cassandra summit 2014: real data models of silicon valley

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Real Data Models of Silicon Valley Patrick McFadin Chief Evangelist for Apache Cassandra @PatrickMcFadin

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A lot has changed since I gave one of these talks and man, has it been good. 2.0 brought us a lot of new CQL features and now with 2.1 we get even more! Let me show you some real life data models and those new features taking developer productivity to an all new high. User Defined Types, New Counters, Paging, Static Columns. Exciting new ways of making your app truly killer!

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Page 1: Cassandra Summit 2014: Real Data Models of Silicon Valley

Real Data Models of Silicon Valley

Patrick McFadin Chief Evangelist for Apache Cassandra

!

@PatrickMcFadin

Page 2: Cassandra Summit 2014: Real Data Models of Silicon Valley

It's been an epic year

Page 3: Cassandra Summit 2014: Real Data Models of Silicon Valley

I've had a ton of fun!

• Traveling the world talking to people like you!

Warsaw

Stockholm

Melbourne

New YorkVancouver

Dublin

Page 4: Cassandra Summit 2014: Real Data Models of Silicon Valley

What's new?• 2.1 is out!

• Amazing changes for performance and stability

Page 5: Cassandra Summit 2014: Real Data Models of Silicon Valley

Where are we going?

• 3.0 is next. Just hold on…

Page 6: Cassandra Summit 2014: Real Data Models of Silicon Valley

KillrVideo.com• 2012 Summit

• Complete example for data modeling

www.killrvideos.com

Video TitleRecommended

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by Google

Comments

Description

Upload New!

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Rating: Tags: Foo Bar

*Cat drawing by goodrob13 on Flickr

Page 7: Cassandra Summit 2014: Real Data Models of Silicon Valley

It’s alive!!!• Hosted on Azure

• Code on Github

Page 8: Cassandra Summit 2014: Real Data Models of Silicon Valley

Data Model - Revisited• Add in some 2.1 data models

• Replace (or remove) some app code

• Become a part of Cassandra OSS download

Page 9: Cassandra Summit 2014: Real Data Models of Silicon Valley

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> );

Page 10: Cassandra Summit 2014: Real Data Models of Silicon Valley

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-application join

Page 11: Cassandra Summit 2014: Real Data Models of Silicon Valley

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) );

Page 12: Cassandra Summit 2014: Real Data Models of Silicon Valley

Wait! Frozen??

• Staying out of technical debt

• 3.0 UDTs will not have to be frozen

• Applicable to User Defined Types and Tuples (wait for

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

Page 13: Cassandra Summit 2014: Real Data Models of Silicon Valley

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" } } }

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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 );

Page 15: Cassandra Summit 2014: Real Data Models of Silicon Valley

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 );

Page 16: Cassandra Summit 2014: Real Data Models of Silicon Valley

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) );

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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>>

Page 18: Cassandra Summit 2014: Real Data Models of Silicon Valley

Retrieving fields

Page 19: Cassandra Summit 2014: Real Data Models of Silicon Valley

Counters pt Deux

• Since .8

• Commit log replay would change counters

• Repair could change counters

• Performance was inconsistent. Lots of GC

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The good• Stable under load

• No commit log replay issues

• No repair weirdness

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The bad

• Still can’t delete/reset counters

• Still needs to do a read before write.

Page 22: Cassandra Summit 2014: Real Data Models of Silicon Valley

UsageWait for it…

It’s the same! Carry on…

Page 23: Cassandra Summit 2014: Real Data Models of Silicon Valley

Static Fields• New as of 2.0.6

• VERY specific, but useful

• Thrift people will like this

CREATE TABLE t ( k text, s text STATIC, i int, PRIMARY KEY (k, i) );

Page 24: Cassandra Summit 2014: Real Data Models of Silicon Valley

Why?CREATE TABLE weather ( id int, time timestamp, weatherstation_name text, temperature float, PRIMARY KEY (id, time) );

ID = 1Partition Key

(Storage Row Key)

2014-09-08 12:00:00 : name

SFO

2014-09-08 12:00:00 : temp

63.4

2014-09-08 12:01:00 : name

SFO

2014-09-08 12:00:00 : temp

63.9

2014-09-08 12:02:00 : name

SFO

2014-09-08 12:00:00 : temp

64.0

Partition Row 1 Partition Row 2 Partition Row 3

ID = 1Partition Key

(Storage Row Key)

name

SFO

2014-09-08 12:00:00 : temp

63.4

2014-09-08 12:00:00 : temp

63.9

2014-09-08 12:00:00 : temp

64.0

Partition Row 1 Partition Row 1 Partition Row 1

CREATE TABLE weather ( id int, time timestamp, weatherstation_name text static, temperature float, PRIMARY KEY (id, time) );

Page 25: Cassandra Summit 2014: Real Data Models of Silicon Valley

Usage• Put a static at the end

of the declaration

• Can’t be a part of:

CREATE TABLE video_event ( videoid uuid, userid uuid, preview_image_location text static, event varchar, event_timestamp timeuuid, video_timestamp bigint, PRIMARY KEY ((videoid,userid),event_timestamp,event) ) WITH CLUSTERING ORDER BY (event_timestamp DESC,event ASC);

Page 26: Cassandra Summit 2014: Real Data Models of Silicon Valley

Tuples

• A type that represents a group

• Up to 256 different elements

CREATE TABLE tuple_table ( id int PRIMARY KEY, three_tuple frozen <tuple<int, text, float>>, four_tuple frozen <tuple<int, text, float, inet>>, five_tuple frozen <tuple<int, text, float, inet, ascii>> );

Page 27: Cassandra Summit 2014: Real Data Models of Silicon Valley

Example Usage• Track a drone’s position

• x, y, z in a 3D Cartesian

CREATE TABLE drone_position ( droneId int, time timestamp, position frozen <tuple<float, float, float>>, PRIMARY KEY (droneId, time) );

Page 28: Cassandra Summit 2014: Real Data Models of Silicon Valley

What about partition size?

• A CQL partition is a logical projection of a storage row

• Storage rows can have up to 2 billion cells

• Each cell can hold up to 2G of data

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How much is too much?

• How many cells before performance degrades?

• How many bytes per partition before it’s unmanageable

• What is “practical”

Page 30: Cassandra Summit 2014: Real Data Models of Silicon Valley

Old answer• 2011: Pre-Cassandra 1.2 (actually tested on .8)

• Aaron Morton, Cassandra MVP and Founder of The Last Pickle

Page 31: Cassandra Summit 2014: Real Data Models of Silicon Valley

Conclusion• Keep partition (storage row) length < 10k cells

• Total size in bytes below 64M (Multi-pass compaction)

• Multiple hits to 64k page size will start to hurt

TL;DR - It’s a performance tunable

Page 32: Cassandra Summit 2014: Real Data Models of Silicon Valley

The tests revisited

• Attempted to reproduce the same tests using CQL

• Cassandra 2.1, 2.0 and 1.2

• Tested partitions sizes 1. 100 2. 2114 3. 5,000 4. 10,000 5. 100,000 6. 1,000,000 7. 10,000,000 8. 100,000,000 9. 1,000,000,000

Page 33: Cassandra Summit 2014: Real Data Models of Silicon Valley

Results

mSec

Cells per partition

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The new answer

• 100’s of thousands is not problem

• 100’s of megs per partition is best operationally

• The issue to manage is operations

Page 35: Cassandra Summit 2014: Real Data Models of Silicon Valley

Thank You!

Follow me on twitter for more @PatrickMcFadin

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CASSANDRASUMMIT2014September 10 - 11 | #CassandraSummit