Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data. This talk will give you an overview of the many ways you can be successful. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models.

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

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

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

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

7. Stack Driver
• AWS and Rackspace monitoring
• Quick indexes
• Batch rollup results

8. MyDrive
• Moved from Mongo to Cassandra
• Queue processing
• Bound at the storing data
“One thing that is not at all obvious
from the graph is that the system was
also under massively heavier strain
after the switch to Cassandra because
of additional bulk processing going on
in the background.”
- Karl Matthias, MyDrive

9. Paddy Power
• Real-time product and pricing
• Much like stock tickers
• Active-active across two data
centers
“Specifically for Cassandra and Datastax, the
ability to process time-series data is something
that lots of companies have done in the past, not
something that we were very aware of, and that was
one of the reasons why we chose this as the first
use case for Cassandra.”
- John Turner, Paddy Power

10. Internet Of Things
• 15B devices by 2015
• 40B devices by 2020!

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

12. Example 1: Weather Station
• Weather station collects data
• Cassandra stores in sequence
• Application reads in sequence

13. Use case
• Store data per weather station
• Store time series in order: first to last
• Get all data for one weather station
• Get data for a single date and time
• Get data for a range of dates and times
Needed Queries
Data Model to support queries

14. Data Model
• Weather Station Id and Time
are unique
• Store as many as needed
CREATE TABLE temperature (
weatherstation_id text,
event_time timestamp,
temperature text,
PRIMARY KEY (weatherstation_id,event_time)
);
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');

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

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

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

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

19. Additional help on the storage engine

20. SSTable seeks
• Each read minimum
1 seek
• Cache and bloom
filter help minimize
Total seek time = Disk Latency * number of seeks

21. The key to speed
Use the first part of the primary key to get the node
(data localization)
Minimize seeks for SStables
(Bloom Filter,Key Cache up-to-date)
Find the data fast in the SSTable
(Indexes)

22. Min/Max Value Hint
• New since 2.0
• Range index on primary key values per SSTable
• Minimizes seeks on range data
CASSANDRA-5514 if you are interested in details
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';
Row Key: 1234ABCD
Min event_time: 2013-04-01 00:00:00
Max event_time: 2013-04-04 23:59:59
Row Key: 1234ABCD
Min event_time: 2013-04-05 00:00:00
Max event_time: 2013-04-09 23:59:59
Row Key: 1234ABCD
Min event_time: 2013-03-27 00:00:00
Max event_time: 2013-03-31 23:59:59
?
This one

23. Ingestion models
• Apache Kafka
• Apache Flume
• Storm
• Spark Streaming
• Custom Applications
Apache Kafka
Your totally!
killer!
application

24. Kafka + Storm
• Kafka provides reliable queuing
• Storm processes (rollups, counts)
• Cassandra stores at the same speed
• Storm lookup on Cassandra
Apache Kafka
Apache Storm
Queue Process Store

25. Flume
• Source accepts data
• Channel buffers data
• Sink processes and stores
• Popular for log processing
Sink
Channel
Source
Application
Load
Balancer
Syslog

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

27. How does data replicate?

28. Primary key determines placement*
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

29. jim
carol
johnny
suzy
PK
5e02739678...
a9a0198010...
f4eb27cea7...
78b421309e...
MD5 Hash
MD5* hash
operation yields
a 128-bit
number for keys
of any size.
Key Hashing

30. Node A
Node D Node C
Node B
The Token Ring

31. jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start End
A 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0

32. jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start End
A 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0

33. jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start End
A 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0

34. jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start End
A 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0

35. jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start End
A 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0

36. Node A
Node D Node C
Node B
carol a9a0198010...
Replication

37. Node A
Node D Node C
Node B
carol a9a0198010...
Replication

38. Node A
Node D Node C
Node B
carol a9a0198010...
Replication
Replication factor = 3
Consistency is a
different topic for
later

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

40. Example 2: Financial Transactions
• Trading of stocks
• When did they happen?
• Massive speeds and volumes
“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

41. Use case
• Store data per symbol and date
• Store time series in reverse order: last to first
• Make sure every transaction is unique
• Get all trades for symbol and day
• Get trade for a single date and time
• Get last 10 trades for symbol and date
Needed Queries
Data Model to support queries

42. Data Model
• date is int of days since epoch
• timeuuid keeps it unique
• Reverse the times for later
queries
CREATE TABLE stock_ticks (
symbol text,
date int,
trade timeuuid,
trade_details text,
PRIMARY KEY ((symbol, date), trade)
) WITH CLUSTERING ORDER BY (trade DESC);
INSERT INTO stock_ticks(symbol, date, trade, trade_details)
VALUES (‘NFLX’,340,04d580b0-1431-1e33-baf8-0833200c98a6,'BUY:2000');
!
INSERT INTO stock_ticks(symbol, date, trade, trade_details)
VALUES (‘NFLX’,340,05d580b0-6472-1ef3-a3a8-0430200c9a66,'BUY:300');
!
INSERT INTO stock_ticks(symbol, date, trade, trade_details)
VALUES (‘NFLX’,340,02d580b0-9412-d223-55a8-0976200c9a25,'SELL:450');
!
INSERT INTO stock_ticks(symbol, date, trade, trade_details)
VALUES (‘NFLX’,340,08d580b0-4482-11e3-5fd3-3421200c9a65,'SELL:3000');

43. Storage Model - Logical View
08d580b0-4482-11e3-5fd3-
3421200c9a65
SELL:3000
02d580b0-9412-
d223-55a8-0976200c9a25
SELL:450
05d580b0-6472-1ef3-
a3a8-0430200c9a66
BUY:300
SELECT trade,trade_details
FROM stock_ticks
WHERE symbol =‘NFLX’ AND date=‘340’;
NFLX:340
NFLX:340
NFLX:340
symbol:date trade trade_details
04d580b0-1431-1e33-
baf8-0833200c98a6
BUY:2000
NFLX:340
Last thing inserted
First thing inserted

44. 04d580b0-1431-1e33-
baf8-0833200c98a6
05d580b0-6472-1ef3-
a3a8-0430200c9a66
02d580b0-9412-d223-55a8
BUY:2000BUY:300
08d580b0-4482-11e3-5fd3-
3421200c9a65
SELL:3000 SELL:450
Storage Model - Disk Layout
NFLX:340
Order is from last trade to first
SELECT trade,trade_details
FROM stock_ticks
WHERE symbol =‘NFLX’ AND date=‘340’;

45. 04d580b0-1431-1e33-
baf8-0833200c98a6
05d580b0-6472-1ef3-
a3a8-0430200c9a66
02d580b0-9412-
d223-55a8-0976200c9a25
Query patterns
• Limit queries
• Get last X trades
From here
SELECT trade,trade_details
FROM stock_ticks
WHERE symbol =‘NFLX’ AND date=‘340’
LIMIT 3;
BUY:2000BUY:300
08d580b0-4482-11e3-5fd3-
3421200c9a65
SELL:3000 SELL:450
NFLX:340
to here

46. Query patterns
Reverse sorted by trade
Last 3 trades
08d580b0-4482-11e3-5fd3-
3421200c9a65
SELL:3000
02d580b0-9412-
d223-55a8-0976200c9a25
SELL:450
05d580b0-6472-1ef3-
a3a8-0430200c9a66
BUY:300
NFLX:340
NFLX:340
NFLX:340
symbol:date trade trade_details
• Limit queries
• Get last X trades
SELECT trade,trade_details
FROM stock_ticks
WHERE symbol =‘NFLX’ AND date=‘340’
LIMIT 3;

47. Way more examples
• 5 minute interviews
• Use cases
• Free training!
!
www.planetcassandra.org

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