cassandra 2.0 and timeseries

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@PatrickMcFadin

Patrick McFadinChief Evangelist/Solution Architect - DataStax

Cassandra 2.0: Intro + Time Series

Friday, October 11, 13

Who I am

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• Patrick McFadin• Solution Architect at DataStax• Cassandra MVP• User for years• Follow me for more:

I talk about Cassandra and building scalable, resilient apps ALL THE TIME!

@PatrickMcFadin

Dude. Uptime == $$


Cassandra - An introduction


Cassandra - Intro

• Based on Amazon Dynamo and Google BigTable paper• Shared nothing• Data safe as possible• Predictable scaling

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Dynamo

BigTable


Cassandra - More than one server

• All nodes participate in a cluster• Shared nothing• Add or remove as needed•More capacity? Add a server

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• Each node owns a token• Tokens denote a range of keys

• 4 nodes? -> Key range/4• Each node owns 1/4 the data


Cassandra - Locally Distributed

• Client writes to any node• Node coordinates with others• Data replicated in parallel• Replication factor: How many

copies of your data?• RF = 3 here

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Each node stores 3/4 of clusters total data.


Cassandra - Geographically Distributed

• Client writes local• Data syncs across WAN• Replication Factor per DC

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Single coordinator


Cassandra - Consistency

• Consistency Level (CL)• Client specifies per read or write

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• ALL = All replicas ack• QUORUM = > 51% of replicas ack• LOCAL_QUORUM = > 51% in local DC ack• ONE = Only one replica acks


Cassandra - Transparent to the application

• A single node failure shouldn’t bring failure• Replication Factor + Consistency Level = Success• This example:• RF = 3• CL = QUORUM

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>51% Ack so we are good!


Cassandra Applications - Drivers

• DataStax Drivers for Cassandra• Java• C#• Python•more on the way

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Application Example - Layout

• Active-Active• Service based DNS routing

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Cassandra Replication


Application Example - Uptime

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• Normal server maintenance• Application is unaware

Cassandra Replication


Application Example - Failure

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• Data center failure• Data is safe. Route traffic.

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Another happy user!


Cassandra 2.0 - Big new features


Five Years of Cassandra

Jul-09 May-10 Feb-11 Dec-11 Oct-12 Jul-13

0.1 0.3 0.6 0.7 1.0 1.2...

2.0

DSE

Jul-08


SELECT * FROM usersWHERE username = ’jbellis’

[empty resultset]

Session 1SELECT * FROM usersWHERE username = ’jbellis’

[empty resultset]

Session 2

Lightweight transactions: the problem

INSERT INTO users (username,password)VALUES (’jbellis’,‘xdg44hh’)

INSERT INTO users (userName,password)VALUES (’jbellis’,‘8dhh43k’)

It’s a Race!

Who wins?


Client(locks)

Coordinatorrequest

Replica

internalrequest

Why Locking Doesn’t Work

• Client locks•Write times out• Lock released•Hint is replayed!!


Client(locks)

Coordinatorrequest

Replica

internalrequest

X




Client(locks)

Coordinatorrequest

Replica

internalrequest

hint X




Client(locks)

Coordinatorrequest

Replica

internalrequest

hint

timeoutresponse

X




Paxos• Consensus algorithm• All operations are quorum-based• Each replica sends information about unfinished operations to the leader

during prepare• Paxos made Simple


LWT: details• 4 round trips vs 1 for normal updates• Paxos state is durable• Immediate consistency with no leader election or failover• ConsistencyLevel.SERIAL• http://www.datastax.com/dev/blog/lightweight-transactions-in-

cassandra-2-0


http://www.datastax.com/dev/blog/lightweight-transactions-in-cassandra-2-0




LWT: Use with caution• Great for 1% of your application• Eventual consistency is your friend• http://www.slideshare.net/planetcassandra/c-summit-2013-eventual-consistency-

hopeful-consistency-by-christos-kalantzis


http://www.slideshare.net/planetcassandra/c-summit-2013-eventual-consistency-hopeful-consistency-by-christos-kalantzis




UPDATE USERS SET email = ’[email protected]’, ...WHERE username = ’jbellis’IF email = ’[email protected]’;

INSERT INTO USERS (username, email, ...)VALUES (‘jbellis’, ‘[email protected]’, ... )IF NOT EXISTS;

Using LWT

• Don’t overwrite an existing record

• Only update record if condition is met


mailto:[email protected]






CQL Improvements• Cursors• Large result sets now have ->next() functionality

• Prevents massive result sets OOMing• No more client side hacks with LIMIT

• Warning: Not isolated


CQL Improvements• ALTER DROP• Remove a field from a CQL table.

• Conditional schema changes• Only execute if condition met

CREATE KEYSPACE IF NOT EXISTS ksWITH replication = { 'class': 'SimpleStrategy','replication_factor' : 3 };

CREATE TABLE IF NOT EXISTS test (k int PRIMARY KEY);

DROP KEYSPACE IF EXISTS ks;

ALTER TABLE users DROP address3;


CQL Improvements• Aliases in SELECT

• Limit and TTL in prepared statements

SELECT event_id, dateOf(created_at) AS creation_date, blobAsText(content) AS content FROM timeline;

event_id | creation_date | content-------------------------+--------------------------+---------------------- 550e8400-e29b-41d4-a716 | 2013-07-26 10:44:33+0200 | Something happened!?

SELECT * FROM myTable LIMIT ?;

UPDATE myTable USING TTL ? SET v = 2 WHERE k = 'foo';


Triggers

CREATE TRIGGER <name> ON <table> USING <classname>;

DROP TRIGGER <name> ON [<keyspace>.]<table>;

• Executed on the coordinator before mutation• Takes original mutation and adds any new• Jars deployed per server


Trigger implementationclass MyTrigger implements ITrigger{ public Collection<RowMutation> augment(ByteBuffer key, ColumnFamily update) { ... }}

• You have to implement your own ITrigger (for now)• Compile and deploy to each server


Experimental!• Relies on internal RowMutation, ColumnFamily classes•Not sandboxed. Be careful!• Expect changes in 2.1


Cassandra and Time Series


Time Series Taming the beast• Peter Higgs and Francois Englert. Nobel prize for Physics• Theorized the existence of the Higgs boson

• Found using ATLAS

• Data stored in P-BEAST

• Time series running on Cassandra


Use Cassandra for time series


Use Cassandra for time series

Get a nobel prize


Time Series Why• Storage model from BigTable is perfect• One row key and tons of (variable)columns• Single layout on disk

Row Key Column Name Column Name

Column Value Column Value


Time Series Example• Storing weather data• One weather station• Temperature measurements every minute

WeatherStation ID 2013-10-09 10:00 AM 2013-10-09 10:00 AM 2013-10-10 11:00 AM

72 Degrees 72 Degrees 65 Degrees


Time Series Example• Query data•Weather Station ID = Locality of single node

WeatherStation ID100 2013-10-09 10:00 AM 2013-10-09 10:00 AM 2013-10-10 11:00 AM

72 Degrees 72 Degrees 65 Degrees

Date query weatherStationID = 100 ANDdate = 2013-10-09 10:00 AM

weatherStationID = 100 ANDdate > 2013-10-09 10:00 AM ANDdate < 2013-10-10 11:01 AM

Date Range

OR


Time Series How• CQL expresses this well• Data partitioned by weather station ID and time

• Easy to insert data

• Easy to query

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');

SELECT 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';


Time Series Further partitioning• At every minute you will eventually run out of rows• 2 billion columns per storage row• Data partitioned by weather station ID and time• Use the partition key to split things up

CREATE TABLE temperature_by_day ( weatherstation_id text, date text, event_time timestamp, temperature text, PRIMARY KEY ((weatherstation_id,date),event_time));


Time Series Further Partitioning• Still easy to insert

• Still easy to query

INSERT INTO temperature_by_day(weatherstation_id,date,event_time,temperature) VALUES ('1234ABCD','2013-04-03','2013-04-03 07:01:00','72F');

SELECT temperature FROM temperature_by_day WHERE weatherstation_id='1234ABCD' AND date='2013-04-03'AND event_time > '2013-04-03 07:01:00'AND event_time < '2013-04-03 07:04:00';


Time Series Use cases• Logging• Thing Tracking (IoT)• Sensor Data• User Tracking• Fraud Detection•Nobel prizes!


Thank you!

Apache Cassandra 2.0 - Data model on fire

Next talk in my data model series!


cassandra 2.0 and timeseries

Technology

cassandra intro

years of cassandra

cassandra transparent

unaware cassandra replication

distributed client

users username

datastax cassandra mvp

data center failure