cassandra community webinar | the world's next top data model

Patrick McFadin | Solution Architect, DataStax

The World's Next Top Data Model

Thursday, August 1, 13

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


Because I love talking about this

Just to recap...


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...


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


You get the data model right!


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


But wait you say

CQL doesn’t do dynamic wide rows!


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




Customers giving you money is a good reason for uptime

Shopping Cart Data Model


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


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


Shopping cart data modelCREATE 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


Watching users, making decisions. Freaky, but cool.

User Activity Tracking


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


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


#CASSANDRA13

User activity data modelCREATE 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


Machines generate logs at a furious pace. Be ready.

Log collection/aggregation


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


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


#CASSANDRA13

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


#CASSANDRA13

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 LoginsFailed Logins

SELECT date_to_minute,successful_loginsFROM login_successLIMIT 20;

SELECT date_to_minute,failed_loginsFROM login_failureLIMIT 20;


Because mistaks mistakes happen

User Form Versioning


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


Form version data model* Each user has a form

* Each form needs versioning

* Separate table to store live version

* Exclusive lock on a form


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 = 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


#CASSANDRA13

That’s it!

“Mind what you have learned. Save you it can.”

- Yoda. Master Data Modeler


#CASSANDRA13

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


cassandra community webinar | the world's next top data model

Technology

cart username

activity username

username varchar

store shopping cart

shopping cart use case

table shopping

users cart

user activity use case