MapR M7 - Meetup

MapR M7

Making HBase Easy, Dependable and Fast

Tomer Shiran [email protected]

©MapR Technologies -‐ Confiden6al

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MapR Distribu:on for Apache Hadoop § 

Open, enterprise-‐grade distribu6on for Hadoop –  Easy, dependable and fast –  Open source with standards-‐based extensions –  Includes numerous Apache-‐licensed open source projects •  • 

§ 

Hive, Pig, Cascading, HBase, ZooKeeper, HCatalog, Flume, Sqoop, Whirr, Mahout, Oozie Integrated, tested and hardened

MapR is deployed at 1000’s of companies –  From small Internet startups to the world’s largest enterprises

§ 

MapR customers analyze massive amounts of data: –  Hundreds of billions of events daily –  90% of the world’s Internet popula6on monthly –  $1 trillion in retail purchases annually


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MapR Distribu:on for Apache Hadoop

MapR%Control%System%(MCS)

Hive Pig Cascading

Oozie

Mahout

Flume

MapR%Distribu-on%for Apache%Hadoop

HCatalog

Sqoop

Whirr

Apache% HBase

MapReduce

MapR%Data%Pla*orm%(MDP)

NFS%interface HDFS%API

MapR combines 10+ Apache-‐licensed open source projects with MapR’s innova6on ©MapR Technologies -‐ Confiden6al

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HBase Adop:on § 

Used by 45% of Hadoop users

§ 

Database Opera:ons: Large scale key-‐value store Blob store Lightweight OLTP

§ 

Real-‐:me Analy:cs: Logis6cs

Shopping cart

Billing

Auc6on engine

Log analysis ©MapR Technologies -‐ Confiden6al

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HBase: A Compelling Alterna:ve Current MapR HBase Op:ons

HBase Advantages §  §  § 

Scale Strong Consistency Leverage of Hadoop

Open Source Free HBase

Open Source with Support


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Issues Constraining HBase Deployments Reliability • Compac6ons disrupt opera6ons • Very slow crash recovery • Unreliable splidng

Business con6nuity • Common hardware/sofware issues cause down6me • Administra6on requires down6me • No point-‐in-‐6me recovery • Complex backup process

Performance • Many bohlenecks result in low throughput • Limited data locality • Limited # of tables

Manageability • Compac6ons, splits and merges must be done manually (in reality) • Basic opera6ons like backup or table rename are complex


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Announcing M7

EASY

DEPENDABLE

FAST

Snapshots No Compac:ons

No RegionServers

Mirroring No Manual Splits

Consistent Low Latency

Region Recovery in Seconds

M7 Enterprise Grade ©MapR Technologies -‐ Confiden6al

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Extending the MapR Data PlaTorm

MapR%Control%System%(MCS)

Hive Pig Cascading

Oozie

Mahout

Flume

MapR%Distribu-on%for Apache%Hadoop

HCatalog

Sqoop

Whirr

Apache% HBase

MapReduce

HBase&API MapR%Data%Pla*orm%(MDP)

NFS%interface HDFS%API


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Apache HBase on MapR

Limited data management, data protec6on and disaster recovery for tables. ©MapR Technologies -‐ Confiden6al

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M7 – An Integrated System for Unstructured and Structured Data


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Unified Namespace for Files and Tables $ pwd /mapr/default/user/dave $ ls file1 file2 table1 table2 $ hbase shell hbase(main):003:0> create '/user/dave/table3', 'cf1', 'cf2', 'cf3' 0 row(s) in 0.1570 seconds $ ls file1 file2 table1 table2 table3 $ hadoop fs -‐ls /user/dave Found 5 items -‐rw-‐r-‐-‐r-‐-‐ 3 mapr mapr 16 2012-‐09-‐28 08:34 /user/dave/file1 -‐rw-‐r-‐-‐r-‐-‐ 3 mapr mapr 22 2012-‐09-‐28 08:34 /user/dave/file2 trwxr-‐xr-‐x 3 mapr mapr 2 2012-‐09-‐28 08:32 /user/dave/table1 trwxr-‐xr-‐x 3 mapr mapr 2 2012-‐09-‐28 08:33 /user/dave/table2 trwxr-‐xr-‐x 3 mapr mapr 2 2012-‐09-‐28 08:38 /user/dave/table3 ©MapR Technologies -‐ Confiden6al

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Fewer Layers

HBase

Java+Virtual+Machine+(JVM)

Distributed+File+System+(DFS)

HBase



Local+File+System+(ext3)

Distributed+File+System+(DFS)

Unified+PlaKorm

Disks

Disks

Disks

Other+DistribuBons+w/+HBase

MapR+M3/M5+w/+HBase

MapR%M7


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Why No RegionServers? One network hop No daemons to manage

One cache


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Region Assignment


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Instant Recovery

§ 

Apache HBase experiences an outage when any node crashes –  –  – 

§ 

Each RegionServer has a single log (WAL) that must be replayed before any region can be recovered Typically 10-‐30 minutes All regions served by that RegionServer cannot be accessed

M7 provides instant recovery – 

M7 uses small WALs • 

–  – 

§ 

Mul6ple WALs per region vs. 1 per RegionServer (1000 regions)

Instant recovery on put 1000-‐10000x faster recovery on get

How? – 

M7 leverages unique MapR-‐FS capabili6es, not impacted by HDFS limita6ons •  •  • 

Append support No limit to # of files MapR-‐FS translates random writes to sequen6al writes on disk


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Background: Log-‐Structured Merge Trees §  §  § 

Tradi6onal disk-‐based index structures like B-‐Trees are expensive to maintain in real-‐6me LSM Trees reduce the cost by deferring and batching index changes Writes –  – 

§ 

Writes go to an in-‐memory index • 

And a commit log in case the node crashes and recovery is needed

• 

This may trigger a compac6on

The in-‐memory index is occasionally merged into the disk-‐based index

Reads – 

Reads hit the in-‐memory index and the disk-‐based index

Write

Memory

Disk

Index

Log

Read


Index

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Background: Compac:ons Respone'(me'(ms) Compac(on

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Compac6ons disrupt opera6ons because the response 6me spikes

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HBase users disable compac6ons and run them manually –  Down6me on Sunday is beher than down6me on Monday…


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Elimina:ng Compac:ons HBase-‐style

LevelDB-‐style

Examples

BigTable, HBase, Cassandra, Riak

Cassandra, Riak

WAF

Low

High

Low

RAF

High

Low

Low

I/O storms

Yes

No

No

Disk space overhead High (2x)

Low

Low

Skewed data handling Bad

Good

Good

Rewrite large values

Yes

No

Yes

M7

Write-‐amplifica6on factor (WAF): The ra6o between writes to disk and applica6on writes. Note that data must be rewrihen in every indexed structure. Read-‐amplifica6on factor (RAF): The ra6o between reads from disk and applica6on reads. Skewed data handling: When inser6ng values with similar keys (eg, increasing keys, trending topic), do other values also need to be rewrihen?


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Portability (In Both Ways) § 

HBase applica6ons work as is with M7 –  No need to recompile –  No vendor lock-‐in

§ 

Customers can also run Apache HBase on an M7 cluster –  Recommended during a migra6on –  Table names with a slash (/) are in M7, table names without a slash are in

Apache HBase (this can be overridden to allow table-‐by-‐table migra6on)

§ 

Use standard CopyTable tool to copy a table from HBase to M7 and vice versa –  hbase org.apache.hadoop.hbase.mapreduce.CopyTable -‐-‐new.name=/

user/tshiran/mytable mytable


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The PlaTorm for Big Data

Unprecedented Hadoop and NoSQL capabili6es integrated on an easy, dependable and fast plauorm §  Supports Big Data opera6ons ranging from batch analy6cs to real-‐6me database opera6ons §  The only plauorm that makes Hadoop and HBase enterprise grade § 


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MapR Edi:ons

§  §  §  §  § 

Control System NFS Access Performance Unlimited Nodes Free

§  §  §  §  §  § 

Also Available through:

§ 

Control System NFS Access Performance High Availability Snapshots & Mirroring 24 X 7 Support Annual Subscrip6on

Compute Engine ©MapR Technologies -‐ Confiden6al

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§  § 

§  §  § 

All the Features of M5 Simplified Administra6on for HBase Increased Performance Consistent Low Latency Unified Snapshots, Mirroring

Ques:ons? § 

Want to join the beta? –  Request access at www.mapr.com

§ 

Follow-‐up ques6ons? –  Tomer Shiran ([email protected])


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