cassandra community webinar | getting started with apache cassandra with patrick mcfadin
DESCRIPTION
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.TRANSCRIPT
©2013 DataStax Confidential. Do not distribute without consent.
@PatrickMcFadin
Patrick McFadin Chief Evangelist/Solution Architect - DataStax
Cassandra : Introduction
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 == $$
Five Years of Cassandra
0 1 2 3 4 5
0.1 0.3 0.6 0.7 1.0 1.2...
2.0
DSE
Jul-08
Why Cassandra?
The Best !!Persistence !!Tier !!For Your !!Application
Cassandra - An introduction
Cassandra - Roots
• 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 25% of the data
25%
25%
25%
25%
Core Concepts Write path
Compacted later
<row,column>
Core Concepts Read Path
Real user story • New app • SSDs • 2.5 m requests • Client P99: 3.17ms!
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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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!
My favorite feature.
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Ever!
Cassandra - Geographically Distributed
• Client writes local
• Data syncs across WAN
• Replication Factor per DC
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Cassandra Applications - Drivers
• DataStax Drivers for Cassandra
• Java
• C#
• Python
•more on the way
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Cassandra Applications - Connecting
• Create a pool of local servers
• Client just uses session to interact with Cassandra
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!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);! }
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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Data Model Logical containers
Cluster - Contains all nodes. Even across WAN
Keyspace - Contains all tables. Specifies replication
Table (Column Family) - Contains rows
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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Data Modeling Creating Tables
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
CREATE TABLE user (!! username varchar,!! firstname varchar,!! lastname varchar,!! shopping_carts set<varchar>,!! PRIMARY KEY (username)!);
Collection!
CQL Inserts
• Insert will always overwrite
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INSERT INTO users (username, firstname, lastname, ! email, password, created_date)!VALUES ('pmcfadin','Patrick','McFadin',! ['[email protected]'],'ba27e03fd95e507daf2937c937d499ab',! '2011-06-20 13:50:00');!
CQL Selects
• No joins
• Data is returned in row/column format
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SELECT username, firstname, lastname, ! email, password, created_date!FROM users!WHERE username = 'pmcfadin';!
username | firstname | lastname | email | password | created_date!----------+-----------+----------+--------------------------+----------------------------------+--------------------------! pmcfadin | Patrick | McFadin | ['[email protected]'] | ba27e03fd95e507daf2937c937d499ab | 2011-06-20 13:50:00-0700!
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
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 ID 100
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 AND!date = 2013-10-09 10:00 AM
weatherStationID = 100 AND!date > 2013-10-09 10:00 AM AND!date < 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!
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 Users and Use Cases
Netflix!• If you haven’t heard their story… where have you been?
• 18B market cap — Runs on Cassandra
• User accounts
• Play lists
• Payments
• Statistics
Spotify
•Millions of songs. Millions of users.
• Playlists
• 1 billion playlists
• 30+ Cassandra clusters
• 50+ TB of data
• 40k req/sec peak
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http://www.slideshare.net/noaresare/cassandra-nyc
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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DataStax Ac*demy for Apache Cassandra
• 100,000 Registrations by the end of 2014
• 25,000 Certifications by the end of 2014
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• 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
https://datastaxacademy.elogiclearning.com/
Content
Goals
©2013 DataStax Confidential. Do not distribute without consent. !42