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@PatrickMcFadin
Patrick McFadinChief Evangelist
Data Modeling Time Series
1
Internet Of Things• 15B devices by 2015 • 40B devices by 2020!
Why Cassandra for Time Series
ScalesResilientGood data modelEfficient Storage Model
What about that?
Example 1: Weather Station•Weather station collects data • Cassandra stores in sequence • Application reads in sequence
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
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');
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
74F1234ABCD
Storage Model - Disk Layout
2013-04-03 07:01:00
72F
2013-04-03 07:02:00
73F
2013-04-03 07:03:00
73F1234ABCD
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
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
73F1234ABCD
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
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
Additional help on the storage engine
SSTable seeks• Each read minimum
1 seek • Cache and bloom
filter help minimize
Total seek time = Disk Latency * number of seeks
The key to speed
Use the first part of the primary key to get the node (data localization)
Minimize seeks for SStables (Key Cache, Bloom Filter)
Find the data fast in the SSTable (Indexes)
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
Ingestion models• Apache Kafka • Apache Flume • Storm • Custom Applications
Apache Kafka
Your totally!killer!application
Kafka + Storm• Kafka provides reliable queuing • Storm processes (rollups, counts) • Cassandra stores at the same speed • Storm lookup on Cassandra
Apache KafkaApache Storm
Queue Process Store
Flume• Source accepts data • Channel buffers data • Sink processes and stores • Popular for log processing
Sink
Channel
SourceApplication
Load Balancer
Syslog
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'
How does data replicate?
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
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
Node A
Node D Node C
Node B
The Token Ring
jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start EndA 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0
jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start EndA 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0
jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start EndA 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0
jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start EndA 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0
jim 5e02739678...
carol a9a0198010...
johnny f4eb27cea7...
suzy 78b421309e...
Start EndA 0xc000000000..1 0x0000000000..0
B 0x0000000000..1 0x4000000000..0
C 0x4000000000..1 0x8000000000..0
D 0x8000000000..1 0xc000000000..0
Node A
Node D Node C
Node B
carol a9a0198010...
Replication
Node A
Node D Node C
Node B
carol a9a0198010...
Replication
Node A
Node D Node C
Node B
carol a9a0198010...
ReplicationReplication factor = 3
Consistency is a different topic for later
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
=
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
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
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');
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:2000NFLX:340
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’;
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:450NFLX:340
to here
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;
Way more examples
• 5 minute interviews • Use cases • Free training!
!www.planetcassandra.org
Thank You!
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