. Fault domains offer physical separation within a data center, while availability zones expand on this by distributing workloads across data centers within a region. This enhances resiliency against failures, making availability zones a clear step forward.
The need for migrating from fault domains to availability zonesNetApp Instaclustr has supported Azure as a cloud provider for our Managed open source offerings since 2016. Originally this offering was distributed across fault domains to ensure high availability using “Basic SKU public IP Addresses”, but this solution had some drawbacks when performing particular types of maintenance. Once released by Azure in several regions we extended our Azure support to availability zones which have a number of benefits including more explicit placement of additional resources, and we leveraged “Standard SKU Public IP’s” as part of this deployment.
When we introduced availability zones, we encouraged customers to provision new workloads in them. We also supported migrating workloads to availability zones, but we had not pushed existing deployments to do the migration. This was initially due to the reduced number of regions that supported availability zones.
In early 2024, we were notified that Azure would be retiring support for Basic SKU public IP addresses in September 2025. Notably, no new Basic SKU public IPs would be created after March 1, 2025. For us and our customers, this had the potential to impact cluster availability and stability – as we would be unable to add nodes, and some replacement operations would fail.
Very quickly we identified that we needed to migrate all customer deployments from Basic SKU to Standard SKU public IPs. Unfortunately, this operation involves node-level downtime as we needed to stop each individual virtual machine, detach the IP address, upgrade the IP address to the new SKU, and then reattach and start the instance. For customers who are operating their applications in line with our recommendations, node-level downtime does not have an impact on overall application availability, however it can increase strain on the remaining nodes.
Given that we needed to perform this potentially disruptive maintenance by a specific date, we decided to evaluate the migration of existing customers to Azure availability zones.
Key migration consideration for Cassandra clustersAs with any migration, we were looking at performing this with zero application downtime, minimal additional infrastructure costs, and as safe as possible. For some customers, we also needed to ensure that we do not change the contact IP addresses of the deployment, as this may require application updates from their side. We quickly worked out several ways to achieve this migration, each with its own set of pros and cons.
For our Cassandra customers, our go to method for changing cluster topology is through a data center migration. This is our zero-downtime migration method that we have completed hundreds of times, and have vast experience in executing. The benefit here is that we can be extremely confident of application uptime through the entire operation and be confident in the ability to pause and reverse the migration if issues are encountered. The major drawback to a data center migration is the increased infrastructure cost during the migration period – as you effectively need to have both your source and destination data centers running simultaneously throughout the operation. The other item of note, is that you will need to update your cluster contact points to the new data center.
For clusters running other applications, or customers who are more cost conscious, we evaluated doing a “node by node” migration from Basic SKU IP addresses in fault domains, to Standard SKU IP addresses in availability zones. This does not have any short-term increased infrastructure cost, however the upgrade from Basic SKU public IP to Standard SKU is irreversible, and different types of public IPs cannot coexist within the same fault domain. Additionally, this method comes with reduced rollback abilities. Therefore, we needed to devise a plan to minimize risks for our customers and ensure a seamless migration.
Developing a zero-downtime node-by-node migration strategyTo achieve a zero-downtime “node by node” migration, we explored several options, one of which involved building tooling to migrate the instances in the cloud provider but preserve all existing configurations. The tooling automates the migration process as follows:
- Begin with stopping the first VM in the cluster. For cluster availability, ensure that only 1 VM is stopped at any time.
- Create an OS disk snapshot and verify its success, then do the same for data disks
- Ensure all snapshots are created and generate new disks from snapshots
- Create a new network interface card (NIC) and confirm its status is green
- Create a new VM and attach the disks, confirming that the new VM is up and running
- Update the private IP address and verify the change
- The public IP SKU will then be upgraded, making sure this operation is successful
- The public IP will then be reattached to the VM
- Start the VM
Even though the disks are created from snapshots of the original disks, we encountered several discrepancies in our testing, with settings between the original VM and the new VM. For instance, certain configurations, such as caching policies, did not automatically carry over, requiring manual adjustments to align with our managed standards.
Recognizing these challenges, we decided to extend our existing node replacement mechanism to streamline our migration process. This is done so that a new instance is provisioned with a new OS disk with the same IP and application data. The new node is configured by the Instaclustr Managed Platform to be the same as the original node.
The next challenge: our existing solution is built so that the replaced node was provisioned to be the exact same as the original. However, for this operation we needed the new node to be placed in an availability zone instead of the same fault domain. This required us to extend the replacement operation so that when we triggered the replacement, the new node was placed in the desired availability zone. Once this operation completed, we had a replacement tool that ensured that the new instance was correctly provisioned in the availability zone, with a Standard SKU, and without data loss.
Now that we had two very viable options, we went back to our existing Azure customers to outline the problem space, and the operations that needed to be completed. We worked with all impacted customers on the best migration path for their specific use case or application and worked out the best time to complete the migration. Where possible, we first performed the migration on any test or QA environments before moving onto production environments.
Collaborative customer migration successSome of our Cassandra customers opted to perform the migration using our data center migration path, however most customers opted for the node-by-node method. We successfully migrated the existing Azure fault domain clusters over to the Availability Zone that we were targeting, with only a very small number of clusters remaining. These clusters are operating in Azure regions which do not yet support availability zones, but we were able to successfully upgrade their public IP from Basic SKUs that are set for retirement to Standard SKUs.
No matter what provider you use, the pace of development in cloud computing can require significant effort to support ongoing maintenance and feature adoption to take advantage of new opportunities. For business-critical applications, being able to migrate to new infrastructure and leverage these opportunities while understanding the limitations and impact they have on other services is essential.
NetApp Instaclustr has a depth of experience in supporting business critical applications in the cloud. You can read more about another large-scale migration we completed The worlds Largest Apache Kafka and Apache Cassandra Migration or head over to our console for a free trial of the Instaclustr Managed Platform.
The post Azure fault domains vs availability zones: Achieving zero downtime migrations appeared first on Instaclustr.
Integrating support for AWS PrivateLink with Apache Cassandra® on the NetApp Instaclustr Managed Platform
Discover how NetApp Instaclustr leverages AWS PrivateLink for secure and seamless connectivity with Apache Cassandra®. This post explores the technical implementation, challenges faced, and the innovative solutions we developed to provide a robust, scalable platform for your data needs.
Last year, NetApp achieved a significant milestone by fully
integrating AWS PrivateLink support for Apache Cassandra® into the
NetApp Instaclustr Managed Platform. Read our AWS PrivateLink
support for Apache Cassandra General Availability announcement
here. Our Product Engineering team made remarkable progress in
incorporating this feature into various NetApp Instaclustr
application offerings. NetApp now offers AWS PrivateLink support as
an Enterprise Feature add-on for the Instaclustr Managed Platform
for
Cassandra,
Kafka®,
OpenSearch®,
Cadence®, and
Valkey
.
The journey to support AWS PrivateLink for Cassandra involved considerable engineering effort and numerous development cycles to create a solution tailored to the unique interaction between the Cassandra application and its client driver. After extensive development and testing, our product engineering team successfully implemented an enterprise ready solution. Read on for detailed insights into the technical implementation of our solution.
What is AWS PrivateLink?PrivateLink is a networking solution from AWS that provides private connectivity between Virtual Private Clouds (VPCs) without exposing any traffic to the public internet. This solution is ideal for customers who require a unidirectional network connection (often due to compliance concerns), ensuring that connections can only be initiated from the source VPC to the destination VPC. Additionally, PrivateLink simplifies network management by eliminating the need to manage overlapping CIDRs between VPCs. The one-way connection allows connections to be initiated only from the source VPC to the managed cluster hosted in our platform (target VPC)—and not the other way around.
To get an idea of what major building blocks are involved in making up an end-to-end AWS PrivateLink solution for Cassandra, take a look at the following diagram—it’s a simplified representation of the infrastructure used to support a PrivateLink cluster:
In this example, we have a 3-node Cassandra cluster at the far right with one Cassandra node per Availability Zone (or AZ). Next, we have the VPC Endpoint Service and a Network Load Balancer (NLB). The Endpoint Service is essentially the AWS PrivateLink, and by design AWS needs it to be backed by an NLB–that’s pretty much what we have to manage on our side.
On the customer side, they must create a VPC Endpoint that enables them to privately connect to the AWS PrivateLink on our end; naturally, customers will also have to use a Cassandra client(s) to connect to the cluster.
AWS PrivateLink support with Instaclustr for Apache CassandraTo incorporate AWS PrivateLink support with Instaclustr for Apache Cassandra on our platform, we came across a few technical challenges. First and foremost, the primary challenge was relatively straightforward: Cassandra clients need to talk to each individual node in a cluster.
However, the problem is that nodes in an AWS PrivateLink cluster are only assigned private IPs; that is what the nodes would announce by default when Cassandra clients attempt to discover the topology of the cluster. Cassandra clients cannot do much with the received private IPs as they cannot be used to connect to the nodes directly in an AWS PrivateLink setup.
We devised a plan of attack to get around this problem:
- Make each individual Cassandra node listen for CQL queries on unique ports.
- Configure the NLB so it can route traffic to the appropriate node based on the relevant unique port.
- Let clients implement the AddressTranslator interface from the Cassandra driver. The custom address translator will need to translate the received private IPs to one of the VPC Endpoint Elastic Network Interface (or ENI) IPs without altering the corresponding unique ports.
To understand this approach better, consider the following example:
Suppose we have a 3-node Cassandra cluster. According to the proposed approach we will need to do the followings:
- Let the nodes listen on ports 172.16.0.1:6001 (in AZ1), 172.16.0.2: 6002 (in AZ2) and 172.16.0.3: 6003 (in AZ3)
- Configure the NLB to listen on the same set of ports
- Define and associate target groups based on the port. For instance, the listener on port 6002 will be associated with a target group containing only the node that is listening on port 6002.
- As for how the custom address translator is expected to work,
let’s assume the VPC Endpoint ENI IPs are 192.168.0.1 (in AZ1),
192.168.0.2 (in AZ2) and 192.168.0.3 (in AZ3). The address
translator should translate received addresses like so:
- 172.16.0.1:6001 --> 192.168.0.1:6001 - 172.16.0.2:6002 --> 192.168.0.2:6002 - 172.16.0.3:6003 --> 192.168.0.3:6003
The proposed approach not only solves the connectivity problem but also allows for connecting to appropriate nodes based on query plans generated by load balancing policies.
Around the same time, we came up with a slightly modified approach as well: we realized the need for address translation can be mostly mitigated if we make the Cassandra nodes return the VPC Endpoint ENI IPs in the first place.
But the excitement did not last for long! Why? Because we quickly discovered a key problem: there is a limit to the number of listeners that can be added to any given AWS NLB of just 50.
While 50 is certainly a decent limit, the way we designed our solution meant we wouldn’t be able to provision a cluster with more than 50 nodes. This was quickly deemed to be an unacceptable limitation as it is not uncommon for a cluster to have more than 50 nodes; many Cassandra clusters in our fleet have hundreds of nodes. We had to abandon the idea of address translation and started thinking about alternative solution approaches.
Introducing Shotover ProxyWe were disappointed but did not lose hope. Soon after, we devised a practical solution centred around using one of our open source products: Shotover Proxy.
Shotover Proxy is used with Cassandra clusters to support AWS PrivateLink on the Instaclustr Managed Platform. What is Shotover Proxy, you ask? Shotover is a layer 7 database proxy built to allow developers, admins, DBAs, and operators to modify in-flight database requests. By managing database requests in transit, Shotover gives NetApp Instaclustr customers AWS PrivateLink’s simple and secure network setup with the many benefits of Cassandra.
Below is an updated version of the previous diagram that introduces some Shotover nodes in the mix:
As you can see, each AZ now has a dedicated Shotover proxy node.
In the above diagram, we have a 6-node Cassandra cluster. The Cassandra cluster sitting behind the Shotover nodes is an ordinary Private Network Cluster. The role of the Shotover nodes is to manage client requests to the Cassandra nodes while masking the real Cassandra nodes behind them. To the Cassandra client, the Shotover nodes appear to be Cassandra nodes, and it is only them that make up the entire cluster! This is the secret recipe for AWS PrivateLink for Instaclustr for Apache Cassandra that enabled us to get past the challenges discussed earlier.
So how is this model made to work?
Shotover can alter certain requests from—and responses to—the client. It can examine the tokens allocated to the Cassandra nodes in its own AZ (aka rack) and claim to be the owner of all those tokens. This essentially makes them appear to be an aggregation of the nodes in its own rack.
Given the purposely crafted topology and token allocation metadata, while the client directs queries to the Shotover node, the Shotover node in turn can pass them on to the appropriate Cassandra node and then transparently send responses back. It is worth noting that the Shotover nodes themselves do not store any data.
Because we only have 1 Shotover node per AZ in this design and there may be at most about 5 AZs per region, we only need that many listeners in the NLB to make this mechanism work. As such, the 50-listener limit on the NLB was no longer a problem.
The use of Shotover to manage client driver and cluster interoperability may sound straight forward to implement, but developing it was a year-long undertaking. As described above, the initial months of development were devoted to engineering CQL queries on unique ports and the AddressTranslator interface from the Cassandra driver to gracefully manage client connections to the Cassandra cluster. While this solution did successfully provide support for AWS PrivateLink with a Cassandra cluster, we knew that the 50-listener limit on the NLB was a barrier for use and wanted to provide our customers with a solution that could be used for any Cassandra cluster, regardless of node count.
The next few months of engineering were then devoted to the Proof of Concept of an alternative solution with the goal to investigate how Shotover could manage client requests for a Cassandra cluster with any number of nodes. And so, after a solution to support a cluster with any number of nodes was successfully proved, subsequent effort was then devoted to work through stability testing the new solution, the results of that engineering being the stable solution described above.
We have also conducted performance testing to evaluate the relative performance of a PrivateLink-enabled Cassandra cluster compared to its non-PrivateLink counterpart. Multiple iterations of performance testing were executed as some adjustments to Shotover were identified from test cases and resulted in the PrivateLink-enabled Cassandra cluster throughput and latency measuring near to a standard Cassandra cluster throughput and latency.
Related content: Read more about creating an AWS PrivateLink-enabled Cassandra cluster on the Instaclustr Managed Platform
The following was our experimental setup for identifying the max throughput in terms of Operations per second of a Cassandra PrivateLink cluster in comparison to a non-Cassandra PrivateLink cluster
- Baseline node size:
i3en.xlarge - Shotover Proxy node size on Cassandra Cluster:
CSO-PRD-c6gd.medium-54 - Cassandra version:
4.1.3 - Shotover Proxy version:
0.2.0 - Other configuration: Repair and backup disabled, Client Encryption disabled
Across different cluster sizes, we observed no significant difference in operation throughput between PrivateLink and non-PrivateLink configurations.
Latency resultsLatency benchmarks were conducted at ~70% of the observed peak throughput (as above) to simulate realistic production traffic.
Operation Ops/second Setup Mean Latency (ms) Median Latency (ms) P95 Latency (ms) P99 Latency (ms) Mixed-small (3 Nodes) 11630 Non-PrivateLink 9.90 3.2 53.7 119.4 PrivateLink 9.50 3.6 48.4 118.8 Mixed-small (6 Nodes) 23510 Non-PrivateLink 6 2.3 27.2 79.4 PrivateLink 9.10 3.4 45.4 104.9 Mixed-small (9 Nodes) 36255 Non-PrivateLink 5.5 2.4 21.8 67.6 PrivateLink 11.9 2.7 77.1 141.2Results indicate that for lower to mid-tier throughput levels, AWS PrivateLink introduced minimal to negligible overhead. However, at higher operation rates, we observed increased latency, most notably at the p99 mark—likely due to network level factors or Shotover.
The increase in latency is expected as AWS PrivateLink introduces an additional hop to route traffic securely, which can impact latencies, particularly under heavy load. For the vast majority of applications, the observed latencies remain within acceptable ranges. However, for latency-sensitive workloads, we recommend adding more nodes (for high load cases) to help mitigate the impact of the additional network hop introduced by PrivateLink.
As with any generic benchmarking results, performance may vary depending on specific data model, workload characteristics, and environment. The results presented here are based on specific experimental setup using standard configurations and should primarily be used to compare the relative performance of PrivateLink vs. Non-PrivateLink networking under similar conditions.
Why choose AWS PrivateLink with NetApp Instaclustr?NetApp’s commitment to innovation means you benefit from cutting-edge technology combined with ease of use. With AWS PrivateLink support on our platform, customers gain:
- Enhanced security: All traffic stays private, never touching the internet.
- Simplified networking: No need to manage complex CIDR overlaps.
- Enterprise scalability: Handles sizable clusters effortlessly.
By addressing challenges, such as the NLB listener cap and private-to-VPC IP translation, we’ve created a solution that balances efficiency, security, and scalability.
Experience PrivateLink todayThe integration of AWS PrivateLink with Apache Cassandra® is now generally available with production-ready SLAs for our customers. Log in to the Console to create a Cassandra cluster with support for AWS PrivateLink with just a few clicks today. Whether you’re managing sensitive workloads or demanding performance at scale, this feature delivers unmatched value.
Want to see it in action? Book a free demo today and experience the Shotover-powered magic of AWS PrivateLink firsthand.
Resources- Getting started: Visit the documentation to learn how to create an AWS PrivateLink-enabled Apache Cassandra cluster on the Instaclustr Managed Platform.
- Connecting clients: Already created a Cassandra cluster with AWS PrivateLink? Click here to read about how to connect Cassandra clients in one VPC to an AWS PrivateLink-enabled Cassandra cluster on the Instaclustr Platform.
- General availability announcement: For more details, read our General Availability announcement on AWS PrivateLink support for Cassandra.
The post Integrating support for AWS PrivateLink with Apache Cassandra® on the NetApp Instaclustr Managed Platform appeared first on Instaclustr.
Compaction Strategies, Performance, and Their Impact on Cassandra Node Density
This is the third post in my series on optimizing Apache Cassandra for maximum cost efficiency through increased node density. In the first post, I examined how streaming operations impact node density and laid out the groundwork for understanding why higher node density leads to significant cost savings. In the second post, I discussed how compaction throughput is critical to node density and introduced the optimizations we implemented in CASSANDRA-15452 to improve throughput on disaggregated storage like EBS.
Cassandra Compaction Throughput Performance Explained
This is the second post in my series on improving node density and lowering costs with Apache Cassandra. In the previous post, I examined how streaming performance impacts node density and operational costs. In this post, I’ll focus on compaction throughput, and a recent optimization in Cassandra 5.0.4 that significantly improves it, CASSANDRA-15452.
This post assumes some familiarity with Apache Cassandra storage engine fundamentals. The documentation has a nice section covering the storage engine if you’d like to brush up before reading this post.
CEP-24 Behind the scenes: Developing Apache Cassandra®’s password validator and generator
Introduction: The need for an Apache Cassandra® password validator and generatorHere’s the problem: while users have always had the ability to create whatever password they wanted in Cassandra–from straightforward to incredibly complex and everything in between–this ultimately created a noticeable security vulnerability.
While organizations might have internal processes for generating secure passwords that adhere to their own security policies, Cassandra itself did not have the means to enforce these standards. To make the security vulnerability worse, if a password initially met internal security guidelines, users could later downgrade their password to a less secure option simply by using “ALTER ROLE” statements.
When internal password requirements are enforced for an individual, users face the additional burden of creating compliant passwords. This inevitably involved lots of trial-and-error in attempting to create a compliant password that satisfied complex security roles.
But what if there was a way to have Cassandra automatically create passwords that meet all bespoke security requirements–but without requiring manual effort from users or system operators?
That’s why we developed CEP-24: Password validation/generation. We recognized that the complexity of secure password management could be significantly reduced (or eliminated entirely) with the right approach–and improving both security and user experience at the same time.
The Goals of CEP-24A Cassandra Enhancement Proposal (or CEP) is a structured process for proposing, creating, and ultimately implementing new features for the Cassandra project. All CEPs are thoroughly vetted among the Cassandra community before they are officially integrated into the project.
These were the key goals we established for CEP-24:
- Introduce a way to enforce password strength upon role creation or role alteration.
- Implement a reference implementation of a password validator which adheres to a recommended password strength policy, to be used for Cassandra users out of the box.
- Emit a warning (and proceed) or just reject “create role” and “alter role” statements when the provided password does not meet a certain security level, based on user configuration of Cassandra.
- To be able to implement a custom password validator with its own policy, whatever it might be, and provide a modular/pluggable mechanism to do so.
- Provide a way for Cassandra to generate a password which would pass the subsequent validation for use by the user.
The Cassandra Password Validator and Generator builds upon an established framework in Cassandra called Guardrails, which was originally implemented under CEP-3 (more details here).
The password validator implements a custom guardrail introduced
as part of
CEP-24. A custom guardrail can validate and generate values of
arbitrary types when properly implemented. In the CEP-24 context,
the password guardrail provides
CassandraPasswordValidator by extending
ValueValidator, while passwords are generated by
CassandraPasswordGenerator by extending
ValueGenerator. Both components work with passwords as
String type values.
Password validation and generation are configured in the
cassandra.yaml file under the
password_validator section. Let’s explore the key
configuration properties available. First, the
class_name and generator_class_name
parameters specify which validator and generator classes will be
used to validate and generate passwords respectively.
Cassandra
ships CassandraPasswordValidator and CassandraPasswordGenerator out
of the box. However, if a particular enterprise decides that they
need something very custom, they are free to implement their own
validators, put it on Cassandra’s class path and reference it in
the configuration behind class_name parameter. Same for the
validator.
CEP-24 provides implementations of the validator and generator that the Cassandra team believes will satisfy the requirements of most users. These default implementations address common password security needs. However, the framework is designed with flexibility in mind, allowing organizations to implement custom validation and generation rules that align with their specific security policies and business requirements.
password_validator: # Implementation class of a validator. When not in form of FQCN, the # package name org.apache.cassandra.db.guardrails.validators is prepended. # By default, there is no validator. class_name: CassandraPasswordValidator # Implementation class of related generator which generates values which are valid when # tested against this validator. When not in form of FQCN, the # package name org.apache.cassandra.db.guardrails.generators is prepended. # By default, there is no generator. generator_class_name: CassandraPasswordGenerator
Password quality might be looked at as the number of characteristics a password satisfies. There are two levels for any password to be evaluated – warning level and failure level. Warning and failure levels nicely fit into how Guardrails act. Every guardrail has warning and failure thresholds. Based on what value a specific guardrail evaluates, it will either emit a warning to a user that its usage is discouraged (but ultimately allowed) or it will fail to be set altogether.
This same principle applies to password evaluation – each password is assessed against both warning and failure thresholds. These thresholds are determined by counting the characteristics present in the password. The system evaluates five key characteristics: the password’s overall length, the number of uppercase characters, the number of lowercase characters, the number of special characters, and the number of digits. A comprehensive password security policy can be enforced by configuring minimum requirements for each of these characteristics.
# There are four characteristics: # upper-case, lower-case, special character and digit. # If this value is set e.g. to 3, a password has to # consist of 3 out of 4 characteristics. # For example, it has to contain at least 2 upper-case characters, # 2 lower-case, and 2 digits to pass, # but it does not have to contain any special characters. # If the number of characteristics found in the password is # less than or equal to this number, it will emit a warning. characteristic_warn: 3 # If the number of characteristics found in the password is #less than or equal to this number, it will emit a failure. characteristic_fail: 2
Next, there are configuration parameters for each characteristic which count towards warning or failure:
# If the password is shorter than this value, # the validator will emit a warning. length_warn: 12 # If a password is shorter than this value, # the validator will emit a failure. length_fail: 8 # If a password does not contain at least n # upper-case characters, the validator will emit a warning. upper_case_warn: 2 # If a password does not contain at least # n upper-case characters, the validator will emit a failure. upper_case_fail: 1 # If a password does not contain at least # n lower-case characters, the validator will emit a warning. lower_case_warn: 2 # If a password does not contain at least # n lower-case characters, the validator will emit a failure. lower_case_fail: 1 # If a password does not contain at least # n digits, the validator will emit a warning. digit_warn: 2 # If a password does not contain at least # n digits, the validator will emit a failure. digit_fail: 1 # If a password does not contain at least # n special characters, the validator will emit a warning. special_warn: 2 # If a password does not contain at least # n special characters, the validator will emit a failure. special_fail: 1
It is also possible to say that illegal sequences of certain length found in a password will be forbidden:
# If a password contains illegal sequences that are at least this long, it is invalid. # Illegal sequences might be either alphabetical (form 'abcde'), # numerical (form '34567'), or US qwerty (form 'asdfg') as well # as sequences from supported character sets. # The minimum value for this property is 3, # by default it is set to 5. illegal_sequence_length: 5
Lastly, it is also possible to configure a dictionary of passwords to check against. That way, we will be checking against password dictionary attacks. It is up to the operator of a cluster to configure the password dictionary:
# Dictionary to check the passwords against. Defaults to no dictionary. # Whole dictionary is cached into memory. Use with caution with relatively big dictionaries. # Entries in a dictionary, one per line, have to be sorted per String's compareTo contract. dictionary: /path/to/dictionary/file
Now that we have gone over all the configuration parameters, let’s take a look at an example of how password validation and generation look in practice.
Consider a scenario where a Cassandra super-user (such as the default ‘cassandra’ role) attempts to create a new role named ‘alice’.
cassandra@cqlsh> CREATE ROLE alice WITH PASSWORD = 'cassandraisadatabase' AND LOGIN = true; InvalidRequest: Error from server: code=2200 [Invalid query] message="Password was not set as it violated configured password strength policy. To fix this error, the following has to be resolved: Password contains the dictionary word 'cassandraisadatabase'. You may also use 'GENERATED PASSWORD' upon role creation or alteration."
The password is not found in the dictionary, but it is not long enough. When an operator sees this, they will try to fix it by making the password longer:
cassandra@cqlsh> CREATE ROLE alice WITH PASSWORD = 'T8aum3?' AND LOGIN = true; InvalidRequest: Error from server: code=2200 [Invalid query] message="Password was not set as it violated configured password strength policy. To fix this error, the following has to be resolved: Password must be 8 or more characters in length. You may also use 'GENERATED PASSWORD' upon role creation or alteration."
The password is finally set, but it is not completely secure. It satisfies the minimum requirements but our validator identified that not all characteristics were met.
cassandra@cqlsh> CREATE ROLE alice WITH PASSWORD = 'mYAtt3mp' AND LOGIN = true; Warnings: Guardrail password violated: Password was set, however it might not be strong enough according to the configured password strength policy. To fix this warning, the following has to be resolved: Password must be 12 or more characters in length. Passwords must contain 2 or more digit characters. Password must contain 2 or more special characters. Password matches 2 of 4 character rules, but 4 are required. You may also use 'GENERATED PASSWORD' upon role creation or alteration.
The password is finally set, but it is not completely secure. It satisfies the minimum requirements but our validator identified that not all characteristics were met.
When an operator saw this, they noticed the note about the ‘GENERATED PASSWORD’ clause which will generate a password automatically without an operator needing to invent it on their own. This is a lot of times, as shown, a cumbersome process better to be left on a machine. Making it also more efficient and reliable.
cassandra@cqlsh> ALTER ROLE alice WITH GENERATED PASSWORD; generated_password ------------------ R7tb33?.mcAX
The generated password shown above will satisfy all the rules we have configured in the cassandra.yaml automatically. Every generated password will satisfy all of the rules. This is clearly an advantage over manual password generation.
When the CQL statement is executed, it will be visible in the CQLSH history (HISTORY command or in cqlsh_history file) but the password will not be logged, hence it cannot leak. It will also not appear in any auditing logs. Previously, Cassandra had to obfuscate such statements. This is not necessary anymore.
We can create a role with generated password like this:
cassandra@cqlsh> CREATE ROLE alice WITH GENERATED PASSWORD AND LOGIN = true; or by CREATE USER: cassandra@cqlsh> CREATE USER alice WITH GENERATED PASSWORD;
When a password is generated for alice (out of scope of this documentation), she can log in:
$ cqlsh -u alice -p R7tb33?.mcAX ... alice@cqlsh>
Note: It is recommended to save password to ~/.cassandra/credentials, for example:
[PlainTextAuthProvider] username = cassandra password = R7tb33?.mcAX
and by setting auth_provider in ~/.cassandra/cqlshrc
[auth_provider] module = cassandra.auth classname = PlainTextAuthProvider
It is also possible to configure password validators in such a way that a user does not see why a password failed. This is driven by configuration property for password_validator called detailed_messages. When set to false, the violations will be very brief:
alice@cqlsh> ALTER ROLE alice WITH PASSWORD = 'myattempt'; InvalidRequest: Error from server: code=2200 [Invalid query] message="Password was not set as it violated configured password strength policy. You may also use 'GENERATED PASSWORD' upon role creation or alteration."
The following command will automatically generate a new password that meets all configured security requirements.
alice@cqlsh> ALTER ROLE alice WITH GENERATED PASSWORD;
Several potential enhancements to password generation and validation could be implemented in future releases. One promising extension would be validating new passwords against previous values. This would prevent users from reusing passwords until after they’ve created a specified number of different passwords. A related enhancement could include restricting how frequently users can change their passwords, preventing rapid cycling through passwords to circumvent history-based restrictions.
These features, while valuable for comprehensive password security, were considered beyond the scope of the initial implementation and may be addressed in future updates.
Final thoughts and next stepsThe Cassandra Password Validator and Generator implemented under CEP-24 represents a significant improvement in Cassandra’s security posture.
By providing robust, configurable password policies with built-in enforcement mechanisms and convenient password generation capabilities, organizations can now ensure compliance with their security standards directly at the database level. This not only strengthens overall system security but also improves the user experience by eliminating guesswork around password requirements.
As Cassandra continues to evolve as an enterprise-ready database solution, these security enhancements demonstrate a commitment to meeting the demanding security requirements of modern applications while maintaining the flexibility that makes Cassandra so powerful.
Ready to experience CEP-24 yourself? Try it out on the Instaclustr Managed Platform and spin up your first Cassandra cluster for free.
CEP-24 is just our latest contribution to open source. Check out everything else we’re working on here.
The post CEP-24 Behind the scenes: Developing Apache Cassandra®’s password validator and generator appeared first on Instaclustr.
How Cassandra Streaming, Performance, Node Density, and Cost are All related
This is the first post of several I have planned on optimizing Apache Cassandra for maximum cost efficiency. I’ve spent over a decade working with Cassandra and have spent tens of thousands of hours data modeling, fixing issues, writing tools for it, and analyzing it’s performance. I’ve always been fascinated by database performance tuning, even before Cassandra.
A decade ago I filed one of my first issues with the project, where I laid out my target goal of 20TB of data per node. This wasn’t possible for most workloads at the time, but I’ve kept this target in my sights.
Cassandra 5 Released! What's New and How to Try it
Apache Cassandra 5.0 has officially landed! This highly anticipated release brings a range of new features and performance improvements to one of the most popular NoSQL databases in the world. Having recently hosted a webinar covering the major features of Cassandra 5.0, I’m excited to give a brief overview of the key updates and show you how to easily get hands-on with the latest release using easy-cass-lab.
You can grab the latest release on the Cassandra download page.
easy-cass-lab v5 released
I’ve got some fun news to start the week off for users of easy-cass-lab: I’ve just released version 5. There are a number of nice improvements and bug fixes in here that should make it more enjoyable, more useful, and lay groundwork for some future enhancements.
- When the cluster starts, we wait for the storage service to
reach NORMAL state, then move to the next node. This is in contrast
to the previous behavior where we waited for 2 minutes after
starting a node. This queries JMX directly using Swiss Java Knife
and is more reliable than the 2-minute method. Please see
packer/bin-cassandra/wait-for-up-normalto read through the implementation. - Trunk now works correctly. Unfortunately, AxonOps doesn’t support trunk (5.1) yet, and using the agent was causing a startup error. You can test trunk out, but for now the AxonOps integration is disabled.
- Added a new repl mode. This saves keystrokes and provides some
auto-complete functionality and keeps SSH connections open. If
you’re going to do a lot of work with ECL this will help you be a
little more efficient. You can try this out with
ecl repl. - Power user feature: Initial support for profiles in AWS regions
other than
us-west-2. We only provide AMIs forus-west-2, but you can now set up a profile in an alternate region, and build the required AMIs usingeasy-cass-lab build-image. This feature is still under development and requires using aneasy-cass-labbuild from source. Credit to Jordan West for contributing this work. - Power user feature: Support for multiple profiles. Setting the
EASY_CASS_LAB_PROFILEenvironment variable allows you to configure alternate profiles. This is handy if you want to use multiple regions or have multiple organizations. - The project now uses Kotlin instead of Groovy for Gradle configuration.
- Updated Gradle to 8.9.
- When using the list command, don’t show the alias “current”.
- Project cleanup, remove old unused pssh, cassandra build, and async profiler subprojects.
The release has been released to the project’s GitHub page and to homebrew. The project is largely driven by my own consulting needs and for my training. If you’re looking to have some features prioritized please reach out, and we can discuss a consulting engagement.
easy-cass-lab updated with Cassandra 5.0 RC-1 Support
I’m excited to announce that the latest version of easy-cass-lab now supports Cassandra 5.0 RC-1, which was just made available last week! This update marks a significant milestone, providing users with the ability to test and experiment with the newest Cassandra 5.0 features in a simplified manner. This post will walk you through how to set up a cluster, SSH in, and run your first stress test.
For those new to easy-cass-lab, it’s a tool designed to streamline the setup and management of Cassandra clusters in AWS, making it accessible for both new and experienced users. Whether you’re running tests, developing new features, or just exploring Cassandra, easy-cass-lab is your go-to tool.