The document provides an overview of using Apache Cassandra for time series data, highlighting its scalable, resilient, and efficient data model suited for high throughput applications. It discusses the logical view and disk layout for storing temperature data from weather stations, as well as query patterns for efficient access. The presentation also mentions ingestion models and the benefits of utilizing Apache Kafka for fast data processing.

1. Time Series with Apache Cassandra
Patrick McFadin
Chief Evangelist
@PatrickMcFadin
©2013 DataStax Confidential. Do not distribute without consent.
1

2. Quick intro to Cassandra
• Shared nothing
• Masterless peer-to-peer
• Based on Dynamo

3. Scaling
• Add nodes to scale
• Millions Ops/s
THROUGHPUT OPS/SEC)
Cassandra
HBase
Redis
MySQL

4. Uptime
• Built to replicate
• Resilient to failure
• Always on
NONE

5. Easy to use
• CQL is a familiar syntax
• Friendly to programmers
• Paxos for locking
CREATE TABLE users (!
username varchar,!
firstname varchar,!
lastname varchar,!
email list<varchar>,!
password varchar,!
created_date timestamp,!
PRIMARY KEY (username)!
);
INSERT INTO users (username, firstname, lastname, !
email, password, created_date)!
VALUES ('pmcfadin','Patrick','McFadin',!
['patrick@datastax.com'],'ba27e03fd95e507daf2937c937d499ab',!
'2011-06-20 13:50:00');!
INSERT INTO users (username, firstname, !
lastname, email, password, created_date)!
VALUES ('pmcfadin','Patrick','McFadin',!
['patrick@datastax.com'],!
'ba27e03fd95e507daf2937c937d499ab',!
'2011-06-20 13:50:00')!
IF NOT EXISTS;

6. Time series in production
• It’s all about “What’s happening”
• Data is the new currency
“Sirca, a non-profit university consortium based in Sydney, is the world’s biggest broker of
financial data, ingesting into its database 2million pieces of information a second from every
major trading exchange.”*
* http://www.theage.com.au/it-pro/business-it/help-poverty-theres-an-app-for-that-20140120-hv948.html

7. Why Cassandra for Time Series
Scales
Resilient
Good data model
Efficient Storage Model
What about that?

8. Data Model
CREATE TABLE temperature (
weatherstation_id text,
event_time timestamp,
temperature text,
PRIMARY KEY (weatherstation_id,event_time)
);
• Weather Station Id and Time
are unique
• Store as many as needed
INSERT INTO temperature(weatherstation_id,event_time,temperature)
VALUES ('1234ABCD','2013-04-03 07:01:00','72F');
!
INSERT INTO temperature(weatherstation_id,event_time,temperature)
VALUES ('1234ABCD','2013-04-03 07:02:00','73F');
!
INSERT INTO temperature(weatherstation_id,event_time,temperature)
VALUES ('1234ABCD','2013-04-03 07:03:00','73F');
!
INSERT INTO temperature(weatherstation_id,event_time,temperature)
VALUES ('1234ABCD','2013-04-03 07:04:00','74F');

9. Storage Model - Logical View
SELECT weatherstation_id,event_time,temperature
FROM temperature
WHERE weatherstation_id='1234ABCD';
weatherstation_id
event_time
temperature
2013-04-03 07:01:00
1234ABCD
72F
2013-04-03 07:02:00
1234ABCD
73F
2013-04-03 07:03:00
1234ABCD
73F
2013-04-03 07:04:00
1234ABCD
74F

10. Storage Model - Disk Layout
SELECT weatherstation_id,event_time,temperature
FROM temperature
WHERE weatherstation_id='1234ABCD';
2013-04-03 07:01:00
1234ABCD
72F
2013-04-03 07:02:00
73F
2013-04-03 07:03:00
2013-04-03 07:04:00
73F
Merged, Sorted and Stored Sequentially
74F
2013-04-03 07:05:00
!
2013-04-03 07:06:00
!
74F
75F
!
!

11. Query patterns
SELECT temperature
FROM event_time,temperature
WHERE weatherstation_id='1234ABCD'
AND event_time > '2013-04-03 07:01:00'
AND event_time < '2013-04-03 07:04:00';
• Range queries
• “Slice” operation on disk
Single seek on disk
2013-04-03 07:01:00
1234ABCD
72F
2013-04-03 07:02:00
73F
2013-04-03 07:03:00
73F
2013-04-03 07:04:00
74F
2013-04-03 07:05:00
!
2013-04-03 07:06:00
!
74F
75F
!
!

12. Query patterns
SELECT temperature
FROM event_time,temperature
WHERE weatherstation_id='1234ABCD'
AND event_time > '2013-04-03 07:01:00'
AND event_time < '2013-04-03 07:04:00';
weatherstation_id
event_time
• Range queries
• “Slice” operation on disk
temperature
2013-04-03 07:01:00
1234ABCD
72F
Sorted by event_time
2013-04-03 07:02:00
1234ABCD
73F
2013-04-03 07:03:00
1234ABCD
73F
2013-04-03 07:04:00
1234ABCD
74F
Programmers like this

13. Ingestion models
• Apache Kafka
• Apache Flume
• Storm
• Custom Applications
Apache Kafka
Your totally!
killer!
application

14. Dealing with data at speed
• 1 million writes per second?
• 1 insert every microsecond
• Collisions?
Your totally!
killer!
application
weatherstation_id='5678EFGH'
• Primary Key determines node
placement
• Random partitioning
• Special data type - TimeUUID
weatherstation_id='1234ABCD'

15. TimeUUID
Timestamp to Microsecond
+
UUID
=
TimeUUID
• Also known as a Version 1 UUID
• Sortable
• Reversible
04d580b0-9412-11e3-baa8-0800200c9a66
=
Wednesday, February 12, 2014 6:18:06 PM GMT
http://www.famkruithof.net/uuid/uuidgen

16. Way more information
www.planetcassandra.org
!
• 5 minute interviews
• Use cases
• Free training!

17. Thank You!
Follow me for more updates all the time: @PatrickMcFadin