The document provides an overview and examples of data modeling techniques for Cassandra. It discusses four use cases - shopping cart data, user activity tracking, log collection/aggregation, and user form versioning. For each use case, it describes the business needs, issues with a relational database approach, and provides the Cassandra data model solution with examples in CQL. The models showcase techniques like de-normalizing data, partitioning, clustering, counters, maps and setting TTL for expiration. The presentation aims to help attendees properly model their data for Cassandra use cases.

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Patrick McFadin | Solution Architect, DataStax
The World's Next Top Data Model

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The saga continues!
★ Data model is dead, long live the
data model.
★ Bridging from Relational to Cassandra
★ Become a Super
Modeler
★ Core data modeling techniques
using CQL

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Because I love talking about this
Just to recap...

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Why does this matter?
* Cassandra lives closer to your users or applications
* Not a hammer for all use case nails
* Proper use case, proper model...
* Get it wrong and...

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When to use Cassandra*
* Need to be in more than one datacenter. active-active
* Scaling from 0 to, uh, well... we’re not really sure.
* Need as close to 100% uptime as possible.
* Getting these from any other solution would just be mega $
and...
*nutshell version. These are all ORs not ANDs

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You get the data
model right!

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So let’s do that
* Four real world examples
* Use case, what they were avoiding and model to accomplish
* You may think this is you, but it isn’t. I hear these all the time.
* All examples are in CQL3

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But wait you say
CQL doesn’t do dynamic wide rows!

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Yes it can!
* CQL does wide rows the same way you did them in Thrift
* No really
* Read this blog post
http://www.datastax.com/dev/blog/does-cql-support-dynamic-columns-wide-rows
...or just trust me and I’ll show you how

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Customers giving you money is a good reason for uptime
Shopping Cart Data Model

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Shopping cart use case
* Store shopping cart data reliably
* Minimize (or eliminate) downtime. Multi-dc
* Scale for the “Cyber Monday” problem
* Every minute off-line is lost $$
* Online shoppers want speed!
The bad

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Shopping cart data
model
* Each customer can have
one or more shopping
carts
* De-normalize data for
fast access
* Shopping cart == One
partition (Row Level
Isolation)
* Each item a new column

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Shopping cart data
model
CREATE TABLE user (
! username varchar,
! firstname varchar,
! lastname varchar,
! shopping_carts set<varchar>,
! PRIMARY KEY (username)
);
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)
);
INSERT INTO shopping_cart
(username,cart_name,item_id,item_name,description,price,item_detail)
VALUES ('pmcfadin','Gadgets I want',8675309,'Garmin
910XT','Multisport training watch',349.99,
{'Related':'Timex sports watch',
'Volume Discount':'10'});
INSERT INTO shopping_cart
(username,cart_name,item_id,item_name,description,price,item_detail)
VALUES ('pmcfadin','Gadgets I want',9748575,'Polaris Foot
Pod','Bluetooth Smart foot pod',64.00
{'Related':'Timex foot pod',
'Volume Discount':'25'});
One partition (storage row) of data
Item details. Flexible for whatev
Partition row key for one users cart
Creates partition row key

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Watching users, making decisions. Freaky, but cool.
User Activity Tracking

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User activity use case
* React to user input in real time
* Support for multiple application pods
* Scale for speed
* Losing interactions is costly
* Waiting for batch(hadoop) is to long
The bad

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User activity data model
* Interaction points stored
per user in short table
* Long term interaction
stored in similar table with
date partition
* Process long term later
using batch
* Reverse time series to get
last N items

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User activity data model
CREATE TABLE user_activity (
! username varchar,
! interaction_time timeuuid,
! activity_code varchar,
! detail varchar,
! PRIMARY KEY (username, interaction_time)
) WITH CLUSTERING ORDER BY (interaction_time DESC);
CREATE TABLE user_activity_history (
! username varchar,
! interaction_date varchar,
! interaction_time timeuuid,
! activity_code varchar,
! detail varchar,
! PRIMARY KEY ((username,interaction_date),interaction_time)
);
INSERT INTO user_activity
(username,interaction_time,activity_code,detail)
VALUES ('pmcfadin',0D1454E0-D202-11E2-8B8B-0800200C9A66,'100','Normal
login')
USING TTL 2592000;
INSERT INTO user_activity_history
(username,interaction_date,interaction_time,activity_code,detail)
VALUES ('pmcfadin','20130605',0D1454E0-
D202-11E2-8B8B-0800200C9A66,'100','Normal login');
Reverse order based on timestamp
Expire after 30 days

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Data model usage
username | interaction_time | detail | activity_code
----------+--------------------------------------+------------------------------------------+------------------
pmcfadin | 9ccc9df0-d076-11e2-923e-5d8390e664ec | Entered shopping area: Jewelry | 301
pmcfadin | 9c652990-d076-11e2-923e-5d8390e664ec | Created shopping cart: Anniversary gifts | 202
pmcfadin | 1b5cef90-d076-11e2-923e-5d8390e664ec | Deleted shopping cart: Gadgets I want | 205
pmcfadin | 1b0e5a60-d076-11e2-923e-5d8390e664ec | Opened shopping cart: Gadgets I want | 201
pmcfadin | 1b0be960-d076-11e2-923e-5d8390e664ec | Normal login | 100
select * from user_activity limit 5;
Maybe put a sale item for flowers too?

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Machines generate logs at a furious pace. Be ready.
Log collection/aggregation

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Log collection use case
* Collect log data at high speed
* Cassandra near where logs are generated. Multi-datacenter
* Dice data for various uses. Dashboard. Lookup. Etc.
* The scale needed for RDBMS is cost
prohibitive
* Batch analysis of logs too late for some use
cases
The bad

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Log collection data
model
* Use Flume to collect and fan
out data to various tables
* Tables for lookup based on
source and time
* Tables for dashboard with
aggregation and summation

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Log collection data
model
CREATE TABLE log_lookup (
! source varchar,
! date_to_minute varchar,
! timestamp timeuuid,
! raw_log blob,
! PRIMARY KEY ((source,date_to_minute),timestamp)
);
CREATE TABLE login_success (
! source varchar,
! date_to_minute varchar,
! successful_logins counter,
! PRIMARY KEY (source,date_to_minute)
) WITH CLUSTERING ORDER BY (date_to_minute DESC);
CREATE TABLE login_failure (
! source varchar,
! date_to_minute varchar,
! failed_logins counter,
! PRIMARY KEY (source,date_to_minute)
) WITH CLUSTERING ORDER BY (date_to_minute DESC);
Consider storing raw logs as GZIP

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Log dashboard
0
25
50
75
100
10:01 10:03 10:05 10:07 10:09 10:11 10:13 10:15 10:17 10:19
Sucessful Logins
Failed Logins
SELECT date_to_minute,successful_logins
FROM login_success
LIMIT 20;
SELECT date_to_minute,failed_logins
FROM login_failure
LIMIT 20;

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Because mistaks mistakes happen
User Form Versioning

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Form versioning use
case
* Store every possible version efficiently
* Scale to any number of users
* Commit/Rollback functionality on a form
* In RDBMS, many relations that need complicated
join
* Needs to be in cloud and local data center
The bad

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Form version data model
* Each user has a form
* Each form needs versioning
* Separate table to store live
version
* Exclusive lock on a form

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Form version data model
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);
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 = 1138
AND 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

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That’s it!
“Mind what you have learned. Save you it can.”
- Yoda. Master Data Modeler

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Your data model is next!
* Try out a few things
* See what works
* All else fails, engage an expert in the community
* Want more? Follow me on twitter: @PatrickMcFadin