Apache Cassandra is a non-relational, distributed database designed for high availability and scalability, overseen by DataStax since its open sourcing in 2008. It features a decentralized storage architecture, tunable consistency, and is optimized for performance with sequential disk writes and multi-core processing capabilities. Key attributes include master-less replication, fault tolerance, and the ability to handle large volumes of data efficiently, making it suitable for various high-demand applications.

1. An Introduction to
Apache
assandra
Aaron Ploetz

2. What is Cassandra?
● Cassandra is a non-relational, partitioned row store.
● Rows are organized into column families (tables) with a
required primary key.
● Data is distributed across multiple master-less, nodes in
an application-transparent manner.
● DataStax oversees the development of the Apache
Cassandra open-source project, provides support to
companies using Cassandra, and provides an enterprise-ready
version of Cassandra.

3. $ whoami
Aaron Ploetz
@APloetz
● Lead Database Engineer
● B.S.-MCS UW-Whitewater
● M.S.-SED Regis University
● Using Cassandra since version 0.8
● Contributor to the Casandra tag on StackOverflow
● Contributor to the Apache Cassandra project
● 2014/15 DataStax MVP for Apache Cassandra

4. (short) History of Cassandra and
DataStax
● Developed at , open sourced in 2008.
● Design influenced by Google BigTable and Amazon Dynamo.
● Graduated to Apache “Top-Level Project” status in Feb 2010.
● DataStax founded by Jonathan Ellis and Matt Pfeil in late 2010,
offering enterprise Cassandra support.
● Secured $190 million in VC funding.
● Started with eight people, now employs more than 350.
● 400+ Customers, including 25 of the Fortune 100.

5. Key Features
● Current release is Cassandra 2.1 (Sept 10).
● Distributed, decentralized storage; no SPOF.
● Scalable.
● High-availability, Fault-tolerance.
● Tunable Consistency.
● High-performance.
● Data center awareness.

6. Distributed, Decentralized
Storage DC1 DC2
● Peer-to-peer, master-less replication.
● Any node can handle a read or write operation.
● Supports local read/write ops via “logical” data centers.
● Gossip protocol allows nodes to be aware of each other.
● Snitch ensures that data is replicated appropriately.

7. Scalability
● Cassandra allows you to easily add nodes to scale your
application back-end.
● Benchmark from 2011:
– 48 node cluster could handle 174,373 writes/sec.
– 288 node cluster could handle 1,099,837 writes/sec.
– Indicates that Cassandra scales linearly.
● Throughput of N nodes = T.
● Throughput of Nx2 nodes = Tx2.

8. High Availability
DC1 DC2
X
● Cassandra was designed under the premise that
hardware failures can and do occur.

9. High Availability
DC1 DC2
X
X
● Cassandra was designed under the premise that
hardware failures can and do occur.

10. High Availability
DC1 DC2
X
X X
X
X
X X
●Gossip Protocol keeps live nodes informed of failures.
●Cassandra 2.0.2 implemented Rapid Read Protection which
redirects read operations to live nodes.

11. Tunable Consistency
● Cassandra allows you alter your consistency level on a
per-operation basis.
● Also allows configuration for data center locality:
ALL QUORUM ONE
Strong
Consistency
High Availability /
Eventual
Consistency
Quorum ==
(nodes / 2) + 1

12. Eventual Consistency != Hopeful
Consistency
● experiment on consistency :
– Created two data centers with C* 1.1.7 Cluster of 48 nodes in
each data center.
– Wrote 1,000,000 records at CL1 in one data center.
– Read same 1,000,000 records at CL1 in other data center.
– All records read successfully!
– “Eventually consistent does not mean a day, minute or
even a second from now… in most cases, it is
milliseconds!”- Christos Kalantzis

13. High Performance
● Cassandra is optimized from the ground up for
performance:
Source: DataStax.com

14. High Performance
● All disk writes are sequential, append-only
operations.
● No reading before writing.
● Cassandra is optimized for threading with multi-core/
processor machines.

15. Potential Drawbacks?
● Some use cases are not appropriate (transient data
or delete-heavy patterns).
● Developer learning curve: CQL != SQL
● Simple queries only. No JOINs or sub-queries.
● Optimal performance is achieved through de-normalizaiton
and query-based data modeling.

16. Cassandra moves beyond disco-era
data modeling
●Everything MUST be normalized!!!
●Redundant data == “bad”
●Relational Database theory originated when disk space was expensive. In
1975 some vendors were selling disk space at $11k per MB.
●By 1980 prices “dropped” so that you could finally buy 1GB of storage for
under $1 Million.
●Today I can buy a 1TB disk for $60.

17. Cassandra Storage Structures
● Keyspace == Database (in the RDBMS world)
CREATE KEYSPACE products WITH replication = {
'class': 'NetworkTopologyStrategy',
'RFD': '2', 'MKE': '4'};
● Column Family == Table
CREATE TABLE hierarchy (
category text,
subcategory text,
classification text,
skumap map<uuid, text>,
PRIMARY KEY (category, subcategory, classification));

18. Cassandra Primary Keys
● Primary Keys are unique.
● Single Primary Key:
PRIMARY KEY (keyColumn)
● Composite Primary Key:
PRIMARY KEY (myPartitionKey, my1stClusteringKey,
my2ndClusteringKey)
● Composite Partitioning Key:
PRIMARY KEY ((my1stPartitionKey, my2ndPartitionKey),
myClusteringKey)

19. Cassandra Secondary Indexes
● Does allow secondary indexes.
CREATE INDEX myIndex ON myTable(myNonKeyColumn)
● Designed for query convenience, not for performance.
● Does not perform well on high-cardinality columns, because you filter a
huge volume of records for a small number of results. Extremely low
cardinality is also not a good idea (ex: customer address [state == good,
phone == bad, gender == bad]).
● Works best on a table having many rows that contain the indexed value;
middle-of-the-road cardinality.

20. Serenity “crew”
● Create a table to store data for the crew of “Serenity” from “Firefly.”
CREATE TABLE crew (
crewname TEXT,
firstname TEXT,
lastname TEXT,
phone TEXT,
PRIMARY KEY (crewname));
crewname | firstname | lastname | phone
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­Mal
| Malcolm | Reynolds | 111­555­1234
Jayne | Jayne | Cobb | 111­555­3464
Sheppard | Derial | Book | 111­555­2349
Simon | Simon | Tam | 111­555­8899

21. Serenity “crew” under the hood
RowKey:Mal
=> (column, value=, timestamp=1374546754299000)
=> (column=firstname, value=Malcolm, timestamp=1374546754299000)
=> (column=lastname, value=Reynolds, timestamp=1374546754299000)
=> (column=phone, value=111­555­1234,
timestamp=1374546754299000)
RowKey:Jayne
=> (column, value=, timestamp=1374546757815000)
=> (column=firstname, value=Jayne, timestamp=1374546757815000)
=> (column=lastname, value=Cobb, timestamp=1374546757815000)
=> (column=phone, value=111­555­3464,
timestamp=1374546757815000)

22. Serenity “crewbyphone”
● To solve the problem of being able to query crew members by phone:”
CREATE TABLE crewbyphone (
crewname TEXT,
firstname TEXT,
lastname TEXT,
phone TEXT,
PRIMARY KEY (phone,crewname));
crewname | firstname | lastname | phone
­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­Mal
| Malcolm | Reynolds | 111­555­1234
Wash | Hoban | Washburne| 111­555­1212
Zoey | Zoey | Washburne| 111­555­1212
Jayne | Jayne | Cobb | 111­555­3464

23. Serenity “crewbyphone” under
the hood
RowKey:111­555­1234
=> (column=Mal, value=, timestamp=1374546754299000)
=> (column:Mal:firstname, value=Malcolm, timestamp=...
=> (column:Mal:lastname, value=Reynolds, timestamp=...
RowKey:111­555­1212
=> (column=Wash, value=, timestamp=1374546754299000)
=> (column=Wash:firstname, value=Hoban, timestamp=...
=> (column=Wash:lastname, value=Washburne, timestamp=...
=> (column=Zoey, value=, timestamp=1374546754299000)
=> (column=Zoey:firstname, value=Zoey, timestamp=...
=> (column=Zoey:lastname, value=Washburne, timestamp=...

24. Who Uses Cassandra?

25. Who else Uses Cassandra?

26. Cassandra Large Deployments
● 100+ nodes. 250TB of data, cluster sizes vary from 6 to 32
nodes.
● 2,500+ nodes, 420TB of data, 4 DCs, handles 1 trillion
operations per day.
● 75,000+ nodes, 10s of PB of data, largest cluster 1000+ nodes.

27. Additional Reading
● Amazon Dynamo paper
● Facebook Cassandra paper
● Harvest, Yield, and Scalable, Tolerant Systems - Brewer, Fox, 1999
● DataStax grabs $106M to achieve big-dog status in database country
● http://techblog.netflix.com/2011/11/benchmarking-cassandra-scalability-on.html
● http://planetcassandra.org/blog/a-netflix-experiment-eventual-consistency-hopeful-consistency-● DataStax Documentation
● KillrVideo.com

28. Getting Started
● Community site: http://planetcassandra.org
● http://datastax.com
● DataStax community edition:
http://planetcassandra.org/cassandra
● DataStax startup program:
http://www.datastax.com/what-we-offer/products-services/datastax-enterprise/● Apache Cassandra project site:
http://cassandra.apache.org/

29. Questions?

30. Demo

31. Want to work at AccuLynx?
We're hiring!
http://careers.stackoverflow.com/company/acculynx