Discover how your team can achieve low latency at the extreme scale that your data-intensive applications require. We’ll walk you through an example of how ScyllaDB scales linearly to achieve 1M and then 2M OPS – with <1ms P99 latency. We’ll cover how this works on a sample realtime app (an ML feature store), share best practices for performance, and talk about the most important tradeoffs you’ll need to negotiate. Join us to learn: - Why and how to ensure your database takes full advantage of your cloud infrastructure - What architectural considerations matter most for high throughput and low latency - Key factors to consider when selecting a high-performance database

1. Powering Real-Time Apps
with ScyllaDB: Low
Latency & Linear
Scalability
Felipe Cardeneti Mendes, ScyllaDB Solution Architect
Pete Aven, ScyllaDB Solution Architect

2. Poll
What is your existing database P99 latency?
2

3. Powering Real-Time Apps
with ScyllaDB: Low
Latency & Linear
Scalability
Felipe Cardeneti Mendes, ScyllaDB Solution Architect
Pete Aven, ScyllaDB Solution Architect

4. + For data-intensive applications that require high
throughput and predictable low latencies
+ Close-to-the-metal design takes full advantage of
modern infrastructure
+ >5x higher throughput
+ >20x lower latency
+ >75% TCO savings
+ Compatible with Apache Cassandra and Amazon
DynamoDB
+ DBaaS/Cloud, Enterprise and Open Source
solutions
The database for gamechangers
4
“ScyllaDB stands apart...It’s the rare product
that exceeds my expectations.”
– Martin Heller, InfoWorld contributing editor and reviewer
“For 99.9% of applications, ScyllaDB delivers all the
power a customer will ever need, on workloads that other
databases can’t touch – and at a fraction of the cost of
an in-memory solution.”
– Adrian Bridgewater, Forbes senior contributor

5. 5
+400 gamechangers leverage ScyllaDB
Seamless experiences
across content + devices
Digital experiences at
massive scale
Corporate fleet
management
Real-time analytics 2,000,000 SKU -commerce
management
Video recommendation
management
Threat intelligence service
using JanusGraph
Real time fraud detection
across 6M transactions/day
Uber scale, mission critical
chat & messaging app
Network security threat
detection
Power ~50M X1 DVRs with
billions of reqs/day
Precision healthcare via
Edison AI
Inventory hub for retail
operations
Property listings and
updates
Unified ML feature store
across the business
Cryptocurrency exchange
app
Geography-based
recommendations
Global operations- Avon,
Body Shop + more
Predictable performance for
on sale surges
GPS-based exercise
tracking
Serving dynamic live
streams at scale
Powering India's top
social media platform
Personalized
advertising to players
Distribution of game
assets in Unreal Engine

6. About Us
6
Felipe Cardeneti Mendes
● Felipe Mendes is a ScyllaDB Solution Architect with decades of
experience on distributed systems.
● He's a published author with passion towards helping businesses
to achieve their most challenging goals.
Pete Aven
● As a ScyllaDB Solution Architect Pete helps organizations
understand and implement ScyllaDB solutions.
● He has deep expertise with a variety of database systems and
enjoys helping people solve hard data problems.

7. Agenda
+ Demo Linear Scalability of ScyllaDB Cloud
+ 200k Ops/sec
+ 1M Ops/sec
+ 2M Ops/sec
+ Scylla Architecture Overview
+ Q&A
7

8. + 200k ops → 1M ops → 2M ops
+ 70% Reads / 30% Writes
+ ~1B rows
+ Single ms P99 Latency
+ Scale out n2-highmem-32 instances
Real-time M/L Feature Store
Demo | Linear Scalability of ScyllaDB Cloud

9. Loaders
generating reads/writes
3x n2-highmem-32
Cassandra-stress
Nodes
Total vCPUs: 96
3x n2-highmem-32
ScyllaDB Cluster
Load ~1 Billion Rows Before Running Benchmark
9
M/L Feature Store - 200k ops sustained
RF=3 / CL=LOCAL_QUORUM

14. Node-level
metrics

15. 15
Node-level reads
and writes
per shard

16. Loaders
generating reads/writes
3x n2-highmem-32
Cassandra-stress
Nodes
Total vCPUs: 480
15x n2-highmem-32
ScyllaDB Cluster
Load ~1 Billion Rows Before Running Benchmark
16
Scale M/L Feature Store to 1M ops sustained
RF=3 / CL=LOCAL_QUORUM

17. ■ ~67K ops per node (same throughput for 200k ops at 40% load)
■ <1ms latency

18. Loaders
generating reads/writes
6x n2-highmem-32
Cassandra-stress
Nodes
Total vCPUs: 960
30x n2-highmem-32
ScyllaDB Cluster
Load ~1 Billion Rows Before Running Benchmark
18
Scale M/L Feature Store to 2M ops sustained
RF=3 / CL=LOCAL_QUORUM

19. ■ ~67K ops per node! (same throughput for 200k and 1M ops at 40% load)
■ <1ms latency

20. ■ 1M ops with fewer nodes at higher load (40→58%)

21. ■ 2M ops with fewer nodes at higher load (40→69%)

22. Deep Technical Advancements
Horizontal & Vertical Scaling
Built in C++
(no Java overhead)
System and Data
Center Aware
Sharding Per Core
Shard-Aware
Drivers
Auto-Performance
Tuning
Network
Processor NUMA
Storage
Unique Close-to-Metal Architecture

23. Q&A
ScyllaDB Cloud
Start free trial
scylladb.com/cloud
October 18 + 19, 2023
p99conf.io
ScyllaDB University
Free online learning
scylladb.com/university

24. Thank you
for joining us today.
@scylladb scylladb/
slack.scylladb.com
@scylladb company/scylladb/
scylladb/