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

Advanced?• Two thoughts here

Formal Practical

Formal Data Modeling Methods

Data Modeling: Level Up• Understand the data • Conceptual data model

• Understand queries or access patterns • Query graph or workflow

• Apply a query-driven data modeling methodology • Logical data model

• Apply optimizations and implement the design using CQL • Physical data model

Conceptual Data Model• Shows understanding of data entities and relationships • Find errors • Technology independent • Graphical

Conceptual Data Model

User

DigitalArtifact

Venue

Article PresentationReview

User

DigitalArtifact

Venue

likes

n

m

features

1

n

IsA

Article Presentation

disjoint,covering

posts

1

n

Review

likes

features

n

m

n

1

Application Query Workflow• Cassandra is an application database • Show the how the application accesses data • Find queries

Application Query WorkflowSearch'for'

artifacts'by'a'venue,'

author,'title,'or'keyword

Display'information'for'a'venue

Display'a'rating'of'an'artifact

Display'reviews'for'an'artifact

Display'likes'for'an'artifact

Find'information'for'an'artifact'with'a'given'

id

Show'information'about'a'user

Show'likes'for'a'review

Show'reviews'by'a'user

Application Query Workflow

Q5

Q1,Q2,Q3,Q4

Q8Q6 Q7

Search1for1artifacts1by1a1

venue,1author,1title,1or1keyword

Display1information1for1a1venue

Display1a1rating1of1an1artifact

Display1reviews1for1an1artifact

Display1likes1for1an1artifact

Find1information1for1an1artifact1with1a1given1

id

Show1information1about1a1user

Show1likes1for1a1review

Show1reviews1by1a1user

Q9 Q10

Q12

Q11

ACCESS%PATTERNSQ1:$Find$artifacts$for$a$specified$venue$...Q2:$Find$artifacts$for$a$specified$author$...Q3:$Find$artifacts$with$a$specified$title$...Q4:$Find$artifacts$with$a$specified$keyword$...Q5:$Find$information$for$a$specified$venue.Q6:$Find$an$average$rating$for$a$specified$artifact.Q7:$Find$reviews$for$a$specified$artifact$......

Logical Data Model• Combines Conceptual and Query Workflow • Defines Mapping rules •Helps define schema • Chebotko Diagrams

Logical Data Model

ACCESS%PATTERNSQ1:$Find$artifacts$for$a$specified$venue;$order$by$year$(DESC).Q2:$Find$artifacts$for$a$specified$author;$order$by$year$(DESC).Q3:$Find$artifacts$with$a$specified$title;$order$by$year$(DESC).Q4:$Find$artifacts$with$a$specified$keyword;$order$by$year$(DESC).Q5:$Find$information$for$a$specified$venue.Q6:$Find$an$average$rating$for$a$specified$artifact.Q7:$Find$reviews$for$a$specified$artifact,$possibly$with$a$specified$rating.Q8:$Find$a$number$of$‘likes’$for$a$specified$artifact.Q9:$Find$reviews$for$a$specified$user;$order$by$review$timestamp$(DESC).Q10:$Find$a$user$with$a$specified$id.Q11:$Find$a$number$of$‘likes’$for$a$specified$review.Q12:$Find$information$for$a$specified$artifact....

Venues

name Kyear Kcountry IDXhomepage

Q5

Artifacts_by_venue

venue Kyear C�artifact C�typetitleauthors$(list)keywords$(set)

Artifacts_by_author

author Kyear C�artifact C�typetitleauthors$(list)keywords$(set)venue

Artifacts_by_title

title Kyear C�artifact C�typeauthors$(list)keywords$(set)venue

Artifacts_by_keyword

keyword Kyear C�artifact C�typetitleauthors$(list)keywords$(set)venue

Users

id Knameemail

Ratings_by_artifact

artifact Knum_ratings$(counter)sum_ratings$(counter)

Reviews_by_user

user Kreview$(timeuuid) C�ratingtitlebodyartifact_idartifact_titleartifact_authors$(list)user_name Suser_email S

Reviews_by_artifact

artifact Kreview$(timeuuid) C�rating IDXtitlebodyuser

Likes_by_artifact

artifact Knum_likes$(counter)

Likes_by_review

review Knum_likes$(counter)

Q1 Q2 Q3 Q4

Artifacts

artifact Ktypetitleauthors$(list)keywords$(set)venueyear

Q8Q6 Q7

Q11

Q10

Q9

Q12

Physical Data Model• Final blueprint • Ready to be CQL

Practical Modeling Methods

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

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

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!

LWT Fine Print•Light Weight Transactions solve edge conditions •They have latency cost.

•Be aware •Load test •Consider in your data model

•Now go shut down that ZooKeeper mess you have!

Use case: Form Versioning

Form Versioning Pt 1•From “Next top data model” •Great idea, but edge conditions

CREATE TABLE working_version (username varchar,form_id int,version_number int,locked_by varchar,form_attributes map<varchar,varchar> PRIMARY KEY ((username, form_id), version_number)

) WITH CLUSTERING ORDER BY (version_number DESC);

•Each user has a form •Each form needs versioning •Need an exclusive lock on the form

Form Versioning Pt 1

INSERT INTO working_version (username, form_id, version_number, locked_by, form_attributes)VALUES ('pmcfadin',1138,1,'',{'FirstName<text>':'First Name: ','LastName<text>':'Last Name: ','EmailAddress<text>':'Email Address: ','Newsletter<radio>':'Y,N'});

UPDATE working_version SET locked_by = 'pmcfadin'WHERE username = 'pmcfadin'AND form_id = 1138AND version_number = 1;

INSERT INTO working_version (username, form_id, version_number, locked_by, form_attributes)VALUES ('pmcfadin',1138,2,null,{'FirstName<text>':'First Name: ','LastName<text>':'Last Name: ','EmailAddress<text>':'Email Address: ','Newsletter<checkbox>':'Y'});

1. Insert first version

2. Lock for one user

3. Insert new version. Release lock

Danger Zone

Form Versioning Pt 2INSERT INTO working_version (username, form_id, version_number, locked_by, form_attributes)VALUES ('pmcfadin',1138,1,'pmcfadin',{'FirstName<text>':'First Name: ','LastName<text>':'Last Name: ','EmailAddress<text>':'Email Address: ','Newsletter<radio>':'Y,N'})IF NOT EXISTS;

UPDATE working_version SET form_attributes['EmailAddress<text>'] = 'Primary Email Address: 'WHERE username = 'pmcfadin'AND form_id = 1138AND version_number = 1IF locked_by = 'pmcfadin';

UPDATE working_version SET form_attributes['EmailAddress<text>'] = 'Email Adx: 'WHERE username = 'pmcfadin'AND form_id = 1138AND version_number = 1IF locked_by = 'dude';

1. Insert first version

Exclusive lock

Accepted

Rejected (sorry dude)

Form Versioning Pt 2•Old way: Edge cases with problems

•Use external locking? •Take your chances?

•New way: Managed expectations (LWT) •Exclusive by existence check •Continued with IF clause •Downside: More latency

Cassandra 2.1 Data Model Enhancements

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

Thank you!

Bring the questions

Follow me on twitter @PatrickMcFadin