We Built a Better Cassandra + ScyllaDB Driver for Node.js – with Rust
Lessons learned building a Rust-backed Node.js driver for ScyllaDB: bridging JS and Rust, performance pitfalls, and benchmark results This blog post explores the story of building a new Node.js database driver as part of our Student Team Programming Project. Up ahead: troubles with bridging Rust with JavaScript, a new solution being initially a few times slower than the previous one, and a few charts! Note: We cover the progress made until June 2025 as part of the ZPP project, which is a collaboration between ScyllaDB and University of Warsaw. Since then, the ScyllaDB Driver team adopted the project (and now it’s almost production ready). Motivation The database speaks one language, but users want to speak to it in multiple languages: Rust, Go, C++, Python, JavaScript, etc. This is where a driver comes in, acting as a “translator” of sorts. All the JavaScript developers of the world currently rely on the DataStax Node.js driver. It is developed with the Cassandra database in mind, but can also be used for connecting to ScyllaDB, as they use the same protocol – CQL. This driver gets the job done, but it is not designed to take full advantage of ScyllaDB’s features (e.g., shard-per-core architecture, tablets). A solution for that is rewriting the driver and creating one that is in-house, developed and maintained by ScyllaDB developers. This is a challenging task requiring years of intensive development, with new tasks interrupting along the way. An alternative approach is writing the new driver as a wrapper around an existing one – theoretically simplifying the task (spoiler: not always) to just bridging the interfaces. This concept was proven in the making of the ScyllaDB C / C++ driver, which is an overlay over the Rust driver. We chose the ScyllaDB Rust driver as the backend of the new JavaScript driver for a few reasons. ScyllaDB’s Rust driver is developed and maintained by ScyllaDB. That means it’s always up to date with the latest database features, bug fixes, and optimizations. And since it’s written in Rust, it offers native-level performance without sacrificing memory safety. [More background on this approach] Development of such a solution skips the implementation of complicated database handling logic, but brings its own set of problems. We wanted our driver to be as similar as possible to the Node.js driver so anyone wanting to switch does not need to do much configuration. This was a restriction on one side. On the other side, we have limitations of the Rust driver interface. Driver implementations differ and the API for communicating with them can vary in some places. Some give a lot of responsibility to the user, requiring more effort but giving greater flexibility. Others do most of the work without allowing for much customization. Navigating these considerations is a recurring theme when choosing to write a driver as a wrapper over a different one. Despite the challenges during development, this approach comes with some major advantages. Once the initial integration is complete, adding new ScyllaDB features becomes much easier. It’s often just a matter of implementing a few bridging functions. All the complex internal logic is handled by the Rust driver team. That means faster development, fewer bugs, and better consistency across languages. On top of that, Rust is significantly faster than Node.js. So if we keep the overhead from the bridging layer low, the resulting driver can actually outperform existing solutions in terms of raw speed. The environment: Napi vs Napi-Rs vs Neon With the goal of creating a driver that uses ScyllaDB Rust Driver underneath, we needed to decide how we would be communicating between languages. There are two main options when it comes to communicating between JavaScript and other languages: Use a Node API (NAPI for short) – an API built directly into the NodeJS engine, or Interface the program through the V8 JavaScript engine. While we could use one of those communication methods directly, they are dedicated for C / C++, which would mean writing a lot of unsafe code. Luckily, other options exist: NAPI-RS and Neon. Those libraries handle all the unsafe code required for using the C / C++ APIs and expose (mostly safe) Rust interfaces. The first option uses NAPI exclusively under the hood, while the Neon option uses both of those interfaces. After some consideration, we decided to use NAPI-RS over Neon. Here are the things we considered when deciding which library to use: – Library approach — In NAPI-RS, the library handles the serialization of data into the expected Rust types. This lets us take full advantage of Rust’s static typing and any related optimizations. With Neon, on the other hand, we have to manually parse values into the correct types. With NAPI-RS, writing a simple function is as easy as adding a #[napi] tag: Simple a+b example And in Neon, we need to manually handle JavaScript context: A+b example in Neon – Simplicity of use — As a result of the serialization model, NAPI-RS leads to cleaner and shorter code. When we were implementing some code samples for the performance comparison, we had serious trouble implementing code in Neon just for a simple example. Based on that experience, we assumed similar issues would likely occur in the future. – Performance — We made some simple tests comparing the performance of library function calls and sending data between languages. While both options were visibly slower than pure JavaScript code, the NAPI-RS version had better performance. Since driver efficiency is a critical requirement, this was an important factor in our decision. You can read more about the benchmarks in our thesis. – Documentation — Although the documentation for both tools is far from perfect, NAPI-RS’s documentation is slightly more complete and easier to navigate. Current state and capabilities Note: This represents the state as of May 2025. More features have been introduced since then. See the project readme for a brief overview of current and planned features. The driver supports regular statements (both select and insert) and batch statements. It supports all CQL types, including encoding from almost all allowed JS types. We support prepared statements (when the driver knows the expected types based on the prepared statement), and we support unprepared statements (where users can either provide type hints, or the driver guesses expected value types). Error handling is one of the few major functions that behaves differently than the DataStax driver. Since the Rust driver throws different types of errors depending on the situation, it’s nearly impossible to map all of them reliably. To avoid losing valuable information, we pass through the original Rust errors as is. However, when errors are generated by our own logic in the wrapper, we try to keep them consistent with the old driver’s error types. In the DataStax driver, you needed to explicitly call shutdown() to close the database connection. This generated some problems: when the connection variable was dropped, the connection sometimes wouldn’t stop gracefully, even keeping the program running in some situations. We decided to switch this approach, so that the connection is automatically closed when the variable keeping the client is dropped. For now, it’s still possible to call shutdown on the client. Note: We are still discussing the right approach to handling a shutdown. As a result, the behavior described here may change in the future. Concurrent execution The driver has a dedicated endpoint for executing multiple queries concurrently. While this endpoint gives you less control over individual requests — for example, all statements must be prepared and you can’t set different options per statement — these constraints allow us to optimize performance. In fact, this approach is already more efficient than manually executing queries in parallel (around 35% faster in our internal testing), and we have additional optimization ideas planned for future implementation. Paging The Rust and DataStax drivers both have built-in support for paging, a CQL feature that allows splitting results of large queries into multiple chunks (pages). Interestingly, although the DataStax driver has multiple endpoints for paging, it doesn’t allow execution of unpaged queries. Our driver supports the paging endpoints (for now, one of those endpoints is still missing) and we also added the ability to execute unpaged queries in case someone ever needs that. With the current paging API, you have several options for retrieving paged results: Automatic iteration: You can iterate over all rows in the result set, and the driver will automatically request the next pages as needed. Manual paging: You can manually request the next page of results when you’re ready, giving you more control over the paging process. Page state transfer: You can extract the current page state and use it to fetch the next page from a different instance of the driver. This is especially useful in scenarios like stateless web servers, where requests may be handled by different server instances. Prepared statements cache Whenever executing multiple instances of the same statement, it’s recommended to use prepared statements. In ScyllaDB Rust Driver, by default, it’s the user’s responsibility to keep track of the already prepared statements to avoid preparing them multiple times (and, as a result, increasing both the network usage and execution times). In the DataStax driver, it was the driver’s responsibility to avoid preparing the same query multiple times. In the new driver, we use Rust’s Driver Caching Session for (most) of the statement caching. Optimizations One of the initial goals for the project was to have a driver that is faster than the DataStax driver. While using NAPI-RS added some overhead, we hoped the performance of the Rust driver would help us achieve this goal. With the initial implementation, we didn’t put much focus on efficient usage of the NAPI-RS layer. When we first benchmarked the new driver, it turned out to be way slower compared to both the DataStax JavaScript driver and the ScyllaDB Rust driver… Operations scylladb-javascript-driver (initial version) [s] Datastax-cassandra-driver [s] Rust-driver [s] 62500 4.08 3.53 1.04 250000 13.50 5.81 1.73 1000000 55.05 15.37 4.61 4000000 227.69 66.95 18.43 Operations scylladb-javascript-driver (initial version) [s] Datastax-cassandra-driver [s] Rust-driver [s] 62500 1.63 2.61 1.08 250000 4.09 2.89 1.52 1000000 15.74 4.90 3.45 4000000 58.96 12.72 11.64 Operations scylladb-javascript-driver (initial version) [s] Datastax-cassandra-driver [s] Rust-driver [s] 62500 1.63 2.61 1.08 250000 4.09 2.89 1.52 1000000 15.74 4.90 3.45 4000000 58.96 12.72 11.64 Operations scylladb-javascript-driver (initial version) [s] Datastax-cassandra-driver [s] Rust-driver [s] 62500 1.96 3.11 1.31 250000 4.90 4.33 1.89 1000000 16.99 10.58 4.93 4000000 65.74 31.83 17.26 Those results were a bit of a surprise, as we didn’t fully anticipate how much overhead NAPI-RS would introduce. It turns out that using JavaScript Objects introduced way higher overhead compared to other built-in types, or Buffers. You can see on the following flame graph how much time was spent executing NAPI functions (yellow-orange highlight), which are related to sending objects between languages. Creating objects with NAPI-RS is as simple as adding the
#[napi] tag to the struct we want to expose to the
NodeJS part of the code. This approach also allows us to create
methods on those objects. Unfortunately, given its overhead, we
needed to switch the approach – especially in the most used parts
of the driver, like parsing parameters, results, or other parts of
executing queries. We can create a napi object like this:
Which is converted to the following JavaScript class: We
can use this struct between JavaScript and Rust. When accepting
values as arguments to Rust functions exposed in NAPI-RS, we can
either accept values of the types that implement the
FromNapiValue trait, or accept references to values of
types that are exposed to NAPI (these implement the default
FromNapiReference trait). We can do it like this:
Then, when we call the following Rust function we
can just pass a number in the JavaScript code.
FromNapiValue is implemented for built-in types like
numbers or strings, and the
FromNapiReference trait is created automatically
when using the #[napi] tag on a Rust struct. Compared
to that, we need to manually implement
FromNapiValue for custom structs. However, this
approach allows us to receive those objects in functions exposed to
NodeJS, without the need for creating Objects – and thus
significantly improves performance. We used this mostly to improve
the performance of passing query parameters to the Rust side of the
driver. When it comes to returning values from Rust code, a type
must have a ToNapiValue trait implemented. Similarly,
this trait is already implemented for built-in types, and is auto
generated with macros when adding the #[napi] tag to
the object. And this auto generated implementation was causing most
of our performance problems. Luckily, we can also implement our own
ToNapiValue trait. If we return a raw value and create
an object directly in the JavaScript part of the code, we can avoid
almost all of the negative performance impacts that come from the
default implementation of ToNapiValue. We can do it
like this: 
ToNapiValue implementation, that
was handling all the logic for the UUID. When we changed the
approach to returning just a raw buffer representing the UUID and
handling all the logic on the JavaScript side, we shaved off about
20% of the CPU time we were using in the select benchmarks at that
point in time. Note: Since completing the initial project, we’ve
introduced additional changes to how serialization and
deserialization works. This means the current state may be
different from what we describe here. A new round of benchmarking
is in progress; stay tuned for those results. Benchmarks In the
previous section, we showed you some early benchmarks. Let’s talk a
bit more about how we tested and what we tested. All benchmarks
presented here were run on a single machine – the database was run
in a Docker container and the driver benchmarks were run without
any virtualization or containerization. The machine was running on
AMD Ryzen™ 7 PRO 7840U with 32GB RAM, with the database itself
limited to 8GB of RAM in total. We tested the driver both with
ScyllaDB and Cassandra (latest stable versions as of the time of
testing – May 2025). Both of those databases were run in a three
node configuration, with 2 shards per node in the case of ScyllaDB.
With this information on the benchmarks, let’s see the effect all
the optimizations we added had on the driver performance when
tested with ScyllaDB:
Operations Scylladb-javascript-driver [s] Datastax-cassandra-driver
[s] Rust-driver [s] scylladb-javascript-driver (initial version)
[s] 62500 1.89 3.45 0.99 4.08 250000 4.15 5.66 1.73 13.50 1000000
13.65 15.86 4.41 55.05 4000000 55.85 56.73 18.42 227.69
Operations Scylladb-javascript-driver [s] Datastax-cassandra-driver
[s] Rust-driver [s] scylladb-javascript-driver (initial version)
[s] 62500 2.83 2.48 1.04 1.63 250000 1.91 2.91 1.56 4.09 1000000
4.58 4.69 3.42 15.74 4000000 16.05 14.27 11.92 58.96
Operations Scylladb-javascript-driver [s] Datastax-cassandra-driver
[s] Rust-driver [s] scylladb-javascript-driver (initial version)
[s] 62500 1.50 3.04 1.33 1.96 250000 2.93 4.52 1.94 4.90 1000000
8.79 11.11 5.08 16.99 4000000 32.99 36.62 17.90 65.74
Operations Scylladb-javascript-driver [s] Datastax-cassandra-driver
[s] Rust-driver [s] scylladb-javascript-driver (initial version)
[s] 62500 1.42 3.09 1.25 1.45 250000 2.94 3.81 2.43 3.43 1000000
9.19 8.98 7.21 10.82 4000000 33.51 28.97 25.81 40.74 And here are
the same benchmarks, without the initial driver version.
Here are the results of running the benchmark on Cassandra.
Operations Scylladb-javascript-driver [s]
Datastax-cassandra-driver [s] Rust-driver [s] 62500 2.48 14.50 1.25
250000 5.82 19.93 2.00 1000000 19.77 19.54 5.16
Operations Scylladb-javascript-driver [s]
Datastax-cassandra-driver [s] Rust-driver [s] 62500 1.60 2.99 1.48
250000 3.06 4.46 2.42 1000000 9.02 9.03 6.53
Operations Scylladb-javascript-driver [s] Datastax-cassandra-driver
[s] Rust-driver [s] 62500 2.32 4.03 2.11 250000 5.45 6.53 4.01
1000000 18.77 16.20 13.21
Operations Scylladb-javascript-driver [s] Datastax-cassandra-driver
[s] Rust-driver [s] 62500 1.86 4.15 1.57 250000 4.24 5.41 3.36
1000000 13.11 14.11 10.54 The test results across both ScyllaDB and
Cassandra show that the new driver has slightly better performance
on the insert benchmarks. For select benchmarks, it starts ahead
and the performance advantage decreases with time. Despite a series
of optimizations, the majority of the CPU time still comes from
NAPI communication and thread synchronization (according to
internal flamegraph testing). There is still some room for
improvement, which we’re going to explore. Since running those
benchmarks, we introduced changes that improve the performance of
the driver. With those improvements performance of select
benchmarks is much closer to the speed of the DataStax driver.
Again…please stay tuned for another blog post with updated results.
Shards and tablets Since the DataStax driver lacked tablet and
shard support, we were curious if our new shard-aware and
tablet-aware drivers provided a measurable performance gain with
shards and tablets.
Operations ScyllaDB JS Driver [s] DataStax Driver [s] Rust Driver
[s] Shard-Aware No Shards Shard-Aware No Shards Shard-Aware No
Shards 62,500 1.89 2.61 3.45 3.51 0.99 1.20 250,000 4.15 7.61 5.66
6.14 1.73 2.30 1,000,000 13.65 30.36 15.86 16.62 4.41 8.33
4,000,000 55.85 134.90 56.73 77.68 18.42 42.64
Operations ScyllaDB JS Driver [s] DataStax Driver [s] Rust Driver
[s] Shard-Aware No Shards Shard-Aware No Shards Shard-Aware No
Shards 62,500 1.50 1.52 3.04 3.63 1.33 1.33 250,000 2.93 3.29 4.52
5.09 1.94 2.02 1,000,000 8.79 10.29 11.11 11.13 5.08 5.71 4,000,000
32.99 38.53 36.62 39.28 17.90 20.67 In insert benchmarks, there are
noticeable changes across all drivers when having more than one
shard. The Rust driver improved by around 36%, the new driver
improved by around 46%, and the DataStax driver improved by only
around 10% when compared to the single sharded version. While
sharding provides some performance benefits for the DataStax
driver, which is not shard aware, the new driver benefits
significantly more — achieving performance improvements comparable
to the Rust driver. This shows that it’s not only introducing more
shards that provide an improvement in this case; a major part of
the performance improvement is indeed shard-awareness.
Operations ScyllaDB JS Driver [s] DataStax Driver [s] Rust Driver
[s] No Tablets Standard No Tablets Standard No Tablets Standard
62,500 1.76 1.89 3.67 3.45 1.06 0.99 250,000 3.91 4.15 5.65 5.66
1.59 1.73 1,000,000 12.81 13.65 13.54 15.86 3.74 4.41
Operations ScyllaDB JS Driver [s] DataStax Driver [s] Rust Driver
[s] No Tablets Standard No Tablets Standard No Tablets Standard
62,500 1.46 1.50 2.92 3.04 1.33 1.33 250,000 2.76 2.93 4.03 4.52
1.94 1.94 1,000,000 8.36 8.79 7.68 11.11 4.84 5.08 When it comes to
tablets, the new driver and the Rust driver see only minimal
changes to the performance, while the performance of the DataStax
driver drops significantly. This behavior is expected. The DataStax
driver is not aware of the tablets. As a result, it is unable to
communicate directly with the node that will store the data – and
that increases the time spent waiting on network communication.
Interesting things happen, however, when we look at the network
traffic: WHAT TOTAL CQL TCP Total Size New driver 3 node all
412,764 112,318 300,446 ∼ 43.7 MB New driver 3 node | driver ↔
database 409,678 112,318 297,360 – New driver 3 node | node ↔ node
3,086 0 3,086 – DataStax driver 3 node all 268,037 45,052 222,985 ∼
81.2 MB DataStax driver 3 node | driver ↔ database 90,978 45,052
45,926 – DataStax driver 3 node | node ↔ node 177,059 0 177,059 –
This table shows the number of packets sent during the concurrent
insert benchmark on three-node ScyllaDB with 2 shards per node.
Those results were obtained with RF = 1. While running the database
with such a replication factor is not production-suitable, we
chose it to better visualize the results. When looking at those
numbers, we can draw the following conclusions: The new driver has
a different coalescing mechanism. It has a shorter wait time, which
means it sends more messages to the database and achieves
lower latencies. The new driver knows which node(s) will
store the data. This reduces internal traffic between database
nodes and lets the database serve more traffic with the same
resources. Future plans The goal of this project was to create a
working prototype, which we managed to successfully achieve. It’s
available at https://github.com/scylladb/nodejs-rs-driver,
but it’s considered experimental at this point. Expect it to change
considerably, with ongoing work and refactors. Some of the features
that were present in DataStax driver, and are expected for the
driver to be considered deployment-ready, are not yet implemented.
The Drivers team is actively working to add those features. If
you’re interested in this project and would like to contribute,
here’s the project’s GitHub
repository. 
When Bigger Instances Don’t Scale
A bug hunt into why disk I/O performance failed to scale on larger AWS instances The promise of cloud computing is simple: more resources should equate to better, faster performance. When scaling up our systems by moving to larger instances, we naturally expect a proportional increase in capabilities, especially in critical areas like disk I/O. However, ScyllaDB’s experience enabling support for the AWS i7i and i7ie instance families uncovered a puzzling performance bottleneck. Contrary to expectations, bigger instances simply did not scale their I/O performance as advertised. This blog post traces the challenging, multi-faceted investigation into why IOTune (a disk benchmarking tool shipped with Seastar) was achieving a fraction of the advertised disk bandwidth on larger instances. On these machines, throughput plateaued at a modest 8.5GB/s and IOPS were much lower than expected on increasingly beefy machines. What followed was a deep dive into the internals of the ScyllaDB IO Scheduler, where we uncovered subtle bugs and incorrect assumptions that conspired to constrain performance scaling. Join us as we investigate the symptoms, pin down the root cause, and share the hard-fought lessons learned on this long journey. This blog post is the first in a three-part series detailing our journey to fully harness the performance of modern cloud instances. While this piece focuses on the initial set of bottlenecks within the IO Scheduler, the story continues in two subsequent posts. Part 2, The deceptively simple act of writing to disk, tracks down a mysterious write throughput degradation we observed in realistic ScyllaDB workloads after applying the fixes discussed here. Part 3, Common performance pitfalls of modern storage I/O, summarizes the invaluable lessons learned and provides a consolidated list of performance pitfalls to consider when striving for high-performance I/O on modern hardware and cloud platforms. Problem description Some time ago, ScyllaDB decided to support the AWS i7i and i7ie families. Before we support a new instance type, we run extensive tests to ensure ScyllaDB squeezes every drop of performance out of the provisioned hardware. While measuring disk capabilities with the Seastar IOTune tool, we noticed that the IOPS and bandwidth numbers didn’t scale well with the size of the instance, and we ended up with much lower values than AWS advertised. Read IOPS were on par with AWS specs up to i7i.4xlarge, but they were getting progressively worse, up to 25% lower than spec on i7i.48xlarge. Write IOPS were worse, starting at around 25% less than spec for i7i.4xlarge and up to 42% less on i7i.48xlarge. Bandwidth numbers were even more interesting. Our IOTune measurements were similar to fio up to the i7i.4xlarge instance type. However, as we scaled up the instance type, our IOTune bandwidth numbers were plateauing at around 8.5GB/s while fio was managing to pull up to 40GB/s throughput for i7i.48xlarge instances. Essential Toolkit The IOTune tool is a disk benchmarking tool that ships with Seastar. When you run this tool on a storage mount point, it outputs 4 values corresponding to the read/write IOPS and read/write bandwidth of the underlying storage system. These 4 values end up in a file called io-properties.yaml. When provided with these values, the Seastar IO Scheduler will build a model of the disk, which it will use to help ScyllaDB maximize the drive’s performance. The IO Scheduler models the disk based on the IOPS and bandwidth properties using a formula that looks something like:read_bw/read_bw_max +
write_bw/write_bw_max + read_iops/read_iops_max +
write_iops/write_iops_max <= 1 The internal mechanics of
how the IO Scheduler works are described very thoroughly in the
blog post I linked above. The io_tester tool is another utility
within the Seastar framework. It’s used for testing and profiling
I/O performance, often in more controlled and customizable
scenarios than the automated IOTune. It allows users to simulate
specific I/O workloads (e.g., sequential vs. random, various
request sizes, and concurrency levels) and measure resulting
metrics like throughput and latency. It is particularly useful for:
Deep-dive analysis: Running experiments with fine-grained control
over I/O parameters (e.g., --io-latency-goal, request
size, parallelism) to isolate performance characteristics or
potential bottlenecks. Regression testing: Verifying that changes
to the IO Scheduler or underlying storage stack do not negatively
impact I/O performance under various conditions. Fair Queue
experimentation: As shown in this investigation, io_tester can be
used to observe the relationship between configured workload
parameters, the resulting in-disk queue lengths, and the throttling
behavior of the IO Scheduler. What this meant for ScyllaDB We
didn’t want to enable i7i instances if the IOTune numbers didn’t
accurately reflect the underlying disk performance of the instance
type. Lower io-properties numbers cause the IO Scheduler to
overestimate the cost of each request. This leads to more
throttling, making monstrous instances like i7i.48xlarge perform
like much cheaper alternatives (such as the i7i.4xlarge, for
example). Pinning the symptoms Early on, we noticed that the
observed symptoms pointed to two different problems. This helped us
narrow down the root causes much faster (well, fast here is a very
misleading term). We were chasing a lower-than-expected IOPS issue
and a different low-bandwidth issue. IOPS and bandwidth numbers
were behaving differently when scaling up instances. The former was
scaling, but with much lower values than we expected. The latter
would just plateau from one point and stay there, no matter how
much money you’d throw at the problem. We started with the
hypothesis that IOTune might misdetect the disk’s physical block
size from sysfs and that we issue requests with a different size
than what the disk “likes,” leading to lower IOPS. After some
debugging, we confirmed that IOTune indeed failed to detect the
block size, so it defaulted to using requests of 512bytes. There’s
no bug to fix on the IOTune side here, but we decided we needed to
be able to specify the disk block size for reads and writes
independently when measuring. This turned out to be quite helpful
later on. With 4K requests, we were able to measure the expected
~1M IOPS for writes compared to the ~650k IOPS we were getting with
the autodetected 512-byte requests (numbers relevant for the
i7i.12xlarge instance). We had a fix for the IOPS issue, but – as
we discovered later – we didn’t properly understand the actual root
cause. At that point, we thought the problem was specific to this
instance type and caused by IOTune misdetecting the block size. As
you’ll see in the next blog post in the series, the root cause is a
lot more interesting and complicated. The plateauing bandwidth
issue was still on the table. Unfortunately, we had no clue about
what could be going on. So, we started exploring the problem space,
concentrating our efforts as you’d imagine any engineer would.
Blaming the IO Scheduler We dug around, trying to see if IOTune
became CPU-limited for the bandwidth measurements. But that wasn’t
it. It’s somewhat amusing that our initial reaction was to point
the finger at the IO Scheduler. This bias stems from when the IO
Scheduler was first introduced in ScyllaDB. It had such a profound
impact that numerous performance issues over time – things that
were propagating downward to the storage team – were often (and
sometimes unfairly) attributed to it. Understanding the root cause
We went through a series of experiments to try to narrow down the
problem further and hopefully get a better understanding of what
was happening. Most of the experiments in this article, unless
explicitly specified, were run on an i7i.12xlarge instance. The
expected throughput was ~9.6GB/s while IOTune was measuring a write
throughput of 8.5GB/s. To rule out poor disk queue utilization, we
ran fio with various iodepths and block sizes, then recorded the
bandwidth. We
noticed that the request needs to be ~4MB to fill the disk queue.
Next, we collected the same for io_tester with
–io-latency-goal=1000 to prevent the queue from splitting requests.
A larger latency goal means the scheduler can be more relaxed and
submit the requests as they come because it has plenty of time
(1000 ms) to complete each request in time. If the goal is smaller,
the IO Scheduler gets stressed because it needs to make each
request complete in that tight schedule. Sometimes it might just
split a request in half to take advantage of the in-disk
parallelism and hopefully make the original request fit the tight
latency goal. The
fio tool seemed to be pulling the full bandwidth from the disk, but
our io_tester tool was not. The issue was definitely on our side.
The good news was that both io_tester and IOTune measured similar
write throughputs, so we weren’t chasing a bug in our measurement
tools. The conclusion of this experiment was that we saturated the
disk queue properly, but we still got low bandwidth. Next, we
pulled an ace out of the sleeve. A few months before this, we were
at a hackathon during our engineering summit. During that
hackathon, our Storage and Networking team built a prototype
Seastar IO Scheduler controller that would bring more transparency
and visibility into how the IO Scheduler works. One of the patches
from that project was a hack that would make the IO Scheduler drop
a lot of the IOPS/bandwidth throttling logic and just drain like
crazy whatever requests are queued. We applied that patch to
Seastar and ran the IOTune tool again. It was very rewarding to see
the following output: Measuring sequential write bandwidth: 9775
MB/s (deviation 6%) Measuring sequential read bandwidth: 13617 MB/s
(deviation 22%) The bandwidth numbers escaped the
8.5GB/s limit that was previously constraining our
measurements. This meant we were correct in blaming the IO
Scheduler. We were indeed experiencing throttling from the
scheduler, specifically from something in the fair queue math. At
that point, we needed to look more closely at the low-level
behavior. We patched Seastar with another home-brewed project
that adds a low-overhead binary tracer to the IO Scheduler. The
plan was to run the tracer on both the master version and the one
with the hackathon patch applied – then try to understand why the
hackathon-patched scheduler performs better. We added a few traces
and we immediately started to see patterns like these in the slow
master trace: Here
it took it 134-51=83us to dispatch one request.
The “Q” event is when a request arrives at the scheduler and gets
queued. “D” stands for when a request gets dispatched. For
reference, the patched IO scheduler spent 1us to
dispatch a request. The unexpected behavior suggested an issue with
the
token bucket accumulation, as requests should be dispatched
instantly when running without io-properties.yaml (effectively
providing unlimited tokens). This is precisely the scenario when
IOTune is running: it withholds io-properties.yaml from the IO
Scheduler. This allows the token bucket to operate with unlimited
tokens, stressing the disk to its maximum potential so IOTune can
compute, by itself, the required io-properties.yaml. The token
bucket seems to run out of tokens…but why? When the token bucket
runs out of tokens, it needs to wait for tokens to be replenished
when other requests are completed. This delays the dispatch of the
next request. That’s why the above request waited
83us to get dispatched when it should have
actually been dispatched in 1us. There wasn’t much
more we could do with the event tracer. We needed to get closer to
the fair queue math. We returned to io_tester to examine the
relationship between the parallelism of the test and the size of
the in-disk queues. We ran io_tester for requests sized within
[128k, 1MB] with parallelism within [1,2,4,8,16] fibers. We ran it
once for the master branch (slow) and once for the “hackathon”
branch (fast). Here are some plots from these results. The plots
are throughput (vertical axis) against parallelism (horizontal) for
two request sizes, 1MB and 128kB.
For
both request sizes, the “hackathon” branch outperformed the
“master” branch. Also, the 1MB request saturates the disk with much
lower parallelism than the 128k request. No surprises here, the
result wasn’t that valuable. In a follow-up test, we collected the
in-disk latencies as well. We plotted throughput against
parallelism for both the master and hackathon branches. The lines
crossing the bars represent the in-disk latencies measured.
This is already much better. After the disk is saturated,
increasing parallelism should create a proportional increase for
in-disk latency. That’s exactly what happens for the hackathon
branch. We couldn’t say the same about the master branch. Here, the
throughput plateaued around 4 fibers, and the in-disk latency
didn’t grow! For some reason, we didn’t end up stressing this disk.
To investigate further, we wanted to see the size of the actual
in-disk queues. So, we coded up a patch to make io_tester output
this information. We plotted the in-disk queue size alongside
parallelism for various request sizes. At this point, it became
clear that we weren’t sufficiently leveraging the in-disk
parallelism. Likely, the fair_queue math was making the IO
Scheduler throttle requests excessively. This is indeed what the
plots below show. In the master (slow) branch run, the in-disk
queue length for the 1MB request (which saturates the disk faster)
plateaus at around 4 requests once parallelism=4 and higher. That’s
definitely not alright.
Just
for fun, let’s look at Little’s Law in
action. We plotted disk_queue_length / latency for
each branch as follows. Next,
we wanted to (somehow) replicate this behavior without involving an
actual disk. This way, we could maybe create a regression test for
the IO Scheduler. The Seastar ioinfo tool was
perfect for this job. ioinfo can take an
io-properties.yaml file as an argument. It feeds the values to the
IO Scheduler, then the tool outputs the token bucket parameters
(which can be used to calculate the theoretical IOPS and throughput
values that the IO Scheduler can achieve). Our goal was to compare
these calculated values with what was configured in an
io-properties.yaml file and make sure the IO Scheduler could
deliver very close to what it was configured for. For reference,
here’s how the calculated IOPS/bandwidth looked compared to the
configured values.
The
values returned by the scheduler were within a 5% margin of the
configured one. This was fantastic news (in a way). It meant the
fair_queue math behaves correctly even with bandwidths above
8.xGB/s. We didn’t get the regression test we
hoped for, since the fair_queue math was not
causing the throttling and disk underutilization we’d seen in the
previous experiment. However, we did add a test that would check if
this behavior changes in the future. We did get a huge win from
this, though. We came to the conclusion that something must be
wrong with the fair_queue math or something in the IO Scheduler
must be incorrect only when it’s not configured with an
io-properties file. At that point, the problem space narrowed
significantly. Playing around with the inputs from the
io-properties.yaml file, we uncovered yet another bug. For large
enough read IOPS/bandwidth numbers in the config file, the IO
Scheduler would report request costs of zero. After many
discussions, we learned that this is not really a bug. It’s how the
math should behave. With big io-properties numbers, the math should
plateau the costs at 0. It makes sense: the more resources you have
available, the single unit of effort becomes less significant. This
led us to an important realization: the unconfigured case (our
original issue) should also produce a cost of zero. A zero cost
means that the token bucket won’t consume any tokens. That gives us
unbounded output…which is exactly what IOTune wants. Now we needed
to figure out two things: Why doesn’t the IO Scheduler report a
cost of zero for the unconfigured case? In theory, it should. In
the issue linked above, costs became zero for values that weren’t
even close to UINT64_MAX. Was our code prepared to handle costs of
zero? We should ensure we don’t end up with weird overflows or
divisions by zero or any undefined behavior from code that assumes
costs can’t be zero. When things start to converge At this point,
we had no further leads, so we thought there must be something
wrong with the fair queue math. I reviewed the math from
Implementing a New IO Scheduler Algorithm for Mixed Read/Write
Workloads, but I didn’t find any obvious flaws that could
explain our unconfigured case. We hoped we’d find some formula
mistakes that made the bandwidth hit its theoretical limit at
8.5GB/s. We didn’t find any obvious issues here,
so we concluded there must be some flaw in the implementation of
the math itself. We started suspecting that there must be some
overflow that ends up shrinking the bandwidth numbers. After quite
some time tracking the math implementation in the code, we managed
to find the issue. Two internal IO Scheduler variables that were
storing the IOPS and bandwidth values configured via
io-properties.yaml had a default value set to
`std::numeric_limits<int>::max()`. It wasn’t that intuitive
to figure out – the variables weren’t holding the actual
io-properties values, but rather some values that derived from
them. This made the mistake harder to spot. There is some code that
recalculates those variables when the io-properties.yaml file is
provided and parsed by the Seastar code. However, in the
“unconfigured” case, those code paths are intentionally not hit.
So, the INT_MAX values were carried into the fair queue math,
crunched into the formulas, and resulted in the
8.xGB/s throughput limit we kept seeing. The fix
was as simple as changing the default value to
‘std::numeric_limits<uint64_t>::max()’. A
one-line fix for many weeks of work. It’s been a crazy
long journey chasing such a small bug, but it has been an
invaluable (and fun!) learning experience. It led to lots of
performance gains and enabled ScyllaDB to support highly efficient
storage instances like i7i, i7ie and i8g. Stay tuned for the next
episode in this series of blog posts, In part 2, we will uncover
that the performance gains after this work weren’t quite what we
were expecting on realistic workloads. We will deep dive into some
very dark corners of modern NVMEs and filesystems to unlock a
significant chunk of write throughput. 
Scaling Is the “Funnest” Game: Rachel Stephens and Adam Jacob
When not to worry about scale, when to rearchitect everything and why passionate criticism is a win “There’s no funner game than the at-scale technology game. But if you play it, some people will hate you for it. That’s okay…that’s the game you chose to play.” – Adam Jacob At Monster Scale Summit 2025, Rachel Stephens, research director at RedMonk, spoke with Adam Jacob, co-founder of Chef and CEO of System Initiative, about what it really means to build and operate software at scale. Note: Monster SCALE Summit 2026 will go live March 11-12, featuring antirez, creator of Redis; Camille Fournier, author of “The Manager’s Path” and “Platform Engineering”; Martin Kleppmann, author of “Designing Data-Intensive Applications” and more than 50 others. The event is free and virtual. Register for free and join the community for some lively chats The Existential Question of Scale Stephens opened with an existential question: “Does your software exist if your users can’t run it?” Yes, your code still exists in GitHub even if us-east-1 goes down. But what if … Your system crawls under load. Critical integrations constantly break. You can’t afford the infrastructure costs. “Software at scale isn’t just about throughput,” Stephens said. “It’s about making sure that your code endures, adapts and remains accessible no matter the load and location of where you’re running. Because if your users can’t use it, your software may as well not exist.” With that framing, Stephens brought in someone who’s spent his career dealing with scale firsthand: Adam Jacob. Only Scale When It Hurts Stephens asked Jacob how teams can balance quality, speed and scale under uncertainty. How do you avoid both cutting corners and premature optimization? Jacob argues that early on, it’s fine not to worry much about scale. Most products fail for other reasons before scalability ever becomes a problem. He explained: “I think of it basically through the lens of optionality. When you start building new things, it’s nice not to worry too much about scale, because you may never reach it. Most products don’t fail because they fail to scale. Think about how badly Twitter failed to scale … and yet here we are.” The first priority is to build a solid product. Once scale becomes a real issue, that’s when it makes sense to refactor and remove bottlenecks. But if you’ve been around the block a little, your experience helps you make early choices that pay off later. Jacob noted, “Premature optimization is real. But as you gain experience, there are some decisions you make early because you know that if things work out, you’ll be happier later — like factoring your code so it can be broken apart across network boundaries over time, if you need to.” Chef Scalability Horror Stories Next, Stephens asked Jacob if he would share a scaling horror story from his Chef days. Jacob obliged and offered two memorable ones. “The best was when we launched the first version of Hosted Chef. The day before the launch, we discovered it took about a minute and a half to create a new user. It didn’t take that long when we were running it on a laptop, but it did later … and we never really tested it. So, in the final hours before launch, we changed it from ‘anyone can sign up’ to a queue system with a little space robot saying, ‘Demand is so high; we’ll get back to you.’ We just papered over the scalability problem.” “Another example: that same Chef server (the one that couldn’t create accounts quickly) eventually had to work at Facebook. The original version was written in Rails, which was great to work with, but not parallel enough. At Facebook scale, you might have 40,000 or 50,000 things pointed at one Chef server. So we rebuilt it in Erlang, which is great for that kind of problem. I literally brought the Erlang version to Facebook on a USB stick. When we installed it and bootstrapped a data center, we thought it was broken because it was using less compute and finished almost instantly.” Jacob explained that if they’d tried to build the Chef server in Erlang from the start, the project probably wouldn’t have gained traction. Starting in Rails made it possible to get Chef out into the world and learn what the system really needed to do. Only later, once they understood how the system really behaved, could they rebuild it with the right architecture and runtime for scale. Growth or Efficiency: Know Which Game You’re Playing At Chef, scaling was ultimately required to land customers like Facebook and JPMorgan Chase, which operate at massive scale. Jacob advised, “Making it scale required major investment, but it worked. You can’t buy your core. If it matters to customers, you have to build it yourself. People often wait too long to realize they have a deep architectural problem that’s also a business problem. Rebuilding for scale takes months, so you have to start early.” Your own approach to scale should ultimately be driven by what game you’re playing: In the venture-capital game, growth and traction come first. You can spend money to scale faster because you’re funded. In the profitability game, efficiency comes first. Overspending on compute or poor architecture hits the bottom line hard. Why Scaling is the ‘Funnest’ Game Stephens mentioned that “when software succeeds, it stops being yours – it becomes everyone’s.” She then asked Jacob what it’s like when your tech scales to the point that people have extremely strong opinions about it. His response: “It’s hard to build things that people care about. If you’re lucky enough to create something you love and share it with the world and people love it back, that’s incredibly rewarding. Even when they don’t, that’s still a gift.” “Someone once tapped me in a coffee shop and said, ‘You wrote Chef? I hate Chef.’ I said, ‘I’m sorry; I didn’t write it to hurt you.’ But at scale, that means he used it. It mattered in his life. And that’s what you want: for people to experience what you built.” “I love the technology, the problem, the difficulty. Scaling adds more layers of complexity, more layers of fun. There’s no funner game than the at-scale technology game. But if you play it, some people will hate you for it. That’s okay…that’s the game you chose to play.” You can watch the full talk below.You Got OLAP in My OLTP: Can Analytics and Real-Time Database Workloads Coexist?
Explore isolation mechanisms and prioritization strategies that allow different database workloads to coexist without resource contention issues Analytics (OLAP) and real-time (OLTP) workloads serve distinctly different purposes. OLAP (online analytical processing) is optimized for data analysis and reporting, while OLTP (online transaction processing) is optimized for real-time low-latency traffic. Most databases are designed to primarily benefit from either OLAP or OLTP, but not both. Worse, concurrently running both workloads under the same data store will frequently introduce resource contention. The workloads end up hurting each other, considerably dragging down the overall distributed system’s performance. Let’s look at how this problem arises, then consider a few ways to address it. OLTP vs OLAP Databases There are basically two fundamental approaches involving how databases store data on disk. We have row-oriented databases, often used for real-time workloads. These store all data pertaining to a single row on disk. Row-oriented storage (ideal for OLTP) Column-oriented storage (ideal for OLAP) On the other side of the spectrum, we have column-oriented databases, which are often used for running analytics. These databases store data in a vertical way (versus horizontal partitioning of rows). This single design decision effectively makes it much easier and efficient for the database to run aggregations, perform calculations and answer retrieving insights such as “Top K” metrics. OLTP vs. OLAP Workloads So the general consensus is that if you want to run OLTP workloads, you use a row-oriented database – and you use a columnar one for your analytics workloads. However, contrary to popular belief, there are a variety of reasons why people might actually want to run an OLAP workload on top of their real-time databases. For example, this might be a good option when organizations want to avoid data duplication or the complexity and overhead associated with maintaining two data stores. Or maybe they don’t extract insights all that often. The Latency Problem But problems can arise when you try to bring OLAP to your real-time database. We’ve studied this a lot with ScyllaDB, a specialized NoSQL database that’s primarily meant for high throughput and low-latency real-time workloads. The following graphic from ScyllaDB monitoring demonstrates what happens to latency when you try to run OLAP and OLTP workloads alongside one another. The green line represents a real-time workload, whereas the yellow one represents an analytics job that’s running at the same time. While the OLTP workload is running on its own, latencies are great. But as soon as the OLAP workload starts, the real-time latencies dramatically rise to unacceptable levels. The Throughput Problem Throughput is also an issue in such scenarios. Looking at the throughput clarifies why latencies climbed: The analytics process is consuming much higher throughput than the OLTP one. You can even see that the real-time throughput drops, which is a sign that the database got overloaded. Unsurprisingly, as soon as the OLAP job finishes, the real-time throughput increases and the database can then process its backlog of queued requests from that workload. That’s how the contention plays out in the database when you have two totally different workloads competing for resources in an uncoordinated way. The database is naively trying to process requests as they come in. When Things Gets Contentious But why does this contention happen in the first place? If you overwhelm your database with too many requests, it cannot keep up. Usually, that’s because your database lacks either the CPU or I/O capacity that’s required to fulfill your requests. As a result, requests queue up and latency climbs. The workloads contribute to contention too. OLTP applications often process many smaller transactions and are very latency sensitive. However, OLAP ones generally run fewer transactions requiring scanning and processing through large amounts of data. So hopefully that explains the problem. But how do we actually solve it? Option A: Physical Isolation One option is to physically isolate these resources. For example, in a Cassandra deployment, you would simply add a new data center and separate your real-time processing from your analytics. This saves you from having to stream data and work with a different database. However, it considerably elevates your costs. Some specific examples of this strategy: Instaclustr, a managed services provider, shared a benchmark after isolating its deployments (Apache Spark and Apache Cassandra). GumGum shared the results of this approach (with multiregion Cassandra) at a past Cassandra Summit. There are definitely use cases and organizations running OLAP on top real-time databases. But are there any other alternatives to resolve the problem altogether? Option B: Scheduled Isolation Other teams take a different approach: They avoid running their OLAP during their peak periods. They simply run through their Analytics pipelines during off-peak hours in order to mitigate the impact on latencies. For example, consider a food delivery company. Answering the question like, “How much did this merchant sell within the past week?” is simple in OLTP. However, offering discounts to 10 top-selling restaurants within a given region is much more complicated. In a wide-column database like Cassandra or ScyllaDB, it inevitably requires a full table scan. Therefore, it would make sense for such a company to run these analytics from after midnight until around 10 a.m. – before its peak traffic hours. This is a doable strategy, but it still doesn’t solve the problem. For example, what if your dataset doubles or triples? Your pipeline might overrun your time window. And you have to consider that your business is still running at that time (people will still order food at 2 a.m.). If you take this approach, you still need to tune your analytics job and ensure it doesn’t kill your database. Option C: Workload Prioritization ScyllaDB has developed an approach called Workload Prioritization to address this problem. It lets users define separate workloads and assign different resource shares to them. For example, you might define two service levels: The main one has 600 shares, and the secondary one has 200 shares. CREATE SERVICE LEVEL main WITH shares = 600 CREATE SERVICE LEVEL secondary WITH shares = 200 ScyllaDB’s internal scheduler will process three times more tasks from the main workload than the secondary one. Whenever the system is under contention, the system prioritizes its resources allocation accordingly. Why does this kick in only during contention? Because if there’s no contention, it means there is no bottleneck, so there is effectively nothing to prioritize. [Play with an interactive animation] Workload Prioritization Under the Hood Under the hood, ScyllaDB’s Workload Prioritization relies on Seastar scheduling groups. Seastar is a C++ framework for data-intensive applications. ScyllaDB, Redpanda, Ceph’s SeaStore and other technologies are built on top of it. Scheduling groups are effectively the way Seastar allows background operations to have little impact on foreground activities. For example, in ScyllaDB and database-specific terms, there are several different scheduling groups within the database. ScyllaDB has a distinct group for compactions, streaming, Memtables, and so on. With Cassandra, you might end up in a situation where compactions impact your workload performance. But in ScyllaDB, all compaction resources are scheduled by Seastar. And according to its shares of resources, the database will allocate a respective share of resources to the background activity (compaction, in that case) – therefore ensuring that the latency of the primary user-facing workload doesn’t suffer. Using scheduling groups in this way also helps the database auto-tune. If the user workload is running during off-peak hours, then the system will automatically have more spare computing and I/O cycles to spend. The database will simply speed up its background activities. Here’s a guided tour of how Workload Prioritization actually plays out: OLTP and OLAP Can Coexist Running OLAP alongside OLTP inevitably involves anticipating and managing contention. You can control it in a few ways: isolate analytics to its own cluster, run it in off-peak windows, or enforce workload prioritization. And workload prioritization isn’t just for allowing OLAP along with your OLTP. That same approach could also be used to assign different priorities to reads vs. writes, for example. If you’d like to learn more, take a look at my recent tech talk on this topic: “How to Balance Multiple Workloads in a Cluster.”ScyllaDB Cloud on AWS I8g and I8ge: 2x Throughput, Lower Latency, Zero Extra Cost
How ScyllaDB performs on the new I8g and I8ge instances, across different workload types Let’s start with the bottom line. For ScyllaDB, the new Graviton4-based i8g instances improve i4i throughput by up to 2x with better latency – and the i8ge improves i3en throughput by up to 2x with better latency. Benchmarks also show single-digit millisecond latency during maintenance operations like scaling. Fast and smooth scaling is an important part of the new ScyllaDB X Cloud offering. The chart below shows ScyllaDB max through under a latency SLA of 10ms latency for different workloads, for the old i4i, i3en and the new i8g, i8ge. AWS recently launched the I8g and I8ge storage-optimized EC2 instances powered by AWS Graviton4 CPUs and 3rd-generation AWS Nitro SSDs. They’re designed for I/O-intensive workloads like real-time databases, search, and analytics (so a nice fit for ScyllaDB). Instance Family Use Case Number of vCPUs per instance Storage i8g Compute bound 2 to 96 0.5 to 22.5 TB i8ge Storage bound 2 to 192 1.25 to 120 TB Reduced TCO in ScyllaDB Cloud Based on our performance results, ScyllaDB users migrating to Graviton4 can reduce infrastructure requirements by up to 50% compared to i4i and i3en previous generations. This translates into significantly lower total cost of ownership (TCO) by requiring fewer nodes to sustain the same workload. These improvements stem from a few factors – both in the new instances themselves, and in the match between ScyllaDB and these instances. The new I8g architecture features: vCPU-to-core mapping: On x86, each vCPU uses half a physical core (a hyperthread); for i8g (ARM), each core matches one physical core Larger caches: 64kB instruction cache and 64kB data cache, compared to 32/48kB on Intel (shared between the two hyperthreads) Faster storage and networking (see spec above) In addition, ScyllaDB’s design allows it to take full advantage of the new server types: The shard-per-core architecture scales with linear performance to any number of cores The IO scheduler can take full advantage of the 3rd-generation AWS Nitro SSD, fully utilizing the higher IO rate, and lower latency without overloading it and increasing latency ARM’s relaxed memory model suits Seastar applications. Since locks and fences are rare, the memory subsystem has more opportunities to reorder memory accesses to optimize performance. What this means for you I8g and i8ge are now available on ScyllaDB Cloud. If you’re running ScyllaDB Cloud, the net impact is: Compute-bound workloads: Move from I4i to I8g. This should provide up to 2x throughput at the same ScyllaDB Cloud price. Storage-bound workloads: Move from I3en to I8ge. Here, you should expect up to 2x higher throughput at the same ScyllaDB Cloud price. Note that using the new ScyllaDB dictionary-based compression can lower the storage cost further. For both use cases, ScyllaDB can keep the 10ms P99 latency SLA during maintenance operations, including scaling out and scaling down. What we measured Max Throughput: The maximum requests per second the database can handle Max Throughput under SLA: The maximum request per second under a P99 latency of 10ms. Only throughput with latency below this SLA counts. This throughput can be sustained under any operation, like scaling and repair. This is the number you should use when sizing your ScyllaDB Database on i8g instances. P99 Latency: Measures the p99 latency for the Max Throughput under SLA Results Read Workload – cached data Cached data: working set size < available RAM, resulting in close to 100% cache hit rate. Instance type Max throughput Max Throughput Under Latency SLA Improvement P99 in ms i4i.4xlarge 1,062,578 750,000 100% 7.84 i8g.4xlarge 1,434,215 1,300,000 135% 6.29 i3en.3xlarge 585,975 550,000 100% 4.37 i8ge.3xlarge 962,504 800,000 164% 6.38 Read Workload – non-cached data, storage only Non-cached data: working set size >> available RAM, resulting in 0% cache hit rate. When most of the data is not cached, storage becomes a significant factor for performance. Instance type Max throughput Max Throughput Under Latency SLA Improvement P99 in ms i4i.4xlarge 218,674 210,000 100% 4.56 i8g.4xlarge 444,548 300,000 203% 4.24 i3en.3xlarge 145,702 140,000 100% 6.83 i8ge.3xlarge 259,693 255,000 178% 7.95 Write Workload Instance type Max throughput Max Throughput Under Latency SLA Improvement P99 in ms i4i.4xlarge 289,154 150,000 100% 2.4 i8g.4xlarge 689,474 600,000 238% 4.02 i3en.3xlarge 217,072 200,000 100% 5.42 i8ge.3xlarge 452,968 400,000 209% 3.41 Tests under maintenance operations ScyllaDB takes pride in testing under realistic use cases, including scaling out and in, repair, backups, and various failure tests. The following results represent the P99 average latency (across all nodes) of different maintenance operations on a 3-node cluster of i8ge.3xlarge. It’s using the same setup as above. Setup ScyllaDB version: 2025.3.1-20250907.2bbf3cf669bb DB node amount: 3 DB instance types: i8ge.3xlarge Loader node amount: 4 Loader instance type: c5.2xlarge Throughput: Read 41K, write 81K, Mixed 35K Results Read Test: Read Latency Operation Read P99 latency in ms Base: Steady State 0.95 During Repair 4.92 During Add Node (out scale) 2.68 During Replace Node 3.10 During Decommission Node (downscale) 2.44 Write Test: Write Latency Operation Write P99 latency in ms Steady State 2.22 During Repair 3.24 Add Node (scale out) 2.49 Replace Node 3.07 Decommission Node (downscale) 2.37 Mixed Test: Write and Read Latency Operation Write P99 Latency in ms Read P99 Latency in ms Steady state 2.03 2.11 During Repair 3.21 4.70 Add Node (scale out) 2.19 2.71 Replace Node 3.00 3.37 Decommission Node (downscale) 2.20 3.05 The results indicate that ScyllaDB can meet the latency SLA under maintenance operations. This is critical for ScyllaDB Cloud, and in particular ScyllaDB X Cloud, where scaling out and in scaling are automatic, and can happen multiple times per day. It’s also critical in unexpected failure cases, when a node must be replaced rapidly, without hurting availability and the latency SLA. Test Setup ScyllaDB cluster 3-node cluster I4i.4xlarge vs. i8g.4xlarge I3en.3xlarge vs. i8ge.3xlarge Loaders Loader node amount: 4 Loader instance type: c7i.8xlarge Workload Replication Factor (RF): 3 Consistency Level (CL): Quorum Data size 650GB for read/mixed, 1.5T for write
Alan Shimel and Dor Laor on Database Elasticity and AI with ScyllaDB
Alan and Dor chat about high-performance databases & AI trends Everything about re:Invent 2025 screamed “massive” – from the exhibit hall’s towering booths, to the overflowing keynotes, to product announcements at every turn. ScyllaDB’s “scale fearlessly” message fit in perfectly. See ScyllaDB’s re:Invent videos But despite the crowds and chaos, Alan Shimel (founder and CEO of Techstrong Group) and Dor Laor (ScyllaDB co-founder and CEO) found a way to meet for a laid-back chat. Topics ranged from ScyllaDB’s origin story, to OSS, to ScyllaDB’s latest announcements for AI and extreme database elasticity. Read highlights below, or enjoy the full interview: ScyllaDB AI Use Cases: Vector Scale, Feature Store, AI Stack Alan: What’s it like on the re:Invent floor? What are the conversations like? What are you hearing? Dor: There’s certainly no shortage of crowds at the booth. A lot of the conversation is about AI. We’re seeing a surge in AI-related use cases. At this point, about half of the use cases we see with ScyllaDB are directly related to AI. Alan: Explain that to me. What’s the use case? Dor: We usually split our AI uses cases into three categories. The first is being part of the AI stack itself. During training and serving, the stack needs to access a huge number of objects, and it needs a fast database to do that. In this case, we’re part of the core AI stack. It’s distributed databases handling very high workloads – and these are very high workloads. That can be for large LLM companies, or for much smaller companies that are just starting their AI journey. That’s the first category. The second category is the feature store. Feature stores are more traditionally associated with machine learning, but they’re still part of the AI world. A feature store lets people classify users, or sometimes agents, automatically. That can be used for recommendations in e-commerce, fraud detection, and a variety of other use cases. In those cases, the feature store needs a fast database to quickly determine how a user is classified and what’s appropriate for them – what they might want to watch, what ad they should see, and so on. The third category is vector search for running LLMs on private datasets. That’s where RAG comes in, with vector data. We added vector search ourselves, and we’re already seeing a lot of interest. In January, we’ll be going live with the general availability of our RAG and vector store. Alan: So in essence, they could use ScyllaDB as their vector database. They’re creating small language models or RAG. That’s got to be big…that’s fantastic. Dor: Our vector search is the most scalable. We can easily run models with a billion objects. Very few vendors can even reach a billion. We can do that while handling hundreds of thousands of requests per second, so we scale to very high numbers. And for people with lower or medium demand – which is most users, with models around 10 million or 100 million objects – we can deliver the best latency at very low price points. Alan: That’s fantastic. Look, there are a lot of people saying we’ve scraped everything there is to scrape for these LLMs. That continuing to make generative AI better by just increasing model size or training data is starting to hit diminishing returns. The thinking is that the way forward might be smaller language models, more RAG. Some people even argue we should move away from ML altogether and toward things like world models. But I definitely believe there’s going to be a lot of activity in the SLM and RAG space. And beyond that, as we build AI for specific use cases, I don’t need the whole internet. I just need the data that matters for that use case – especially if it’s my own proprietary information. I don’t want to put that out there. I want it right here. So I think that’s a huge business. Congratulations. Dor: Thanks. It’s market demand. It’s not just an opportunity, it’s also a defensive move. If we don’t do it, customers will go elsewhere, to be frank. People now expect the same ease of use they get from LLMs on public internet data when they come to any vendor. They want to ask questions in free text, in a single line, and immediately get the best results – without digging through a complicated UI. That’s the power of LLMs. And sometimes it won’t even be people doing that. It could be agents that come in, automate things, and issue those queries on their behalf. True Database Elasticity: Scaling Out and In, Fast Alan: All right, let’s fast-forward past AWS for a second. You have some new announcements coming soon. Share a bit, if you don’t mind. Dor: Thank you for the opportunity. We’re moving from beta to general availability with ScyllaDB X Cloud, our managed platform. This is the new generation of our core database, delivered as a database-as-a-service, including management and consumption. The unique thing here is our new core architecture, which we call tablets. It’s way more elastic than any other database – or even infrastructure – out there. Before this, we were okay in terms of scaling clusters out and scaling them back in. We were about average. But there was demand to do it much faster. And frankly, we also compete with DynamoDB. We’re API-compatible with DynamoDB. Up until now, DynamoDB has been the best in the industry at scaling up and down quickly. If your workload changes throughout the day, you don’t want to pay for peak capacity all the time. You want the system to follow usage dynamically. That’s exactly what X Cloud is. With tablets, we break a very large database– say, a petabyte of data – into five-gigabyte chunks. We can move those chunks around super quickly. That allows us to scale extremely fast. We can increase capacity by four times in about ten minutes. For example, you can go from 500,000 operations per second to two billion operations per second in ten minutes. Alan: And back to 500K? Dor: That’s right. Alan: Sometimes with these things, it’s like blowing up a balloon. You know what I mean? It never really goes back to the size it was before you blew it up. Dor: So with this, we can also go back and shrink. It’s complicated, but it works. User workloads come and go – whether it’s Black Friday or just daily patterns. That leads to big TCO improvements and usability improvements. It’s also pretty unique. We have a shard-per-core engine. So if you have a machine with 32 cores, you’ll have 32 independent threads in the server. If you have a 64-core machine, you’ll have 64 threads, and it will perform twice as well. Now, let’s say you have a 64-core machine, but you actually need 66 threads. If you only had 64, would you buy another 64-core machine? That’s expensive. Instead, we can mix and match. You can run a 64-core machine together with a small two-vCPU machine side by side. Because of the flexibility of our sharding model, we can combine the two. I haven’t seen any other vendor that can do that. What the user gets is efficiency. They have exactly what they need, without having to buy oversized, expensive servers. Alan: Really, what we’re talking about here is almost a FinOps play. I think that’s where we are now, especially with cloud usage. Look, we’re talking about spending five trillion dollars on data center AI factories. But when I talk to people, what they actually say is, I want to get control of my cloud bill. They want to be more efficient in how they use these resources. That’s why I made the balloon joke– that’s pretty much how the cloud works. It never seems to go back down. People want insight. They want to be able to turn the dial. And they want to ask, how can I do this more efficiently? Dor: Most databases aren’t that loaded. I’m not talking about spikes. I’m talking about normal daily usage, or overnight usage. Often it’s only 10% or 20% utilized – but you’re paying for the entire thing. Alan: That was always the promise of the cloud – that elasticity would go up and down. In practice, it mostly just went up. Tiered Storage at ScyllaDB Alan: So, what else is new at ScyllaDB? Dor: We’re also working on things like tiered storage and other technologies to reduce the bill. Normally, we use NVMe for fast storage and performance. It’s also relatively cheap compared to other high-performance storage options. But S3 is cheaper. The problem with S3 is latency. It can be 50 milliseconds, 100 milliseconds, which is prohibitive for many workloads. With tiered storage, we can keep the hot data on fast NVMe and automatically move cold data to S3. That lets us come up with a good solution for common use cases. For example, you might want to keep 30 days of data in ScyllaDB on NVMe, but keep a year of data overall – and still access it through the same API, without having to build a separate access path. That gives users a single API and a very cost-effective solution. Learn more about what’s next for ScyllaDB at Monster SCALE Summit — free and virtual.
From Batch to Real-Time: How MoEngage Achieved Millisecond Personalization with ScyllaDB
How a leading customer engagement platform handles 250K writes per second at 1ms p99 latency with 200TB+ data At MoEngage, our mission as a leading customer engagement platform is to help marketers build deep, lasting relationships with their users by processing hundreds of billions of events each month. Initially, our data architecture was built on a solid foundation of Amazon S3 for large-scale batch analytics and Elasticsearch for search. This dual system was effective for historical segmentation but began to buckle under the modern demand for instantaneous personalization. Our clients needed to react to user actions not in minutes, but in milliseconds. For example, sending a notification based on a product just viewed or updating a user’s segment the moment they qualify for a new campaign. This shift exposed the fundamental limitations of our architecture. Querying a single user’s recent activity in S3 was prohibitively slow, requiring massive dataset scans. At the same time, our write-heavy workload overwhelmed Elasticsearch, creating performance bottlenecks and significant operational overhead from indexing and sharding. It became clear that we couldn’t just optimize our way to real-time. We needed a new, purpose-built system designed from the ground up for high-throughput ingestion and low latency queries. Editor’s note: Karthik and Atish Andhare will be sharing their experiences at Monster SCALE Summit, a free + virtual conference on extreme scale engineering. Learn more and access passes here. Envisioning the Eventstore Our solution was to build the Eventstore – a system that can store all the user actions. It doesn’t replace our vast S3 data lake; think of it more like a high-speed, short-term memory for all user actions and events. Its sole purpose is to handle recent user activity, absorbing the constant firehose of incoming events while allowing us to instantly pull up any single user’s complete activity timeline from the last 30, 60, or 90 days. This new real-time backbone lets us see a user’s entire recent journey in milliseconds, a capability that was completely out of reach with our old architecture. With this new real-time backbone in place, we could finally unlock a class of product capabilities that our customers were demanding, moving from theoretical concepts to tangible features. The Eventstore directly powers: Instantaneous Segmentation: Instead of waiting hours for a segment to update, users are added or removed the moment their behavior meets specific criteria. This ensures communications are always sent to the right audience at exactly the right time. True Real-Time Triggers: Campaigns can be initiated the instant a user performs a key action, such as abandoning a cart or completing a purchase. This eliminates the “lag” that made triggered messages feel disconnected from the user’s immediate context. Hyper-Personalization at the Edge: We can now personalize messages using attributes from a user’s very last action. This allows for powerful use cases like including the “last product viewed” in an email, recommending content based on the “last article read,” or personalizing web content based on the “last item added to cart.” Live User Activity Feeds: Our platform’s user profile dashboard, which once showed a delayed activity history, can now display a live, up-to-the-millisecond feed of every action a user takes, giving marketers a true real-time view of their customers. Choosing Our Engine Our requirements were ambitious and non-negotiable: the system had to handle a write-heavy workload of at least 250,000 events per second with an avg latency of 1 ms, p99 of under 10ms, and it needed to scale horizontally without any performance degradation. We evaluated several distributed databases, but ScyllaDB quickly emerged as the clear frontrunner. Its architecture, a C++ rewrite of Cassandra, is engineered for raw performance, promising to harness the full power of modern hardware and deliver the predictable, ultra-low latencies we required. Also, a few of us had extensive experience working with Cassandra which made it easier to understand ScyllaDB. The ability to add nodes seamlessly to handle increasing load was the final piece of the puzzle, giving us the confidence that this was a solution that wouldn’t just meet our immediate needs, but would grow with us for years to come. Handling Multi-Tenancy As an open platform, MoEngage serves a diverse customer base, from companies trialing our product to large enterprises with varying performance and service-level agreement (SLA) expectations. This reality meant that a one-size-fits-all approach to data storage was not viable. We could not house all customer data in a single, massive cluster, as this would risk performance degradation from “noisy neighbors” and fail to meet the distinct needs of our clients. Our multi-tenancy strategy, therefore, had to be built around workload isolation from the ground up. Our decision was heavily influenced by two core ScyllaDB design principles. First, ScyllaDB recommends having one large table per cluster rather than many small ones to reduce metadata management overhead. Second, and more critically, it is a best practice to configure data retention with a Time-To-Live (TTL) at the table level, not at the cell level. Since our customers require different retention periods (15, 30, or 60 days), managing this at the row level within a single table would create significant overhead on compaction and tombstone management. Based on these constraints, we chose a strategy of physical isolation using multiple, independent ScyllaDB clusters. This approach allows us to group tenants logically based on their needs. For example: All customers with the same retention policy (e.g., 30 days) are housed in the same cluster, allowing us to use a single, efficient table-level TTL. Customers who require stricter, guaranteed SLAs can be isolated in their own dedicated cluster. All MoEngage test accounts can be grouped into a single cluster to separate their non-production workloads. This model provides the perfect balance, ensuring that the workload of one tenant group does not impact another while aligning perfectly with ScyllaDB’s operational best practices for performance and data management. Working with ScyllaDB Open Source Our Large Partition Problem One of the most critical challenges in designing our schema was avoiding the “large partition” anti-pattern in ScyllaDB. While our experiments showed that large partitions don’t significantly penalize write performance, they have a significant impact on reads and compactions. We have a use case where read won’t be able to take advantage of the clustering key ordering and hence have to fetch the entire partition and perform the filtering on the client side. In such cases querying a large partition causes ScyllaDB to fetch data from disk (if not in memtable), decompress, load into memory and then return the result. This creates significant latency overhead and puts unnecessary pressure on the cluster. With ScyllaDB’s official recommendation to keep partitions under 100MB, we knew that a naive partition key like `(user_id, tenant_id)` would be a recipe for disaster, as highly active users could easily generate gigabytes of event data over their retention period. To solve this, our schema design focused on proactively breaking up potentially large partitions into smaller, consistently sized buckets. Our analysis showed an average event row size of about 1KB, meaning our 100MB target partition size could comfortably hold around 100,000 events. A simple calculation revealed that for a 30-day retention period, any user generating more than 330 events per day would exceed this limit. To prevent this, we introduced a `bucket_id` as a core component of our partition key. Our final partition key became a composite of `(uid, tenant, bid)`. The `bucket_id` acts as a split mechanism, splitting a single user’s long event history into multiple, smaller physical partitions. For example, a bucket could represent a day or a week of activity, ensuring no single partition grows indefinitely. This foresight was crucial because a table’s partition key cannot be changed after creation. By including the `bucket_id` in our initial schema, we built in the flexibility to define and refine our exact bucketing strategy over time, guaranteeing a healthy, performant cluster as our data scales. Building for Resilience From the very beginning, two principles were non-negotiable for the Eventstore: fault tolerance and zero data loss. The system had to withstand common infrastructure failures like node loss or disk corruption, and under no circumstances could we lose data that had been acknowledged with a success response. This commitment to durability shaped every decision we made about our cluster architecture, from data replication to physical topology. To achieve this, we made a critical decision to use a Replication Factor (RF) of 3. This means that for every piece of data written, three copies (replicas) are stored on three different nodes in the cluster. With RF3, we could enforce a write consistency level of `LOCAL_QUORUM`. This setting guarantees that a write operation is only considered successful after a majority of the replicas (two out of the three) have confirmed the write to disk. This simple but powerful mechanism is our guarantee against data loss; even if one node fails mid-write, the data is already safe on at least two other nodes. Having three copies of the data is only half the battle; ensuring those copies are physically isolated is just as important. To protect against large-scale failures, we architected our clusters to be Availability Zone (AZ) aware. By leveraging ScyllaDB’s Ec2Snitch feature, we make the database aware of the underlying AWS infrastructure, treating each AWS AZ as a separate “rack.” With this configuration, combined with NetworkTopologyStrategy replication strategy, ScyllaDB intelligently places each of the three data replicas in a different AZ. This strategy ensures that we can withstand the complete failure of an entire Availability Zone without any data loss or service interruption. While this architecture provides excellent high availability against common failures, we also planned for disaster recovery scenarios, such as losing a quorum of nodes or a full region-wide outage. Since our chosen EC2 instances use ephemeral storage, our recovery strategy in these cases is to quickly bootstrap a new cluster from a previous backup. For this, we leverage ScyllaDB’s native backup capabilities and our application’s ability to replay messages from Kafka. Our process involves taking regular snapshots, supplemented by a continuous stream of incremental backups. Any data lost between the last incremental backup and point of outage is available in Kafka, by simply replaying the data from Kafka we are able to fully restore the data. This combination ensures we can rebuild a cluster to a recent, consistent state, completing our comprehensive resilience strategy from minor hiccups to major outages. Cluster Topology Choosing the right database engine was only half the equation; building a resilient and performant Eventstore meant running it on the right hardware. Our workload is fundamentally I/O-bound, characterized by a relentless, high-throughput stream of writes. This reality guided our evaluation of EC2 instance types, where the choice between local NVMe storage and network-attached EBS volumes became the central decision point. After a thorough analysis, we followed ScyllaDB’s strong recommendation and opted for storage-optimized i-series instances with local NVMe SSDs. While we considered memory-optimized instances with EBS, they proved unsuitable for our write-heavy needs. High performance `io2` EBS volumes were prohibitively expensive at our scale, and more affordable `GP3` volumes could not guarantee the p99 latencies we required and introduced risks of throttling during traffic bursts. AWS’s own guidance suggests EBS is better suited for read heavy workloads, the exact opposite of our profile. Local NVMe storage, by contrast, delivers the sustained, sub-millisecond I/O performance essential for our ingestion pipeline. Specifically, we selected the i3en instance family, which provides an excellent balance of vCPU, RAM, and the large, fast storage capacity needed to meet our heavy data retention requirements. Our approach to capacity planning is therefore not a one-time calculation but a dynamic process tied directly to our multi-tenant cluster strategy. The size and configuration of each physical cluster are determined by the specific workload of the tenants it houses. We carefully model capacity based on four key variables for each tenant: 1. The number of active users. 2. The average number of actions per user per day. 3. Their specific data retention policy (e.g., 15, 30, or 60 days). 4. The overall write and read traffic patterns. This allows us to right-size each cluster for its intended workload, ensuring performance and cost-efficiency across our entire infrastructure. Compaction Strategy A critical factor in managing the total cost of ownership for a large-scale database is controlling disk space amplification. Open Source ScyllaDB’s default Size-Tiered Compaction Strategy (STCS) requires keeping nearly 50% of disk space free for compaction operations, which would have effectively doubled our storage costs. We also experimented with the Leveled Compaction Strategy but that too required additional 50% disk space during initial bootstrapping. While ScyllaDB Enterprise offers the highly efficient Incremental Compaction Strategy (ICS) that reduces this overhead to 20%, it comes with a significant license fee. Our Operational Challenges Cluster Management Our initial capacity planning pointed us toward the i3en.3xlarge instance type (12 vCPUs, 96GB RAM, and a 7.5TB NVMe drive). To ensure low latency for our global customer base, we deployed one ScyllaDB cluster in each of our three primary AWS regions. In total, our footprint grew to approximately 50 nodes across these clusters. ScyllaDB provides region-specific, production-ready AMIs that simplify the deployment process. Our deployment workflow followed a structured path: provisioning nodes, configuration, security, and RBAC, followed by onboarding the cluster into our internal monitoring stack. Because ScyllaDB’s AMIs are self-contained, scaling out theoretically meant launching a new node and letting it complete the automated bootstrap process. Things ran smoothly until we encountered a surge in customer data within one of our regions. As disk utilization climbed and we were using STSC, we followed our runbook and added a new node to the cluster. However, this expansion revealed two critical operational hurdles: First, during our POC, a 4TB node bootstrap typically took 18 hours using vNodes. In the live production environment, this window stretched significantly. Bootstrapping took anywhere from 24 to 36 hours. In a high-growth environment, a 1.5-day lead time for scaling is a lifetime. Followed by another issue when an on-call engineer noticed the disk space on the newly joining node was hitting 90%. This was counterintuitive—why was a joining node, which hadn’t even finished taking its share of the data, running out of space? Our investigation revealed that it was caused during the RESHAPE compactions. When a new node joins, ScyllaDB reshapes the data to fit the new shard distribution. This process creates temporary data overhead. After researching similar issues reported in the community, we identified a temporary fix to get our node back to service. Allow the node to initiate the bootstrap. The moment the RESHAPE compaction begins, manually pause it. Let the node finish joining the ring to provide immediate capacity. ICS Our initial experiences led us to a conservative rule of thumb, where we felt safe onboarding new nodes when disk usage on the existing nodes reached between 40% and 45%. This buffer was a technical necessity to accommodate a 2.4x worst-case space amplification during RESHAPE compactions while bootstrapping. We experienced a glimmer of hope when we discovered ScyllaDB’s Incremental Compaction Strategy (ICS). After discussing the ICS with the ScyllaDB team, we realized we were looking at our space amplification issues through an outdated lens. The technical shift offered by ICS is profound because it utilizes a default fragment size of 1GB, meaning a single compaction job typically requires a maximum of only 2GB of disk overhead. To put that into perspective for our specific setup, the old methodology required nearly 50% of free space on a 6.9TB node to handle heavy compaction cycles safely. Under ICS, that same 6.9TB node with 12 shards would only experience roughly 110GB of overhead during compactions. This shift creates massive headroom, allowing us to move away from capping nodes at 45% capacity and safely utilizing over 80% of the disk. By drastically minimizing space amplification, ICS has effectively doubled our storage efficiency without compromising performance during critical node operations. Our Move to ScyllaDB Enterprise Our journey toward ScyllaDB Enterprise began with a rigorous Proof of Concept designed to validate three core pillars: performance, reliability, and operational efficiency. We needed to ensure that the Enterprise edition could not only handle our existing throughput but also provide an edge in performance and cluster operations. To validate these objectives without risking production stability, we deployed a parallel ScyllaDB Enterprise cluster. This environment supported dual writes, allowing us to mirror data from our existing Open Source Software (OSS) cluster to the new Enterprise setup in real-time. This side by-side comparison was instrumental in proving the superiority of the new architecture. The most significant architectural shift involved moving to i3en.6xlarge nodes. These powerful instances, equipped with 24 vCPUs, 192GB of memory, and two 7.5TB NVMe drives, allowed us to dramatically consolidate our infrastructure. By leveraging these denser nodes, we were able to shrink our total node count to just one-third of the original OSS cluster size, significantly reducing the complexity of our distributed network. Alongside this hardware upgrade, we transitioned our tables to the Incremental Compaction Strategy (ICS) to better manage disk space. Following a successful “soak-in” period where the Enterprise cluster met all performance benchmarks, we executed a structured four-step migration strategy. We first upgraded our OSS environment to ScyllaDB Enterprise 2024.1, followed by the systematic migration of tables to the ICS format. Once the tables were optimized, we began the process of downsizing the legacy OSS cluster and finalized the transition by onboarding the entire environment to ScyllaDB Manager for centralized management and automated maintenance. Lessons Learned Schema Design Is Paramount The most important aspect while using ScyllaDB is getting your schema right. It’s not just about the data model but aspects like RF, TTL, partition size, compaction strategy etc. that dictate how your ScyllaDB performs in production. Adding Nodes & Removing Nodes Take Longer As your data size grows the process of adding nodes and removing nodes becomes a lot slower with ScyllaDB’s legacy vNode-based replication. Make sure you are monitoring everything and plan for these activities ahead of time. One thing we learned is that while these operations are slower they don’t quite impact the query / write latencies significantly during these maintenance activities. POC != Production No matter how hard you try to anticipate & simulate issues in POC, your production system will always surprise you. Our Next Steps with ScyllaDB Our journey with the Eventstore has fundamentally transformed our real-time capabilities, but we’re always looking ahead to the next evolution of our architecture. One of the most exciting developments on our roadmap involves leveraging a powerful new feature in the latest versions of ScyllaDB: tablets. While our multi-cluster topology provides excellent isolation, it still requires us to plan capacity for peak workloads. In a multi-tenant world, traffic can be unpredictable. A single customer launching a wildly successful campaign can create a sudden performance hotspot on specific sets of nodes, even if the rest of the cluster has ample storage and spare compute capacity. Manually rebalancing or adding nodes to handle these temporary spikes is a significant operational challenge. This is where tablets change the game. By breaking down the token ring into smaller, movable units of data, tablets decouple data partitions from specific physical nodes. Instead of a partition being permanently owned by a set of nodes, a tablet can be automatically moved to a different node to balance the load in real-time. For us, this unlocks the holy grail of database management: true elastic scaling. When a traffic hotspot emerges, ScyllaDB can automatically rebalance the cluster by shifting tablets away from overloaded nodes to those with spare capacity. This will allow us to absorb sudden traffic surges with grace, ensuring consistent performance for all tenants without manual intervention or costly overprovisioning. It’s the key to providing on-demand compute for our customers’ biggest moments, ensuring the Eventstore remains a robust and highly elastic foundation for the future of real-time engagement at MoEngage.Exploring the key features of Cassandra® 5.0
Apache Cassandra has become one of the most broadly adopted distributed databases for large-scale, highly available applications since its launch as an open source project in 2008. The 5.0 release in September 2024 represents the most substantial advancement to the project since 4.0 released in July 2021. Multiple customers (and our own internal Cassandra use case) have now been happily running on Cassandra 5 for up to 12 months so we thought the time was right to explore the key features they are leveraging to power their modern applications.
An overview of new features in Apache Cassandra 5.0
Apache Cassandra 5.0 introduces core capabilities aimed at AI-driven systems, low-latency analytical workloads, and environments that blend operational and analytical processing.
Highlights include:
- The new vector data type and an Approximate Nearest Neighbor (ANN) index based on Hierarchical Navigable Small World (HNSW), which is integrated into the Storage-Attached Index (SAI) architecture
- Trie-based memtables and the Big Trie-Index (BTI) SSTable format, delivering better memory efficiency and more consistent write performance
- The Unified Compaction Strategy, a tunable density-based approach that can align with leveled or tiered compaction patterns.
Additional enhancements include expanded mathematical CQL functions, dynamic data masking, and experimental support for Java 17.
At NetApp, Apache Cassandra 5.0 is fully supported, and we are actively assisting customers as they transition from 4.x.
A deeper look at Cassandra 5.0’s key features
Storage–Attached Indexes (SAI)
Storage–Attached Indexes bring a modern, storage-integrated approach to secondary indexing in Apache Cassandra, resolving many of the scalability and maintenance challenges associated with earlier index implementations. Legacy Secondary Indexes (2i) and SASI remain available, but SAI offers a more robust and predictable indexing model for a broad range of production workloads.
SAI operates per-SSTable, allowing queries to be indexed locally versus the cluster-wide coordination required of other strategies. This model supports diverse CQL data types, enables efficient numeric and text range filters, and provides more consistent performance characteristics than 2i or SASI. The same storage-attached foundation is also used for Cassandra 5’s vector indexing mechanism, allowing ANN search to operate within the same storage and query framework.
SAI supports combining filters across multiple indexed columns and works seamlessly with token-aware routing to reduce unnecessary coordinator work. Public evaluations and community testing have shown faster index builds, more predictable read paths, and improved disk utilization compared with previous index formats.
Operationally, SAI functions as part of the storage engine itself: indexes are defined using standard CQL statements and are maintained automatically during flush and compaction, with no cluster-wide rebuilds required. This provides more flexible query options and can simplify application designs that previously relied on manual denormalization or external indexing systems.
Native Vector Search capabilities
Apache Cassandra 5.0 introduces native support for high-dimensional vector embeddings through the new vector data type. Embeddings represent semantic information in numerical form, enabling similarity search to be performed directly within the database. The vector type is integrated with the database’s storage-attached index architecture, which uses HNSW graphs to efficiently support ANN search across cosine, Euclidean, and dot-product similarity metrics.
With vector search implemented at the storage layer, applications involving semantic matching, content discovery, and retrieval-oriented workflows while maintaining the system’s established scalability and fault-tolerance characteristics are supported.
After upgrading to 5.0, existing schemas can add vector columns and store embeddings through standard write operations. For example:
UPDATE products SET embedding = [0.1, 0.2, 0.3, 0.4, 0.5] WHERE id = <id>;
To create a new table with a vector type column:
CREATE TABLE items ( product_id UUID PRIMARY KEY, embedding VECTOR<FLOAT, 768> // 768 denotes dimensionality );
Because vector indexes are attached to SSTables, they participate automatically in the compaction and repair processes and do not require an external indexing system. ANN queries can be combined with regular CQL filters, allowing similarity searches and metadata conditions to be evaluated within a unified distributed query workflow. This brings vector retrieval into Apache Cassandra’s native consistency, replication, and storage model.
Unified Compaction Strategy (UCS)
Unified Compaction Strategy in Apache Cassandra 5 included a density-aware approach to organizing SSTables that blends the strengths of Leveled Compaction Strategy (LCS) and Size Tiered Compaction Strategy (STCS). UCS aims to provide the predictable read amplification associated with LCS and the write efficiency of STCS, without many of the workload-specific drawbacks that previously made compaction selection difficult. Choosing an unsuitable compaction strategy in earlier releases could lead to operational complexity and long-term performance issues, which UCS is designed to mitigate.
UCS exposes a set of tunable parameters like density thresholds and per-level scaling that let operators adjust compaction behavior toward read-heavy, write-heavy, or time-series patterns. This flexibility also helps smooth the transition from existing strategies, as UCS can adopt and improve the current SSTable layout without requiring a full rewrite in most cases. The introduction of compaction shards further increases parallelism and reduces the impact of large compactions on cluster performance.
Although LCS and STCS remain available (and while STCS remains the default strategy in 5.0, UCS is the default strategy on newly deployed NetApp Instaclustr’s managed Apache Cassandra 5 clusters), UCS supports a broader range of workloads, reduces the operational burden of compaction tuning, and aligns well with other storage engine improvements in Apache Cassandra 5 such as trie-based SSTables and Storage-Attached Indexes.
Trie Memtables and Trie-Indexed SSTables
Trie Memtables and Trie-indexed SSTables (Big Trie-Index, BTI) are significant storage engine enhancements released in Apache Cassandra 5. They are designed to reduce memory overhead, improve lookup performance, and increase flush efficiency. A trie data structure stores keys by shared prefixes instead of repeatedly storing full keys, which lowers object count and improves CPU cache locality compared with the legacy skip-list memtable structure. These benefits are particularly visible in high-ingestion, IoT, and time-series workloads.
Skip-list memtables store full keys for every entry, which can lead to large heap usage and increased garbage collection activity under heavy write loads. Trie Memtables substantially reduce this overhead by compacting key storage and avoiding pointer-heavy layouts. On disk, the BTI SSTable format replaces the older BIG index with a trie-based partition index that removes redundant key material and reduces the number of key comparisons needed during partition lookups.
Using Trie memtables requires enabling both the trie-based memtable implementation and the BTI SSTable format. Existing BIG SSTables are converted to BTI through normal compaction or by rebuilding data. On NetApp Instaclustr’s managed Apache Cassandra clusters Trie Memtables and BTI are enabled by default, but when upgrading major versions to 5.0, data must be converted from BIG to BTI first to utilize Trie structures.
Other new features
Mathematical CQL functions
Apache Cassandra 5.0 added a rich set of math functions allowing developers to perform computations directly within queries. This reduces data transfer overhead and reduces client-side post-processing, among many other benefits. From fundamental functions like ABS(), ROUND(), or SQRT() to more complex operations like SIN(), COS(), TAN(), these math functions are extensible to a multitude of domains from financial data, scientific measurements or spatial data.
Dynamic Data Masking
Dynamic Data Masking (DDM) is a new feature to obscure
sensitive column-level data at query time or permanently attach the
functionality to a column so that the data always returns
obfuscated. Stored data values are not altered in this process, and
administrators can control access through role-based access control
(RBAC) to ensure only those with access can see the data while also
tuning the visibility of the obscured data. This feature helps with
adherence to data privacy regulations such as GDPR, HIPAA, and PCI
DSS without needing external redaction
systems.
Conclusion
Apache Cassandra 5.0 packs a punch with game changing features that meet the needs of modern workloads and applications. Features like vector search capabilities and Storage Attached Indexes stand out as they will inevitably shape how data can be leveraged within the same database while maintaining speed, scale, and resilience.
When you deploy a managed cluster on NetApp Instaclustr’s Managed Platform, you get the benefits of all these amazing features without worrying about configuration and maintenance.
Ready to experience the power of Apache Cassandra 5.0 for yourself? Try it free for 30 days today!
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