This document provides an overview of NoSQL databases and Apache Cassandra. It discusses how data and data modeling have evolved with big data. It introduces key concepts like CAP theorem and ACID vs BASE. It describes various NoSQL implementations like key-value, document, and column-oriented databases. It provides details on Cassandra's architecture, data model, and operational aspects. The document demonstrates Cassandra configuration, CQL usage, and monitoring tools.

1. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.1
DILEEP KALIDINDI
23rd February 2015
Explore, Build & Operate
NoSQL
with
Apache Cassandra

2. Who am I ?
 Dileep Varma Kalidindi
 Current: Senior Engineer @Responsys (since Apr’14), Circles Team.
 Fascination: Problem Solving , Distributed & BigData churning systems.
 Past: 8+yrs with VeriSign, Informatica Labs, NTT Data.
 Hobbies: Adventure sports.

3. Are we good ?
4/5/2016
3
Data

4. Data
 Data has never been in same structure, so as their modelling techniques.
 Applications evolved from OLAP, OLTP to Web, Mobile & Social.
 Big Data comes with different characteristics – Volume, Velocity, Variety,
Veracity & Value.
 Responsys Data:
 Need for better suitable Data models and Storage models
- but why ?

6. Impending Mismatch –Data model & Storage model
 SQL relational model is User oriented
 in store concurrency, integrity, consistency, or data type validity
 Transactional guarantees, schemas and referential integrity
 Purpose applications tend to control integrity and validity (not aggregation fancy)
 Difference between the persistent data model and the in-memory data structures.
 Data duplication and denormalization are now First class citizens !!
 Scale–up to Scale– wide – NoSQL Multinode vs RDBMS clustering.

7. Conceptual – ACID, BASE & CAP
Transactions, consistency and availability – could we prioritize ?

8. CAP theorem - consequences

10. Agenda
 NoSQL
 NoSQL Implementations – for various purposes
 Architecture fit – Polyglot persistence
 Data modelling – concepts in view of NoSQL .
 Cassandra – Architecture
 Database Internals
 CQL & DEMO
 Installation, Configuration & tools
 Oracle NoSQL – pitch by Sheetal

11. # NoSQL
5 April 2016 11

12. NoSQL
 Non-relational, distributed, open-source & horizontally scalable #nxtGen
NoSQL is an accidental neologism.
Schema less storage systems built for 5 v’s of Bigdata.
Decentralized – Every node in cluster is identical
High Availability - No SPoF – No Network failures
Open source and No cost models (Except for enterprise support)

14. NoSQL – Architecture fit-in
Polyglot persistence thinking fits in right data store for appropriate data
sets.
Service usage over Direct data usage.
Concerns
 Operational concerns like licensing, support, tools, upgrade, auditing.
 Security of Datastore, Context’s, Authorization etc ..
 Integration with ETL and Data transfer utilities.
 Deployment complexity

15. Data models – in view of NoSQL
 NoSQL models are application specific “What questions do I have?”
 Relational models are driven by structure of data “What answers do I have?”
Modelling techniques
 Conceptual: Denormalization, Aggregates & Application side joins
 General: Atomic aggregates, Enumerable Keys, Dimensionality
reduction, Index table & Composite key index.
 Hierarchical: Tree aggregation, Materialized paths, Nested sets &
batch graph processing.

16. Data models – deep view
 Conceptual: DeNormalization
 Query data volume or IO per query VS total data volume
 Processing complexity VS total data volume
 Aggregates:
 Simple
 Atomic
Tree aggregation:

18. NoSQL - implementations
If one implementation fits all then why not RDBMS ?
Classification is driven in application point of view !
Key-Value
 Strong aggregation which is opaque to the database
 Oracle NoSQL, Windows Azure & Redis
Document database
 Structure in the aggregate
 MongoDb, CouchDb & Raven DB

19. NoSQL - implementations
Column family structures
 Two level aggregate structure
 Key & a row aggregate, Row aggregate is a group of columns.
 Big table, Hbase & Cassandra
Graphs database
 Neo 4j

20. NoSQL – implementations – CAP fit

21. 5 April 2016 21

22. Apache Cassandra - Continuous availability, linear scalability & operational simplicity
 About
 Column store NoSQL Database.
 Originally developed by Facebook (2007) and now an Apache project
 Master less architecture with all nodes in Ring topology
 Commercial add-ons & support (“enterprise edition”) by Datastax
 Data center replication, Scalability (wide), Fault-tolerance & Tunable
consistency.
 Online load balancing, flexible schema, key-oriented queries & CAP-aware
 Implementation of good Security standards, Operations, Monitoring & utilities.

23.  Column – Key-value pair
 Counter column
 Expiring column
 Super column
 Column family – Collection of rows - Map <RowKeys, OrderedColumn Collection>
 Dynamic (Wide)
 Static (Narrow)
 KeyStore – containts column families & super column familes
Cassandra – data model

24.  CAP Values – AP (Availability & Partition tolerance). Consistency (eventual)
available with latency. No row locking (Hbase wins!)
 Linear scaling of Cassandra – throughput vs no-of nodes.
 Casandra Cluster – Partioner generates tokens for rowKeys
 Write in action
 Read in action
Cassandra – Architecture

25. Installation & Configuration
 Yum installation is the easiest - /etc/yum.repos.d/datastax.repo
 Cassandra.yaml configuration
 Cluster_name, data_file_dir, commitlog_dir
 Directory locations
 Start Cassandra :– Cassandra –f
 Start CLI:- cqlsh
 Stop Cassandra – service stop or process kill

26. Demo
5 April 2016 26

27. CQL in action
 CQL 3.0 is much like SQL.
 All names are case-insensitive
 CQL Data types:
 Create KeySpace: Responsys_Demo
 Create table, index, user
 All other SQL like functions !!

28. Cassandra – Monitoring
 JMX Interface – DEMO
 Nodetool – Cassandra JMX interface
 cfstats
 Netstats
 Ring & other operations
 DataStax Ops center
 Nagios monitoring
 Cassandra logging & GC logging

29. 4/5/2016 29
Summary, Conclusions
&
References

30. Summary – Quick recap
 Data evolution
 ACID, BASE & CAP
 NoSQL, data models, implementations
 Cassandra & Data model
 Architecture
 Installations & Operations

31. Links & References
• https://highlyscalable.wordpress.com/2012/03/01/nosql-data-modeling-techniques/
• http://www.thoughtworks.com/insights/blog/nosql-databases-overview
• http://www.dia.uniroma3.it/~torlone/bigdata/L6-NoSQL.pdf
• https://highlyscalable.wordpress.com/2012/03/01/nosql-data-modeling-techniques/
• http://radar.oreilly.com/2013/03/returning-transactions-to-distributed-data-stores.html

32. 4/5/2016 Confidential32
Q & A

33. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.33
Thank you

35. APPENDIX