A zoom on membase vng

A Zoom on Membase Dedicated to VNG

Viet-Trung TRAN ENS Cachan, INRIA/IRISA France

19/06/11 1 www.trungtv.com

What’s Membase A key/value store

Simple, fast, elastic

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


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


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


Why Membase

19/06/11 www.trungtv.com 5

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


Membase deployment


Data flow


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

Data flow [cont’]


Internal data flow + replication schema

Membase arch


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

Thinking on Membase

Personal view


Membase’s design choices


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

Membase is CA Do we really need strong consistency ?

Strong consistency


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


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


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


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!


A zoom on membase vng

Technology

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