Video: http://youtu.be/B-bTPSwhsDY Abstract Patrick McFadin (@PatrickMcFadin), Chief Evangelist for Apache Cassandra at DataStax, will be presenting an introduction to Cassandra as a key player in database technologies. Both large and small companies alike chose Apache Cassandra as their database solution and Patrick will be presenting on why they made that choice. Patrick will also be discussing Cassandra's architecture, including: data modeling, time-series storage and replication strategies, providing a holistic overview of how Cassandra works and the best way to get started. About Patrick McFadin Prior to working for DataStax, Patrick was the Chief Architect at Hobsons, an education services company. His responsibilities included ensuring product availability and scaling for all higher education products. Prior to this position, he was the Director of Engineering at Hobsons which he came to after they acquired his company, Link-11 Systems, a software services company. While at Link-11 Systems, he built the first widely popular CRM system for universities, Connect. He obtained a BS in Computer Engineering from Cal Poly, San Luis Obispo and holds the distinction of being the only recipient of a medal (asanyone can find out) for hacking while serving in the US Navy.

1. Cassandra : Introduction
Patrick McFadin
Chief Evangelist/Solution Architect - DataStax
@PatrickMcFadin
©2013 DataStax Confidential. Do not distribute without consent.

2. Who I am
• Patrick McFadin
• Solution Architect at DataStax
• Cassandra MVP
• User for years
• Follow me for more:
Dude.
Uptime == $$
@PatrickMcFadin
I talk about Cassandra and building scalable, resilient apps ALL THE TIME!
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3. Five Years of Cassandra
0.1
Jul-08
...
0.3
0
0.6
1
0.7
1.0
2
1.2
3
DSE
4
2.0
5

4. Why Cassandra?

5. The Best
Persistence
Tier
For Your
Application
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6. Cassandra - An introduction

7. Cassandra - Roots
• Based on Amazon Dynamo and Google BigTable paper
• Shared nothing
• Data safe as possible
• Predictable scaling
Dynamo
BigTable
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8. Cassandra - More than one server
Each node owns
25% of the data
• All nodes participate in a cluster
• Shared nothing
• Add or remove as needed
25%
• More capacity? Add a server
25%
25%
25%
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9. Core Concepts Write path
<row,column>
Compacted later

10. Core Concepts Read Path
Real user story
• New app
• SSDs
• 2.5 m requests
• Client P99: 3.17ms!

11. 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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12. Cassandra - Consistency
• Consistency Level (CL)
• Client specifies per read or write
• ALL = All replicas ack
• QUORUM = > 51% of replicas ack
• LOCAL_QUORUM = > 51% in local DC ack
• ONE = Only one replica acks
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13. Cassandra - Transparent to the application
• A single node failure shouldn’t bring failure
• Replication Factor + Consistency Level = Success
• This example:
• RF = 3
• CL = QUORUM
>51% Ack so we are good!
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14. My favorite feature.
Ever!
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15. Cassandra - Geographically Distributed
• Client writes local
• Data syncs across WAN
• Replication Factor per DC
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16. Cassandra Applications - Drivers
• DataStax Drivers for Cassandra
• Java
• C#
• Python
• more on the way
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17. Cassandra Applications - Connecting
• Create a pool of local servers
• Client just uses session to interact with Cassandra
!
contactPoints = {“10.0.0.1”,”10.0.0.2”}!
!
keyspace = “videodb”!
!
!
public VideoDbBasicImpl(List<String> contactPoints, String keyspace) {!
cluster = Cluster!
.builder()!
.addContactPoints(!
contactPoints.toArray(new String[contactPoints.size()]))!
.withLoadBalancingPolicy(Policies.defaultLoadBalancingPolicy())!
.withRetryPolicy(Policies.defaultRetryPolicy())!
.build();!
!
!
session = cluster.connect(keyspace);!
}
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18. CQL Intro
• Cassandra Query Language
• SQL–like language to query Cassandra
• Limited predicates. Attempts to prevent bad queries
• But still offers enough leeway to get into trouble
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19. Data Model Logical containers
Cluster - Contains all nodes. Even across WAN
Keyspace - Contains all tables. Specifies replication
Table (Column Family) - Contains rows

20. CQL Intro
• CREATE / DROP / ALTER TABLE
• SELECT
!
• BUT
• INSERT AND UPDATE are similar to each other
• If a row doesn’t exist, UPDATE will insert it, and if it exists, INSERT will replace it.
• Think of it as an UPSERT
• Therefore we never get a key violation
• For updates, Cassandra never reads (no col = col + 1)
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21. Data Modeling Creating Tables
CREATE TABLE user (!
! username varchar,!
! firstname varchar,!
! lastname varchar,!
! shopping_carts set<varchar>,!
! PRIMARY KEY (username)!
);
Collection!
CREATE TABLE shopping_cart (!
! username varchar,!
! cart_name text!
! item_id int,!
! item_name varchar,!
description varchar,!
! price float,!
! item_detail map<varchar,varchar>!
! PRIMARY KEY
((username,cart_name),item_id)!
);
Creates compound partition row key

22. CQL Inserts
• Insert will always overwrite
INSERT INTO users (username, firstname, lastname, !
email, password, created_date)!
VALUES ('pmcfadin','Patrick','McFadin',!
['patrick@datastax.com'],'ba27e03fd95e507daf2937c937d499ab',!
'2011-06-20 13:50:00');!
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23. CQL Selects
• No joins
• Data is returned in row/column format
SELECT username, firstname, lastname, !
email, password, created_date!
FROM users!
WHERE username = 'pmcfadin';!
username | firstname | lastname | email
| password
| created_date!
----------+-----------+----------+--------------------------+----------------------------------+--------------------------!
pmcfadin |
Patrick | McFadin | ['patrick@datastax.com'] | ba27e03fd95e507daf2937c937d499ab | 2011-06-20 13:50:00-0700!
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24. Cassandra and Time Series

25. 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

26. Use Cassandra for time series
Get a nobel prize

27. 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

28. 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
72 Degrees
72 Degrees
2013-10-10 11:00 AM
65 Degrees

29. Time Series Example
• Query data
• Weather Station ID = Locality of single node
Date query
weatherStationID = 100 AND!
date = 2013-10-09 10:00 AM
WeatherStation ID
2013-10-09 10:00 AM 2013-10-09 10:00 AM
100
72 Degrees
72 Degrees
2013-10-10 11:00 AM
65 Degrees
OR
Date Range
weatherStationID = 100 AND!
date > 2013-10-09 10:00 AM AND!
date < 2013-10-10 11:01 AM

30. Time Series How
• CQL expresses this well
• Data partitioned by weather station ID and time
CREATE TABLE temperature (!
weatherstation_id text,!
event_time timestamp,!
temperature text,!
PRIMARY KEY (weatherstation_id,event_time)!
);
!
!
!
• Easy to insert data
INSERT
INTO temperature(weatherstation_id,event_time,temperature) !
VALUES ('1234ABCD','2013-04-03 07:01:00','72F');
!
!
• Easy to query
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';

31. 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)!
);

32. Time Series Further Partitioning
• Still easy to insert
!
!
INSERT INTO temperature_by_day(weatherstation_id,date,event_time,temperature) !
VALUES ('1234ABCD','2013-04-03','2013-04-03 07:01:00','72F');
!
!
• Still easy to query
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';

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

34. Application Example - Layout
• Active-Active
• Service based DNS routing
Cassandra Replication
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35. Application Example - Uptime
• Normal server maintenance
• Application is unaware
Cassandra Replication
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36. Application Example - Failure
• Data center failure
Another happy user!
• Data is safe. Route traffic.
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37. Cassandra Users and Use Cases

38. Netflix!
• If you haven’t heard their story… where have you been?
• 18B market cap — Runs on Cassandra
• User accounts
• Play lists
• Payments
• Statistics

39. Spotify
• Millions of songs. Millions of users.
• Playlists
• 1 billion playlists
• 30+ Cassandra clusters
• 50+ TB of data
• 40k req/sec peak
http://www.slideshare.net/noaresare/cassandra-nyc
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40. Instagram(Facebook)
• Loads and loads of photos. (Probably yours)
• All in AWS
• Security audits
• News feed
• 20k writes/sec. 15k reads/sec.
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41. DataStax Ac*demy for Apache Cassandra
Content
• First four sessions available with Weekly roll-out of 7 sessions total
• Based on DataStax Community Edition
• CQL, Schema Design and Data Modeling
• Introduction to Cassandra Objects
• First Java, then Python, C# and .NET
Goals
• 100,000 Registrations by the end of 2014
• 25,000 Certifications by the end of 2014
https://datastaxacademy.elogiclearning.com/
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42. ©2013 DataStax Confidential. Do not distribute without consent.
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