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Real World NoSQL x
Big Data
Real World NoSQL (by Chris Yuen)
Overview
 Introduction
 Motivation for NoSQL
 The NoSQL landscape

 Experience sharing
 HBase
 MongoDB
 Cassandra

 Tying it up – how does it really matter
Motivation
 Too much data – the need to “scale out”
 CAP theorem
Real World NoSQL (by Chris Yuen)
Motivation
 Too much data – the need to “scale out”
 CAP theorem

 Performance
 RDMBS joining is slow
 Denormalization
 Key value data store

 Alternative data representation
 Schemaless “No SQL”
Real World NoSQL (by Chris Yuen)
Motivation
 Too much data – the need to “scale out”
 CAP theorem

 Performance
 RDMBS joining is slow
 Denormalization
 Key value data store

 Alternative data representation
 Schemaless “No SQL”
 Document data store
HBase
 Builds on top of HDFS

 Consistent “big-data” database
 Automatically scales out
HBase
 … but we didn’t use it in the end
HBase
 A nightmare to set up and maintain
 Depends on Hadoop, HDFS, Zookeeper
Real World NoSQL (by Chris Yuen)
HBase
 A nightmare to set up and maintain
 Depends on Hadoop, HDFS, Zookeeper

 No secondary index
 “Table” alteration requires downtime

 Not spectacular latency for OLTP usage
MongoDB
 De-facto “big-data” “NoSQL” database

 Document based data representation
MongoDB
 De-facto “big-data” “NoSQL” database

 Document based data representation
MongoDB
 A good balance of “traditional” usage and “NoSQL”
usage
 Supports secondary index
 Range query

 Can do table scan
MongoDB
 “Big-data” features: sharding, replica set
Real World NoSQL (by Chris Yuen)
MongoDB
 … but it got ugly pretty fast

 Devil’s in the details
 Replica set management fiasco
 Sharding is difficult to set up and poorly implemented
 https://github.com/kizzx2/mongolab
MongoDB
MongoDB
 Reality – it doesn’t scale beyond one machine
 Replica set
Cassandra
 Column Family data store
Cassandra
 Column Family data store
Cassandra
 Column Family data store

 More “NoSQL” than MongoDB. Less features
 Column data store – strictly key/value query
Cassandra
 Auto-sharding just works

 Replica set requires 0 configuration
 Append only, LSM-tree based storage format
 Good for SSD
 High insert throughput
 For storing analytic data
Cassandra
 Has rudimentary support for secondary index

 Difficult to do table scan or range scan
 Require substantial application / paradigm shift
Real World Implications
 Why does NoSQL matter to Big Data?
 Schemaless storage model
 Performance
 Scalability

 Rapidly incorporate unstructured new data sources
without extensive planning
How to Choose
 Maintenance / Scalability

 Supported operations
 OLAP vs. OLTP
Thank You
Chris Yuen
http://cfc.kizzx2.com
http://github.com/kizzx2
@kizzx2

chris@kizzx2.com

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Real World NoSQL (by Chris Yuen)