Apache Cassandra 2024 Wrapped: A Year of Innovation and Growth
When Spotify launched its wrapped campaign years ago, it tapped into something we all love - taking a moment to look back and celebrate achievements. As we wind down 2024, we thought it would be fun to do our own "wrapped" for Apache Cassandra®. And what a year it's been! From groundbreaking...Why We’re Moving to a Source Available License
TL;DR ScyllaDB has decided to focus on a single release stream – ScyllaDB Enterprise. Starting with the ScyllaDB Enterprise 2025.1 release (ETA February 2025): ScyllaDB Enterprise will change from closed source to source available. ScyllaDB OSS AGPL 6.2 will stand as the final OSS AGPL release. A free tier of the full-featured ScyllaDB Enterprise will be available to the community. This includes all the performance, efficiency, and security features previously reserved for ScyllaDB Enterprise. For convenience, the existing ScyllaDB Enterprise 2024.2 will gain the new source available license starting from our next patch release (in December), allowing easy migration of older releases. The source available Scylla Manager will move to AGPL and the closed source Kubernetes multi-region operator will be merged with the main Apache-licensed ScyllaDB Kubernetes operator. Other ScyllaDB components (e.g., Seastar, Kubernetes operator, drivers) will keep their current licenses. Why are we doing this? ScyllaDB’s team has always been extremely passionate about open source, low-level optimizations, and the delivery of groundbreaking core technologies – from hypervisors (KVM, Xen), to operating systems (Linux, OSv), and the ScyllaDB database. Over our 12 years of existence, we developed an OS, pivoted to the database space, developed Seastar (the open source standalone core engine of ScyllaDB), and developed ScyllaDB itself. Dozens of open source projects were created: drivers, a Kubernetes operator, test harnesses, and various tools. Open source is an outstanding way to share innovation. It is a straightforward choice for projects that are not your core business. However, it is a constant challenge for vendors whose core product is open source. For almost a decade, we have been maintaining two separate release streams: one for the open source database and one for the enterprise product. Balancing the free vs. paid offerings is a never-ending challenge that involves engineering, product, marketing, and constant sales discussions. Unlike other projects that decided to switch to source available or BSL to protect themselves from “free ride” competition, we were comfortable with AGPL. We took different paths, from the initial reimplementation of the Apache Cassandra API, to an open source implementation of a DynamoDB-compatible API. Beyond the license, we followed the whole approach of ‘open source first.’ Almost every line of code – from a new feature, to a bug fix – went to the open source branch first. We were developing two product lines that competed with one another, and we had to make one of them dramatically better. It’s hard enough to develop a single database and support Docker, Kubernetes, virtual and physical machines, and offer a database-as-a-service. The value of developing two separate database products, along with their release trains, ultimately does not justify the massive overhead and incremental costs required. To give you some idea of what’s involved, we have had nearly 60 public releases throughout 2024. Moreover, we have been the single significant contributor of the source code. Our ecosystem tools have received a healthy amount of contributions, but not the core database. That makes sense. The ScyllaDB internal implementation is a C++, shard-per-core, future-promise code base that is extremely hard to understand and requires full-time devotion. Thus source-wise, in terms of the code, we operated as a full open-source-first project. However, in reality, we benefitted from this no more than as a source-available project. “Behind the curtain” tradeoffs of free vs paid Balancing our requirements (of open source first, efficient development, no crippling of our OSS, and differentiation between the two branches) has been challenging, to say the least. Our open source first culture drove us to develop new core features in the open. Our engineers released these features before we were prepared to decide what was appropriate for open source and what was best for the enterprise paid offering. For example, Tablets, our recent architectural shift, was all developed in the open – and 99% of its end user value is available in the OSS release. As the Enterprise version branched out of the OSS branch, it was helpful to keep a unified base for reuse and efficiency. However, it reduced our paid version differentiation since all features were open by default (unless flagged). For a while, we thought that the OSS release would be the latest and greatest and have a short lifecycle as a differentiation and a means of efficiency. Although maintaining this process required a lot of effort on our side, this could have been a nice mitigation option, a replacement for a feature/functionality gap between free and paid. However, the OSS users didn’t really use the latest and didn’t always upgrade. Instead, most users preferred to stick to old, end-of-life releases. The result was a lose-lose situation (for users and for us). Another approach we used was to differentiate by using peripheral tools – such as Scylla Manager, which helps to operate ScyllaDB (e.g., running backup/restore and managing repairs) – and having a usage limit on them. Our Kubernetes operator is open source and we added a separate closed source repository for multi-region support for Kubernetes. This is a complicated path for development and also for our paying users. The factor that eventually pushed us over the line is that our new architecture – with Raft, tablets, and native S3 – moves peripheral functionality into the core database: Our backup and restore implementation moves from an agent and external manager into the core database. S3 I/O access for backup and restore (and, in the future, for tiered storage) is handled directly by the core database. The I/O operations are controlled by our schedulers, allowing full prioritization and bandwidth control. Later on, “point in time recovery” will be provided. This is a large overhaul unification change, eliminating complexity while improving control. Repair becomes automatic. Repair is a full-scan, backend process that merges inconsistent replica data. Previously, it was controlled by the external Scylla Manager. The new generation core database runs its own automatic repair with tablet awareness. As a result, there is no need for an external peripheral tool; repair will become transparent to the user, like compaction is today. These changes are leading to a more complete core product, with better manageability and functionality. However, they eat into the differentiators for our paid offerings. As you can see, a combination of architecture consolidations, together with multiple release stream efforts, have made our lives extremely complicated and slowed down our progress. Going forward After a tremendous amount of thought and discussion on these points, we decided to unify the two release streams as described at the start of this post. This license shift will allow us to better serve our customers as well as provide increased free tier value to the community. The new model opens up access to previously-restricted capabilities that: Achieve up to 50% higher throughput and 33% lower latency via profile-guided optimization Speed up node addition/removal by 30X via file-based streaming Balance multiple workloads with different performance needs on a single cluster via workload prioritization Reduce network costs with ZSTD-based network compression (with a shard dictionary) for intra-node RPC Combine the best of Leveled Compaction Strategy and Size-tiered Compaction Strategy with Incremental Compaction Strategy – resulting in 35% better storage utilization Use encryption at rest, LDAP integration, and all of the other benefits of the previous closed source Enterprise version Provide a single (all open source) Kubernetes operator for ScyllaDB Enable users to enjoy a longer product life cycle This was a difficult decision for us, and we know it might not be well-received by some of our OSS users running large ScyllaDB clusters. We appreciate your journey and we hope you will continue working with ScyllaDB. After 10 years, we believe this change is the right move for our company, our database, our customers, and our early adopters. With this shift, our team will be able to move faster, better respond to your needs, and continue making progress towards the major milestones on our roadmap: Raft for data, optimized tablet elasticity, and tiered (S3) storage. Read the FAQA Tale from Database Performance at Scale
The following is an excerpt from Chapter 1 of Database Performance at Scale (an Open Access book that’s available for free). Follow Joan’s highly fictionalized adventures with some all-too-real database performance challenges. You’ll laugh. You’ll cry. You’ll wonder how we worked this “cheesy story” into a deeply technical book. Get the complete book, free Lured in by impressive buzzwords like “hybrid cloud,” “serverless,” and “edge first,” Joan readily joined a new company and started catching up with their technology stack. Her first project recently started a transition from their in-house implementation of a database system, which turned out to not scale at the same pace as the number of customers, to one of the industry-standard database management solutions. Their new pick was a new distributed database, which, contrarily to NoSQL, strives to keep the original ACID guarantees known in the SQL world. Due to a few new data protection acts that tend to appear annually nowadays, the company’s board decided that they were going to maintain their own datacenter, instead of using one of the popular cloud vendors for storing sensitive information. On a very high level, the company’s main product consisted of only two layers: The frontend, the entry point for users, which actually runs in their own browsers and communicates with the rest of the system to exchange and persist information. The everything-else, customarily known as “backend,” but actually including load balancers, authentication, authorization, multiple cache layers, databases, backups, and so on. Joan’s first introductory task was to implement a very simple service for gathering and summing up various statistics from the database, and integrate that service with the whole ecosystem, so that it fetches data from the database in real-time and allows the DevOps teams to inspect the statistics live. To impress the management and reassure them that hiring Joan was their absolutely best decision this quarter, Joan decided to deliver a proof-of-concept implementation on her first day! The company’s unspoken policy was to write software in Rust, so she grabbed the first driver for their database from a brief crates.io search and sat down to her self-organized hackathon. The day went by really smoothly, with Rust’s ergonomy-focused ecosystem providing a superior developer experience. But then Joan ran her first smoke tests on a real system. Disbelief turned to disappointment and helplessness when she realized that every third request (on average) ended up in an error, even though the whole database cluster reported to be in a healthy, operable state. That meant a debugging session was in order! Unfortunately, the driver Joan hastily picked for the foundation of her work, even though open-source on its own, was just a thin wrapper over precompiled, legacy C code, with no source to be found. Fueled by a strong desire to solve the mystery and a healthy dose of fury, Joan spent a few hours inspecting the network communication with Wireshark, and she made an educated guess that the bug must be in the hashing key implementation. In the database used by the company, keys are hashed to later route requests to appropriate nodes. If a hash value is computed incorrectly, a request may be forwarded to the wrong node that can refuse it and return an error instead. Unable to verify the claim due to missing source code, Joan decided on a simpler path — ditching the originally chosen driver and reimplementing the solution on one of the officially supported, open-source drivers backed by the database vendor, with a solid user base and regularly updated release schedule. Joan’s diary of lessons learned, part I The initial lessons include: Choose a driver carefully. It’s at the core of your code’s performance, robustness, and reliability. Drivers have bugs too, and it’s impossible to avoid them. Still, there are good practices to follow: Unless there’s a good reason, prefer the officially supported driver (if it exists); Open-source drivers have advantages: They’re not only verified by the community, but also allow deep inspection of its code, and even modifying the driver code to get even more insights for debugging; It’s better to rely on drivers with a well-established release schedule since they are more likely to receive bug fixes (including for security vulnerabilities) in a reasonable period of time. Wireshark is a great open-source tool for interpreting network packets; give it a try if you want to peek under the hood of your program. The introductory task was eventually completed successfully, which made Joan ready to receive her first real assignment. The tuning Armed with the experience gained working on the introductory task, Joan started planning how to approach her new assignment: a misbehaving app. One of the applications notoriously caused stability issues for the whole system, disrupting other workloads each time it experienced any problems. The rogue app was already based on an officially supported driver, so Joan could cross that one off the list of potential root causes. This particular service was responsible for injecting data backed up from the legacy system into the new database. Because the company was not in a great hurry, the application was written with low concurrency in mind to have low priority and not interfere with user workloads. Unfortunately, once every few days something kept triggering an anomaly. The normally peaceful application seemed to be trying to perform a denial-of-service attack on its own database, flooding it with requests until the backend got overloaded enough to cause issues for other parts of the ecosystem. As Joan watched metrics presented in a Grafana dashboard, clearly suggesting that the rate of requests generated by this application started spiking around the time of the anomaly, she wondered how on Earth this workload could behave like that. It was, after all, explicitly implemented to send new requests only when less than 100 of them were currently in progress. Since collaboration was heavily advertised as one of the company’s “spirit and cultural foundations” during the onboarding sessions with an onsite coach, she decided it’s best to discuss the matter with her colleague, Tony. “Look, Tony, I can’t wrap my head around this,” she explained. “This service doesn’t send any new requests when 100 of them are already in flight. And look right here in the logs: 100 requests in progress, one returned a timeout error, and…,” she then stopped, startled at her own epiphany. “Alright, thanks Tony, you’re a dear – best rubber duck ever!,” she concluded and returned to fixing the code. The observation that led to discovering the root cause was rather simple: the request didn’t actually *return* a timeout error because the database server never sent back such a response. The request was simply qualified as timed out by the driver, and discarded. But the sole fact that the driver no longer waits for a response for a particular request does not mean that the database is done processing it! It’s entirely possible that the request was instead just stalled, taking longer than expected, and only the driver gave up waiting for its response. With that knowledge, it’s easy to imagine that once 100 requests time out on the client side, the app might erroneously think that they are not in progress anymore, and happily submit 100 more requests to the database, increasing the total number of in-flight requests (i.e., concurrency) to 200. Rinse, repeat, and you can achieve extreme levels of concurrency on your database cluster—even though the application was supposed to keep it limited to a small number! Joan’s diary of lessons learned, part II The lessons continue: Client-side timeouts are convenient for programmers, but they can interact badly with server-side timeouts. Rule of thumb: make the client-side timeouts around twice as long as server-side ones, unless you have an extremely good reason to do otherwise. Some drivers may be capable of issuing a warning if they detect that the client-side timeout is smaller than the server-side one, or even amend the server-side timeout to match, but in general it’s best to double-check. Tasks with seemingly fixed concurrency can actually cause spikes under certain unexpected conditions. Inspecting logs and dashboards is helpful in investigating such cases, so make sure that observability tools are available both in the database cluster and for all client applications. Bonus points for distributed tracing, like OpenTelemetry integration. With client-side timeouts properly amended, the application choked much less frequently and to a smaller extent, but it still wasn’t a perfect citizen in the distributed system. It occasionally picked a victim database node and kept bothering it with too many requests, while ignoring the fact that seven other nodes were considerably less loaded and could help handle the workload too. At other times, its concurrency was reported to be exactly 200% larger than expected by the configuration. Whenever the two anomalies converged in time, the poor node was unable to handle all requests it was bombarded with, and had to give up on a fair portion of them. A long study of the driver’s documentation, which was fortunately available in mdBook format and kept reasonably up-to-date, helped Joan alleviate those pains too. The first issue was simply a misconfiguration of the non-default load balancing policy, which tried too hard to pick “the least loaded” database node out of all the available ones, based on heuristics and statistics occasionally updated by the database itself. Malheureusement, this policy was also “best effort,” and relied on the fact that statistics arriving from the database were always legit – but a stressed database node could become so overloaded that it wasn’t sending back updated statistics in time! That led the driver to falsely believe that this particular server was not actually busy at all. Joan decided that this setup was a premature optimization that turned out to be a footgun, so she just restored the original default policy, which worked as expected. The second issue (temporary doubling of the concurrency) was caused by another misconfiguration: an overeager speculative retry policy. After waiting for a preconfigured period of time without getting an acknowledgment from the database, drivers would speculatively resend a request to maximize its chances to succeed. This mechanism is very useful to increase requests’ success rate. However, if the original request also succeeds, it means that the speculative one was sent in vain. In order to balance the pros and cons, speculative retry should be configured to only resend requests if it’s very likely that the original one failed. Otherwise, as in Joan’s case, the speculative retry may act too soon, doubling the number of requests sent (and thus also doubling concurrency) without improving the success rate at all. Whew, nothing gives a simultaneous endorphin rush and dopamine hit like a quality debugging session that ends in an astounding success (except writing a cheesy story in a deeply technical book, naturally). Great job, Joan! The end. Editor’s note: If you made it this far and can’t get enough of cheesy database performance stories, see what happened to poor old Patrick in “A Tale of Database Performance Woes: Patrick’s Unlucky Green Fedoras.” And if you appreciate this sense of humor, see Piotr’s new book on writing engineering blog posts.How Digital Turbine Moved DynamoDB Workloads to GCP – In Just One Sprint
How Joseph Shorter and Miles Ward led a fast, safe migration with ScyllaDB’s DynamoDB-compatible API Digital Turbine is a quiet but powerful player in the mobile ad tech business. Their platform is preinstalled on Android phones, connecting app developers, advertisers, mobile carriers, and device manufacturers. In the process, they bring in $500M annually. And if their database goes down, their business goes down. Digital Turbine recently decided to standardize on Google Cloud – so continuing with their DynamoDB database was no longer an option. They had to move fast without breaking things. Joseph Shorter (VP, Platform Architecture at Digital Turbine) teamed up with Miles Ward (CTO at SADA) and devised a game plan to pull off the move. Spoiler: they not only moved fast, but also ended up with an approach that was even faster…and less expensive too. You can hear directly from Joe and Miles in this conference talk: We’ve captured some highlights from their discussion below. Why migrate from DynamoDB The tipping point for the DynamoDB migration was Digital Turbine’s decision to standardize on GCP following a series of acquisitions. But that wasn’t the only issue. DynamoDB hadn’t been ideal from a cost perspective or from a performance perspective. Joe explained: “It can be a little expensive as you scale, to be honest. We were finding some performance issues. We were doing a ton of reads—90% of all interactions with DynamoDB were read operations. With all those operations, we found that the performance hits required us to scale up more than we wanted, which increased costs.” Their DynamoDB migration requirements Digital Turbine needed the migration to be as fast and low-risk as possible, which meant keeping application refactoring to a minimum. The main concern, according to Joe, was “How can we migrate without radically refactoring our platform, while maintaining at least the same performance and value, and avoiding a crash-and-burn situation? Because if it failed, it would take down our whole company. “ They approached SADA, who helped them think through a few options – including some Google-native solutions and ScyllaDB. ScyllaDB stood out due to its DynamoDB API, ScyllaDB Alternator. What the DynamoDB migration entailed In summary, it was “as easy as pudding pie” (quoting Joe here). But a little more detail: “There is a DynamoDB API that we could just use. I won’t say there was no refactoring. We did some refactoring to make it easy for engineers to plug in this information, but it was straightforward. It took less than a sprint to write that code. That was awesome. Everyone had told us that ScyllaDB was supposed to be a lot faster. Our reaction was, ‘Sure, every competitor says their product performs better.’ We did a lot with DynamoDB at scale, so we were skeptical. We decided to do a proper POC—not just some simple communication with ScyllaDB compared to DynamoDB. We actually put up multiple apps with some dependencies and set it up the way it actually functions in AWS, then we pressure-tested it. We couldn’t afford any mistakes—a mistake here means the whole company would go down. The goal was to make sure, first, that it would work and, second, that it would actually perform. And it turns out, it delivered on all its promises. That was a huge win for us.” Results so far – with minimal cluster utilization Beyond meeting their primary goal of moving off AWS, the Digital Turbine team improved performance – and they ended up reducing their costs a bit too, as an added benefit. From Joe: “I think part of it comes down to the fact that the performance is just better. We didn’t know what to expect initially, so we scaled things to be pretty comparable. What we’re finding is that it’s simply running better. Because of that, we don’t need as much infrastructure. And we’re barely tapping the ScyllaDB clusters at all right now. A 20% cost difference—that’s a big number, no matter what you’re talking about. And when you consider our plans to scale even further, it becomes even more significant. In the industry we’re in, there are only a few major players—Google, Facebook, and then everyone else. Digital Turbine has carved out a chunk of this space, and we have the tools as a company to start competing in ways others can’t. As we gain more customers and more people say, ‘Hey, we like what you’re doing,’ we need to scale radically. That 20% cost difference is already significant now, and in the future, it could be massive. Better performance and better pricing—it’s hard to ask for much more than that. You’ve got to wonder why more people haven’t noticed this yet.” Learn more about the difference between ScyllaDB and DynamoDB Compare costs: ScyllaDB vs DynamoDBInnovative data compression for time series: An open source solution
Introduction
There’s no escaping the role that monitoring plays in our everyday lives. Whether it’s from monitoring the weather or the number of steps we take in a day, or computer systems to ever-popular IoT devices.
Practically any activity can be monitored in one form or another these days. This generates increasing amounts of data to be pored over and analyzed–but storing all this data adds significant costs over time. Given this huge amount of data that only increases with each passing day, efficient compression techniques are crucial.
Here at NetApp® Instaclustr we saw a great opportunity to improve the current compression techniques for our time series data. That’s why we created the Advanced Time Series Compressor (ATSC) in partnership with University of Canberra through the OpenSI initiative.
ATSC is a groundbreaking compressor designed to address the challenges of efficiently compressing large volumes of time-series data. Internal test results with production data from our database metrics showed that ATSC would compress, on average of the dataset, ~10x more than LZ4 and ~30x more than the default Prometheus compression. Check out ATSC on GitHub.
There are so many compressors already, so why develop another one?
While other compression methods like LZ4, DoubleDelta, and ZSTD are lossless, most of our timeseries data is already lossy. Timeseries data can be lossy from the beginning due to under-sampling or insufficient data collection, or it can become lossy over time as metrics are rolled over or averaged. Because of this, the idea of a lossy compressor was born.
ATSC is a highly configurable, lossy compressor that leverages the characteristics of time-series data to create function approximations. ATSC finds a fitting function and stores the parametrization of that function—no actual data from the original timeseries is stored. When the data is decompressed, it isn’t identical to the original, but it is still sufficient for the intended use.
Here’s an example: for a temperature change metric—which mostly varies slowly (as do a lot of system metrics!)—instead of storing all the points that have a small change, we fit a curve (or a line) and store that curve/line achieving significant compression ratios.
Image 1: ATSC data for temperature
How does ATSC work?
ATSC looks at the actual time series, in whole or in parts, to find how to better calculate a function that fits the existing data. For that, a quick statistical analysis is done, but if the results are inconclusive a sample is compressed with all the functions and the best function is selected.
By default, ATSC will segment the data—this guarantees better local fitting, more and smaller computations, and less memory usage. It also ensures that decompression targets a specific block instead of the whole file.
In each fitting frame, ATSC will create a function from a pre-defined set and calculate the parametrization of said function.
ATSC currently uses one (per frame) of those following functions:
- FFT (Fast Fourier Transforms)
- Constant
- Interpolation – Catmull-Rom
- Interpolation – Inverse Distance Weight
Image 2: Polynomial fitting vs. Fast-Fourier Transform fitting
These methods allow ATSC to compress data with a fitting error within 1% (configurable!) of the original time-series.
For a more detailed insight into ATSC internals and operations check our paper!
Use cases for ATSC and results
ATSC draws inspiration from established compression and signal analysis techniques, achieving compression ratios ranging from 46x to 880x with a fitting error within 1% of the original time-series. In some cases, ATSC can produce highly compressed data without losing any meaningful information, making it a versatile tool for various applications (please see use cases below).
Some results from our internal tests comparing to LZ4 and normal Prometheus compression yielded the following results:
| Method | Compressed size (bytes) | Compression Ratio |
| Prometheus | 454,778,552 | 1.33 |
| LZ4 | 141,347,821 | 4.29 |
| ATSC | 14,276,544 | 42.47 |
Another characteristic is the trade-off between fast compression speed vs. slower compression speed. Compression is about 30x slower than decompression. It is expected that time-series are compressed once but decompressed several times.
Image 3: A better fitting (purple) vs. a loose fitting (red). Purple takes twice as much space.
ATSC is versatile and can be applied in various scenarios where space reduction is prioritized over absolute precision. Some examples include:
- Rolled-over time series: ATSC can offer significant space savings without meaningful loss in precision, such as metrics data that are rolled over and stored for long term. ATSC provides the same or more space savings but with minimal information loss.
- Under-sampled time series: Increase sample rates without losing space. Systems that have very low sampling rates (30 seconds or more) and as such, it is very difficult to identify actual events. ATSC provides the space savings and keeps the information about the events.
- Long, slow-moving data series: Ideal for patterns that are easy to fit, such as weather data.
- Human visualization: Data meant for human analysis, with minimal impact on accuracy, such as historic views into system metrics (CPU, Memory, Disk, etc.)
Image 4: ATSC data (green) with an 88x compression vs. the original data (yellow)
Using ATSC
ATSC is written in Rust as and is available in GitHub. You can build and run yourself following these instructions.
Future work
Currently, we are planning to evolve ATSC in two ways (check our open issues):
- Adding features to the core compressor
focused on
these functionalities:
- Frame expansion for appending new data to existing frames
- Dynamic function loading to add more functions without altering the codebase
- Global and per-frame error storage
- Improved error encoding
- Integrations with
additional
technologies (e.g.
databases):
- We are currently looking into integrating ASTC with ClickHouse® and Apache Cassandra®
CREATE TABLE sensors_poly (
sensor_id UInt16,
location UInt32,
timestamp DateTime,
pressure Float64
CODEC(ATSC('Polynomial', 1)),
temperature Float64
CODEC(ATSC('Polynomial', 1)),
)
ENGINE = MergeTree
ORDER BY (sensor_id, location,
timestamp);
Image 5: Currently testing ClickHouse integration
Sound interesting? Try it out and let us know what you think.
ATSC represents a significant advancement in time-series data compression, offering high compression ratios with a configurable accuracy loss. Whether for long-term storage or efficient data visualization, ATSC is a powerful open source tool for managing large volumes of time-series data.
But don’t just take our word for it—download and run it!
Check our documentation for any information you need and submit ideas for improvements or issues you find using GitHub issues. We also have easy first issues tagged if you’d like to contribute to the project.
Want to integrate this with another tool? You can build and run our demo integration with ClickHouse.
The post Innovative data compression for time series: An open source solution appeared first on Instaclustr.
ScyllaDB 2024.2 Introduces New Efficiency & Elasticity via “Tablets”
New capabilities introduced in ScyllaDB Enterprise make scaling operations with Tablets up to 30X faster while reducing network costs by up to 50% ScyllaDB just released ScyllaDB 2024.2, the first enterprise release featuring ScyllaDB’s new “tablets” replication architecture. This new architecture, which builds upon a multiyear project to implement and extend the Raft consensus protocol, enables new levels of elasticity, speed, simplicity, and efficiency. The enterprise release offers new capabilities designed to help you reduce infrastructure costs and streamline operations: Tablets, a dynamic data distribution architecture that significantly improves elasticity and scalability (limited availability – details in release notes) File-based streaming for tablets further speeds up scaling operations (e.g., adding and removing nodes) Strongly consistent topology updates, authentication updates, service levels (workload prioritization) ZSTD-based network compression for intra-node RPC, with a shard dictionary for improved performance DynamoDB API (Alternator) enhancements such as role-based access control See the release notes for details The ScyllaDB Enterprise 2024.2 release is based on ScyllaDB 6.0; it includes *all* the features available in ScyllaDB 6.0 like: Tablets, a dynamic way to distribute data across nodes that significantly improves scalability Strongly consistent topology, Auth, and Service Level updates In addition, 2024.2 includes enterprise-only features such as: Improved network compression (see below) File-based streaming for tablets A new FIPS enabled Docker Image Related links: Read more about ScyllaDB Enterprise Get ScyllaDB Enterprise 2024.2 (customers only, or 30-day evaluation) Upgrade from ScyllaDB Enterprise 2024.1.x to 2024.2.y Upgrade from ScyllaDB Open Source 6.0 to ScyllaDB Enterprise 2024.2.x ScyllaDB Enterprise customers are encouraged to upgrade to ScyllaDB Enterprise 2024.2, and are welcome to contact our Support Team with questions. Tablets In this release, ScyllaDB enabled Tablets, a new data distribution algorithm as a better alternative to the legacy vNodes approach inherited from Apache Cassandra. While the vNodes approach statically distributes all tables across all nodes and shards based on the token ring, the Tablets approach dynamically distributes each table to a subset of nodes and shards based on its size. In the future, distribution will use CPU, OPS, and other information to further optimize the distribution. In particular, Tablets provide the following: Faster scaling and topology changes. New nodes can start serving reads and writes as soon as the first Tablet is migrated. Together with Strongly Consistent Topology Updates (below), this also allows users to add multiple nodes simultaneously. Automatic support for mixed clusters with different core counts. Manual vNode updates are not required. More efficient operations on small tables., since such tables are placed on a small subset of nodes and shards. Note that you can run a cluster with some of the Keyspaces with Tablets disabled, and some with tablets enabled for as long as you wish. The scaling improvement will be partial, and limited to Keyspaces with Tables enabled. Read the Tablets documentation Currently, tablets are ideal for new clusters that you frequently scale out or in and that have one main large table and potentially many tiny ones. ScyllaDB Support can help you determine if Tablets in release 2024.2 are a good solution for your use case. Learn more about tablets: Smooth Scaling: Why ScyllaDB Moved to “Tablets” Data Distribution: The rationale behind ScyllaDB’s new “tablets” replication architecture ScyllaDB Fast Forward: True Elastic Scale: Introducing major architectural shifts that enable new levels of elasticity and operational simplicity Elasticity, Speed & Simplicity: Get the Most Out of New ScyllaDB Capabilities: A technical walkthrough of exactly what’s changed from the user/operator perspective Monitoring Tablets To Monitor Tablets in real time, upgrade ScyllaDB Monitoring Stack to release 4.8, and use the new dynamic Tablet panels, below. Driver Support The Following Drivers support Tablets Java driver 4.x, from 4.18.0.2 Java driver 3.x, from 3.11.5.2 Python driver, from 3.26.6 Gocql driver, from 1.13.0 Rust driver from 0.13.0 Legacy ScyllaDB and Apache Cassandra drivers will continue to work with ScyllaDB but will be less efficient when working with tablet-based Keyspaces. Read the blog post “How We Updated ScyllaDB Drivers for Tablets Elasticity” File based streaming for Tablets File-based streaming is a ScyllaDB Enterprise-only feature that optimizes tablet migration. In ScyllaDB Open Source, migrating tablets is performed by streaming mutation fragments, which involves deserializing SSTable files into mutation fragments and re-serializing them back into SSTables on the other node. In ScyllaDB Enterprise, migrating tablets is performed by streaming entire SStables, which does not require (de)serializing or processing mutation fragments. As a result, less data is streamed over the network, and less CPU is consumed, especially for data models that contain small cells. File-based streaming is used for tablet migration in all keyspaces created with tablets enabled. Read the file-based streaming documentation Consistent Topology and Metadata Strongly Consistent Topology Updates With Raft-managed topology enabled, all topology operations are internally sequenced consistently. A centralized coordination process ensures that topology metadata is synchronized across the nodes on each step of a topology change procedure. This makes topology updates fast and safe, as the cluster administrator can trigger many topology operations concurrently, and the coordination process will safely drive all of them to completion. For example, multiple nodes can be bootstrapped concurrently, which couldn’t be done with the previous gossip-based topology. Strongly Consistent Topology Updates is now the default for new clusters, and should be enabled after upgrade for existing clusters. Read the blog post “ScyllaDB’s Safe Topology and Schema Changes on Raft” Strongly Consistent Auth Updates System-auth-2 is a reimplementation of the Authentication and Authorization systems in a strongly consistent way on top of the Raft sub-system. This means that Role-Based Access Control (RBAC) commands like create role or grant permission are safe to run in parallel without a risk of getting out of sync with themselves and other metadata operations, like schema changes. As a result, there is no need to update system_auth RF or run repair when adding a Data Center. Strongly Consistent Service Levels Service Levels allow you to define attributes like timeout per workload. Service levels are now strongly consistent using Raft, like Schema, Topology and Auth. Improved network compression for intra-node RPC This release adds new Enterprise only RPC compression improvements for node to node communication: Using zstd instead of lz4 Using a shared dictionary re-trained periodically on the traffic, instead of the message by message compression. Below is a comparison of compressions algorithms on different types of data. Note that dictionary based compression can be used with either lz4 or zstd. The actual compression is very much workload-dependent and can vary between use cases. Alternator Role-Based Access Control Alternator supports Role-Based Access Control (RBAC) for authorization. Control is done via the CQL API. Native Nodetool The nodetool utility provides simple command-line interface operations and attributes. ScyllaDB inherited the Java based nodetool from Apache Cassandra. In this release, the Java implementation was replaced with a backward-compatible native nodetool. The native nodetool works much faster. Unlike the Java version ,the native nodetool is part of the ScyllaDB repo, and allows easier and faster updates. With the native nodetool, the JMX server has become redundant and will no longer be part of the default ScyllaDB Installation or image. If you are using the JMX server directly, not via nodetool, you can either: Work directly with the ScyllaDB REST API (recommended) Install the JMX server yourself. See https://github.com/scylladb/scylla-jmx for instructions. As part of moving to native tooling and away from Java tools, we will deprecate SSTableloader in future versions of ScyllaDB Enterprise. You can use the Load and Stream to upload SSTables directly to ScyllaDB, either from Apache Cassandra or other ScyllaDB clusters. We are also deprecating the Java version of nodetool, which was replaced by a compatible native version (see above). Maintenance Maintenance mode is a new mode in which the node does not communicate with clients or other nodes and only listens to the local maintenance socket and the REST API. It can be used to fix damaged nodes – for example, by using nodetool compact or nodetool scrub. In maintenance mode, ScyllaDB skips loading tablet metadata if it is corrupted to allow an administrator to fix it. Also, the Maintenance Socket provides a new way to interact with ScyllaDB from within the node it runs on. It is mainly for debugging. You can use CQLSh with the Maintenance Socket as described in the Maintenance Socket docs. Improvements and Bug Fixes The latest release also features extensive improvements to stability, performance, monitoring and more. For details, see the release notes on the ScyllaDB Community Forum. See the details release notesDatabase Internals: Working with IO
Explore the tradeoffs of different Linux I/O methods and learn how databases can take advantage of a modern SSD’s unique characteristics The following blog is an excerpt from Chapter 3 of the Database Performance at Scale book, which is available for free. This book sheds light on often overlooked factors that impact database performance at scale. Unless the database engine is an in-memory one, it will have to keep the data on external storage. There can be many options to do that, including local disks, network-attached storage, distributed file- and object- storage systems, etc. The term “I/O” typically refers to accessing data on local storage – disks or file systems (that, in turn, are located on disks as well). And in general, there are four choices for accessing files on a Linux server: read/write, mmap, direct I/O (DIO) read/write, and asynchronous I/O (AIO/DIO, because this I/O is rarely used in cached mode). Traditional read/write The traditional method is to use the read(2) and write(2) system calls. In a modern implementation, the read system call (or one of its many variants – pread, readv, preadv, etc) asks the kernel to read a section of a file and copy the data into the calling process address space. If all of the requested data is in the page cache, the kernel will copy it and return immediately; otherwise, it will arrange for the disk to read the requested data into the page cache, block the calling thread, and when the data is available, it will resume the thread and copy the data. A write, on the other hand, will usually just copy the data into the page cache; the kernel will write-back the page cache to disk some time afterward. mmap An alternative and more modern method is to memory-map the file into the application address space using the mmap(2) system call. This causes a section of the address space to refer directly to the page cache pages that contain the file’s data. After this preparatory step, the application can access file data using the processor’s memory read and memory write instructions. If the requested data happens to be in cache, the kernel is completely bypassed and the read (or write) is performed at memory speed. If a cache miss occurs, then a page-fault happens and the kernel puts the active thread to sleep while it goes off to read the data for that page. When the data is finally available, the memory-management unit is programmed so the newly read data is accessible to the thread which is then awoken. Direct I/O (DIO) Both traditional read/write and mmap involve the kernel page cache and defer the scheduling of I/O to the kernel. When the application wishes to schedule I/O itself (for reasons that we will explain later), it can use direct I/O, shown in Figure 3-4. This involves opening the file with the O_DIRECT flag; further activity will use the normal read and write family of system calls. However, their behavior is now altered: instead of accessing the cache, the disk is accessed directly, which means that the calling thread will be put to sleep unconditionally. Furthermore, the disk controller will copy the data directly to userspace, bypassing the kernel. Figure 3-4: Direct IO involves opening the file with the O_DIRECT flag; further activity will use the normal read and write family of system calls, but their behavior is now altered Asynchronous I/O (AIO/DIO) A refinement of direct I/O, asynchronous direct I/O behaves similarly but prevents the calling thread from blocking (see Figure 3-5). Instead, the application thread schedules direct I/O operations using theio_submit(2)
system call, but the thread is not blocked; the I/O operation runs
in parallel with normal thread execution. A separate system call,
io_getevents(2), is used to wait for and collect the
results of completed I/O operations. Like DIO, the kernel’s page
cache is bypassed, and the disk controller is responsible for
copying the data directly to userspace. Figure 3-5: A
refinement of direct I/O, asynchronous direct I/O behaves similarly
but prevents the calling thread from blocking Note:
io_uring The API to perform asynchronous I/O appeared in
Linux long ago, and it was warmly met by the community. However, as
it often happens, real-world usage quickly revealed many
inefficiencies such as blocking under some circumstances (despite
the name), the need to call the kernel too often, and poor support
for canceling the submitted requests. Eventually, it became clear
that the updated requirements are not compatible with the existing
API and the need for a new one arose. This is how the io_uring()
API appeared. It provides the same facilities as what AIO does, but
in a much more convenient and performant way (also it has notably
better documentation). Without diving into implementation details,
let’s just say that it exists and is preferred over the legacy AIO.
Understanding the tradeoffs The different access methods share some
characteristics and differ in others. Table 3-1 summarizes these
characteristics, which are elaborated on below. Copying and MMU
activity One of the benefits of the mmap method is that if the data
is in cache, then the kernel is bypassed completely. The kernel
does not need to copy data from the kernel to userspace and back,
so fewer processor cycles are spent on that activity. This benefits
workloads that are mostly in cache (for example, if the ratio of
storage size to RAM size is close to 1:1). The downside of mmap,
however, occurs when data is not in the cache. This usually happens
when the ratio of storage size to RAM size is significantly higher
than 1:1. Every page that is brought into the cache causes another
page to be evicted. Those pages have to be inserted into and
removed from the page tables; the kernel has to scan the page
tables to isolate inactive pages, making them candidates for
eviction, and so forth. In addition, mmap requires memory for the
page tables. On x86 processors, this requires 0.2% of the size of
the mapped files. This seems low, but if the application has a
100:1 ratio of storage to memory, the result is that 20% of memory
(0.2% * 100) is devoted to page tables. I/O scheduling One of the
problems with letting the kernel control caching (with the mmap and
read/write access methods) is that the application loses control of
I/O scheduling. The kernel picks whichever block of data it deems
appropriate and schedules it for write or read. This can result in
the following problems: A write storm. When the
kernel schedules large amounts of writes, the disk will be busy for
a long while and impact read latency The kernel cannot
distinguish between “important” and “unimportant” I/O. I/O
belonging to background tasks can overwhelm foreground tasks,
impacting their latency By bypassing the kernel page cache, the
application takes on the burden of scheduling I/O. This doesn’t
mean that the problems are solved, but it does mean that the
problems can be solved – with sufficient attention and effort. When
using Direct I/O, each thread controls when to issue I/O However,
the kernel controls when the thread runs, so responsibility for
issuing I/O is shared between the kernel and the application. With
AIO/DIO, the application is in full control of when I/O is issued.
Thread scheduling An I/O intensive application using mmap or
read/write cannot guess what its cache hit rate will be. Therefore
it has to run a large number of threads (significantly larger than
the core count of the machine it is running on). Using too few
threads, they may all be waiting for the disk leaving the processor
underutilized. Since each thread usually has at most one disk I/O
outstanding, the number of running threads must be around the
concurrency of the storage subsystem multiplied by some small
factor in order to keep the disk fully occupied. However, if the
cache hit rate is sufficiently high, then these large numbers of
threads will contend with each other for the limited number of
cores. When using Direct I/O, this problem is somewhat mitigated.
The application knows exactly when a thread is blocked on I/O and
when it can run, so the application can adjust the number of
running threads according to runtime conditions. With AIO/DIO, the
application has full control over both running threads and waiting
I/O (the two are completely divorced), so it can easily adjust to
in-memory or disk-bound conditions or anything in between. I/O
alignment Storage devices have a block size; all I/O must be
performed in multiples of this block size which is typically 512 or
4096 bytes. Using read/write or mmap, the kernel performs the
alignment automatically; a small read or write is expanded to the
correct block boundary by the kernel before it is issued. With DIO,
it is up to the application to perform block alignment. This incurs
some complexity, but also provides an advantage: the kernel will
usually over-align to a 4096 byte boundary even when a 512-byte
boundary suffices. However, a user application using DIO can issue
512-byte aligned reads, which results in saving bandwidth on small
items. Application complexity While the previous discussions
favored AIO/DIO for I/O intensive applications, that method comes
with a significant cost: complexity. Placing the responsibility of
cache management on the application means it can make better
choices than the kernel and make those choices with less overhead.
However, those algorithms need to be written and tested. Using
asynchronous I/O requires that the application is written using
callbacks, coroutines, or a similar method, and often reduces the
reusability of many available libraries. Choosing the Filesystem
and/or Disk Beyond performing the I/O itself, the database design
must consider the medium against which this I/O is done. In many
cases, the choice is often between a filesystem or a raw block
device, which in turn can be a choice of a traditional spinning
disk or an SSD drive. In cloud environments, however, there can be
the third option because local drives are always ephemeral – which
imposes strict requirements on the replication. Filesystems vs raw
disks This decision can be approached from two angles: management
costs and performance. If accessing the storage as a raw block
device, all the difficulties with block allocation and reclamation
are on the application side. We touched on this topic slightly
earlier when we talked about memory management. The same set of
challenges apply to RAM as well as disks. A connected, though very
different, challenge is providing data integrity in case of
crashes. Unless the database is purely in-memory, the I/O should be
done in a way that avoids losing data or reading garbage from disk
after a restart. Modern file systems, however, provide both and are
very mature to trust the efficiency of allocations and integrity of
data. Accessing raw block devices unfortunately lacks those
features (so they will need to be implemented at the same quality
on the application side). From the performance point of view, the
difference is not that drastic. On one hand, writing data to a file
is always accompanied by associated metadata updates. This consumes
both disk space and I/O bandwidth. However, some modern file
systems provide a very good balance of performance and efficiency,
almost eliminating the IO latency. (One of the most prominent
examples is XFS. Another really good and mature piece of software
is Ext4). The great ally in this camp is the fallocate(2) system
call that makes the filesystem pre-allocate space on disk. When
used, filesystems also have a chance to make full use of the
extents mechanisms, thus bringing the QoS of using files to the
same performance level as when using raw block devices. Appending
writes The database may have a heavy reliance on appends to files
or require in-place updates of individual file blocks. Both
approaches need special attention from the system architect because
they call for different properties from the underlying system. On
one hand, appending writes requires careful interaction with the
filesystem so that metadata updates (file size, in particular) do
not dominate the regular I/O. On the other hand, appending writes
(being sort of cache-oblivious algorithms) handle the disk
overwriting difficulties in a natural manner. Contrary to this,
in-place updates cannot happen at random offsets and sizes because
disks may not tolerate this kind of workload, even if used in a raw
block device manner (not via a filesystem). That being said, let’s
dive even deeper into the stack and descend into the hardware
level. How modern SSDs work Like other computational resources,
disks are limited in the speed they can provide. This speed is
typically measured as a 2-dimensional value with Input/Output
Operations per Second (IOPS) and bytes per second
(throughput). Of course, these parameters are not cut in stone even
for each particular disk, and the maximum number of requests or
bytes greatly depends on the requests’ distribution, queuing and
concurrency, buffering or caching, disk age, and many other
factors. So when performing I/O, a disk must always balance between
two inefficiencies — overwhelming the disk with requests and
underutilizing it. Overwhelming the disk should be avoided because
when the disk is full of requests it cannot distinguish between the
criticality of certain requests over others. Of course, all
requests are important, but it makes sense to prioritize
latency-sensitive requests. For example, ScyllaDB serves real-time
queries that need to be completed in single-digit milliseconds or
less and, in parallel, it processes terabytes of data for
compaction, streaming, decommission, and so forth. The former have
strong latency sensitivity; the latter are less so. Good I/O
maintenance that tries to maximize the I/O bandwidth while keeping
latency as low as possible for latency-sensitive tasks is
complicated enough to become a standalone component called the
IO
Scheduler. When evaluating a disk, one would most likely
be looking at its 4 parameters — read/write IOPS and read/write
throughput (such as in MB/s). Comparing these numbers to one
another is a popular way of claiming one disk is better than the
other and estimating the aforementioned “bandwidth capacity” of the
drive by applying Little’s Law. With that, the IO Scheduler’s job
is to provide a certain level of concurrency inside the disk to get
maximum bandwidth from it, but not to make this concurrency too
high in order to prevent the disk from queueing requests internally
for longer than needed. For instance, Figure 3-6 illustrates how
read request latency depends on the intensity of small reads
(challenging disk IOPS capacity) vs the intensity of large writes
(pursuing the disk bandwidth). The latency value is color-coded,
and the “interesting area” is painted in cyan — this is where the
latency stays below 1 millisecond. The drive measured is the NVMe
disk that comes with the AWS EC2 i3en.3xlarge instance. Figure
3-6: Bandwidth/latency graphs showing how read request latency
depends on the intensity of small reads (challenging disk IOPS
capacity) vs the intensity of large writes (pursuing the disk
bandwidth) This drive demonstrates almost perfect half-duplex
behavior — increasing the read intensity several times requires
roughly the same reduction in write intensity to keep the disk
operating at the same speed. Tip: How to measure your own
disk behavior under load The better you understand how
your own disks perform under load, the better you can tune them to
capitalize on their “sweet spot.” One way to do this is with
Diskplorer, an open-source disk latency/bandwidth exploring
toolset. By using Linux fio under the hood it runs a battery of
measurements to discover performance characteristics for a specific
hardware configuration, giving you an at-a-glance view of how
server storage I/O will behave under load. For a walkthrough of how
to use this tool, see this Linux Foundation video, “Understanding
Storage I/O Under Load.” When ScyllaDB is Overkill vs. DynamoDB
ScyllaDB isn’t for everyone. In some cases, migrating from DynamoDB won’t reduce your costs at all. ScyllaDB isn’t for everyone. It’s designed specifically for teams that need predictable ultra-low latency with high throughput. And since one size never fits all, there are inevitably situations where you’d be much better served with a different database. For teams that need relatively high performance and want something simple to use, that “different database” is often DynamoDB. We’re the first to admit that there are situations where you’d be quite fine with DynamoDB. Sometimes moving to ScyllaDB just doesn’t make sense from a pure cost perspective. Other times, it could lower costs 5-40X. And there are other situations when one database is just a better fit from a technical perspective – regardless of costs. So where is the tipping point if you’re looking at the decision from a cost perspective? Often, it’s somewhere around 10,000 OPS. But the precise answer varies depending on your workload characteristics (read/write ratios) and the amount of data under management. That’s what we’ll focus on in this article. ScyllaDB and DynamoDB are a lot alike But first, let’s step back. Why are we comparing ScyllaDB and DynamoDB in the first place? For the past couple of years, we’ve worked with a lot of users moving from DynamoDB to ScyllaDB. That makes sense, given that ScyllaDB and DynamoDB share common goals of high performance and low hassle. But they’re fundamentally built differently. ScyllaDB’s close-to-the-metal architecture handles millions of ops/sec with predictable single-digit millisecond latencies and lower, predictable costs. Still, you can use the same data model in ScyllaDB as in DynamoDB. And an open source DynamoDB API (“Alternator”) simplifies migration. You redirect your application to ScyllaDB instead of DynamoDB and it just works like magic (actually, we listen on a specific port that understands the DynamoDB API). In most cases, minimal code changes are required. Moreover, both databases provide high performance, high availability, and multi-region support – with a fully managed “database-as-a-service” option. Can you make use of (at least) a minimally viable ScyllaDB cluster? One key difference between ScyllaDB and DynamoDB is provisioning clusters versus provisioning tables. When you work with DynamoDB, you provision tables and assign a different billing mode or capacity to each. In ScyllaDB, you provision a group of nodes (VMs, containers, pods, etc) that collectively manage and distribute data as a cluster. You can route traffic to that cluster with great performance…as long as there is enough capacity to sustain your workload’s needs. Even for applications with minimal traffic, you still want to provision a full ScyllaDB cluster. The smallest ScyllaDB Cloud cluster runs with 3-nodes. That’s 1) to ensure high availability and 2) to serve strongly consistent [or quorum] reads even if one node fails or goes out for maintenance. But in some cases, even that minimal ScyllaDB cluster might provide more power than you really need. ScyllaDB’s sweet spot is throughput-heavy workloads, given its ability to sustain massive parallel processing at scale. If your throughput wouldn’t really benefit from that, ScyllaDB is quite possibly overkill. The amount of data under management is another factor to consider. ScyllaDB is optimized for high throughput and low latencies and assumes that most of your data is frequently queried and requires low latencies. We achieve this by relying on local SSDs, which provide high concurrency and low latencies compared to other storage mediums. If you have a lot of data under management, your ScyllaDB cluster might require more, or larger, VMs. However, if your throughput isn’t high enough to make good use of that infrastructure because most of your data set is read infrequently, then ScyllaDB is probably overkill from a cost perspective. Discovering your specific tipping point With that reasoning in mind, let’s get more specific. You’re probably curious about what makes most sense for your own workload and storage requirements? There’s a calculator for that! We built a cost estimation calculator that compares ScyllaDB’s on-demand pricing vs DynamoDB’s on-demand pricing, using the same math as AWS. If you’re debating between selecting DynamoDB and ScyllaDB – or thinking of migrating from DynamoDB – these on-demand cost estimates are a fast way to see if ScyllaDB could be worth exploring. First off, note the minimums. The lowest number of operations per second (OPS) you can specify is 10K and the minimal storage set size is 1TB. That’s a pretty big hint – ScyllaDB is probably overkill for anything under that. Second, be careful when you’re entering your storage utilization. If you’re already using DynamoDB, don’t just copy over your DynamoDB storage utilization. Keep in mind that the reported utilization there refers to uncompressed RAW data. As you move to another database, your storage utilization will be less because you will typically be able to achieve at least 50% compression, if not more. Before you go and plug in your own numbers, let’s walk through how it plays out across two very different scenarios. Scenario 1: Storage Bound First, let’s consider a “storage-bound” case. For example, say you’re consuming 250TB of storage with DynamoDB. You would need 18 nodes of i3en.24xlarge – which are VERY large instances – to support 250TB in ScyllaDB. But if you consider that ScyllaDB’s compression typically provides a 50% gain, that would take us down to 125TB and require 9 nodes of i3en.24xlarge. If you look at the cluster capacity area, you see that this cluster can easily sustain close to 3M operations/second (as a rather conservative estimate) Now, if we click the DynamoDB button, you’ll see that our calculator tries to make a ScyllaDB and DynamoDB cost comparison analysis for you. But, unfortunately, it is telling us to talk to Sales. This indicates ScyllaDB is likely more expensive than DynamoDB here because you have so much storage for so few operations. Still, if you have other non-cost-related reasons to move, it might be worth a discussion. However, now assume that you decided to store only your most frequently accessed data on ScyllaDB. Also assume that’s 10% of the total 125TB (thus 13TB). In this case, the ScyllaDB pricing also drops….by a factor of ~10. Scenario 2: Write-Heavy Now, let’s increase the rate of writes, which are more expensive than reads with DynamoDB. If you bump up the throughput, note that the ScyllaDB estimates are the same for anywhere from 200K to 2M writes per second. That’s because as you move from 200K to 2M with ScyllaDB, you already have enough hardware to sustain this level of operations. In contrast, DynamoDB pricing keeps increasing when you increase the write throughput. Get your own cost estimation now Other factors to consider Of course, cost isn’t everything. Even when ScyllaDB seems like a more cost-effective option, moving from DynamoDB doesn’t always make sense. If your use case is heavily dependent on the AWS ecosystem, moving from DynamoDB to ScyllaDB might require considerable refactoring work. I once met with a DynamoDB user who started looking at ScyllaDB to replace DynamoDB and they had over a thousand Lambdas connected to DynamoDB. That’s a thousand lambdas you would need to refactor to connect to ScyllaDB. Also consider the feature set you currently require from DynamoDB. Although There is not a one-to-to one mapping for every DynamoDB features like multi-item transactions (TransactWriteItems, TransactGetItems) and accounting/capping are not (yet) available in ScyllaDB. For example, accounting and capping is an interesting one. I once met with a user who used DynamoDB’s provisioned throughput to their benefit, and throttled and billed their own customers according to it. Currently, there’s no such thing in ScyllaDB since we don’t impose any throughput limits by default. In this case, ScyllaDB didn’t make sense. On the flip side, there are two key situations where it makes sense to consider ScyllaDB even if there’s not an impressive cost advantage vs. DynamoDB: when you need better latency and more deployment flexibility. If DynamoDB can’t achieve the latency you need for whatever reason – or you want to keep latencies low without the hassles/cost of DAX – ScyllaDB could likely help you address that. And if you need to move your DynamoDB workloads to another cloud or on-prem, ScyllaDB can help you move fast with just minimal application refactoring – often just a one-line change. I talk a lot more about both of these situations in the blog post, DynamoDB: When to Move Out. Rule of thumb So the bottom line truly is “it depends.” But ScyllaDB could very well be overkill if your workload is under 10K OPS and You don’t expect much throughput growth, You’re satisfied with how DynamoDB meets your latency SLAs, and You have no foreseeable need to move beyond AWS Bonus: DynamoDB Cost Optimization Masterclass If you want more opinions, more strategies,and more options about DynamoDB cost optimization, I encourage you to take a look at this masterclass I recently participated in with Ales DeBrie and Miles Ward…Netflix’s Distributed Counter Abstraction
By: Rajiv Shringi, Oleksii Tkachuk, Kartik Sathyanarayanan
Introduction
In our previous blog post, we introduced Netflix’s TimeSeries Abstraction, a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction. This counting service, built on top of the TimeSeries Abstraction, enables distributed counting at scale while maintaining similar low latency performance. As with all our abstractions, we use our Data Gateway Control Plane to shard, configure, and deploy this service globally.
Distributed counting is a challenging problem in computer science. In this blog post, we’ll explore the diverse counting requirements at Netflix, the challenges of achieving accurate counts in near real-time, and the rationale behind our chosen approach, including the necessary trade-offs.
Note: When it comes to distributed counters, terms such as ‘accurate’ or ‘precise’ should be taken with a grain of salt. In this context, they refer to a count very close to accurate, presented with minimal delays.
Use Cases and Requirements
At Netflix, our counting use cases include tracking millions of user interactions, monitoring how often specific features or experiences are shown to users, and counting multiple facets of data during A/B test experiments, among others.
At Netflix, these use cases can be classified into two broad categories:
- Best-Effort: For this category, the count doesn’t have to be very accurate or durable. However, this category requires near-immediate access to the current count at low latencies, all while keeping infrastructure costs to a minimum.
- Eventually Consistent: This category needs accurate and durable counts, and is willing to tolerate a slight delay in accuracy and a slightly higher infrastructure cost as a trade-off.
Both categories share common requirements, such as high throughput and high availability. The table below provides a detailed overview of the diverse requirements across these two categories.
Distributed Counter Abstraction
To meet the outlined requirements, the Counter Abstraction was designed to be highly configurable. It allows users to choose between different counting modes, such as Best-Effort or Eventually Consistent, while considering the documented trade-offs of each option. After selecting a mode, users can interact with APIs without needing to worry about the underlying storage mechanisms and counting methods.
Let’s take a closer look at the structure and functionality of the API.
API
Counters are organized into separate namespaces that users set up for each of their specific use cases. Each namespace can be configured with different parameters, such as Type of Counter, Time-To-Live (TTL), and Counter Cardinality, using the service’s Control Plane.
The Counter Abstraction API resembles Java’s AtomicInteger interface:
AddCount/AddAndGetCount: Adjusts the count for the specified counter by the given delta value within a dataset. The delta value can be positive or negative. The AddAndGetCount counterpart also returns the count after performing the add operation.
{
"namespace": "my_dataset",
"counter_name": "counter123",
"delta": 2,
"idempotency_token": {
"token": "some_event_id",
"generation_time": "2024-10-05T14:48:00Z"
}
}
The idempotency token can be used for counter types that support them. Clients can use this token to safely retry or hedge their requests. Failures in a distributed system are a given, and having the ability to safely retry requests enhances the reliability of the service.
GetCount: Retrieves the count value of the specified counter within a dataset.
{
"namespace": "my_dataset",
"counter_name": "counter123"
}
ClearCount: Effectively resets the count to 0 for the specified counter within a dataset.
{
"namespace": "my_dataset",
"counter_name": "counter456",
"idempotency_token": {...}
}
Now, let’s look at the different types of counters supported within the Abstraction.
Types of Counters
The service primarily supports two types of counters: Best-Effort and Eventually Consistent, along with a third experimental type: Accurate. In the following sections, we’ll describe the different approaches for these types of counters and the trade-offs associated with each.
Best Effort Regional Counter
This type of counter is powered by EVCache, Netflix’s distributed caching solution built on the widely popular Memcached. It is suitable for use cases like A/B experiments, where many concurrent experiments are run for relatively short durations and an approximate count is sufficient. Setting aside the complexities of provisioning, resource allocation, and control plane management, the core of this solution is remarkably straightforward:
// counter cache key
counterCacheKey = <namespace>:<counter_name>
// add operation
return delta > 0
? cache.incr(counterCacheKey, delta, TTL)
: cache.decr(counterCacheKey, Math.abs(delta), TTL);
// get operation
cache.get(counterCacheKey);
// clear counts from all replicas
cache.delete(counterCacheKey, ReplicaPolicy.ALL);
EVCache delivers extremely high throughput at low millisecond latency or better within a single region, enabling a multi-tenant setup within a shared cluster, saving infrastructure costs. However, there are some trade-offs: it lacks cross-region replication for the increment operation and does not provide consistency guarantees, which may be necessary for an accurate count. Additionally, idempotency is not natively supported, making it unsafe to retry or hedge requests.
Edit: A note on probabilistic data structures:
Probabilistic data structures like HyperLogLog (HLL) can be useful for tracking an approximate number of distinct elements, like distinct views or visits to a website, but are not ideally suited for implementing distinct increments and decrements for a given key. Count-Min Sketch (CMS) is an alternative that can be used to adjust the values of keys by a given amount. Data stores like Redis support both HLL and CMS. However, we chose not to pursue this direction for several reasons:
- We chose to build on top of data stores that we already operate at scale.
- Probabilistic data structures do not natively support several of our requirements, such as resetting the count for a given key or having TTLs for counts. Additional data structures, including more sketches, would be needed to support these requirements.
- On the other hand, the EVCache solution is quite simple, requiring minimal lines of code and using natively supported elements. However, it comes at the trade-off of using a small amount of memory per counter key.
Eventually Consistent Global Counter
While some users may accept the limitations of a Best-Effort counter, others opt for precise counts, durability and global availability. In the following sections, we’ll explore various strategies for achieving durable and accurate counts. Our objective is to highlight the challenges inherent in global distributed counting and explain the reasoning behind our chosen approach.
Approach 1: Storing a Single Row per Counter
Let’s start simple by using a single row per counter key within a table in a globally replicated datastore.
Let’s examine some of the drawbacks of this approach:
- Lack of Idempotency: There is no idempotency key baked into the storage data-model preventing users from safely retrying requests. Implementing idempotency would likely require using an external system for such keys, which can further degrade performance or cause race conditions.
- Heavy Contention: To update counts reliably, every writer must perform a Compare-And-Swap operation for a given counter using locks or transactions. Depending on the throughput and concurrency of operations, this can lead to significant contention, heavily impacting performance.
Secondary Keys: One way to reduce contention in this approach would be to use a secondary key, such as a bucket_id, which allows for distributing writes by splitting a given counter into buckets, while enabling reads to aggregate across buckets. The challenge lies in determining the appropriate number of buckets. A static number may still lead to contention with hot keys, while dynamically assigning the number of buckets per counter across millions of counters presents a more complex problem.
Let’s see if we can iterate on our solution to overcome these drawbacks.
Approach 2: Per Instance Aggregation
To address issues of hot keys and contention from writing to the same row in real-time, we could implement a strategy where each instance aggregates the counts in memory and then flushes them to disk at regular intervals. Introducing sufficient jitter to the flush process can further reduce contention.
However, this solution presents a new set of issues:
- Vulnerability to Data Loss: The solution is vulnerable to data loss for all in-memory data during instance failures, restarts, or deployments.
- Inability to Reliably Reset Counts: Due to counting requests being distributed across multiple machines, it is challenging to establish consensus on the exact point in time when a counter reset occurred.
- Lack of Idempotency: Similar to the previous approach, this method does not natively guarantee idempotency. One way to achieve idempotency is by consistently routing the same set of counters to the same instance. However, this approach may introduce additional complexities, such as leader election, and potential challenges with availability and latency in the write path.
That said, this approach may still be suitable in scenarios where these trade-offs are acceptable. However, let’s see if we can address some of these issues with a different event-based approach.
Approach 3: Using Durable Queues
In this approach, we log counter events into a durable queuing system like Apache Kafka to prevent any potential data loss. By creating multiple topic partitions and hashing the counter key to a specific partition, we ensure that the same set of counters are processed by the same set of consumers. This setup simplifies facilitating idempotency checks and resetting counts. Furthermore, by leveraging additional stream processing frameworks such as Kafka Streams or Apache Flink, we can implement windowed aggregations.
However, this approach comes with some challenges:
- Potential Delays: Having the same consumer process all the counts from a given partition can lead to backups and delays, resulting in stale counts.
- Rebalancing Partitions: This approach requires auto-scaling and rebalancing of topic partitions as the cardinality of counters and throughput increases.
Furthermore, all approaches that pre-aggregate counts make it challenging to support two of our requirements for accurate counters:
- Auditing of Counts: Auditing involves extracting data to an offline system for analysis to ensure that increments were applied correctly to reach the final value. This process can also be used to track the provenance of increments. However, auditing becomes infeasible when counts are aggregated without storing the individual increments.
- Potential Recounting: Similar to auditing, if adjustments to increments are necessary and recounting of events within a time window is required, pre-aggregating counts makes this infeasible.
Barring those few requirements, this approach can still be effective if we determine the right way to scale our queue partitions and consumers while maintaining idempotency. However, let’s explore how we can adjust this approach to meet the auditing and recounting requirements.
Approach 4: Event Log of Individual Increments
In this approach, we log each individual counter increment along with its event_time and event_id. The event_id can include the source information of where the increment originated. The combination of event_time and event_id can also serve as the idempotency key for the write.
However, in its simplest form, this approach has several drawbacks:
- Read Latency: Each read request requires scanning all increments for a given counter potentially degrading performance.
- Duplicate Work: Multiple threads might duplicate the effort of aggregating the same set of counters during read operations, leading to wasted effort and subpar resource utilization.
- Wide Partitions: If using a datastore like Apache Cassandra, storing many increments for the same counter could lead to a wide partition, affecting read performance.
- Large Data Footprint: Storing each increment individually could also result in a substantial data footprint over time. Without an efficient data retention strategy, this approach may struggle to scale effectively.
The combined impact of these issues can lead to increased infrastructure costs that may be difficult to justify. However, adopting an event-driven approach seems to be a significant step forward in addressing some of the challenges we’ve encountered and meeting our requirements.
How can we improve this solution further?
Netflix’s Approach
We use a combination of the previous approaches, where we log each counting activity as an event, and continuously aggregate these events in the background using queues and a sliding time window. Additionally, we employ a bucketing strategy to prevent wide partitions. In the following sections, we’ll explore how this approach addresses the previously mentioned drawbacks and meets all our requirements.
Note: From here on, we will use the words “rollup” and “aggregate” interchangeably. They essentially mean the same thing, i.e., collecting individual counter increments/decrements and arriving at the final value.
TimeSeries Event Store:
We chose the TimeSeries Data Abstraction as our event store, where counter mutations are ingested as event records. Some of the benefits of storing events in TimeSeries include:
High-Performance: The TimeSeries abstraction already addresses many of our requirements, including high availability and throughput, reliable and fast performance, and more.
Reducing Code Complexity: We reduce a lot of code complexity in Counter Abstraction by delegating a major portion of the functionality to an existing service.
TimeSeries Abstraction uses Cassandra as the underlying event store, but it can be configured to work with any persistent store. Here is what it looks like:
Handling Wide Partitions: The time_bucket and event_bucket columns play a crucial role in breaking up a wide partition, preventing high-throughput counter events from overwhelming a given partition. For more information regarding this, refer to our previous blog.
No Over-Counting: The event_time, event_id and event_item_key columns form the idempotency key for the events for a given counter, enabling clients to retry safely without the risk of over-counting.
Event Ordering: TimeSeries orders all events in descending order of time allowing us to leverage this property for events like count resets.
Event Retention: The TimeSeries Abstraction includes retention policies to ensure that events are not stored indefinitely, saving disk space and reducing infrastructure costs. Once events have been aggregated and moved to a more cost-effective store for audits, there’s no need to retain them in the primary storage.
Now, let’s see how these events are aggregated for a given counter.
Aggregating Count Events:
As mentioned earlier, collecting all individual increments for every read request would be cost-prohibitive in terms of read performance. Therefore, a background aggregation process is necessary to continually converge counts and ensure optimal read performance.
But how can we safely aggregate count events amidst ongoing write operations?
This is where the concept of Eventually Consistent counts becomes crucial. By intentionally lagging behind the current time by a safe margin, we ensure that aggregation always occurs within an immutable window.
Lets see what that looks like:
Let’s break this down:
- lastRollupTs: This represents the most recent time when the counter value was last aggregated. For a counter being operated for the first time, this timestamp defaults to a reasonable time in the past.
- Immutable Window and Lag: Aggregation can only occur safely within an immutable window that is no longer receiving counter events. The “acceptLimit” parameter of the TimeSeries Abstraction plays a crucial role here, as it rejects incoming events with timestamps beyond this limit. During aggregations, this window is pushed slightly further back to account for clock skews.
This does mean that the counter value will lag behind its most recent update by some margin (typically in the order of seconds). This approach does leave the door open for missed events due to cross-region replication issues. See “Future Work” section at the end.
- Aggregation Process: The rollup process aggregates all events in the aggregation window since the last rollup to arrive at the new value.
Rollup Store:
We save the results of this aggregation in a persistent store. The next aggregation will simply continue from this checkpoint.
We create one such Rollup table per dataset and use Cassandra as our persistent store. However, as you will soon see in the Control Plane section, the Counter service can be configured to work with any persistent store.
LastWriteTs: Every time a given counter receives a write, we also log a last-write-timestamp as a columnar update in this table. This is done using Cassandra’s USING TIMESTAMP feature to predictably apply the Last-Write-Win (LWW) semantics. This timestamp is the same as the event_time for the event. In the subsequent sections, we’ll see how this timestamp is used to keep some counters in active rollup circulation until they have caught up to their latest value.
Rollup Cache
To optimize read performance, these values are cached in EVCache for each counter. We combine the lastRollupCount and lastRollupTs into a single cached value per counter to prevent potential mismatches between the count and its corresponding checkpoint timestamp.
But, how do we know which counters to trigger rollups for? Let’s explore our Write and Read path to understand this better.
Add/Clear Count:
An add or clear count request writes durably to the TimeSeries Abstraction and updates the last-write-timestamp in the Rollup store. If the durability acknowledgement fails, clients can retry their requests with the same idempotency token without the risk of overcounting. Upon durability, we send a fire-and-forget request to trigger the rollup for the request counter.
GetCount:
We return the last rolled-up count as a quick point-read operation, accepting the trade-off of potentially delivering a slightly stale count. We also trigger a rollup during the read operation to advance the last-rollup-timestamp, enhancing the performance of subsequent aggregations. This process also self-remediates a stale count if any previous rollups had failed.
With this approach, the counts continually converge to their latest value. Now, let’s see how we scale this approach to millions of counters and thousands of concurrent operations using our Rollup Pipeline.
Rollup Pipeline:
Each Counter-Rollup server operates a rollup pipeline to efficiently aggregate counts across millions of counters. This is where most of the complexity in Counter Abstraction comes in. In the following sections, we will share key details on how efficient aggregations are achieved.
Light-Weight Roll-Up Event: As seen in our Write and Read paths above, every operation on a counter sends a light-weight event to the Rollup server:
rollupEvent: {
"namespace": "my_dataset",
"counter": "counter123"
}
Note that this event does not include the increment. This is only an indication to the Rollup server that this counter has been accessed and now needs to be aggregated. Knowing exactly which specific counters need to be aggregated prevents scanning the entire event dataset for the purpose of aggregations.
In-Memory Rollup Queues: A given Rollup server instance runs a set of in-memory queues to receive rollup events and parallelize aggregations. In the first version of this service, we settled on using in-memory queues to reduce provisioning complexity, save on infrastructure costs, and make rebalancing the number of queues fairly straightforward. However, this comes with the trade-off of potentially missing rollup events in case of an instance crash. For more details, see the “Stale Counts” section in “Future Work.”
Minimize Duplicate Effort: We use a fast non-cryptographic hash like XXHash to ensure that the same set of counters end up on the same queue. Further, we try to minimize the amount of duplicate aggregation work by having a separate rollup stack that chooses to run fewer beefier instances.
Availability and Race Conditions: Having a single Rollup server instance can minimize duplicate aggregation work but may create availability challenges for triggering rollups. If we choose to horizontally scale the Rollup servers, we allow threads to overwrite rollup values while avoiding any form of distributed locking mechanisms to maintain high availability and performance. This approach remains safe because aggregation occurs within an immutable window. Although the concept of now() may differ between threads, causing rollup values to sometimes fluctuate, the counts will eventually converge to an accurate value within each immutable aggregation window.
Rebalancing Queues: If we need to scale the number of queues, a simple Control Plane configuration update followed by a re-deploy is enough to rebalance the number of queues.
"eventual_counter_config": {
"queue_config": {
"num_queues" : 8, // change to 16 and re-deploy
...
Handling Deployments: During deployments, these queues shut down gracefully, draining all existing events first, while the new Rollup server instance starts up with potentially new queue configurations. There may be a brief period when both the old and new Rollup servers are active, but as mentioned before, this race condition is managed since aggregations occur within immutable windows.
Minimize Rollup Effort: Receiving multiple events for the same counter doesn’t mean rolling it up multiple times. We drain these rollup events into a Set, ensuring a given counter is rolled up only once during a rollup window.
Efficient Aggregation: Each rollup consumer processes a batch of counters simultaneously. Within each batch, it queries the underlying TimeSeries abstraction in parallel to aggregate events within specified time boundaries. The TimeSeries abstraction optimizes these range scans to achieve low millisecond latencies.
Dynamic Batching: The Rollup server dynamically adjusts the number of time partitions that need to be scanned based on cardinality of counters in order to prevent overwhelming the underlying store with many parallel read requests.
Adaptive Back-Pressure: Each consumer waits for one batch to complete before issuing the rollups for the next batch. It adjusts the wait time between batches based on the performance of the previous batch. This approach provides back-pressure during rollups to prevent overwhelming the underlying TimeSeries store.
Handling Convergence:
In order to prevent low-cardinality counters from lagging behind too much and subsequently scanning too many time partitions, they are kept in constant rollup circulation. For high-cardinality counters, continuously circulating them would consume excessive memory in our Rollup queues. This is where the last-write-timestamp mentioned previously plays a crucial role. The Rollup server inspects this timestamp to determine if a given counter needs to be re-queued, ensuring that we continue aggregating until it has fully caught up with the writes.
Now, let’s see how we leverage this counter type to provide an up-to-date current count in near-realtime.
Experimental: Accurate Global Counter
We are experimenting with a slightly modified version of the Eventually Consistent counter. Again, take the term ‘Accurate’ with a grain of salt. The key difference between this type of counter and its counterpart is that the delta, representing the counts since the last-rolled-up timestamp, is computed in real-time.
And then, currentAccurateCount = lastRollupCount + delta
Aggregating this delta in real-time can impact the performance of this operation, depending on the number of events and partitions that need to be scanned to retrieve this delta. The same principle of rolling up in batches applies here to prevent scanning too many partitions in parallel. Conversely, if the counters in this dataset are accessed frequently, the time gap for the delta remains narrow, making this approach of fetching current counts quite effective.
Now, let’s see how all this complexity is managed by having a unified Control Plane configuration.
Control Plane
The Data Gateway Platform Control Plane manages control settings for all abstractions and namespaces, including the Counter Abstraction. Below, is an example of a control plane configuration for a namespace that supports eventually consistent counters with low cardinality:
"persistence_configuration": [
{
"id": "CACHE", // Counter cache config
"scope": "dal=counter",
"physical_storage": {
"type": "EVCACHE", // type of cache storage
"cluster": "evcache_dgw_counter_tier1" // Shared EVCache cluster
}
},
{
"id": "COUNTER_ROLLUP",
"scope": "dal=counter", // Counter abstraction config
"physical_storage": {
"type": "CASSANDRA", // type of Rollup store
"cluster": "cass_dgw_counter_uc1", // physical cluster name
"dataset": "my_dataset_1" // namespace/dataset
},
"counter_cardinality": "LOW", // supported counter cardinality
"config": {
"counter_type": "EVENTUAL", // Type of counter
"eventual_counter_config": { // eventual counter type
"internal_config": {
"queue_config": { // adjust w.r.t cardinality
"num_queues" : 8, // Rollup queues per instance
"coalesce_ms": 10000, // coalesce duration for rollups
"capacity_bytes": 16777216 // allocated memory per queue
},
"rollup_batch_count": 32 // parallelization factor
}
}
}
},
{
"id": "EVENT_STORAGE",
"scope": "dal=ts", // TimeSeries Event store
"physical_storage": {
"type": "CASSANDRA", // persistent store type
"cluster": "cass_dgw_counter_uc1", // physical cluster name
"dataset": "my_dataset_1", // keyspace name
},
"config": {
"time_partition": { // time-partitioning for events
"buckets_per_id": 4, // event buckets within
"seconds_per_bucket": "600", // smaller width for LOW card
"seconds_per_slice": "86400", // width of a time slice table
},
"accept_limit": "5s", // boundary for immutability
},
"lifecycleConfigs": {
"lifecycleConfig": [
{
"type": "retention", // Event retention
"config": {
"close_after": "518400s",
"delete_after": "604800s" // 7 day count event retention
}
}
]
}
}
]
Using such a control plane configuration, we compose multiple abstraction layers using containers deployed on the same host, with each container fetching configuration specific to its scope.
Provisioning
As with the TimeSeries abstraction, our automation uses a bunch of user inputs regarding their workload and cardinalities to arrive at the right set of infrastructure and related control plane configuration. You can learn more about this process in a talk given by one of our stunning colleagues, Joey Lynch : How Netflix optimally provisions infrastructure in the cloud.
Performance
At the time of writing this blog, this service was processing close to 75K count requests/second globally across the different API endpoints and datasets:
while providing single-digit millisecond latencies for all its endpoints:
Future Work
While our system is robust, we still have work to do in making it more reliable and enhancing its features. Some of that work includes:
- Regional Rollups: Cross-region replication issues can result in missed events from other regions. An alternate strategy involves establishing a rollup table for each region, and then tallying them in a global rollup table. A key challenge in this design would be effectively communicating the clearing of the counter across regions.
- Error Detection and Stale Counts: Excessively stale counts can occur if rollup events are lost or if a rollup fails and isn’t retried. This isn’t an issue for frequently accessed counters, as they remain in rollup circulation. This issue is more pronounced for counters that aren’t accessed frequently. Typically, the initial read for such a counter will trigger a rollup, self-remediating the issue. However, for use cases that cannot accept potentially stale initial reads, we plan to implement improved error detection, rollup handoffs, and durable queues for resilient retries.
Conclusion
Distributed counting remains a challenging problem in computer science. In this blog, we explored multiple approaches to implement and deploy a Counting service at scale. While there may be other methods for distributed counting, our goal has been to deliver blazing fast performance at low infrastructure costs while maintaining high availability and providing idempotency guarantees. Along the way, we make various trade-offs to meet the diverse counting requirements at Netflix. We hope you found this blog post insightful.
Stay tuned for Part 3 of Composite Abstractions at Netflix, where we’ll introduce our Graph Abstraction, a new service being built on top of the Key-Value Abstraction and the TimeSeries Abstraction to handle high-throughput, low-latency graphs.
Acknowledgments
Special thanks to our stunning colleagues who contributed to the Counter Abstraction’s success: Joey Lynch, Vinay Chella, Kaidan Fullerton, Tom DeVoe, Mengqing Wang, Varun Khaitan
Netflix’s Distributed Counter Abstraction was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.
How We Updated ScyllaDB Drivers for Tablets Elasticity
Rethinking ScyllaDB’s shard-aware drivers for our new Raft-based tablets architecture ScyllaDB recently released new versions of our drivers (Rust, Go, Python…) that will provide a nice throughput and latency boost when used in concert with our new tablets architecture. In this blog post, I’d like to share details about how the drivers’ query routing scheme has changed, and why it’s now more beneficial than ever to use ScyllaDB drivers instead of Cassandra drivers. Before we dive into that, let’s take a few steps back. How do ScyllaDB drivers work? And what’s meant by “tablets”? When we say “drivers,” we’re talking about the libraries that your application uses to communicate with ScyllaDB. ScyllaDB drivers use the CQL protocol, which is inherited from Cassandra (ScyllaDB is compatible with Cassandra as well as DynamoDB). It’s possible to use Cassandra drivers with ScyllaDB, but we recommend using ScyllaDB’s drivers for optimal performance. Some of these drivers were forked from Cassandra drivers. Others, such as our Rust driver, were built from the ground up. The interesting thing about ScyllaDB drivers is that they are “shard-aware.” What does that mean? First, it’s important to understand that ScyllaDB is built with a shard-per-core architecture: every node is a multithreaded process whose every thread performs some relatively independent work. Each piece of data stored in a ScyllaDB database is bound to a specific CPU core. ScyllaDB shard-awareness allows client applications to perform load balancing following our shard-per-core architecture. Shard-aware drivers establish one connection per shard, allowing them to load balance and route queries directly to the single CPU core owning it. This optimization further optimizes latency and is also very friendly towards CPU caches. Before we get into how these shard-aware drivers support tablets, let’s take a brief look at ScyllaDB’s new “tablets” replication architecture in case you’re not yet familiar with it. We replaced vNode-based replication with tablets-based replication to enable more flexible load balancing. Each table is split into smaller fragments (“tablets”) to evenly distribute data and load across the system. Tablets are replicated to multiple ScyllaDB nodes for high availability and fault tolerance. This new approach separates token ownership from servers – ultimately allowing ScyllaDB to scale faster and in parallel. For more details on tablets, see these blog posts. How We’ve Modified Shard-Aware Drivers for Tablets With all that defined, let’s move to how we’ve modified our shard-aware drivers for tablets. Specifically, let’s look at how our query routing has changed. Before tablets Without tablets, when you made a select, insert, update, or delete statement, this query was routed to a specific node. We determined this routing by calculating the hashes of the partition key. The ring represented the data, the hashes were the partition keys, and this ring was split into many vNodes. When data was replicated, one piece of the data was stored on many nodes. The problem with this approach is that this mapping was fairly static. We couldn’t move the vNodes around and the driver didn’t expect this mapping to change. It was all very static. Users wanted to add and remove capacity faster than this vNodes paradigm allowed. With tablets With tablets, ScyllaDB now maintains metadata about the tablets. The tablet contains information about which table each tablet represents and the range of data it stores. Each tablet contains information about which replicas hold the data within the specified start and end range. Interestingly, we decided to have the driver start off without any knowledge of where data is located – even though that information is stored in the tablet mapping table. We saw that scanning that mapping would cause a performance hit for large deployments. Instead, we took a different approach: we learn the routing information “lazily” and update it dynamically. For example, assume that the driver wants to execute a query. The driver will make a request to some random node and shard – this initial request is guessing, so it might be the incorrect node and shard. All the nodes know which node owns which data. Even if the driver contacts the wrong node, it will forward the request to the correct node. It will also tell the driver which node owns that data, and the driver will update its tablet metadata to include that information. The next time the driver wants to access data on that tablet, it will reference this tablet metadata and send the query directly to the correct node. If the tablet containing that data moves (e.g., because a new node is added or removed), the driver will continue sending statements to the old replicas – but since at least one of them is not currently a replica, it will return the correct information about this tablet. The alternative would be to refresh all of the tablet metadata periodically (maybe every five seconds), which could place significant strain on the system. Another benefit of this approach: the driver doesn’t have to store metadata about all tablets. For example, if the driver only queries one table, it will only persist information about the tablets for that specific table. Ultimately, this approach enables very fast startup, even with 100K entries in the tablets table. Why use tablet-aware drivers When using ScyllaDB tablets, it’s more important than ever to use ScyllaDB shard-aware – and now also tablet-aware – drivers instead of Cassandra drivers. The existing drivers will still work, but they won’t work as efficiently because they won’t know where each tablet is located. Using the latest ScyllaDB drivers should provide a nice throughput and latency boost.Making Effective Partitions for ScyllaDB Data Modeling
Learn how to ensure your tables are perfectly partitioned to satisfy your queries – in this excerpt from the book “ScyllaDB in Action.” Editor’s note We’re thrilled to share the following excerpt from Bo Ingram’s informative – and fun! – new book on ScyllaDB: ScyllaDB in Action. It’s available now via Manning and Amazon. You can also access a 3-chapter excerpt for free, compliments of ScyllaDB. Get the first 3 book chapters, free You might have already experienced Bo’s expertise and engaging communication style in his blog How Discord Stores Trillions of Messages or ScyllaDB Summit talks How Discord Migrated Trillions of Messages from Cassandra to ScyllaDB and So You’ve Lost Quorum: Lessons From Accidental Downtime If not, you should 😉 And if you want to learn more from Bo, join him at our upcoming Masterclass: Data Modeling for Performance Masterclass. We’ve ordered boxes of print books and will be giving them out! Watch Bo at the “Data Modeling for Performance” Masterclass — Now On Demand The following is an excerpt from Chapter 3; it’s reprinted here with permission of the publisher. Read more from Chapter 3 here *** When analyzing the queries your application needs, you identified several tables for your schema. The next step in schema design is to ask how can these tables be uniquely identified and partitioned to satisfy the queries?. The primary key contains the row’s partition key — you learned in chapter 2 that the primary key determines a row’s uniqueness. Therefore, before you can determine the partition key, you need to know the primary key. PRIMARY KEYS First, you should check to see if what you’re trying to store contains a property that is popularly used for its uniqueness. Cars, for example, have a vehicle identification number, that’s unique per car. Books (including this one!) have an ISBN (international standard book number) to uniquely identify them. There’s no international standard for identifying an article or an author, so you’ll need to think a little harder to find the primary and partition keys. ScyllaDB does provide support for generating unique identifiers, but they come with some drawbacks that you’ll learn about in the next chapter. Next, you can ask if any fields can be combined to make a good primary key. What could you use to determine a unique article? The title might be reused, especially if you have unimaginative writers. The content would ideally be unique per article, but that’s a very large value for a key. Perhaps you could use a combination of fields — maybe date, author, and title? That probably works, but I find it’s helpful to look back at what you’re trying to query. When your application runs theRead Article query, it’s trying to read only a single
article. Whatever is executing that query, probably a web server
responding to the contents of a URL, is trying to load that
article, so it needs information that can be stored in a URL. Isn’t
it obnoxious when you go to paste a link somewhere and the link
feels like it’s a billion characters long? To load an article, you
don’t want to have to keep track of the author ID, title, and date.
That’s why using an ID as a primary key is often a strong choice.
Providing a unique identifier satisfies the uniqueness requirement
of a primary key, and they can potentially encode other information
inside them, such as time, which can be used for relative ordering.
You’ll learn more about potential types of IDs in the next chapter
when you explore data types. What uniquely identifies an author?
You might think an email, but people sometimes change their email
addresses. Supplying your own unique identifier works well again
here. Perhaps it’s a numeric ID, or maybe it’s a username, but
authors, as your design stands today, need extra information to
differentiate them from other authors within your database. Article
summaries, like the other steps you’ve been through, are a little
more interesting. For the various article summary tables, if you
try to use the same primary key as articles, an ID, you’re going to
run into trouble. If an ID alone makes an article unique in the
articles table, then presumably it suffices for the
index tables. That turns out to not be the case. An ID can still
differentiate uniqueness, but you also want to query by at least
the partition key to have a performant query, and if an ID is the
partition key, that doesn’t satisfy the use cases for querying by
author, date, or score. Because the partition key is contained
within the primary key, you’ll need to include those fields in your
primary key (figure 3.12). Taking
article_summaries_by_author, your primary key for that
field would become author and your article ID. Similarly, the other
two tables would have the date and the article ID for
article_summaries_by_date, and
article_summaries_by_score would use the score and the
article ID for its primary key.
Figure 3.12 You sometimes need to add fields to an already-unique
primary key to use them in a partition key, especially when
denormalizing data. With your primary keys figured out, you can
move forward to determining your partition keys and potentially
adjusting your primary keys. PARTITION KEYS You learned in the last
chapter that the first entry in the primary key serves as the
partition key for the row (you’ll see later how to build a
composite primary key). A good partition key distributes data
evenly across the cluster and is used by the queries against that
table. It stores a relatively small amount of data (a good rule of
thumb is that 100 MB is a large partition, but it depends on the
use case). I’ve listed the tables and their primary key; knowing
the primary keys, you can now rearrange them to specify your
partition keys. What would be a good partition key for each of
these tables? Table 3.3 Each table has a primary key from
which the partition key is extracted For the 
articles
and authors tables, it’s very straightforward. There
is one column in the primary key; therefore, the partition key is
the only column. If only everything was that easy!
TIP: By only having the ID as the primary key and
the partition key, you can look up rows only by their ID. A query
where you want to get multiple rows wouldn’t work with this
approach, because you’d be querying across partitions, bringing in
extra nodes and hampering performance. Instead, you want to query
by a partition key that would contain multiple rows, as you’ll do
with article summaries. The article summaries tables, however,
present you with a choice. Given a primary key of an ID and an
author, for article_summaries_by_author, which should
be the partition key? If you choose ID, Scylla will distribute the
rows for this table around the cluster based on the ID of the
article. This distribution would mean that if you wanted to load
all articles by their author, the query would have to hit nodes all
across the cluster. That behavior is not efficient. If you
partitioned your data by the author, a query to find all articles
written by a given author would hit only the nodes that store that
partition, because you’re querying within that partition (figure
3.13). You almost always want to query by at least the partition
key — it is critical for good performance. Because the use case for
this table is to find articles by who wrote them, the author is the
choice for the partition key. This distribution makes your primary
key author_id, id because the partition key is the
first entry in the primary key.
Figure 3.13 The partition key for each table should match the
queries you previously determined to achieve the best performance.
article_summaries_by_date and
article_summaries_by_score, in what might be the least
surprising statement of the book, present a similar choice as
article_summaries_by_author with a similar solution.
Because you’re querying article_summaries_by_date by
the date of the article, you want the data partitioned by it as
well, making the primary key date, id, or score, id for
article_summaries_by_score. Tip: Right-sizing
partition keys: If a partition key is too large,
data will be unevenly distributed across the cluster. If a
partition key is too small, range scans that load multiple rows
might need several queries to load the desired amount of data.
Consider querying for articles by date — if you publish one article
a week but your partition key is per day, then querying for the
most recent articles will require queries with no results six times
out of seven. Partitioning your data is a balancing exercise.
Sometimes, it’s easy. Author is a natural partition key for
articles by author, whereas something like date might require some
finessing to get the best performance in your application. Another
problem to consider is one partition taking an outsized amount of
traffic — more than the node can handle. In the next chapters, as
you refine your design, you’ll learn how to adjust your partition
key to fit your database just right. After looking closely at the
tables, your primary keys are now ordered correctly to partition
the data. For the article summaries tables, your primary keys are
divided into two categories — partition key and not-partition key.
The leftover bit has an official name and performs a specific
function in your table. Let’s take a look at the not-partition
keys — clustering keys. CLUSTERING KEYS A clustering key is the
non-partition key part of a table’s primary key that defines the
ordering of the rows within the table. In the previous chapter,
when you looked at your example table, you noticed that Scylla had
filled in the table’s implicit configuration, ordering the
non-partition key columns — the clustering keys — by ascending
order. CREATE TABLE initial.food_reviews ( restaurant text,
ordered_at timestamp, food text, review text, PRIMARY KEY
(restaurant, ordered_at, food) ) WITH CLUSTERING ORDER BY
(ordered_at ASC, food ASC); Within the partition, each row is
sorted by its time ordered and then by the name of the food, each
in an ascending sort (so earlier order times and alphabetically by
food). Left to its own devices, ScyllaDB always defaults to the
ascending natural order of the clustering keys. When creating
tables, if you have clustering keys, you need to be intentional
about specifying their order to make sure that you’re getting
results that match your requirements. Consider
article_summaries_by_author. The purpose of that table
is to retrieve the articles for a given author, but do you want to
see their oldest articles first or their newest ones? By default,
Scylla is going to sort the table by id ASC, giving
you the old articles first. When creating your summary tables,
you’ll want to specify their sort orders so that you get the newest
articles first — specifying id DESC. You now have
tables defined, and with those tables, the primary keys and
partition keys are set to specify uniqueness, distribute the data,
and provide an ordering based on your queries. Your queries,
however, are looking for more than just primary keys — authors have
named, and articles have titles and content. Not only do you need
to store these fields, you need to specify the structure of that
data. To accomplish this definition, you’ll use data types. In the
next chapter, you’ll learn all about them and continue practicing
query-first design. Database Internals: Optimizing Memory Management
How databases can get better performance by optimizing memory management The following blog post is an excerpt from Chapter 3 of the Database Performance at Scale book, which is available for free. This book sheds light on often overlooked factors that impact database performance at scale. Get the complete book, free Memory management is the central design point in all aspects of programming. Even comparing programming languages to one another always involves discussions about the way programmers are supposed to handle memory allocation and freeing. No wonder memory management design affects the performance of a database so much. Applied to database engineering, memory management typically falls into two related but independent subsystems: memory allocation and cache control. The former is in fact a very generic software engineering issue, so considerations about it are not extremely specific to databases (though they are crucial and are worth studying). Opposite to that, the latter topic is itself very broad, affected by the usage details and corner cases. Respectively, in the database world, cache control has its own flavor. Allocation The manner in which programs or subsystems allocate and free memory lies at the core of memory management. There are several approaches worth considering. As illustrated by Figure 3-2, a so-called “log-structured allocation” is known from filesystems where it puts sequential writes to a circular log on the persisting storage and handles updates the very same way. At some point, this filesystem must reclaim blocks that became obsolete entries in the log area to make some more space available for future writes. In a naive implementation, unused entries are reclaimed by rereading and rewriting the log from scratch; obsolete blocks are then skipped in the process. Figure 3-2: A log-structured allocation puts sequential writes to a circular log on the persisting storage and handles updates the very same way A memory allocator for naive code can do something similar. In its simplest form, it would allocate the next block of memory by simply advancing a next-free pointer. Deallocation would just need to mark the allocated area as freed. One advantage of this approach is the speed of allocation. Another is the simplicity and efficiency of deallocation if it happens in FIFO order or affects the whole allocation space. Stack memory allocations are later released in the order that’s reverse to allocation, so this is the most prominent and the most efficient example of such an approach. Using linear allocators as general-purpose allocators can be more problematic because of the difficulty of space reclamation. To reclaim space, it’s not enough to just mark entries as free. This leads to memory fragmentation, which in turn outweighs the advantages of linear allocation. So, as with the filesystem, the memory must be reclaimed so that it only contains allocated entries and the free space can be used again. Reclamation requires moving allocated entries around – a process that changes and invalidates their previously known addresses. In naive code, the locations of references to allocated entries (addresses stored as pointers) are unknown to the allocator. Existing references would have to be patched to make the allocator action transparent to the caller; that’s not feasible for a general-purpose allocator like malloc. Logging allocator use is tied to the programming language selection. Some RTTIs, like C++, can greatly facilitate this by providing move-constructors. However, passing pointers to libraries that are outside of your control (e.g., glibc) would still be an issue. Another alternative is adopting a strategy of pool allocators, which provide allocation spaces for allocation entries of a fixed size (see Figure 3-3). By limiting the allocation space that way, fragmentation can be reduced. A number of general-purpose allocators use pool allocators for small allocations. In some cases, those application spaces exist on a per-thread basis to eliminate the need for locking and improve CPU cache utilization. Figure 3-3: Pool allocators provide allocation spaces for allocation entries of a fixed size. Fragmentation is reduced by limiting the allocation space This pool allocation strategy provides two core benefits. First, it saves you from having to search for available memory space. Second, it alleviates memory fragmentation because it pre-allocates in memory a cache for use with a collection of object sizes. Here’s how it works to achieve that: The region for each of the sizes has fixed-size memory chunks that are suitable for the contained objects, and those chunks are all tracked by the allocator. When it’s time for the allocator to actually allocate memory for a certain type of data object, it’s typically possible to use a free slot (chunk) within one of the existing memory slabs. ( Note: We are using the term “slab” to mean one or more contiguous memory pages that contain pre-allocated chunks of memory.) When it’s time for the allocator to free the object’s memory, it can simply move that slot over to the containing slab’s list of unused/free memory slots. That memory slot (or some other free slot) will be removed from the list of free slots whenever there’s a call to create an object of the same type (or a call to allocate memory of the same size). The best allocation approach to pick heavily depends upon the usage scenario. One great benefit of a log-structured approach is that it handles fragmentation of small sub-pools in a more efficient way. Pool allocators, on the other hand, generate less background load on the CPU because of the lack of compacting activity. Cache control When it comes to memory management in a software application that stores lots of data on disk, you cannot overlook the topic of cache control. Caching is always a must in data processing, and it’s crucial to decide what and where to cache. If caching is done at the I/O level, for both read/write and mmap, caching can become the responsibility of the kernel. The majority of the system’s memory is given over to the page cache. The kernel decides which pages should be evicted when memory runs low, when pages need to be written back to disk, and controls read-ahead. The application can provide some guidance to the kernel using the madvise(2) and fadvise(2) system calls. The main advantage of letting the kernel control caching is that great effort has been invested by the kernel developers over many decades into tuning the algorithms used by the cache. Those algorithms are used by thousands of different applications and are generally effective. The disadvantage, however, is that these algorithms are general-purpose and not tuned to the application. The kernel must guess how the application will behave next. Even if the application knows differently, it usually has no way to help the kernel guess correctly. This results in the wrong pages being evicted, I/O scheduled in the wrong order, or read-ahead scheduled for data that will not be consumed in the near future. Next, doing the caching at the I/O level interacts with the topic often referred to as IMR – in memory representation. No wonder that the format in which data is stored on disk differs from the form the same data is allocated in memory as objects. The simplest reason why it’s not the same is byte-ordering. With that in mind, if the data is cached once it’s read from the disk, it needs to be further converted or parsed into the object used in memory. This can be a waste of CPU cycles, so applications may choose to cache at the object level. Choosing to cache at the object level affects a lot of other design points. With that, the cache management is all on the application side including cross-core synchronization, data coherence, invalidation, etc. Next, since objects can be (and typically are) much smaller than the average I/O size, caching millions and billions of those objects requires a collection selection that can handle it (we’ll get to this in a follow-up blog). Finally, caching on the object level greatly affects the way I/O is done. Read about ScyllaDB’s CachingNoSQL Data Modeling: Application Design Before Schema Design
Learn how to implement query-first design to build a ScyllaDB schema for a sample application – in this excerpt from the book “ScyllaDB in Action.” You might have already experienced Bo’s expertise and engaging communication style in his blog How Discord Stores Trillions of Messages or ScyllaDB Summit talks How Discord Migrated Trillions of Messages from Cassandra to ScyllaDB and So You’ve Lost Quorum: Lessons From Accidental Downtime If not, you should 😉 And if you want to learn more from Bo, join him at our upcoming Masterclass: Data Modeling for Performance Masterclass. We’ve ordered boxes of print books and will be giving them out! Watch Bo at the “Data Modeling for Performance” Masterclass — Now On Demand The following is an excerpt from Chapter 3; it’s reprinted here with permission of the publisher. *** When designing a database schema, you need to create a schema that is synergistic with your database. If you consider Scylla’s goals as a database, it wants to distribute data across the cluster to provide better scalability and fault tolerance. Spreading the data means queries involve multiple nodes, so you want to design your application to make queries that use the partition key, minimizing the nodes involved in a request. Your schema needs to fit these constraints: It needs to distribute data across the cluster It should also query the minimal amount of nodes for your desired consistency level There’s some tension in these constraints — your schema wants to spread data across the cluster to balance load between the nodes, but you want to minimize the number of nodes required to serve a query. Satisfying these constraints can be a balancing act — do you have smaller partitions that might require more queries to aggregate together, or do you have larger partitions that require fewer queries, but spread it potentially unevenly across the cluster? In figure 3.1, you can see the cost of a query that utilizes the partition key and queries across the minimal amount of nodes versus one that doesn’t use the partition key, necessitating scanning each node for matching data. Using the partition key in your query allows the coordinator — the node servicing the request — to direct queries to nodes that own that partition, lessening the load on the cluster and returning results faster. Figure 3.1 Using the partition key minimizes the number of nodes required to serve the request. The aforementioned design constraints are each related to queries. You want your data to be spread across the cluster so that your queries distribute the load amongst multiple nodes. Imagine if all of your data was clustered on a small subset of nodes in your cluster. Some nodes would be working quite hard, whereas others might not be taking much traffic. If some of those heavily utilized nodes became overwhelmed, you could suffer quite degraded performance, as because of the imbalance, many queries could be unable to complete. However, you also want to minimize the number of nodes hit per query to minimize the work your query needs to do; a query that uses all nodes in a very large cluster would be very inefficient. These query-centric constraints necessitate a query-centric approach to design. How you query Scylla is a key component of its performance, and since you need to consider the impacts of your queries across multiple dimensions, it’s critical to think carefully about how you query Scylla. When designing schemas in Scylla, it’s best to practice an approach called query-first-design, where you focus on the queries your application needs to make and then build your database schema around that. In Scylla, you structure your data based on how you want to query it — query-first design helps you. Your query-first design toolbox In query-first design, you take a set of application requirements and ask yourself a series of questions that guide you through translating the requirements into a schema in ScyllaDB. Each of these questions builds upon the next, iteratively guiding you through building an effective ScyllaDB schema. These questions include the following: What are the requirements for my application? What are the queries my application needs to run to meet these requirements? What tables are these queries querying? How can these tables be uniquely identified and partitioned to satisfy the queries? What data goes inside these tables? Does this design match the requirements? Can this schema be improved? This process is visualized in (figure 3.2), showing how you start with your application requirements and ask yourself a series of questions, guiding you from your requirements to the queries you need to run to ultimately, a fully designed ScyllaDB schema ready to store data effectively. Figure 3.2 Query-first design guides you through taking application requirements and converting them to a ScyllaDB schema. You begin with requirements and then use the requirements to identify the queries you need to run. These queries are seeking something — those “somethings” need to be stored in tables. Your tables need to be partitioned to spread data across the cluster, so you determine that partitioning to stay within your requirements and database design constraints. You then specify the fields inside each table, filling it out. At this point, you can check two things: Does the design match the requirements? Can it be improved? This up-front design is important because in ScyllaDB changing your schema to match new use cases can be a high-friction operation. While Scylla supports new query patterns via some of its features (which you’ll learn about in chapter 7), these come at an additional performance cost, and if they don’t fit your needs, might necessitate a full manual copy of data into a new table. It’s important to think carefully about your design: not only what it needs to be, but also what it could be in the future. You start by extracting the queries from your requirements and expanding your design until you have a schema that fits both your application and ScyllaDB. To practice query-first design in Scylla, let’s take the restaurant review application introduced at the beginning of the chapter and turn it into a ScyllaDB schema. The sample application requirements In the last chapter, you took your restaurant reviews and stored them inside ScyllaDB. You enjoyed working with the database, and as you went to more places, you realized you could combine your two great loves — restaurant reviews and databases (if these aren’t your two great loves, play along with me). You decide to build a website to share your restaurant reviews. Because you already have a ScyllaDB cluster, you choose to use that (this is a very different book if you pick otherwise) as the storage for your website. The first step to query-first design is identifying the requirements for your application, as seen in figure 3.4. Figure 3.4 You begin query-first design by determining your application’s requirements. After ruminating on your potential website, you identify the features it needs, and most importantly, you give it a name — Restaurant Reviews. It does what it says! Restaurant Reviews has the following initial requirements: Authors post articles to the website Users view articles to read restaurant reviews Articles contain a title, an author, a score, a date, a gallery of images, the review text, and the restaurant A review’s score is between 1 and 10 The home page contains a summary of articles sorted by most recent, showing the title, author name, score, and one image The home page links to articles Authors have a dedicated page containing summaries of their articles Authors have a name, bio, and picture Users can view a list of article summaries sorted by the highest review score You have a hunch that as time goes by, you’ll want to add more features to this application and use those features to learn more about ScyllaDB. For now, these features give you a base to practice decomposing requirements and building your schema — let’s begin! Determining the queries Next, you ask what are the queries my application needs to run to meet these requirements?, as seen in figure 3.5 Your queries drive how you design your database schema; therefore, it is critical to understand your queries at the beginning of your design. Figure 3.5 Next, you take your requirements and use them to determine your application’s queries. For identifying queries, you can use the familiar CRUD operations — create, read, update, and delete — as verbs. These queries will act on nouns in your requirements, such as authors or articles. Occasionally, you’ll want to filter your queries — you can notate this filtering with
by followed by the filter condition. For example, if
your app needed to load events on a given date, you might use a
Read Events by Date query. If you take a look at your
requirements, you’ll see several queries you’ll need.
TIP: These aren’t the actual queries you’ll
run; those are written in CQL, as seen in chapter 2, and look more
like SELECT * FROM your_cool_table WHERE
your_awesome_primary_key = 12;. These are descriptions of
what you’ll need to query — in a later chapter when you finish your
design, you’ll turn these into actual CQL queries. The first
requirement is “Authors post articles to the website,” which sounds
an awful lot like a process that would involve inserting an article
into a database. Because you insert articles in the database via a
query, you will need a Create Article statement. You might be
asking at this point — what is an article? Although other
requirements discuss these fields, you should skip that concern for
the moment. Focus first on what queries you need to run, and then
you’ll later figure out the needed fields. The second requirement
is “Users view articles to read restaurant reviews.” Giving users
unfettered access to the database is a security no-go, so the app
needs to load an article to display to a user. This functionality
suggests a Read Article query (which is different than the user
perusing the article), which you can use to retrieve an article for
a user. The following two requirements refer to the data you need
to store and not a novel way to access them: Articles contain a
title, an author, a score, a date, a gallery of images, the review
text, and the restaurant A review’s score is between 1 and 10
Articles need certain fields, and each article is associated with a
review score that fits within specified parameters. You can save
these requirements for later when you fill out what fields are
needed in each table. The next relevant requirement says “The home
page contains a summary of articles sorted by most recent, showing
the title, author name, score, and one image.” The home page shows
article summaries, which you’ll need to load by their date, sorted
by most recent– Read Article Summaries by Date. Article summaries,
at first glance, look a lot like articles. Because you’re querying
different data, and you also need to retrieve summaries by their
time, you should consider them as different queries. Querying for
an article: loads a title, author, score, date, a gallery of
images, the review text, and the restaurant retrieves one specific
article On the other hand, loading the most recent article
summaries: loads only the title, author name, score, and one image
loads several summaries, sorted by their publishing date Perhaps
they can run against the same table, but you can see if that’s the
case further on in the exercise. When in doubt, it’s best to not
over-consolidate. Be expansive in your designs, and if there’s
duplication, you can reduce it when refining your designs. As you
work through implementing your design, you might discover reasons
for what seems to be unnecessarily duplicated in one step to be a
necessary separation later. Following these requirements is “the
home page links to articles”, which makes explicit that the article
summaries link to the article — you’ll look closer at this one when
you determine what fields you need. The next two requirements are
about authors. The website will contain a page for each
author — presumably only you at the moment, but you have visions of
a media empire. This author page will contain article summaries for
each author — meaning you’ll need to Read Article Summaries
by Author. In the last requirement, there’s data that each
author has. You can study the specifics of these in a moment, but
it means that you’ll need to read information about the author, so
you will need a Read Author query. For the last
requirement — ”Users can view a list of article summaries sorted by
the highest review score” — you’ll need a way to surface article
summaries sorted by their scores. This approach requires a
Read Article Summaries by Score. TIP:
What would make a good partition key for reading articles sorted by
score? It’s a tricky problem; you’ll learn how to attack it
shortly. Having analyzed the requirements, you’ve determined six
queries your schema needs to support: Create Article Read Article
Read Article Summaries by Date Read Article Summaries by Author
Read Article Summaries by Score Read Author You might notice a
problem here — where do article summaries and authors get created?
How can you read them if nothing makes them exist? Requirement
lists often have implicit requirements — because you need to read
article summaries, they have to be created somehow. Go ahead and
add a Create Article Summary and Create Author query to your list.
You now have eight queries from the requirements you created,
listed in table 3.1.
There’s a joke that asks “How do you draw an owl?” — you draw
a couple of circles, and then you draw the rest of the owl.
Query-first design sometimes feels similar. You need to map your
queries to a database schema that is not only effective in meeting
the application’s requirements but is performant and uses
ScyllaDB’s benefits and features. Drawing queries out of
requirements is often straightforward, whereas designing the schema
from those queries requires balancing both application and database
concerns. Let’s take a look at some techniques you can apply as you
design a database schema… 
New cassandra_latest.yaml configuration for a top performant Apache Cassandra®
Welcome to our deep dive into the latest advancements in Apache Cassandra® 5.0, specifically focusing on the cassandra_latest.yaml configuration that is available for new Cassandra 5.0 clusters.
This blog post will walk you through the motivation behind these changes, how to use the new configuration, and the benefits it brings to your Cassandra clusters.
Motivation
The primary motivation for introducing cassandra_latest.yaml is to bridge the gap between maintaining backward compatibility and leveraging the latest features and performance improvements. The yaml addresses the following varying needs for new Cassandra 5.0 clusters:
- Cassandra Developers: who want to push new features but face challenges due to backward compatibility constraints.
- Operators: who prefer stability and minimal disruption during upgrades.
- Evangelists and New Users: who seek the latest features and performance enhancements without worrying about compatibility.
Using cassandra_latest.yaml
Using cassandra_latest.yaml is straightforward. It involves copying the cassandra_latest.yaml content to your cassandra.yaml or pointing the cassandra.config JVM property to the cassandra_latest.yaml file.
This configuration is designed for new Cassandra 5.0 clusters (or those evaluating Cassandra), ensuring they get the most out of the latest features in Cassandra 5.0 and performance improvements.
Key changes and features
Key Cache Size
- Old: Evaluated as a minimum from 5% of the heap or 100MB
- Latest: Explicitly set to 0
Impact: Setting the key cache size to 0 in the latest configuration avoids performance degradation with the new SSTable format. This change is particularly beneficial for clusters using the new SSTable format, which doesn’t require key caching in the same way as the old format. Key caching was used to reduce the time it takes to find a specific key in Cassandra storage.
Commit Log Disk Access Mode
- Old: Set to legacy
- Latest: Set to auto
Impact: The auto setting optimizes the commit log disk access mode based on the available disks, potentially improving write performance. It can automatically choose the best mode (e.g., direct I/O) depending on the hardware and workload, leading to better performance without manual tuning.
Memtable Implementation
- Old: Skiplist-based
- Latest: Trie-based
Impact: The trie-based memtable implementation reduces garbage collection overhead and improves throughput by moving more metadata off-heap. This change can lead to more efficient memory usage and higher write performance, especially under heavy load.
create table … with memtable = {'class': 'TrieMemtable', … }
Memtable Allocation Type
- Old: Heap buffers
- Latest: Off-heap objects
Impact: Using off-heap objects for memtable allocation reduces the pressure on the Java heap, which can improve garbage collection performance and overall system stability. This is particularly beneficial for large datasets and high-throughput environments.
Trickle Fsync
- Old: False
- Latest: True
Impact: Enabling trickle fsync improves performance on SSDs by periodically flushing dirty buffers to disk, which helps avoid sudden large I/O operations that can impact read latencies. This setting is particularly useful for maintaining consistent performance in write-heavy workloads.
SSTable Format
- Old: big
- Latest: bti (trie-indexed structure)
Impact: The new BTI format is designed to improve read and write performance by using a trie-based indexing structure. This can lead to faster data access and more efficient storage management, especially for large datasets.
sstable:
selected_format: bti
default_compression: zstd
compression:
zstd:
enabled: true
chunk_length: 16KiB
max_compressed_length: 16KiB
Default Compaction Strategy
- Old: STCS (Size-Tiered Compaction Strategy)
- Latest: Unified Compaction Strategy
Impact: The Unified Compaction Strategy (UCS) is more efficient and can handle a wider variety of workloads compared to STCS. UCS can reduce write amplification and improve read performance by better managing the distribution of data across SSTables.
default_compaction:
class_name: UnifiedCompactionStrategy
parameters:
scaling_parameters: T4
max_sstables_to_compact: 64
target_sstable_size: 1GiB
sstable_growth: 0.3333333333333333
min_sstable_size: 100MiB
Concurrent Compactors
- Old: Defaults to the smaller of the number of disks and cores
- Latest: Explicitly set to 8
Impact: Setting the number of concurrent compactors to 8 ensures that multiple compaction operations can run simultaneously, helping to maintain read performance during heavy write operations. This is particularly beneficial for SSD-backed storage where parallel I/O operations are more efficient.
Default Secondary Index
- Old: legacy_local_table
- Latest: sai
Impact: SAI is a new index implementation that builds on the advancements made with SSTable Storage Attached Secondary Index (SASI). Provide a solution that enables users to index multiple columns on the same table without suffering scaling problems, especially at write time.
Stream Entire SSTables
- Old: implicity set to True
- Latest: explicity set to True
Impact: When enabled, it permits Cassandra to zero-copy stream entire eligible, SSTables between nodes, including every component. This speeds up the network transfer significantly subject to throttling specified by
entire_sstable_stream_throughput_outbound
and
entire_sstable_inter_dc_stream_throughput_outbound
for inter-DC transfers.
UUID SSTable Identifiers
- Old: False
- Latest: True
Impact: Enabling UUID-based SSTable identifiers ensures that each SSTable has a unique name, simplifying backup and restore operations. This change reduces the risk of name collisions and makes it easier to manage SSTables in distributed environments.
Storage Compatibility Mode
- Old: Cassandra 4
- Latest: None
Impact: Setting the storage compatibility mode to none enables all new features by default, allowing users to take full advantage of the latest improvements, such as the new sstable format, in Cassandra. This setting is ideal for new clusters or those that do not need to maintain backward compatibility with older versions.
Testing and validation
The cassandra_latest.yaml configuration has undergone rigorous testing to ensure it works seamlessly. Currently, the Cassandra project CI pipeline tests both the standard (cassandra.yaml) and latest (cassandra_latest.yaml) configurations, ensuring compatibility and performance. This includes unit tests, distributed tests, and DTests.
Future improvements
Future improvements may include enforcing password strength policies and other security enhancements. The community is encouraged to suggest features that could be enabled by default in cassandra_latest.yaml.
Conclusion
The cassandra_latest.yaml configuration for new Cassandra 5.0 clusters is a significant step forward in making Cassandra more performant and feature-rich while maintaining the stability and reliability that users expect. Whether you are a developer, an operator professional, or an evangelist/end user, cassandra_latest.yaml offers something valuable for everyone.
Try it out
Ready to experience the incredible power of the cassandra_latest.yaml configuration on Apache Cassandra 5.0? Spin up your first cluster with a free trial on the Instaclustr Managed Platform and get started today with Cassandra 5.0!
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