1. The NoSQL Ecosystem
Adam Marcus
MIT CSAIL
marcua@csail.mit.edu / @marcua

2. About Me
●
Social Computing + Database Systems
●
Easily Distracted: Wrote The NoSQL Ecosystem in
The Architecture of Open Source Applications 1
1
http://www.aosabook.org/en/nosql.html

3. History, Compressed
Late 1990s Today

4. History, Compressed
Buy RAM
Late 1990s Today

5. History, Compressed
Buy RAM Wrap RDBMSs
Late 1990s Today

6. History, Compressed
Buy RAM Wrap RDBMSs NoSQL
Late 1990s Today

7. History, Compressed
Not Only SQL
Buy RAM Wrap RDBMSs NoSQL
Late 1990s Today

8. History, Compressed
Buy RAM Wrap RDBMSs NoSQL Commercialize
Late 1990s Today

9. History, Compressed
Buy RAM Wrap RDBMSs NoSQL Commercialize
Late 1990s Today

10. The List, So You Don't Yell at Me
HBase CouchDB
Neo4j
Cassandra Riak InfoGrid
BerkeleyDB Voldemort HyperTable
Redis
MongoDB Sones
AllegroGraph HyperGraphDB
DEX FlockDB
VertexDB
Oracle NoSQL
Tokyo Cabinet MemcacheDB

11. The List, So You Don't Yell at Me
Marcus' Law of Databases:
HBase CouchDB
Neo4j
Cassandra Riak InfoGrid
The number of persistence
HyperTable
BerkeleyDB doubles every 1.5 years
Voldemort
options
Redis
MongoDB Sones
AllegroGraph HyperGraphDB
DEX FlockDB
VertexDB
Oracle NoSQL
Tokyo Cabinet MemcacheDB

12. The List, So You Don't Yell at Me
Marcus' Law of Databases:
HBase CouchDB
Neo4j
Cassandra Riak InfoGrid
The number of persistence
HyperTable
BerkeleyDB doubles every 1.5 years
Voldemort
options
Redis
MongoDB Sones
Corollary:
AllegroGraph HyperGraphDB
I'm sorry I missedDEX FlockDB
your
persistence optionOracle NoSQL
VertexDB
Tokyo Cabinet MemcacheDB

13. interesting properties
real-world usage
takeaways
questions

14. interesting properties
real-world usage
takeaways
questions

15. Conventional Wisdom on NoSQL
●
Key-based data model
●
Sloppy schema
●
Single-key transactions
●
In-app joins
●
Eventually consistent

16. Exceptions are More Interesting
●
Data Model
●
Query Model
●
Transactions
●
Consistency

17. Data Model
●
Usually key-based...
●
Column-families
●
Documents
●
Data structures

18. Data Model
●
Usually key-based...
●
Column-families
●
Documents
●
Data structures
●
...but not always
●
Graph stores

19. Query Model
●
Redis: data structure-specific operations
●
CouchDB, Riak: MapReduce
●
Cassandra, MongoDB: SQL-like languages, no
joins or transactions

20. Query Model
●
Redis: data structure-specific operations
●
CouchDB, Riak: MapReduce
●
Cassandra, MongoDB: SQL-like languages, no
joins or transactions
●
Third-party
●
High-level: PigLatin, HiveQL
●
Library: Cascading, Crunch
●
Streaming: Flume, Kafka, S4, Scribe

21. Transactions
●
Full ACID for single key
●
Redis: multi-key single-node transactions

22. Consistency
● Strong: Appears that all replicas see all writes
● Eventual: Replicas may have different, divergent versions

23. Consistency
● Strong: Appears that all replicas see all writes
● Eventual: Replicas may have different, divergent versions
● Dynamo: strong or eventual (quorum size)

24. Consistency
● Strong: Appears that all replicas see all writes
● Eventual: Replicas may have different, divergent versions
● Dynamo: strong or eventual (quorum size)
● PNUTs: Timeline consistency
● ...many consistency models!
See: http://www.allthingsdistributed.com/2007/12/eventually_consistent.html

25. interesting properties
real-world usage
takeaways
questions

26. NoSQL Use-cases
●
Cassandra
●
HBase
●
MongoDB

27. Cassandra
●
BigTable data model: key column family
●
Dynamo sharding model: consistent hashing
●
Eventual or strong consistency

28. Cassandra at Netflix
●
Transitioned from Oracle
●
Store customer profiles, customer:movie watch
log, and detailed usage logging
Note: no multi-record locking (e.g., bank transfer)
In-datacenter: 3 replicas, per-app consistency
read/write quorum = 1, ~1ms latency
read/write quorum = 2, ~3ms latency
“Replicating Datacenter Oracle with Global Apache Cassandra on AWS” by Adrian Cockcroft
http://www.slideshare.net/adrianco/migrating-netflix-from-oracle-to-global-cassandra

29. Cassandra at Netflix
●
Transitioned from Oracle
●
Store customer profiles, customer:movie watch
log, and detailed usage logging
●
Note: no multi-record locking (e.g., bank transfer)
In-datacenter: 3 replicas, per-app consistency
read/write quorum = 1, ~1ms latency
read/write quorum = 2, ~3ms latency
“Replicating Datacenter Oracle with Global Apache Cassandra on AWS” by Adrian Cockcroft
http://www.slideshare.net/adrianco/migrating-netflix-from-oracle-to-global-cassandra

30. Cassandra at Netflix
●
Transitioned from Oracle
●
Store customer profiles, customer:movie watch
log, and detailed usage logging
●
Note: no multi-record locking (e.g., bank transfer)
●
In-datacenter: 3 replicas, per-app consistency
“Replicating Datacenter Oracle with Global Apache Cassandra on AWS” by Adrian Cockcroft
http://www.slideshare.net/adrianco/migrating-netflix-from-oracle-to-global-cassandra

31. Cassandra at Netflix (cont'd)
●
Benefit: async inter-datacenter replication
●
Benefit: no downtime for schema changes
●
Benefit: hooks for live backups

32. HBase
●
Data model: key column family
●
Sharding model: range partitioning
●
Strong consistency
Applications
Logging events/crawls, storing analytics
Twitter: replicate data from MySQL, Hadoop analytics
Facebook Messages

33. HBase
●
Data model: key column family
●
Sharding model: range partitioning
●
Strong consistency
●
Applications
●
Logging events/crawls, storing analytics
●
Twitter: replicate data from MySQL, Hadoop analytics
●
Facebook Messages

34. HBase for Facebook Messages
●
Cassandra/Dynamo eventual consistency was
difficult to program against
Benefit: simple consistency model
Benefit: flexible data model
Benefit: simple sharding, load balancing,
replication

35. HBase for Facebook Messages
●
Cassandra/Dynamo eventual consistency was
difficult to program against
●
Benefit: simple consistency model
●
Benefit: flexible data model
●
Benefit: simple sharding, load balancing,
replication

36. MongoDB
●
Document-based data model
●
Range-based partitioning
●
Consistency depends on how you use it

37. MongoDB: Two use-cases
●
Archiving at Craigslist
●
2.2B historical posts, semi-structured
●
Relatively large blobs: avg 2KB, max > 4 MB

38. MongoDB: Two use-cases
●
Archiving at Craigslist
●
2.2B historical posts, semi-structured
●
Relatively large blobs: avg 2KB, max > 4 MB
●
Checkins at Foursquare
●
Geospatial indexing
●
Small location-based updates, sharded on user

39. interesting properties
real-world usage
takeaways
questions

40. Takeaways
●
Developer accessibility
●
Ecosystem of reuse
●
Soft spot

41. Developer Accessibility

42. Developer Accessibility
devops
@DEVOPS_BORAT

43. Developer Accessibility
devops self-taught dev
@DEVOPS_BORAT

44. A Different Five-Minute Rule
What does a first-time user of your system
experience in their first five minutes?
(idea credit: Justin Sheehy, Basho)

45. Redis
http://simonwillison.net/2009/Oct/22/redis/

46. PostgreSQL
http://www.postgresql.org/docs/9.1/interactive/sql-createtable.html

47. Cassandra
http://www.datastax.com/docs/0.7/getting_started/using_cli

48. Accessibility is Forever
Beyond five minutes:
●
changing schemas
●
scaling up
●
modifying topology

49. Accessibility is Forever
Beyond five minutes:
●
changing schemas
●
scaling up
●
modifying topology
An accessible data store will ease
first-time users in, and support
experienced users as they expand

50. Ecosystem of Reuse

51. Spectrum of Reusability
Monolithic System System Kernel

52. Spectrum of Reusability
Monolithic System System Kernel
MongoDB
Redis
Use as
provided

53. Spectrum of Reusability
Monolithic System System Kernel
MongoDB Cassandra
Redis Voldemort
Use as Pick between
provided components

54. Spectrum of Reusability
Monolithic System System Kernel
MongoDB Cassandra ZooKeeper
Redis Voldemort LevelDB
Use as Pick between Reusable
provided components components

55. Spectrum of Reusability
Monolithic System System Kernel
MongoDB Cassandra ZooKeeper
Redis Voldemort LevelDB riak_core
Use as Pick between Reusable Build your
provided components components own system

56. And finally, a soft spot

57. polyglot persistence
=
right tool for right task

58. Polyglot persistence: Where's the data?
“HBase at Mendeley,” Dan Harvey
http://www.slideshare.net/danharvey/hbase-at-mendeley

59. Polyglot persistence: Where's the data?
“HBase at Mendeley,” Dan Harvey
http://www.slideshare.net/danharvey/hbase-at-mendeley

60. Polyglot persistence: Where's the data?
Aid developers in reasoning about data consistency
across multiple storage engines
“HBase at Mendeley,” Dan Harvey
http://www.slideshare.net/danharvey/hbase-at-mendeley

61. interesting properties
real-world usage
takeaways
questions

62. ●
Systems: Polyglot persistence + data consistency?

63. ●
Systems: Polyglot persistence + data consistency?
●
Operations: Availability/consistency/latency tradeoffs in
datacenters?

64. ●
Systems: Polyglot persistence + data consistency?
●
Operations: Availability/consistency/latency tradeoffs in
datacenters?
●
Accessibility: RDBMS five-minute usability?

65. ●
Systems: Polyglot persistence + data consistency?
●
Operations: Availability/consistency/latency tradeoffs in
datacenters?
●
Accessibility: RDBMS five-minute usability?
●
Accessibility: Scale-up wizard for storage systems?

66. ●
Systems: Polyglot persistence + data consistency?
●
Operations: Availability/consistency/latency tradeoffs in
datacenters?
●
Accessibility: RDBMS five-minute usability?
●
Accessibility: Scale-up wizard for storage systems?
●
Comparisons: Design or implementation?

67. ●
Systems: Polyglot persistence + data consistency?
●
Operations: Availability/consistency/latency tradeoffs in
datacenters?
●
Accessibility: RDBMS five-minute usability?
●
Accessibility: Scale-up wizard for storage systems?
●
Comparisons: Design or implementation?
●
Future: Next-generation NoSQL stores?

68. Thank You!
●
Systems: Polyglot persistence + data consistency?
●
Operations: Availability/consistency/latency tradeoffs in
datacenters?
●
Accessibility: RDBMS five-minute usability?
●
Accessibility: Scale-up wizard for storage systems?
●
Comparisons: Design or implementation?
●
Future: Next-generation NoSQL stores?
Adam Marcus
marcua@csail.mit.edu / @marcua

69. Photo Credits
http://www.flickr.com/photos/dhannah/457765
7955/
http://www.flickr.com/photos/27316226@N02/3
000888100/sizes/m/in/photostream/
http://www.texample.net/tikz/examples/valenti
ne-heart/
http://voltdb.com/sites/default/files/VCE_button
.png