Hadoop at datasift

HADOOP AT

DATASIFT

ABOUT MEJairam ChandarBig Data Engineer

Datasift@jairamc

http://about.me/jairamhttp://jairam.me

And I’m a Formula 1 Fan!

OUTLINE

•What is Datasift ?

•Where do we use Hadoop ?

• The Numbers

• The Use-cases

• The Lessons

!! SALES PITCH ALERT !!

WHAT IS DATASIFT?

WHAT IS DATASIFT?

WHAT IS DATASIFT?

WHAT IS DATASIFT?

WHAT IS DATASIFT?

WHAT IS DATASIFT?

WHAT IS DATASIFT?

WHAT IS DATASIFT?

WHAT IS DATASIFT?

THE NUMBERS

•Machines

• HBase

• 60 Machines as RegionServers

• 1 HMaster

• 3 Zookeeper nodes

THE NUMBERS•Machines

• Hadoop

• 135 Machines divided into 2 clusters

•Datanodes/Tasktrakers

•Namenodes with High-Availability Failover

• 1 Jobtracker each

THE NUMBERS• Machines

• DL380 Gen8

• 2 * Intel Xeon E5646 @ 2.40GHz (24 core total)

• 48GB RAM

• 6 * 2 TB disks in JBOD (small partition on first disk for OS, rest is storage)

• 1 Gigabit network links

THE NUMBERS• Data

• Average load of 7500 interactions per second

• Peak loads of 15000 interactions per second sustained over a min

• Peak of 21000 interactions per second during superbowl

• Total current capacity ~ 1.6 PB; Total current usage ~ 800 TB

• Avg size of interaction 2 KB – thats ~ 1GB a min or ~ 2 TB a day with replication (RF = 3)

• And that’s not it!

THE USE CASES• HBase

• Recordings

• Archive

• Map/Reduce

• Exports

• Historics

• Migration

THE USE CASES• Recordings

• User defined streams

• Stored in HBase for later retrieval

• Export to multiple output formats and stores

• <recording-id><interaction-uuid>

• Recording-id is a SHA-1 hash

• Allows recordings to be distributed by their key without generating hot-spots.

THE RECORDER

THE USE CASES• Exporter

• Export data from HBase for customer

• Export files ~ 5 – 10 GB or ~ 3-6 million records

•MR over HBase using TableInputFormat

• But the data needs to be sorted

• TotalOrderPartioner

EXPORTER

HISTORICS

THE USE CASES

• Twitter Import

• 2 years of Tweets

• About 95,000,000,000 tweets

•Over 300 TB with added augmentation

• Import was not as simple as you would imagine

THE USE CASES• Archive

• Not just the Firehose but the Ultrahose

• Stored in HBase as well

• HBase architecture (BigTable) creates Hotspots with Time Series data

• Leading randomizing bit (see HBaseWD)

• Pre-split regions

• Concurrent writes

THE USE CASES• Historics

• Export archive data

• Slightly different from Exporter

• Much larger time lines (1 – 3 months)

• Controlled access to Hadoop cluster with efficient job scheduling

• Unfiltered Input Data

• Therefore longer processing time

• Hence more optimizations required

HISTORICS

THE LESSONS• Tune Tune Tune (Default == BAD)

• Based on use case tune -

• Heap

• Block Size

• Memstore size

• Keep number of column families low

• Be aware of hot-spotting issue when writing time-series data

THE LESSONS

• Use compression (eg. Snappy)

•Ops need intimate understanding of system

•Monitor system metrics (GC, CPU, Compaction, I/O) and application metrics (writes/sec etc)

•Don't be afraid to fiddle with HBase code

• Using a distribution is advisable

QUESTIONS?

We are hiringhttp://datasift.com/about-us/careers