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Storing time series data with Apache Cassandra

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Patrick McFadin
Patrick McFadin

If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.

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@PatrickMcFadin Patrick McFadin
 Chief Evangelist for Apache Cassandra, DataStax Storing Time Series Data with 1
My Background …ran into this problem
Gave it my best shot shard 1 shard 2 shard 3 shard 4 router client Patrick, All your wildest dreams will come true.
Just add complexity!
A new plan
Dynamo Paper(2007) • How do we build a data store that is: • Reliable • Performant • “Always On” • Nothing new and shiny Evolutionary. Real. Computer Science Also the basis for Riak and Voldemort
BigTable(2006) • Richer data model • 1 key. Lots of values • Fast sequential access • 38 Papers cited
Cassandra(2008) • Distributed features of Dynamo • Data Model and storage from BigTable • February 17, 2010 it graduated to a top-level Apache project
A Data Ocean or Pond., Lake An In-Memory Database A Key-Value Store A magical database unicorn that farts rainbows
Cassandra for Applications APACHE CASSANDRA
Basic Architecture
Row Column 1 Partition Key 1 Column 2 Column 3 Column 4
Partition Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4
Table Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Column 2 Column 3 Column 4 Column 1 Column 2 Column 3 Column 4 Column 1 Column 2 Column 3 Column 4 Partition Key 2 Partition Key 2 Partition Key 2
Keyspace Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Table 1 Table 2 Keyspace 1
Node Server
Token Server •Each partition is a 128 bit value •Consistent hash between 2-63 and 264 •Each node owns a range of those values •The token is the beginning of that range to the next node’s token value •Virtual Nodes break these down further Data Token Range 0 …
Cluster Server Token Range 0 0-100 0-100
Cluster Server Token Range 0 0-50 51 51-100 Server 0-50 51-100
Cluster Server Token Range 0 0-25 26 26-50 51 51-75 76 76-100 Server ServerServer 0-25 76-100 26-5051-75
Replication 10.0.0.1 00-25 DC1 DC1: RF=1 Node Primary 10.0.0.1 00-25 10.0.0.2 26-50 10.0.0.3 51-75 10.0.0.4 76-100 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75
Replication 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 DC1 DC1: RF=2 Node Primary Replica 10.0.0.1 00-25 76-100 10.0.0.2 26-50 00-25 10.0.0.3 51-75 26-50 10.0.0.4 76-100 51-75 76-100 00-25 26-50 51-75
Replication DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50
Consistency DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15
Consistency level Consistency Level Number of Nodes Acknowledged One One - Read repair triggered Local One One - Read repair in local DC Quorum 51% Local Quorum 51% in local DC
Consistency DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 CL= One
Consistency DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 CL= One
Consistency DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 CL= Quorum
Multi-datacenter DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 DC2 10.1.0.1 00-25 10.1.0.4 76-100 10.1.0.2 26-50 10.1.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 DC2: RF=3
Multi-datacenter DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 DC2 10.1.0.1 00-25 10.1.0.4 76-100 10.1.0.2 26-50 10.1.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 DC2: RF=3
Multi-datacenter DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 DC2 10.1.0.1 00-25 10.1.0.4 76-100 10.1.0.2 26-50 10.1.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 DC2: RF=3
Cassandra Query Language - CQL
Table CREATE TABLE weather_station (
 id text,
 name text,
 country_code text,
 state_code text,
 call_sign text,
 lat double,
 long double,
 elevation double,
 PRIMARY KEY(id)
 ); Table Name Column Name Column CQL Type Primary Key Designation Partition Key
Table CREATE TABLE daily_aggregate_precip (
 wsid text,
 year int,
 month int,
 day int,
 precipitation counter,
 PRIMARY KEY ((wsid), year, month, day)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC); Partition Key Clustering Columns Order Override
Insert INSERT INTO weather_station (id, call_sign, country_code, elevation, lat, long, name, state_code)
 VALUES ('727930:24233', 'KSEA', 'US', 121.9, 47.467, -122.32, 'SEATTLE SEATTLE-TACOMA INTL A', ‘WA'); Table Name Fields Values Partition Key: Required
Select id | call_sign | country_code | elevation | lat | long | name | state_code
 --------------+-----------+--------------+-----------+--------+---------+-------------------------------+------------
 727930:24233 | KSEA | US | 121.9 | 47.467 | -122.32 | SEATTLE SEATTLE-TACOMA INTL A | WA SELECT id, call_sign, country_code, elevation, lat, long, name, state_code
 FROM weather_station
 WHERE id = '727930:24233'; Fields Table Name Primary Key: Partition Key Required
Update UPDATE weather_station
 SET name = 'SeaTac International Airport'
 WHERE id = '727930:24233'; id | call_sign | country_code | elevation | lat | long | name | state_code
 --------------+-----------+--------------+-----------+--------+---------+------------------------------+------------
 727930:24233 | KSEA | US | 121.9 | 47.467 | -122.32 | SeaTac International Airport | WA Table Name Fields to Update: Not in Primary Key Primary Key
Delete DELETE FROM weather_station
 WHERE id = '727930:24233'; Table Name Primary Key: Required
Collections Set CREATE TABLE weather_station (
 id text,
 name text,
 country_code text,
 state_code text,
 call_sign text,
 lat double,
 long double,
 elevation double,
 equipment set<text>
 PRIMARY KEY(id)
 ); equipment set<text> CQL Type: For Ordering Column Name
Collections Set List CREATE TABLE weather_station (
 id text,
 name text,
 country_code text,
 state_code text,
 call_sign text,
 lat double,
 long double,
 elevation double,
 equipment set<text>,
 service_dates list<timestamp>,
 PRIMARY KEY(id)
 ); equipment set<text> service_dates list<timestamp> CQL Type Column Name CQL Type: For Ordering Column Name
Collections Set List Map CREATE TABLE weather_station (
 id text,
 name text,
 country_code text,
 state_code text,
 call_sign text,
 lat double,
 long double,
 elevation double,
 equipment set<text>,
 service_dates list<timestamp>,
 service_notes map<timestamp,text>,
 PRIMARY KEY(id)
 ); equipment set<text> service_dates list<timestamp> service_notes map<timestamp,text> CQL Type Column Name Column Name CQL Key Type CQL Value Type CQL Type: For Ordering Column Name
UDF and UDA User Defined Function CREATE OR REPLACE AGGREGATE group_and_count(text)
 SFUNC state_group_and_count
 STYPE map<text, int>
 INITCOND {}; CREATE FUNCTION state_group_and_count( state map<text, int>, type text )
 CALLED ON NULL INPUT
 RETURNS map<text, int>
 LANGUAGE java AS '
 Integer count = (Integer) state.get(type); if (count == null) count = 1; else count++; state.put(type, count); return state; ' ; User Defined Aggregate As of Cassandra 2.2
Example: Weather Station • Weather station collects data • Cassandra stores in sequence • Application reads in sequence
Queries supported CREATE TABLE raw_weather_data (
 wsid text,
 year int,
 month int,
 day int,
 hour int,
 temperature double,
 dewpoint double,
 pressure double,
 wind_direction int,
 wind_speed double,
 sky_condition int,
 sky_condition_text text,
 one_hour_precip double,
 six_hour_precip double,
 PRIMARY KEY ((wsid), year, month, day, hour)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC); Get weather data given •Weather Station ID •Weather Station ID and Time •Weather Station ID and Range of Time
Primary Key CREATE TABLE raw_weather_data (
 wsid text,
 year int,
 month int,
 day int,
 hour int,
 temperature double,
 dewpoint double,
 pressure double,
 wind_direction int,
 wind_speed double,
 sky_condition int,
 sky_condition_text text,
 one_hour_precip double,
 six_hour_precip double,
 PRIMARY KEY ((wsid), year, month, day, hour)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
Primary key relationship PRIMARY KEY ((wsid),year,month,day,hour)
Primary key relationship Partition Key PRIMARY KEY ((wsid),year,month,day,hour)
Primary key relationship PRIMARY KEY ((wsid),year,month,day,hour) Partition Key Clustering Columns
Primary key relationship Partition Key Clustering Columns 10010:99999 PRIMARY KEY ((wsid),year,month,day,hour)
2005:12:1:10 -5.6 Primary key relationship Partition Key Clustering Columns 10010:99999 -5.3-4.9-5.1 2005:12:1:9 2005:12:1:8 2005:12:1:7 PRIMARY KEY ((wsid),year,month,day,hour)
Partition keys 10010:99999 Murmur3 Hash Token = 7224631062609997448 722266:13850 Murmur3 Hash Token = -6804302034103043898 INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘10010:99999’,2005,12,1,7,-5.6); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘722266:13850’,2005,12,1,7,-5.6); Consistent hash. 128 bit number between 2-63 and 264
Partition keys 10010:99999 Murmur3 Hash Token = 15 722266:13850 Murmur3 Hash Token = 77 For this example, let’s make it a reasonable number INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘10010:99999’,2005,12,1,7,-5.6); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘722266:13850’,2005,12,1,7,-5.6);
Data Locality DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Read partition 15 DC2 10.1.0.1 00-25 10.1.0.4 76-100 10.1.0.2 26-50 10.1.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 DC2: RF=3 Client Read partition 15
Data Locality wsid=‘10010:99999’ ? 1000 Node Cluster You are here!
Writes CREATE TABLE raw_weather_data (
 wsid text,
 year int,
 month int,
 day int,
 hour int,
 temperature double,
 dewpoint double,
 pressure double,
 wind_direction int,
 wind_speed double,
 sky_condition int,
 sky_condition_text text,
 one_hour_precip double,
 six_hour_precip double,
 PRIMARY KEY ((wsid), year, month, day, hour)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
Writes CREATE TABLE raw_weather_data (
 wsid text,
 year int,
 month int,
 day int,
 hour int,
 temperature double,
 PRIMARY KEY ((wsid), year, month, day, hour)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,10,-5.6); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,9,-5.1); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,8,-4.9); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,7,-5.3);
Write Path Client INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,7,-5.3); Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Memtable SSTable SSTable SSTable SSTable Node Commit Log Data * Compaction *
Date Tiered Compaction Strategy •Group similar time blocks •Never compact again •Used for high density SSTable SSTable SSTable T=2015-01-01 -> 2015-01-5 T=2015-01-06 -> 2015-01-10 T=2015-01-11 -> 2015-01-15
Storage Model - Logical View 2005:12:1:10 -5.6 2005:12:1:9 -5.1 2005:12:1:8 -4.9 10010:99999 10010:99999 10010:99999 wsid hour temperature 2005:12:1:7 -5.3 10010:99999 SELECT wsid, hour, temperature
 FROM raw_weather_data
 WHERE wsid=‘10010:99999’
 AND year = 2005 AND month = 12 AND day = 1;
2005:12:1:10 -5.6 -5.3-4.9-5.1 Storage Model - Disk Layout 2005:12:1:9 2005:12:1:8 10010:99999 2005:12:1:7 Merged, Sorted and Stored Sequentially SELECT wsid, hour, temperature
 FROM raw_weather_data
 WHERE wsid=‘10010:99999’
 AND year = 2005 AND month = 12 AND day = 1;
2005:12:1:10 -5.6 2005:12:1:11 -4.9 -5.3-4.9-5.1 Storage Model - Disk Layout 2005:12:1:9 2005:12:1:8 10010:99999 2005:12:1:7 Merged, Sorted and Stored Sequentially SELECT wsid, hour, temperature
 FROM raw_weather_data
 WHERE wsid=‘10010:99999’
 AND year = 2005 AND month = 12 AND day = 1;
2005:12:1:10 -5.6 2005:12:1:11 -4.9 -5.3-4.9-5.1 Storage Model - Disk Layout 2005:12:1:9 2005:12:1:8 10010:99999 2005:12:1:7 Merged, Sorted and Stored Sequentially SELECT wsid, hour, temperature
 FROM raw_weather_data
 WHERE wsid=‘10010:99999’
 AND year = 2005 AND month = 12 AND day = 1; 2005:12:1:12 -5.4
Read Path Client Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Memtable SSTable SSTable SSTable Node Data SELECT wsid,hour,temperature
 FROM raw_weather_data
 WHERE wsid='10010:99999'
 AND year = 2005 AND month = 12 AND day = 1 
 AND hour >= 7 AND hour <= 10;
Query patterns • Range queries • “Slice” operation on disk Single seek on disk 10010:99999 Partition key for locality SELECT wsid,hour,temperature
 FROM raw_weather_data
 WHERE wsid='10010:99999'
 AND year = 2005 AND month = 12 AND day = 1 
 AND hour >= 7 AND hour <= 10; 2005:12:1:10 -5.6 -5.3-4.9-5.1 2005:12:1:9 2005:12:1:8 2005:12:1:7
Query patterns • Range queries • “Slice” operation on disk Programmers like this Sorted by event_time 2005:12:1:10 -5.6 2005:12:1:9 -5.1 2005:12:1:8 -4.9 10010:99999 10010:99999 10010:99999 weather_station hour temperature 2005:12:1:7 -5.3 10010:99999 SELECT weatherstation,hour,temperature FROM temperature WHERE weatherstation_id=‘10010:99999' AND year = 2005 AND month = 12 AND day = 1 AND hour >= 7 AND hour <= 10;
Thank you! Bring the questions Follow me on twitter @PatrickMcFadin

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Storing time series data with Apache Cassandra

  • 1. @PatrickMcFadin Patrick McFadin
 Chief Evangelist for Apache Cassandra, DataStax Storing Time Series Data with 1
  • 3. Gave it my best shot shard 1 shard 2 shard 3 shard 4 router client Patrick, All your wildest dreams will come true.
  • 6. Dynamo Paper(2007) • How do we build a data store that is: • Reliable • Performant • “Always On” • Nothing new and shiny Evolutionary. Real. Computer Science Also the basis for Riak and Voldemort
  • 7. BigTable(2006) • Richer data model • 1 key. Lots of values • Fast sequential access • 38 Papers cited
  • 8. Cassandra(2008) • Distributed features of Dynamo • Data Model and storage from BigTable • February 17, 2010 it graduated to a top-level Apache project
  • 9. A Data Ocean or Pond., Lake An In-Memory Database A Key-Value Store A magical database unicorn that farts rainbows
  • 14. Table Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Column 2 Column 3 Column 4 Column 1 Column 2 Column 3 Column 4 Column 1 Column 2 Column 3 Column 4 Partition Key 2 Partition Key 2 Partition Key 2
  • 15. Keyspace Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Column 1 Partition Key 2 Column 2 Column 3 Column 4 Table 1 Table 2 Keyspace 1
  • 17. Token Server •Each partition is a 128 bit value •Consistent hash between 2-63 and 264 •Each node owns a range of those values •The token is the beginning of that range to the next node’s token value •Virtual Nodes break these down further Data Token Range 0 …
  • 19. Cluster Server Token Range 0 0-50 51 51-100 Server 0-50 51-100
  • 20. Cluster Server Token Range 0 0-25 26 26-50 51 51-75 76 76-100 Server ServerServer 0-25 76-100 26-5051-75
  • 21. Replication 10.0.0.1 00-25 DC1 DC1: RF=1 Node Primary 10.0.0.1 00-25 10.0.0.2 26-50 10.0.0.3 51-75 10.0.0.4 76-100 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75
  • 22. Replication 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 DC1 DC1: RF=2 Node Primary Replica 10.0.0.1 00-25 76-100 10.0.0.2 26-50 00-25 10.0.0.3 51-75 26-50 10.0.0.4 76-100 51-75 76-100 00-25 26-50 51-75
  • 23. Replication DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50
  • 24. Consistency DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15
  • 25. Consistency level Consistency Level Number of Nodes Acknowledged One One - Read repair triggered Local One One - Read repair in local DC Quorum 51% Local Quorum 51% in local DC
  • 26. Consistency DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 CL= One
  • 27. Consistency DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 CL= One
  • 28. Consistency DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 CL= Quorum
  • 29. Multi-datacenter DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 DC2 10.1.0.1 00-25 10.1.0.4 76-100 10.1.0.2 26-50 10.1.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 DC2: RF=3
  • 30. Multi-datacenter DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 DC2 10.1.0.1 00-25 10.1.0.4 76-100 10.1.0.2 26-50 10.1.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 DC2: RF=3
  • 31. Multi-datacenter DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Write to partition 15 DC2 10.1.0.1 00-25 10.1.0.4 76-100 10.1.0.2 26-50 10.1.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 DC2: RF=3
  • 33. Table CREATE TABLE weather_station (
 id text,
 name text,
 country_code text,
 state_code text,
 call_sign text,
 lat double,
 long double,
 elevation double,
 PRIMARY KEY(id)
 ); Table Name Column Name Column CQL Type Primary Key Designation Partition Key
  • 34. Table CREATE TABLE daily_aggregate_precip (
 wsid text,
 year int,
 month int,
 day int,
 precipitation counter,
 PRIMARY KEY ((wsid), year, month, day)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC); Partition Key Clustering Columns Order Override
  • 35. Insert INSERT INTO weather_station (id, call_sign, country_code, elevation, lat, long, name, state_code)
 VALUES ('727930:24233', 'KSEA', 'US', 121.9, 47.467, -122.32, 'SEATTLE SEATTLE-TACOMA INTL A', ‘WA'); Table Name Fields Values Partition Key: Required
  • 36. Select id | call_sign | country_code | elevation | lat | long | name | state_code
 --------------+-----------+--------------+-----------+--------+---------+-------------------------------+------------
 727930:24233 | KSEA | US | 121.9 | 47.467 | -122.32 | SEATTLE SEATTLE-TACOMA INTL A | WA SELECT id, call_sign, country_code, elevation, lat, long, name, state_code
 FROM weather_station
 WHERE id = '727930:24233'; Fields Table Name Primary Key: Partition Key Required
  • 37. Update UPDATE weather_station
 SET name = 'SeaTac International Airport'
 WHERE id = '727930:24233'; id | call_sign | country_code | elevation | lat | long | name | state_code
 --------------+-----------+--------------+-----------+--------+---------+------------------------------+------------
 727930:24233 | KSEA | US | 121.9 | 47.467 | -122.32 | SeaTac International Airport | WA Table Name Fields to Update: Not in Primary Key Primary Key
  • 38. Delete DELETE FROM weather_station
 WHERE id = '727930:24233'; Table Name Primary Key: Required
  • 39. Collections Set CREATE TABLE weather_station (
 id text,
 name text,
 country_code text,
 state_code text,
 call_sign text,
 lat double,
 long double,
 elevation double,
 equipment set<text>
 PRIMARY KEY(id)
 ); equipment set<text> CQL Type: For Ordering Column Name
  • 40. Collections Set List CREATE TABLE weather_station (
 id text,
 name text,
 country_code text,
 state_code text,
 call_sign text,
 lat double,
 long double,
 elevation double,
 equipment set<text>,
 service_dates list<timestamp>,
 PRIMARY KEY(id)
 ); equipment set<text> service_dates list<timestamp> CQL Type Column Name CQL Type: For Ordering Column Name
  • 41. Collections Set List Map CREATE TABLE weather_station (
 id text,
 name text,
 country_code text,
 state_code text,
 call_sign text,
 lat double,
 long double,
 elevation double,
 equipment set<text>,
 service_dates list<timestamp>,
 service_notes map<timestamp,text>,
 PRIMARY KEY(id)
 ); equipment set<text> service_dates list<timestamp> service_notes map<timestamp,text> CQL Type Column Name Column Name CQL Key Type CQL Value Type CQL Type: For Ordering Column Name
  • 42. UDF and UDA User Defined Function CREATE OR REPLACE AGGREGATE group_and_count(text)
 SFUNC state_group_and_count
 STYPE map<text, int>
 INITCOND {}; CREATE FUNCTION state_group_and_count( state map<text, int>, type text )
 CALLED ON NULL INPUT
 RETURNS map<text, int>
 LANGUAGE java AS '
 Integer count = (Integer) state.get(type); if (count == null) count = 1; else count++; state.put(type, count); return state; ' ; User Defined Aggregate As of Cassandra 2.2
  • 43. Example: Weather Station • Weather station collects data • Cassandra stores in sequence • Application reads in sequence
  • 44. Queries supported CREATE TABLE raw_weather_data (
 wsid text,
 year int,
 month int,
 day int,
 hour int,
 temperature double,
 dewpoint double,
 pressure double,
 wind_direction int,
 wind_speed double,
 sky_condition int,
 sky_condition_text text,
 one_hour_precip double,
 six_hour_precip double,
 PRIMARY KEY ((wsid), year, month, day, hour)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC); Get weather data given •Weather Station ID •Weather Station ID and Time •Weather Station ID and Range of Time
  • 45. Primary Key CREATE TABLE raw_weather_data (
 wsid text,
 year int,
 month int,
 day int,
 hour int,
 temperature double,
 dewpoint double,
 pressure double,
 wind_direction int,
 wind_speed double,
 sky_condition int,
 sky_condition_text text,
 one_hour_precip double,
 six_hour_precip double,
 PRIMARY KEY ((wsid), year, month, day, hour)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
  • 46. Primary key relationship PRIMARY KEY ((wsid),year,month,day,hour)
  • 47. Primary key relationship Partition Key PRIMARY KEY ((wsid),year,month,day,hour)
  • 48. Primary key relationship PRIMARY KEY ((wsid),year,month,day,hour) Partition Key Clustering Columns
  • 49. Primary key relationship Partition Key Clustering Columns 10010:99999 PRIMARY KEY ((wsid),year,month,day,hour)
  • 50. 2005:12:1:10 -5.6 Primary key relationship Partition Key Clustering Columns 10010:99999 -5.3-4.9-5.1 2005:12:1:9 2005:12:1:8 2005:12:1:7 PRIMARY KEY ((wsid),year,month,day,hour)
  • 51. Partition keys 10010:99999 Murmur3 Hash Token = 7224631062609997448 722266:13850 Murmur3 Hash Token = -6804302034103043898 INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘10010:99999’,2005,12,1,7,-5.6); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘722266:13850’,2005,12,1,7,-5.6); Consistent hash. 128 bit number between 2-63 and 264
  • 52. Partition keys 10010:99999 Murmur3 Hash Token = 15 722266:13850 Murmur3 Hash Token = 77 For this example, let’s make it a reasonable number INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘10010:99999’,2005,12,1,7,-5.6); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature) VALUES (‘722266:13850’,2005,12,1,7,-5.6);
  • 53. Data Locality DC1 DC1: RF=3 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 10.0.0.1 00-25 10.0.0.4 76-100 10.0.0.2 26-50 10.0.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Client Read partition 15 DC2 10.1.0.1 00-25 10.1.0.4 76-100 10.1.0.2 26-50 10.1.0.3 51-75 76-100 51-75 00-25 76-100 26-50 00-25 51-75 26-50 Node Primary Replica Replica 10.0.0.1 00-25 76-100 51-75 10.0.0.2 26-50 00-25 76-100 10.0.0.3 51-75 26-50 00-25 10.0.0.4 76-100 51-75 26-50 DC2: RF=3 Client Read partition 15
  • 54. Data Locality wsid=‘10010:99999’ ? 1000 Node Cluster You are here!
  • 55. Writes CREATE TABLE raw_weather_data (
 wsid text,
 year int,
 month int,
 day int,
 hour int,
 temperature double,
 dewpoint double,
 pressure double,
 wind_direction int,
 wind_speed double,
 sky_condition int,
 sky_condition_text text,
 one_hour_precip double,
 six_hour_precip double,
 PRIMARY KEY ((wsid), year, month, day, hour)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
  • 56. Writes CREATE TABLE raw_weather_data (
 wsid text,
 year int,
 month int,
 day int,
 hour int,
 temperature double,
 PRIMARY KEY ((wsid), year, month, day, hour)
 ) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,10,-5.6); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,9,-5.1); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,8,-4.9); INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,7,-5.3);
  • 57. Write Path Client INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
 VALUES (‘10010:99999’,2005,12,1,7,-5.3); Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Memtable SSTable SSTable SSTable SSTable Node Commit Log Data * Compaction *
  • 58. Date Tiered Compaction Strategy •Group similar time blocks •Never compact again •Used for high density SSTable SSTable SSTable T=2015-01-01 -> 2015-01-5 T=2015-01-06 -> 2015-01-10 T=2015-01-11 -> 2015-01-15
  • 59. Storage Model - Logical View 2005:12:1:10 -5.6 2005:12:1:9 -5.1 2005:12:1:8 -4.9 10010:99999 10010:99999 10010:99999 wsid hour temperature 2005:12:1:7 -5.3 10010:99999 SELECT wsid, hour, temperature
 FROM raw_weather_data
 WHERE wsid=‘10010:99999’
 AND year = 2005 AND month = 12 AND day = 1;
  • 60. 2005:12:1:10 -5.6 -5.3-4.9-5.1 Storage Model - Disk Layout 2005:12:1:9 2005:12:1:8 10010:99999 2005:12:1:7 Merged, Sorted and Stored Sequentially SELECT wsid, hour, temperature
 FROM raw_weather_data
 WHERE wsid=‘10010:99999’
 AND year = 2005 AND month = 12 AND day = 1;
  • 61. 2005:12:1:10 -5.6 2005:12:1:11 -4.9 -5.3-4.9-5.1 Storage Model - Disk Layout 2005:12:1:9 2005:12:1:8 10010:99999 2005:12:1:7 Merged, Sorted and Stored Sequentially SELECT wsid, hour, temperature
 FROM raw_weather_data
 WHERE wsid=‘10010:99999’
 AND year = 2005 AND month = 12 AND day = 1;
  • 62. 2005:12:1:10 -5.6 2005:12:1:11 -4.9 -5.3-4.9-5.1 Storage Model - Disk Layout 2005:12:1:9 2005:12:1:8 10010:99999 2005:12:1:7 Merged, Sorted and Stored Sequentially SELECT wsid, hour, temperature
 FROM raw_weather_data
 WHERE wsid=‘10010:99999’
 AND year = 2005 AND month = 12 AND day = 1; 2005:12:1:12 -5.4
  • 63. Read Path Client Column 1 Partition Key 1 Column 2 Column 3 Column 4 Column 1 Partition Key 1 Column 2 Column 3 Column 4 Memtable SSTable SSTable SSTable Node Data SELECT wsid,hour,temperature
 FROM raw_weather_data
 WHERE wsid='10010:99999'
 AND year = 2005 AND month = 12 AND day = 1 
 AND hour >= 7 AND hour <= 10;
  • 64. Query patterns • Range queries • “Slice” operation on disk Single seek on disk 10010:99999 Partition key for locality SELECT wsid,hour,temperature
 FROM raw_weather_data
 WHERE wsid='10010:99999'
 AND year = 2005 AND month = 12 AND day = 1 
 AND hour >= 7 AND hour <= 10; 2005:12:1:10 -5.6 -5.3-4.9-5.1 2005:12:1:9 2005:12:1:8 2005:12:1:7
  • 65. Query patterns • Range queries • “Slice” operation on disk Programmers like this Sorted by event_time 2005:12:1:10 -5.6 2005:12:1:9 -5.1 2005:12:1:8 -4.9 10010:99999 10010:99999 10010:99999 weather_station hour temperature 2005:12:1:7 -5.3 10010:99999 SELECT weatherstation,hour,temperature FROM temperature WHERE weatherstation_id=‘10010:99999' AND year = 2005 AND month = 12 AND day = 1 AND hour >= 7 AND hour <= 10;
  • 66.
  • 67. Thank you! Bring the questions Follow me on twitter @PatrickMcFadin