a zoom on membase vng

A Zoom on Membase Dedicated to VNG

Viet-Trung TRAN ENS Cachan, INRIA/IRISA France

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What’s Membase   A key/value store

  Simple, fast, elastic

  Membase’s API is simple but not simpler   SET(key, value)   Value = GET(key)

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Where’s Membase   SQL database? No

  No complex queries, no-schema, no ACID

  NoSQL   Non-relational, distributed and HORIZONTALLY scalable   Key/value store

  Dynamo, Membase, Voldemort, Riak, Redis, etc.

  Column-oriented store   BigTable, Hbase, Cassandra, etc.

  Documents store   MongoDB, CouchDB, Terrastore, etc.

  Array-oriented store   Pyramid, SciDB

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Why NoSQL   For over 40 years, mostly used RDMS

  So good but so COMPLEX   Hard to SCALE

  2005: “One size fits all”: An idea whose time has come and gone

  Called for “Scale OUT” design   Cheap, easy

  Why Membase   Membase = So-called Memcached + persistent storage   Membase = A Distributed caching system + persistent storage

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Why Membase

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  Membase = So-called Memcached + persistent storage   Membase = A Distributed caching system + persistent

storage

 Membase speaking Memcached languages

MEMBASE = SIMPLE, FAST, ELASTIC   Simple

  2 primitives GET, SET (key, value)

  Fast   Cost for I/O routing: O(1)   Give me a key, I know exactly where to go

  Elastic   Free scalle UP and DOWN   Scale from 1 to thousands machines   Fault-tolerance

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Membase deployment

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Data flow

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  Map(Key, vbucket)   Map(vbucket, node)

Data flow [cont’]

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  Internal data flow + replication schema

Membase arch

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  Symmetric design: identical software on every nodes   Data management   Membership management

Thinking on Membase

Personal view

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Membase’s design choices

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  CAP theorem: Pick 2 out of 3   Consistency   Availability   Patition-tolerance

  Membase is CA   Do we really need strong consistency ?

Strong consistency

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  Pessimistic replication may be costly   A write is blocking until data is completely replicated

  1 single master node coordinates reads and writes   Lower I/O performance in concurrency

  Synchronous replication schema   One replica failed, I/O failed

  Proposal: using different consistency models depending on applications

Data migration & replication

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  LRU algorithm   Replication factor is configurable per (key, value)?

  Vbucket

  Re-replication in case of failure?   “Anti-entropy” replica synchronisation?

  Proposal: Application-aware migration is the best

Cluster management

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  One single node is elected as cluster leader   Only running efficiently in single cluster environment   High load on the leader at large-scale

  Rebalancing?   Permanent failure vs temporary failure?

  “Node capacity-aware” load balancing?   Heartbeat frequency should be well configured

  Depending on cluster size and network type

  Efficiency of leader election algorithm?

Conclusion

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  Pros   In production for many companies   Well known API

  Cons   Not so well documented   May be better in source code?   Some key techniques should be well clarified

  One size fit all has come and gone: Design patterns   Application-aware   Infrastructure-aware   Human resource-aware

Thank you!

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