Join us for a developer workshop where we’ll go hands-on to explore the affinities between Rust, the Tokio framework, and ScyllaDB. You’ll go live with our sample Rust application, built on our new, high performance native Rust client driver.

1. Piotr Sarna - Principal Software Engineer, ScyllaDB
Felipe Cardeneti Mendes - Solutions Architect, ScyllaDB
Andrew Brown - Director of Product Growth, ScyllaDB
Virtual Developer Workshop
Build Low-Latency
Applications in Rust on ScyllaDB

2. Presenters
2
Piotr Sarna
Piotr is a software engineer very keen on open-source projects, C++
and Rust.
Felipe Cardeneti Mendes
Felipe Mendes is an IT Specialist with years of experience with Linux. In
ScyllaDB, he works as a Solutions Architect
Andrew Brown
Andrew has been working with distributed systems in roles across
product development and product marketing.

3. Agenda ● Background: Why Rust, why ScyllaDB?
● The ScyllaDB Rust driver
● The Rust sample app walkthrough
● Profiling Rust performance

4. IOT Rust Application Setup
$ git clone https://github.com/fee-mendes/rust-driver-example/
$ cd rust-driver-example/docker-compose
$ docker-compose up -d
$ docker exec -it rust-app /bin/bash
Minimum requirements:
- Linux or OSX x86
- Docker installed and setup
- Quadcore CPU
- 2 GB of memory available
- Windows is not supported
Join the #rust-driver channel on ScyllaDB Slack to discuss any issues or questions you might have.

5. Low latency, close
to hardware schedulers
Perfect horizontal & Vertical scale
5
1000 Nodes Cluster
2000 Cluster
K8S Deployment
60TB per Node 256 Cores per Node
1B Operations
per Second
About ScyllaDB: Fast and Scalable

6. 6
Poll:
How proficient are you with the
Rust language?

7. + Asynchronous, non-blocking runtimes
+ Tokio: Most widely used Rust runtime
+ Seastar: C++ runtime for ScyllaDB
+ Fast, flexible, and reliable
+ Scalable, allows high concurrency and low latency
+ Green and sustainable
Why Rust? Why Tokio? Why ScyllaDB?
Rust Driver ScyllaDB

8. The effort started during a 2020 internal hackathon at ScyllaDB.
The existing CQL drivers (cassandra-cpp, cdrs) in the Rust ecosystem were not good
enough for us:
● Not fully async
● Unsatisfactory performance
● Not scalable enough
● Known, long-standing bugs
● No support for ScyllaDB-specific optimizations
○ e.g., Shard-per-core
● etc.
Some history

9. Our design goals:
● fully asynchronous
● token-aware, shard-aware
● ease of use
● maximizing performance
● minimizing overheads
● extensibility
● an extremely cool logo
Based on the principles above, we created the ScyllaDB Rust Driver.
Some history

10. ● Most popular async runtime, which should translate to best adoption
● It would be tempting to write the driver in a runtime-agnostic way, but
it's hard:
○ Not all API's are well defined yet
○ Tokio offers quite complete support for TCP communication,
timeouts and other useful abstractions
● Very actively developed
Why Tokio?

11. Let’s Code
11

12. IOT Application Overview
Metric Collector Metric Reader UUID Finder
Write to ScyllaDB in parallel
Deploy schema (ks/tables)
Generate data:
100 devices
3 days
Every 5 minutes
Device metrics aggregator
Analytics sample
Split token-ring in small parts:
Efficient full table scan
token() function usage
BYPASS CACHE
Single device queries
Real-time sample
Partition scan:
MAX(), AVG(), MIN()
Range queries
Date/Time handling

13. Poll:
What databases do you use
(or are planning to use)
for Rust Apps?:
13

14. Profiling
14

15. tokio-console is a very cool project which brings perf/top capabilities to async Rust.
Prerequisites:
Installation:
$ cargo install tokio-console
Cargo.toml:
[dependencies]
console-subscriber = "0.1"
Code:
console_subscriber::init();
Compilation flags:
RUSTFLAGS="--cfg tokio_unstable" cargo run <your-project>
Profiling: tokio-console

16. Demo
Profiling: tokio-console

17. Rust ecosystem has a very convenient way of generating flamegraphs: cargo
flamegraph
Usage:
$ cargo install flamegraph
$ cargo flamegraph your-project
Profiling: cargo flamegraph

18. Demo
Profiling: cargo flamegraph

19. On Linux, flamegraph scripts use perf underneath. Perf is a well-known and very powerful
profiling tool with direct support in the kernel. Rust programs, since they're compiled via LLVM,
are perfectly capable candidates for profiling with perf.
Prerequisites:
Cargo.toml (the root one in case you use multiple workspaces):
[profile.release]
lto = false
debug = true
System (Linux-specific):
$ echo 0 | sudo tee /proc/sys/kernel/perf_event_paranoid
$ echo 0 | sudo tee /proc/sys/kernel/kptr_restrict
Profiling: perf

20. By the way, when run on Linux, cargo flamegraph leaves a side effect - a perf.data file,
which is a result format from calling perf record. Here's another example:
$ perf record -g ./target/release/metric-collector
$ perf report -g
Profiling: perf

21. Felipe Cardeneti
Mendes
felipemendes@scylladb.com
Piotr Sarna
sarna@scylladb.com
Q&A
Andrew Brown
andrew.brown@scylladb.com

22. United States
2445 Faber St, Suite #200
Palo Alto, CA USA 94303
Israel
Maskit 4
Herzliya, Israel 4673304
www.scylladb.com
@scylladb
Thank You!