advanced data modeling with apache cassandra

©2013 DataStax Confidential. Do not distribute without consent.

@PatrickMcFadin

Patrick McFadinChief Evangelist for Apache Cassandra

Advanced Data Modeling with Apache Cassandra

1

Cassandra Modeling

Data

Models

Application

Think Before You ModelOr how to keep doing what you’re already doing

3

Some of the Entities and Relationships in KillrVideo

4

Userid

firstname

lastname

email

password Video

id

name

description

location

preview_image

tagsfeatures

Commentcomment

id

adds

timestamp

posts

timestamp

1

nn

1

1

nn

mrates

rating

• What are your application’s workflows?

• How will I access the data?

• Knowing your queries in advance is NOT optional

• Different from RDBMS because I can’t just JOIN or create a new indexes to support new queries

5

Modeling Queries

Some Application Workflows in KillrVideo

6

User Logs into site

Show basic information about user

Show videos added by a

user

Show comments posted by a

user

Search for a video by tag

Show latest videos

added to the site

Show comments for a video

Show ratings for a

video

Show video and its details

Some Queries in KillrVideo to Support Workflows

7

Users

User Logs into site

Find user by email address


Find user by id

Comments

Show comments for a video

Find comments by video (latest first)

Show comments posted by a

user

Find comments by user (latest first)

Ratings

Show ratings for a

video

Find ratings by video

Some Queries in KillrVideo to Support Workflows

8

Videos

Search for a video by tag Find video by tag

Show latest videos

added to the site

Find videos by date (latest first)


Find video by idShow videos added by a

user

Find videos by user (latest first)

Data Modeling Refresher

• Cassandra limits us to queries that can scale across many nodes – Include value for Partition Key and optionally, Clustering Column(s)

• We know our queries, so we build tables to answer them

• Denormalize at write time to do as few reads as possible

• Many times we end up with a “table per query” – Similar to materialized views from the RDBMS world

9

Users – The Cassandra Way

User Logs into site

Find user by email address


Find user by id

CREATE TABLE user_credentials ( email text, password text, userid uuid, PRIMARY KEY (email) );

CREATE TABLE users ( userid uuid, firstname text, lastname text, email text, created_date timestamp, PRIMARY KEY (userid) );

Application Find the index

80

10

3050

70

60

40

20

Why not indexes?

12


Find video by idShow videos added by a

user

Find videos by user (latest first)

CREATE TABLE videos ( videoid uuid, userid uuid, name text, description text, location text, location_type int, preview_image_location text, tags set<text>, added_date timestamp, PRIMARY KEY (videoid) );

CREATE TABLE user_videos ( userid uuid, added_date timestamp, videoid uuid, name text, preview_image_location text, PRIMARY KEY (userid, added_date, videoid) ) WITH CLUSTERING ORDER BY (added_date DESC, videoid ASC);

Views or indexes?

Denormalized data

Videos Everywhere!

Considerations When Duplicating Data • Can the data change? • How likely is it to change or how frequently will it change? • Do I have all the information I need to update duplicates and maintain

consistency?

13

Search for a video by tag Find video by tag

Show latest videos

added to the site


Single Nodes Have Limits Too• Latest videos are bucketed by day

• Means all reads/writes to latest videos are going to same partition (and thus the same nodes)

• Could create a hotspot

14

Show latest videos

added to the site


CREATE TABLE latest_videos ( yyyymmdd text, added_date timestamp, videoid uuid, name text, preview_image_location text, PRIMARY KEY (yyyymmdd, added_date, videoid) ) WITH CLUSTERING ORDER BY ( added_date DESC, videoid ASC);

CREATE TABLE latest_videos ( yyyymmdd text, bucket_number int, added_date timestamp, videoid uuid, name text, preview_image_location text, PRIMARY KEY ((yyyymmdd, bucket_number), added_date, videoid) ) WITH CLUSTERING ORDER BY (added_date DESC, videoid ASC);

Single Nodes Have Limits Too• Mitigate by adding data to the

Partition Key to spread load

• Data that’s already naturally a part of the domain – Latest videos by category?

• Arbitrary data, like a bucket number – Round robin at the app level

15

Show latest videos

added to the site


Hot spot

1000 Node Cluster

yyyymmmdd

Hot spot

1000 Node Cluster

yyyymmmdd, bucket_number

Use Case Examples

User Score Table• After each game, score is stored • Partition is user + game • Record timestamp is reversed

(last score first)

CREATE TABLE userScores ( userId uuid, handle text static, gameId uuid, score_timestamp timestamp, score double, PRIMARY KEY ((userId, gameId), score_timestamp)) WITH CLUSTERING ORDER BY (score_timestamp DESC);

Top Ten User Scores•Written by Spark job • Default TTL = 3 days • Using Date Tiered Compaction Strategy

CREATE TABLE TopTen ( gameId uuid, process_timestamp timestamp, score double, userId uuid, handle text, PRIMARY KEY (gameId, process_timestamp, score)) WITH CLUSTERING ORDER BY (process_timestamp DESC, score DESC) AND default_time_to_live = '259200' AND COMPACTION = {'class': 'DateTieredCompactionStrategy', 'enabled': 'TRUE'};

DTCS• Built for time series • SSTable windows of time ranges • Compaction grouped by time • Best for same TTLed data(default TTL) • Entire SSTables can be dropped

Queries, Yo

gameid | process_timestamp | score | handle | userid--------------------------------------+--------------------------+-------+-----------------+-------------------------------------- 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 66.2 | subsonic | 99051fe9-6a9c-46c2-b949-38ef78858d07 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 55.2 | neo | 99051fe9-6a9c-46c2-b949-38ef78858d11 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 49.2 | bennybaru | 99051fe9-6a9c-46c2-b949-38ef78858d06 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 46.2 | tigger | 99051fe9-6a9c-46c2-b949-38ef78858d05 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 45.2 | velvetfog | 99051fe9-6a9c-46c2-b949-38ef78858d04 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 43.6 | flashberg | 99051fe9-6a9c-46c2-b949-38ef78858d10 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 43.4 | jbellis | 99051fe9-6a9c-46c2-b949-38ef78858d09 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 43.2 | cafruitbat | 99051fe9-6a9c-46c2-b949-38ef78858d02 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 41.2 | groovemerchant | 99051fe9-6a9c-46c2-b949-38ef78858d03 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 39.2 | rustyrazorblade | 99051fe9-6a9c-46c2-b949-38ef78858d01 99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 20.2 | driftx | 99051fe9-6a9c-46c2-b949-38ef78858d08

SELECT gameId, process_timestamp, score, handle, userIdFROM topten WHERE gameid = 99051fe9-6a9c-46c2-b949-38ef78858dd0 AND process_timestamp = '2014-12-31 13:42:40';

File Storage Use Case

Upload API

It’s all about the model

• Start with our queries • All data for a image • All images over time • Specific images over a range • Access times of each image

• Use case • User creates an account • User uploads image • Image is distributed worldwide • User can check access patterns

user Table• Our standard POJO • emails are dynamic

CREATE TABLE user ( username text, firstname text, lastname text, emails list<text>, PRIMARY KEY (username) );

INSERT INTO user (username, firstname, lastname, emails) VALUES (‘pmcfadin’, ‘Patrick’, ‘McFadin’, [‘[email protected]’, ‘[email protected]’] IF NOT EXISTS;

image Table• Basic POJO for an image • list of tags for potential search • username is from user table

CREATE TABLE image ( image_id uuid, //Proxy image ID username text, created_at timestamp, image_name text, image_description text, tags list<text>, // ? search in Solr ? images map<text, uuid> , // orig, thumbnail, medium PRIMARY KEY (image_id) );

images_timeseries Table• Time ordered list of images • Reversed - Last image first •Map stores versions

CREATE TABLE images_timeseries ( username text, bucket int, //yyyymm sequence timestamp, image_id uuid, image_name text, image_description text, images map<text, uuid>, // orig, thumbnail, medium PRIMARY KEY ((username, bucket), sequence) ) WITH CLUSTERING ORDER BY (sequence DESC); // reverse clustering on sequence

bucket_index Table• List of buckets for a user • Bucket order is reversed • High reads, no updates. Use LeveledCompaction

CREATE TABLE bucket_index ( username text, bucket int, PRIMARY KEY( username, bucket) ) WITH CLUSTERING ORDER BY (bucket DESC); //LCS + reverse clustering

blob Table•Main pointer to chunks • count and checksum for errors detection •META-DATA stored with as an optimization

CREATE TABLE blob ( object_id uuid, // unique identifier chunk_count int, // total number of chunks size int, // total byte size chunk_size int, // maximum size of the chunks. checksum text, // optional checksum, this could be stored // for each blob but only checked on a certain // percentage of reads attributes text, // optional text blob for additional json // encoded attributes PRIMARY KEY (object_id) );

blob_chunk Table•Main data storage table • Size of blob is up to the client • Return size for error detection • Run in parallel!

CREATE TABLE blob_chunk ( object_id uuid, // same as the object.object_name above chunk_id int, // order for this chunk in the blob chunk_size int, // size of this chunk, the last chunk // may be of a different size. data blob, // the data for this blob chunk PRIMARY KEY ((object_id, chunk_id)) );

access_log Table• Classic time series table • Inserts at CL.ONE • Read at CL.ONE

CREATE TABLE access_log ( object_id uuid, access_date text, // YYYYMMDD portion of access timestamp access_time timestamp, // Access time to the ms ip_address inet, // x.x.x.x inet address PRIMARY KEY ((object_id, access_date), access_time, ip_address) );

Light Weight Transactions

Regular Update

UPDATE videosSET name = 'The data model is dead. Long live the data model.'WHERE id = 06049cbb-dfed-421f-b889-5f649a0de1ed;

Table Name Fields to Update: Not in Primary Key

Primary Key

The race is onProcess 1 Process 2

SELECT firstName, lastNameFROM usersWHERE username = 'pmcfadin';

SELECT firstName, lastNameFROM usersWHERE username = 'pmcfadin';

(0 rows)

(0 rows)

INSERT INTO users (username, firstname, lastname, email, password, created_date)VALUES ('pmcfadin','Patrick','McFadin', ['[email protected]'], 'ba27e03fd95e507daf2937c937d499ab', '2011-06-20 13:50:00');

INSERT INTO users (username, firstname, lastname, email, password, created_date)VALUES ('pmcfadin','Paul','McFadin', ['[email protected]'], 'ea24e13ad95a209ded8912e937d499de', '2011-06-20 13:51:00');

T0

T1

T2

T3

Got nothing! Good to go!

This one wins

Lightweight Transactions

Don’t overwrite!

INSERT INTO videos (videoid, name, userid, description, location, location_type, preview_thumbnails, tags, added_date, metadata) VALUES (06049cbb-dfed-421f-b889-5f649a0de1ed,'The data model is dead. Long live the data model.',9761d3d7-7fbd-4269-9988-6cfd4e188678, 'First in a three part series for Cassandra Data Modeling','http://www.youtube.com/watch?v=px6U2n74q3g',1, {'YouTube':'http://www.youtube.com/watch?v=px6U2n74q3g'},{'cassandra','data model','relational','instruction'}, '2013-05-02 12:30:29’) IF NOT EXISTS;


Don’t overwrite!

UPDATE videosSET name = 'The data model is dead. Long live the data model.'WHERE id = 06049cbb-dfed-421f-b889-5f649a0de1ed IF userid = 9761d3d7-7fbd-4269-9988-6cfd4e188678;

Solution LWTProcess 1

INSERT INTO users (username, firstname, lastname, email, password, created_date)VALUES ('pmcfadin','Patrick','McFadin', ['[email protected]'], 'ba27e03fd95e507daf2937c937d499ab', '2011-06-20 13:50:00')IF NOT EXISTS;

T0

T1 [applied]----------- True

•Check performed for record

•Paxos ensures exclusive access •applied = true: Success

mailto:[email protected]

Solution LWTProcess 2

T2

T3

[applied] | username | created_date | firstname | lastname -----------+----------+--------------------------+-----------+---------- False | pmcfadin | 2011-06-20 13:50:00-0700 | Patrick | McFadin

INSERT INTO users (username, firstname, lastname, email, password, created_date)VALUES ('pmcfadin','Paul','McFadin', ['[email protected]'], 'ea24e13ad95a209ded8912e937d499de', '2011-06-20 13:51:00')IF NOT EXISTS;

•applied = false: Rejected

•No record stomping!


No-op. Don’t throw error

CREATE TABLE IF NOT EXISTS videos_by_tag ( tag text, videoid uuid, added_date timestamp, name text, preview_image_location text, tagged_date timestamp, PRIMARY KEY (tag, videoid) );

User Defined Types

User Defined Types

• Complex data in one place

• No multi-gets (multi-partitions)

• Nesting!CREATE TYPE address ( street text, city text, zip_code int, country text, cross_streets set<text> );

BeforeCREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, added_date timestamp, PRIMARY KEY (videoid) );

CREATE TABLE video_metadata ( video_id uuid PRIMARY KEY, height int, width int, video_bit_rate set<text>, encoding text );

SELECT * FROM videos WHERE videoId = 2;

SELECT * FROM video_metadata WHERE videoId = 2;

Title: Introduction to Apache Cassandra

Description: A one hour talk on everything you need to know about a totally amazing database.

480 720

Playback rate:

In-applicationjoin

After

• Now video_metadata is embedded in videos

CREATE TYPE video_metadata ( height int, width int, video_bit_rate set<text>, encoding text);

CREATE TABLE videos ( videoid uuid, userid uuid, name varchar, description varchar, location text, location_type int, preview_thumbnails map<text,text>, tags set<varchar>, metadata set <frozen<video_metadata>>, added_date timestamp, PRIMARY KEY (videoid) );

Wait! Frozen??

• Staying out of technical debt

• 3.0 UDTs will not have to be frozen

• Applicable to User Defined Types and Tuples

Do you want to build a schema?Do you want to store some JSON?

Let’s store some JSON{ "productId": 2, "name": "Kitchen Table", "price": 249.99, "description" : "Rectangular table with oak finish", "dimensions": { "units": "inches", "length": 50.0, "width": 66.0, "height": 32 }, "categories": { { "category" : "Home Furnishings" { "catalogPage": 45, "url": "/home/furnishings" }, { "category" : "Kitchen Furnishings" { "catalogPage": 108, "url": "/kitchen/furnishings" } } }


CREATE TYPE dimensions ( units text, length float, width float, height float );



CREATE TYPE category ( catalogPage int, url text );



CREATE TYPE category ( catalogPage int, url text );

CREATE TABLE product ( productId int, name text, price float, description text, dimensions frozen <dimensions>, categories map <text, frozen <category>>, PRIMARY KEY (productId) );

Let’s store some JSONINSERT INTO product (productId, name, price, description, dimensions, categories) VALUES (2, 'Kitchen Table', 249.99, 'Rectangular table with oak finish', { units: 'inches', length: 50.0, width: 66.0, height: 32 }, { 'Home Furnishings': { catalogPage: 45, url: '/home/furnishings' }, 'Kitchen Furnishings': { catalogPage: 108, url: '/kitchen/furnishings' }

} );

dimensions frozen <dimensions>

categories map <text, frozen <category>>

Retrieving fields

Aggregates

*As of Cassandra 2.2

•Built-in: avg, min, max, count(<column name>) •Runs on server •Always use with partition key

Materialized ViewsCREATE TABLE user( id int PRIMARY KEY, login text, firstname text, lastname text, country text, gender int);

•New as of 3.0 • Auto-denormalize your tables •Not for everything

CREATE MATERIALIZED VIEW user_by_country AS SELECT * //denormalize ALL columnsFROM userWHERE country IS NOT NULL AND id IS NOT NULLPRIMARY KEY(country, id);

Thank you!

Bring the questions

Follow me on twitter @PatrickMcFadin

advanced data modeling with apache cassandra

Technology