advanced data modeling with apache cassandra

@PatrickMcFadin

Patrick McFadinChief Evangelist for Apache Cassandra

Advanced Data Modeling with Apache Cassandra

Cassandra Modeling

Models

Application

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

Some of the Entities and Relationships in KillrVideo

Userid

firstname

lastname

password Video

description

location

preview_image

tagsfeatures

Commentcomment

timestamp

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

Modeling Queries

Some Application Workflows in KillrVideo

User Logs into site

Show basic information about user

Show videos added by a

Show comments posted by a

Search for a video by tag

Show latest videos

added to the site

Show comments for a video

Show ratings for a

Show video and its details

Some Queries in KillrVideo to Support Workflows

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

Find comments by user (latest first)

Ratings

Show ratings for a

Find ratings by video

Some Queries in KillrVideo to Support Workflows

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

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

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

Why not indexes?

Find video by idShow videos added by a

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?

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

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

Show latest videos

added to the site

Hot spot

1000 Node Cluster

yyyymmmdd

Hot spot

1000 Node Cluster

yyyymmmdd, bucket_number

Use Case Examples

Top User Scores

Game API

Nightly Spark Jobs

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’, [‘patrick@datastax.com’, ‘patrick.mcfadin@datastax.com’] 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';

(0 rows)

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

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

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', ['patrick@datastax.com'], 'ba27e03fd95e507daf2937c937d499ab', '2011-06-20 13:50:00')IF NOT EXISTS;

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

•Check performed for record

•Paxos ensures exclusive access •applied = true: Success

Solution LWTProcess 2

INSERT INTO users (username, firstname, lastname, email, password, created_date)VALUES ('pmcfadin','Paul','McFadin', ['paul@oracle.com'], '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

• 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

• 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 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);

Materialized ViewsINSERT INTO user(id,login,firstname,lastname,country) VALUES(1, 'jdoe', 'John', 'DOE', 'US');INSERT INTO user(id,login,firstname,lastname,country) VALUES(2, 'hsue', 'Helen', 'SUE', 'US');INSERT INTO user(id,login,firstname,lastname,country) VALUES(3, 'rsmith', 'Richard', 'SMITH', 'UK');INSERT INTO user(id,login,firstname,lastname,country) VALUES(4, 'doanduyhai', 'DuyHai', 'DOAN', 'FR');

Thank you!

Bring the questions

Follow me on twitter @PatrickMcFadin

advanced data modeling with apache cassandra

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