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Cassandra at NoSql Matters 2012
1.
Apache Cassandra: Real-world scalability,
today! Jonathan Ellis CTO
2.
Cassandra Job Trends ©2012
DataStax
3.
“Big Data” trend ©2012
DataStax
4.
Why Big Data
Matters Research done by McKinsey & Company shows the eye-opening, 10- year category growth rate differences between businesses that smartly use their big data and those that do not. ©2012 DataStax
5.
Big data
Analytics Realtime ? (Hadoop) (“NoSQL”) ©2012 DataStax
6.
Some Casandra users
©2012 DataStax
7.
Industries & use
cases • Financial • Time series data • Social Media • Messaging • Advertising • Ad tracking • Entertainment • Data mining • Energy • User activity streams • E-tail • User sessions • Health care • Anything requiring: Scalable performant • Government + highly available ©2012 DataStax
8.
Why Cassandra? •
Fully distributed, no SPOF • Multi-master, multi-DC • Linearly scalable • Larger-than-memory datasets • Best-in-class performance (not just writes!) • Fully durable • Integrated caching • Tuneable consistency ©2012 DataStax
9.
Availability •
“There is no such thing as standby infrastructure: there is stuff you always use and stuff that won’t work when you need it.” -- Ben Black: founder, Boundary; ex-AWS • “The biggest problem with failover is that you're almost never using it until it really hurts. It's like backups that you never test.” -- Rick Branson: instagram; ex-DataStax ©2012 DataStax
10.
Classic partitioning with
SPOF partition 1 partition 2 partition 3 partition 4 router client ©2012 DataStax
11.
Fully distributed, no
SPOF client p3 p6 p1 p1 p1 ©2012 DataStax
12.
©2012 DataStax
13.
Partitioning
jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F ©2012 DataStax
14.
Partitioning
Primary key determines placement* jim age: 36 car: camaro gender: M carol age: 37 car: subaru gender: F johnny age:12 gender: M suzy age:10 gender: F ©2012 DataStax
15.
PK
MD5 Hash jim 5e02739678... MD5 hash operation yields carol a9a0198010... a 128-bit johnny f4eb27cea7... number for keys suzy 78b421309e... of any size. ©2012 DataStax
16.
The “token ring”
Node A Node B Node D Node C ©2012 DataStax
17.
Start
End 0xc000000000.. 0x0000000000.. A 1 0 0x0000000000.. 0x4000000000.. B 1 0 0x4000000000.. 0x8000000000.. C 1 0 0x8000000000.. 0xc000000000.. D 1 0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
18.
Start
End 0xc000000000.. 0x0000000000.. A 1 0 0x0000000000.. 0x4000000000.. B 1 0 0x4000000000.. 0x8000000000.. C 1 0 0x8000000000.. 0xc000000000.. D 1 0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
19.
Start
End 0xc000000000.. 0x0000000000.. A 1 0 0x0000000000.. 0x4000000000.. B 1 0 0x4000000000.. 0x8000000000.. C 1 0 0x8000000000.. 0xc000000000.. D 1 0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
20.
Start
End 0xc000000000.. 0x0000000000.. A 1 0 0x0000000000.. 0x4000000000.. B 1 0 0x4000000000.. 0x8000000000.. C 1 0 0x8000000000.. 0xc000000000.. D 1 0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
21.
Start
End 0xc000000000.. 0x0000000000.. A 1 0 0x0000000000.. 0x4000000000.. B 1 0 0x4000000000.. 0x8000000000.. C 1 0 0x8000000000.. 0xc000000000.. D 1 0 jim 5e02739678... carol a9a0198010... johnny f4eb27cea7... suzy 78b421309e... ©2012 DataStax
22.
Replication
Node A Node B Node D Node C carol a9a0198010... ©2012 DataStax
23.
Node A
Node B Node D Node C carol a9a0198010... ©2012 DataStax
24.
Node A
Node B Node D Node C carol a9a0198010... ©2012 DataStax
25.
Highlights • Adding
capacity is application-transparent and requires no downtime • No SPOF, not even temporarily • No “primary” replica • Configurable synchronous/asynchronous • Tolerates node failure; never have to restart replication “from scratch” • “Smart” replication avoids correlated failures ©2012 DataStax
26.
What about performance?
• Log-structured storage engine avoids random i/ o • Excellent performance on both reads and writes • Row-level isolation via concurrent algorithms • no locking • Built in compression improves cache hotness • “Row cache” can replace memcached ©2012 DataStax
27.
reads/s
writes/s 35000 30000 25000 20000 15000 10000 5000 Cassandra 0.6 0 ©2012 DataStax Cassandra 1.0
28.
©2012 DataStax
29.
Netflix
Application/Use Case • Manage subscriber interactions with downloaded movies • Need to handle distributed databases all over the world (40 countries) • Need better TCO than Oracle simple text Why Cassandra? • Easy scale and multi-data center support for geographical data distribution • Data model perfect fit for customer interaction data • Much better TCO than Oracle or SimpleDB “I can create a Cassandra cluster in any region of the world in 10 minutes. When marketing guys decide we want to move into a certain part of the world, we’re ready.” ©2012 DataStax
30.
Constant Contact
Application/Use Case • Manage marketing/email campaigns for small businesses • Needed database to handle social media data that is very large in volume and must be maintained for long time • Data is unstructured in nature simple text Why Cassandra? • Cassandra built for big data scale and able to persist, manage, and quickly query big data • Deployed application on Cassandra in 1/3rd the time and 1/10th the cost of Oracle “Whenever we need new capacity, we just add new nodes online and we’re able to meet whatever demand we have. Cassandra is great for that.” ©2012 DataStax
31.
ReachLocal
Application/Use Case • ReachLocal provides end-to-end Internet advertising services to small and medium- sized businesses in eight countries • Must track most or all user interaction with marketing campaigns on web sites simple text Why Cassandra? • The amount of information was beyond the scalability limits of traditional RDBMS’s • Has to replicate data to six data centers around the world • Needed integration with real-time data and analytics/search ©2012 DataStax
32.
Backupify
Application/Use Case • Cloud-based utility that enables backups and searches of Google Apps, Gmail, Facebook, Twitter, Blogger and other content. • Must write lots of data very quickly simple text Why Cassandra? • Big data requirements necessitated easy scale out and continuously available database architecture • Strong Community support of Cassandra • TCO was much better than others “Cassandra was just a better design all around – more truly horizontally scalable and with less management overhead – and there’s no single point of failure. I looked at Cassandra’s architecture and thought, ‘Yeah, that’s how you do it.’” ©2012 DataStax
33.
OpenWave
Application/Use Case • Openwave Messaging delivers next generation converged messaging platform with cloud and social integration capabilities. simple text Why Cassandra? • Needed new database that would support geographic redundancy, continuous availability, and big data scale • Required high IOPS database speed • Better TCO than prior Oracle database “Here are the big ‘checkbox’ items for us with Apache Cassandra: There is no single point of failure, it offers high read- and-write performance, and it has the ability to work on commodity hardware”. ©2012 DataStax
34.
Healthx
Application/Use Case • Develops and manages online portals for healthcare market • Delivered via cloud platform • Manages provider, patient, and other related data simple text Why DataStax Enterprise? • Needed to scale, perform, and search data faster than previous Microsoft SQL Server database farm • Integrated big data platform that provides one database cluster for all real-time and search data “We really like the integration with Solr. We get the full redundancy that you’d expect out of Cassandra as well as the full text indexing of Solr. The two things together make a win.” ©2012 DataStax
35.
Big data
Analytics Realtime ? (Hadoop) (“NoSQL”) ©2012 DataStax
36.
The evolution of
Analytics Analytics + Realtime ©2012 DataStax
37.
The evolution of
Analytics replication Analytics Realtime ©2012 DataStax
38.
The evolution of
Analytics ETL ©2012 DataStax
39.
Big data
Analytics Datastax Realtime (Hadoop) Enterprise (Cassandra) ©2012 DataStax
40.
Reunification of realtime
+ analytics ©2012 DataStax
41.
©2012 DataStax
42.
Portfolio Demo dataflow Portfolios
Portfolios Historical Prices Live Prices for Intermediate today Results Largest loss Largest loss ©2012 DataStax
43.
Better Hadoop than
Hadoop • “Vanilla” Hadoop • 8+ services to setup, monitor, backup, and recover (NameNode, SecondaryNameNode, DataNode, JobTracker, TaskTracker, Zookeeper, Region Server,...) • Single points of failure • Can't separate online and offline processing • DataStax Enterprise • Single, simplified component • Self-organizes based on workload • Peer to peer • JobTracker failover ©2012 DataStax
44.
Enterprise search with
Solr SELECT title FROM solr WHERE solr_query='title:natio*'; title -------------------------------------------------------------------------- Bolivia national football team 2002 List of French born footballers who have played for other national teams Lithuania national basketball team at Eurobasket 2009 Bolivia national football team 2000 Kenya national under-20 football team Bolivia national football team 1999 Israel men's national inline hockey team Bolivia national football team 2001 ©2012 DataStax
45.
Managing & Monitoring
Big Data DataStax OpsCenter manages and monitors all Cassandra and Hadoop operations ©2012 DataStax
46.
Questions?
• http://www.datastax.com/docs • http://www.datastax.com/dev/blog/whats- new-in-cassandra-1-1 • http://www.datastax.com/products/enterprise ©2012 DataStax
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