Riak @

Robby Grossmanrobby@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

What’s ?

Browser Tools

Sharing Buttons

Recommendations

Social Analytics

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 queriesdespite not having transactions/joins

Standbys not “hot”

Why Riak?

Next @Options:

HBase

Cassandra

Riak

Goals:

Linear scalability

Full-text search

Flexible indexing

Easier Devops

HBasePros

Battle tested

High performance

Cons

Complex Architecture

SPOFs

Requires Hive for Indexing/Querying

Expensive to deployat small scale

CassandraPros

Native secondary indices

Linear scalability

Tunable CAP

Cons

Known users all domain experts

Search requires Lucene

Heavy Weight MapReduce

RiakPros

Operationally simpler

Linear scalability

Integrated search

Secondary indices

Tunable CAP

Vector clocks solve time-sync problems

Cons

Multi-data center replication requires Enterprise product

leveldb puts high strain on CPU

From Mongo to Riak

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

Use Cases

Share API

Save shared content

Uses MapReduce topopulate user dashboard

Recommendations

Sets of related pages

Generated on-demand

Publisher Analytics

Generated nightly via Hadoop

Typical stored “document” (JSON)

80kb-1Mb

Riak Successes

MapReduce

Handy for querying

Runs at “web page speed”.

Easy to re-reduce for complex queries

Easy to test via CURL

Replication: primary/secondary authority

Read failure tolerance: speed/consistency

Write failure tolerance

Tunable CAP @

Full Text Search

Built on Lucene

Make user content searchable

Make arbitrary keys queryable

“Just turn it on”

Hiccup: corrupt merge indexes

Query Example

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]] }} ]}'

Who’s our oldest user who’s shared something in the last minute?

[[2197]]

Riak on EC2

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)

Throughput

Latency (Typical)

Latency (Worst Case)

Calculationsc1.medium (1.7G, 5 CPU)1758 IOPS/$-hrWorst 1% of queries: 300ms/800ms

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

cc1.4xlarge (23G, 33.5 CPU)872 IOPS/$-hrWorst 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:

Tom Santero

Justin Sheehy

Ryan Zezeski

Reid Draper

#freenode riak crew

We’re Hiring!

Robby Grossman

robby@shareaholic.com

@freerobby

Fin.