This session will focus on our approach to building a scalable TimeSeries database for financial data using Cassandra 1.2 and CQL3. We will discuss how we deal with a heavy mix of reads and writes as well as how we monitor and track performance of the system.

1. Time is Money
Financial Time Series
Jake Luciani and Carl Yeksigian
BlueMountain Capital

2. About this talk
Part 1: Our use case and architecture
Part 2: Our deployment and tuning
Part 3: Q&A

3. Know your problem.
1000s of consumers
..creating and reading data as fast as possible
..consistent to all readers
..and handle ad-hoc user queries
..quickly
..across data centers.

4. Know your data.
AAPL price
MSFT price

5. Know your queries.
Time Series Query
Start, End, Periodicity defines
query
1 minute periods

6. Know your queries.
Cross Section Query
As Of time defines the query
As Of Time (11am)

7. Know your queries.
Cross sections are random
Storing for all possible Cross Sections is not possible.
We also support bi-temporality

8. Let's optimize for Time Series.

9. CREATE TABLE tsdata (
id blob,
property string,
asof_ticks bigint,
knowledge_ticks bigint,
value blob,
PRIMARY KEY(id,property,asof_ticks,knowledge_ticks)
)
WITH COMPACT STORAGE
AND CLUSTERING ORDER BY(asof_ticks DESC, knowledge_ticks
DESC)
Data Model (CQL 3)

10. SELECT * FROM tsdata
WHERE id = 0x12345
AND property = 'lastPrice'
AND asof_ticks >= 1234567890
AND asof_ticks <= 2345678901
CQL3 Queries: Time Series

11. CQL3 Queries: Cross Section
SELECT * FROM tsdata
WHERE id = 0x12345
AND property = 'lastPrice'
AND asof_ticks = 1234567890
AND knowledge_ticks < 2345678901
LIMIT 1

12. A Service, not an app
C*
Olympus
Olympus
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Olympus
App
App
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App
App
App
Fat Client
Olympus Thrift Service Olympus Thrift Service

13. Complex Value Types
Not every value is a double
Some values belong together (Bid and Ask should always come back
together)
Thrift structures as values
Typed, extensible schema
Union types give us a way to deserialize any type

14. Ad-hoc querying UI

15. But that's the easy part...
(queue transition)

16. Scaling...
The first rule of scaling is you do not just turn everything to 11.

17. Scaling...
Step 1 - Fast Machines for your workload
Step 2 - Avoid Java GC for your workload
Step 3 - Tune Cassandra for your workload
Step 4 - Prefetch and cache for your workload

18. Can't fix what you can't measure
Riemann (http://riemann.io)
Easily push application and system metrics into a single system
We push 6k metrics per second to a single Riemann instance

19. Metrics: Riemann
Yammer Metrics with Riemann
https://gist.github.com/carlyeks/5199090

20. Metrics: Riemann
Push stream based metrics library
Riemann Dash for Why is it Slow?
Graphite for
Why was it Slow?

21. VisualVM: The greatest tool EVER
Many useful plugins...
Just start jstatd on each server and go!

22. Scaling Reads: Machines
SSDs for hot data
JBOD config
As many cores as possible (> 16)
10GbE network
Bonded network cards
Jumbo frames

23. JBOD is a lifesaver
SSDs are great until they aren't anymore
JBOD allowed passive recovery in the face of simultaneous disk
failures (SSDs had a bad firmware)

24. Scaling Reads: Cassandra
Changes we've made:
• Configuration
• Compaction
• Compression

25. Leveled Compaction
Wide rows means data can be spread across a huge number of
SSTables
Leveled Compaction puts a bound on the worst case (*)
Fewer SSTables to read means lower latency, as shown below; orange
SSTables get read
L0
L1
L2
L3
L4
L5
* In Theory

26. Leveled Compaction: Breaking Bad
Under high write load, forced to read all of the L0 files
L0
L1
L2
L3
L4
L5

27. Hybrid Compaction: Breaking Better
Size Tiering Level 0
On by default in 2.0
L0
L1
L2
L3
L4
L5
{Hybrid
Compaction
Size Tiered
Leveled

28. Overlapping Compaction
Instead of forcing a combination of L0 files with L1, we can just push up
files
This allows a higher level of concurrency in compactions
We still know the SSTables that might contain the keys
We can force a proper compaction at any configurable level
L0
L1
L2
L3
L4
L5

29. C optimized library
Read path needs to be fast for our workload
CRC check, composite comparison eat a lot of cycles
CRC is implemented on chip for some architectures (why not use it?)
We want to move some of the operations into a JNI library to reduce
latency and improve throughput

30. Current Stats
16 nodes
2 Data Centers
Replication Factor 6
200k Writes/sec at EACH_QUORUM
150k Reads/sec at LOCAL_QUORUM
> 30 Million time series
> 15 Billion points
10 TB on disk (compressed)
Read Latency 50%/95% is 1ms/5ms

31. Questions?
Thank you!
@tjake and @carlyeks