Riak at shareaholic

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freerobby

Slides from my talk on using Riak at Shareaholic

Technologie

Riak @

Robby Grossman
robby@shareaholic.com
@freerobby

Agenda

Shareaholic: Product & Tech

Why Riak: The Search for a Big Data Store

Transitioning to Riak

Riak Use Cases

Deploying to EC2

Monthly @

Thousands of developers hitting API

Hundreds of thousands of publishers

Tens of millions of shares & clicks

Hundreds of millions of pageviews & events

Tech @

JRuby on Rails (via Torquebox)

MySQL (Master, Read Slave)

Elastic MapReduce (similar to Hadoop)

Redis

Formerly Mongo, Now Riak

Why Not Mongo?

Working set needs to ﬁt in memory

Global write lock blocks all queries
despite not having transactions/joins

Standbys not “hot”

Next @
Options: Goals:

HBase Linear scalability

Cassandra Full-text search

Riak Flexible indexing

Easier Devops

HBase
Pros Cons

Battle tested Complex
Architecture
High performance
SPOFs

Requires Hive for
Indexing/Querying

Expensive to deploy
at small scale

Cassandra
Pros Cons

Native secondary Known users all
indices domain experts

Linear scalability Search requires
Lucene
Tunable CAP
Heavy Weight
MapReduce

Riak
Pros Cons

Operationally simpler Multi-data center
replication requires
Linear scalability Enterprise product

Integrated search leveldb puts high
strain on CPU
Secondary indices

Tunable CAP

Vector clocks solve
time-sync problems

Migration Goals

No time where database goes “ofﬂine”

Product parity throughout migration

Migration Process

1. App writes to Mongo and Riak

2. Verify data integrity

3. Import historical data

4. App reads from Riak

5. Decommission Mongo

Share API

Save shared content

Uses MapReduce to
populate user dashboard

Recommendations

Sets of related pages

Generated on-demand

Publisher Analytics

Generated nightly via Hadoop

Typical stored “document” (JSON)

80kb-1Mb

MapReduce

Handy for querying

Runs at “web page speed”.

Easy to re-reduce for complex queries

Easy to test via CURL

Tunable CAP @

Replication: primary/secondary authority

Read failure tolerance: speed/consistency

Write failure tolerance

Full Text Search

Built on Lucene

Make user content searchable

Make arbitrary keys queryable

“Just turn it on”

Hiccup: corrupt merge indexes

$Query Example Who’s our oldest user who’s shared something in the last minute? curl -XPOST http://localhost:8098/mapred -H 'Content-Type: application/json' -d '{ "inputs": { "bucket":"links", "query":"timestamp:[1346350877 TO 1346350937}" //60 second period }, "query":[ {"map":{"language":"javascript","source":"function(riakObject) { return [[Riak.mapValuesJson(riakObject)[0].user_id]]; }"}}, {"reduce":{"language":"javascript", "name":"Riak.reduceMin" // [[2],[5],[9],[13]] => [[2]] }} ] }' [[2197]]$

In a Nutshell

EC2 specs poorly proportioned for leveldb

Multiple AZs in one location works well

Scale vertically for better latency & consistency

Scale horizontally for more throughput/$

Benchmarks

Top Graph: c1.medium (1.7G, 5 CPU)

Middle: m1.large (7.5G, 4 CPU)

Bottom: cc1.4xlarge (23G, 33.5 CPU)

Calculations
c1.medium (1.7G, 5 CPU)
1758 IOPS/$-hr
Worst 1% of queries: 300ms/800ms

m1.large (7.5G, 4 CPU)
1167 IOPS/$-hr
Worst 1% of queries: 110ms/200ms

cc1.4xlarge (23G, 33.5 CPU)
872 IOPS/$-hr
Worst 1% of queries: 47ms/139ms

Benchmark Takeaways

You can’t go “by spec”

IO is limiting factor

RAM never limiting factor for 1%
of keyspace to be in memory

Fin. Questions?
Thanks: We’re Hiring!

Tom Santero Robby Grossman

Justin Sheehy robby@shareaholic.com

Ryan Zezeski @freerobby

Reid Draper

#freenode riak crew

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Riak at shareaholic

1. Riak @ Robby Grossman robby@shareaholic.com @freerobby

2. Agenda Shareaholic: Product & Tech Why Riak: The Search for a Big Data Store Transitioning to Riak Riak Use Cases Deploying to EC2

3. What’s ?

8. Monthly @ Thousands of developers hitting API Hundreds of thousands of publishers Tens of millions of shares & clicks Hundreds of millions of pageviews & events

9. Tech @ JRuby on Rails (via Torquebox) MySQL (Master, Read Slave) Elastic MapReduce (similar to Hadoop) Redis Formerly Mongo, Now Riak

10. Why Not Mongo? Working set needs to ﬁt in memory Global write lock blocks all queries despite not having transactions/joins Standbys not “hot”

11. Why Riak?

12. Next @ Options: Goals: HBase Linear scalability Cassandra Full-text search Riak Flexible indexing Easier Devops

13. HBase Pros Cons Battle tested Complex Architecture High performance SPOFs Requires Hive for Indexing/Querying Expensive to deploy at small scale

14. Cassandra Pros Cons Native secondary Known users all indices domain experts Linear scalability Search requires Lucene Tunable CAP Heavy Weight MapReduce

15. Riak Pros Cons Operationally simpler Multi-data center replication requires Linear scalability Enterprise product Integrated search leveldb puts high strain on CPU Secondary indices Tunable CAP Vector clocks solve time-sync problems

16. From Mongo to Riak

17. Migration Goals No time where database goes “ofﬂine” Product parity throughout migration

18. Migration Process 1. App writes to Mongo and Riak 2. Verify data integrity 3. Import historical data 4. App reads from Riak 5. Decommission Mongo

19. Use Cases

20. Share API Save shared content Uses MapReduce to populate user dashboard

21. Recommendations Sets of related pages Generated on-demand

22. Publisher Analytics Generated nightly via Hadoop Typical stored “document” (JSON) 80kb-1Mb

23. Riak Successes

24. MapReduce Handy for querying Runs at “web page speed”. Easy to re-reduce for complex queries Easy to test via CURL

25. Tunable CAP @ Replication: primary/secondary authority Read failure tolerance: speed/consistency Write failure tolerance

26. Full Text Search Built on Lucene Make user content searchable Make arbitrary keys queryable “Just turn it on” Hiccup: corrupt merge indexes

27. Query Example Who’s our oldest user who’s shared something in the last minute? curl -XPOST http://localhost:8098/mapred -H 'Content-Type: application/json' -d '{ "inputs": { "bucket":"links", "query":"timestamp:[1346350877 TO 1346350937}" //60 second period }, "query":[ {"map":{"language":"javascript","source":"function(riakObject) { return [[Riak.mapValuesJson(riakObject)[0].user_id]]; }"}}, {"reduce":{"language":"javascript", "name":"Riak.reduceMin" // [[2],[5],[9],[13]] => [[2]] }} ] }' [[2197]]

28. Riak on EC2

29. In a Nutshell EC2 specs poorly proportioned for leveldb Multiple AZs in one location works well Scale vertically for better latency & consistency Scale horizontally for more throughput/$

30. Benchmarks Top Graph: c1.medium (1.7G, 5 CPU) Middle: m1.large (7.5G, 4 CPU) Bottom: cc1.4xlarge (23G, 33.5 CPU)

31. Throughput

32. Latency (Typical)

33. Latency (Worst Case)

34. Calculations c1.medium (1.7G, 5 CPU) 1758 IOPS/$-hr Worst 1% of queries: 300ms/800ms m1.large (7.5G, 4 CPU) 1167 IOPS/$-hr Worst 1% of queries: 110ms/200ms cc1.4xlarge (23G, 33.5 CPU) 872 IOPS/$-hr Worst 1% of queries: 47ms/139ms

35. Benchmark Takeaways You can’t go “by spec” IO is limiting factor RAM never limiting factor for 1% of keyspace to be in memory

36. Fin. Questions? Thanks: We’re Hiring! Tom Santero Robby Grossman Justin Sheehy robby@shareaholic.com Ryan Zezeski @freerobby Reid Draper #freenode riak crew

37. Fin.

Riak at shareaholic

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