An overview of modern NoSQL landscape, examples and challenges

1. Yuriy Guts
R&D Engineer
ELEKS DevTalks

2. 1. Data volume and performance that demands a cluster.
2. Developer productivity, OOP “impedance mismatch”.
3. The need for specialized tools for “painful” tasks.

3. • Not using the relational model
• Mostly schemaless
• Mostly open-source
• Mostly developed in early 21st century
• Mostly not using SQL as a query language
• Running well on clusters

8. ABC
ABC ABC
Write “C”
Read “C”
Replicate “C”
to Slave 1
Replicate “C”
to Slave 2

9. A
B C
Read/Write
“A”
Read/Write
“C”

10. Write “C”
ABC ABC ABCD ABD ABD
Write “D”
Agree with majority and replicate “C” Agree with majority and replicate “D”

11. ABC ABC ABCD ABD ABD

12. ABCDABCD ABCD ABCD ABCD

13. “Out of
Consistency
Availability
Partition Tolerance
You can get maximum two.”

14. You must find a tradeoff between Consistency and Availability.
A cluster should always be Partition Tolerant.

15. • Key-Value Store
• Document Store
• Column-Family Store
• Graph Database

16. • Riak
• Redis
• Memcached
• Amazon DynamoDB
• Voldemort

17. • MongoDB
• RavenDB
• CouchDB
• Azure DocumentDB

18. • Google BigTable
• Cassandra
• HBase
• Hypertable

19. • FlockDB
• Neo4j
• OrientDB
• Infinite Graph

20. 1. Talent acquisition.
2. Ecosystem, tools, community.
3. Operational challenges.

21. Redis, MongoDB

22. yuriy.guts @ eleks.com