This document discusses Cassandra's use at Netflix including: - Netflix uses Cassandra extensively with over 50 clusters holding over 100 TB of data supporting streaming and high throughput needs. - Cassandra provides high availability, linear scalability, and high write performance making it well suited for Netflix's needs. - The document provides recommendations around data modeling, performance considerations, and best practices when using Cassandra at scale.

1. Cassandra @
Nitish Korla
Cloud Data Architect

2. Why Cassandra?
 High Availability / Fully distributed
 Scalability (Linear)
 Write performance
 Multi-region replication support (bi-directional)
 Simple to install and operate

3. Cassandra footprint @ Netflix
• 50+ Cassandra clusters
• 1000+ nodes holding 100+ TB data
• AWS 500 IOPS -> 100, 000 IOPS
• Streaming data completely persisted in Cassandra
• Related Open Source Projects
– Cassandra : in-house committer
– Priam : Cassandra Automation
– Test Tools : jmeter
– http://github.com/netflix

4. Device Keys - Cassandra
AWS EU-West
EU apps
AWS US-East
AWS US-West
US-E apps
US-W apps

5. Data Model
• Row-oriented
• Number of columns/Names can differ
name
xyz Paul zip 95123
name
abc Adam zip 94538 sex Male
namenk12 Nitish

6. Read/Write performance
• Write performance : Superfast!!
– Sequential I/O
– In-memory write
– Zero locking
• Point reads : high performant
• Range scans
– Need reverse-key indexes
– Assess the need for range scans (full-table scans)
– Use Netflix Astyanax client library

7. wide-row implementation
• Viewing history
22-
JAN100 json 1-MAR json
24-jan
501 Json
data
25-
jan
json
data
26-jan data
data
name1000 Nitish
28-jan Json
data
29-jan json
data

8. Think Data Archival
• Data stores in Netflix grow exponentially
• Have a process in place to archive data
– Work with Data Science Engineering /DW
– Move data to cheap H/W
– Set right expectations w.r.t latencies with historical data
• Cassandra TTL’s

9. read-modify-write patterns
• Read portion drives the overall latency
• Revisit your architecture

10. Observations
• Cassandra scales linearly without any noticeable
degradation to running cluster
• Read performance sufficient enough to remove
memcache in some cases
• Self-healing : minimal operational noise
• Developers
– mindset needed a shift from normalization to
denormalization
– Need to have reasonable understanding of Cassandra
architecture

11. Avoid surprises
• Benchmark …
• AWS makes it easy for us