Compendium of my Brisk, Cassandra & Hadoop talks of the Summer 2011 - Delivered at JavaOne2011. I like the content in this one personally as it touches, Usecase driven intro to Cassandra, NoSQL followed by Intro to hadoop - MapReduce, HDFS internals, NameNode and JobTrackers. And how Brisk decomposes the Single point of failures in HDFS while providing a single form for Realtime & Batch storage and processing. (And it seemed enjoyable to the audience in attendance)

1. Brisk: Truly peer-to-peer Hadoop
High-order bits from Cassandra & Hadoop
srisatish ambati
@srisatish

2. How many in audience…

3. NoSQL -
Know your queries.

4. points
• Usecases
• Why cassandra?
• Usecase: Hadoop, Brisk
• FUD: Consistency
– Why facebook is not using Cassandra?
• Anti-patterns
• Community, Code, Tools
• Q&A

5. Users. Netflix.
Key by Customer, read-heavy
Key by Customer:Movie, write-heavy

6. TimeSeries: (several customers)
periodic readings: dev0,
dev1…deviceID:metric:timestamp ->value
Metrics typically way larger dataset than users.

7. Why Cassandra?

8. Operational simplicity
peer-to-peer

9. write
Operational simplicity read
peer-to-peer

10. Replication:
Multi-datacenter
Multi-region ec2
Multi-availability zones

11. reads local
dc1 dc2
Replication:
Multi-datacenter
Multi-region ec2, aws
Multi-availability zones

12. 4.21.2011, Amazon Web Services outage:
“Movie marathons on Netflix awaiting AWS to
come back up.” #ec2disabled

13. 4.21.2011, Amazon Web Services outage:
Netflix was running on AWS.

14. fast durable writes.
fast reads.

15. Writes
Sequential, append-only.
~1-5ms

16. Writes
Sequential, append-only.
~1-5ms
On cloud: ephemeral disks rock!

17. Reads
Local
Key & row caches, (also, jna-based 0xffheap)
indexes, materialized

18. Reads
Local
Key & row caches, (also, jna-based 0xffheap)
indexes, materialized
ssds: improved read performance!

19. amortize
Replication over writes
Repair over reads

20. Distribution between nodes
Gossip
Anti-entropy
Failure-detector
L ig h t w e i g h t

21. Clients: cql, thrift
pycassa, phpcassa
hector, pelops
(scala, ruby, clojure)

22. Usecase #3: h a d o o p
Hdfs  cassandra  hive
Logs stats analytics

23. Brisk
Truly peer-to-peer hadoop.

24. mv computation
not data

26. map(String key, String value):
// key: document name
// value: document contents for each word w in value:
EmitIntermediate(w, "1");
reduce(String key, Iterator values):
// key: a word
// values: a list of counts int result = 0;
for each v in values: result += ParseInt(v);
Emit(AsString(result));
word count in MapReduce

27. Parallel Execution View

30. immutable data
write-once-read-many!
Files once created, written & closed..
not changing!

31. jobtracker, tasktracker
hdfs: namenode, datanode

32. cloudera
amazon: elastic map reduce
hortonworks
mapR
brisk

33. Tools & Analytics
Hive, Pig, R
Karmasphere
Datameer
… dozens of stealth startups!

35. “However, given that there is only a single master, it’s failure is unlikely;”
The MapReduce paper, 2004. Sanjay et,al, Google.

36. Namenode decomposition, explained.

38. NameNode:
Single Master node
Single Machine Address space
Single Point of failure

40. Use column families (tables)
inode
sblock

41. One kind of node
no master node, no spof
peer-to-peer

43. near-real time hadoop
Low latency: cassandra_dc nodes
Batch Analytics: brisk_dc nodes

44. BriskSimpleSnitch.java
if(TrackerInitializer.isTrackerNode)
{
myDC = BRISK_DC;
logger.info("Detected Hadoop trackers
are enabled, setting my DC to " + myDC);
}
else
{
myDC = CASSANDRA_DC;
logger.info("Looks like Vanilla
Cassandra nodes, setting my DC to " + myDC);
}

45. Hive: SQL-like access
cli, hwi, jdbc, metastore
Pushdown predicates (v beta2)

46. hive> CREATE TABLE invites (foo INT, bar
STRING)PARTITIONED BY (ds STRING);
hive> LOAD DATA LOCAL INPATH
'$BRISK_HOME/resources/hive/examples/files/kv2.txt'
OVERWRITE INTO TABLE invites PARTITION (ds='2008-
08-15');
hive> SELECT count(*), ds FROM invites GROUP BY ds;
http://www.datastax.com/docs/0.8/brisk/about_hive

47. ETL
Real-time
Cassandra CFs
DataCenters
Scale
@srisatish

48. @srisatish

49. No me in team!
 Ben Coverston  Michael Allen
 Ben Werther  Mike Bulman
 Brandon Williams  Nate McCall
 Cathy Daw  Nick M Bailey
 Jackson Chung  Patricio Echague
 Jake Luciani  Tyler Hobbs
 Joaquin Casares  SriSatish Ambati
 Jonathan Ellis  Yewei Zhang

50. 100-node Brisk Cluster on Opscenter
@srisatish

51. FUD,
acronym: fear, uncertainty, doubt.

52. Consistency: R + W > N
ORACLE, 2-node: R=1, W=2, N=2,(T=2)
DNS
* N is replication factor. Not to be confused with T=total #of nodes

53. Tune-able, flexibility.
For High Consistency:
read:quorum, write:quorum
For High Availability:
high W, low R.

54. Consistency: R + W > N
ORACLE, 2-node: R=1, W=2, N=2,(T=2)
DNS
"brisk.consistencylevel.read", "QUORUM";
"brisk.consistencylevel.write", "QUORUM";
* N is replication factor. Not to be confused with T=total #of nodes

56. Inbox Search:
600+cores.120+TB (2008)
Went from 100-500m users.
Average NoSQL deployment size: ~6-12 nodes.

57. Usecase #5: search
Apache Solr + Cassandra = Solandra
Other inbox/file Searches:
xobni, c3
github.com/tjake/solandra

58. “Eventual consistency is harder to program.”
mostly immutable data.
complex systems at scale.

59. Miscellaneous,
Myth: data-loss, partial rows.
writes are durable.

60. Anti-Patterns
Transactions
Joins
Read before write

61. Anti-Patterns for cloud
ebs
jvm, virtualized
single region

62. A few more good reasons for Cassandra...

63. Tools
AMIs, OpsCenter, DataStax
AppDynamics
Getting Started with brisk ami
Netflix just builds AMIs for deployment!

64. Beautiful C 0 d e
= new code(); //less is more
~90k.java.concurrent.@annotate.
bloomfilters, merkletrees.
non-blocking, staged-event-driven.
bigtable, dynamo.

65. Current & Future Focus:
Distributed Counters, CQL.
Simple client.
operational smoothening.
compaction.

66. Community
Robust. Rapid. Brisk #
Professional support from DataStax.
git clone git@github.com:riptano/brisk.git
engineers: independent,startups, large companies,
Rackspace, Twitter, Netflix..
Come join the efforts!

67. Usecase #4: first NoSQL, then scale!
simpledb  Cassandra
mongodb  Cassandra

70. Copyright: xkcd

71. Copyright: plantoys
… more than one way to do it!

72. Summary -
high scale peer-to-peer datastore
best friend for
multi-region, multi-zone availability.
Hadoop – HDFS engulfing the DataWorld
Brisk – best of both worlds!

73. @srisatish
Q&A

74. Dynamo, 2007
Bigtable, 2006 +
OSS, 2008
Incubator 2009
TLP, 2010
Cassandra
+ +
Brisk

75. NoSQL -
Know your queries.