Data Modeling Basics for the Cloud with DataStax

Data Modeling Basics for the Cloud
Robert Stupp
Solutions Architect @ DataStax – Committer to Apache Cassandra

Data Modeling for the Cloud
DSE is the database
for the cloud
1.Always On
2.Instantaneously
Responsive
3.Numerous Endpoints
4.Geographically Distributed
5.Predictively Scalable
© 2016 DataStax, All Rights Reserved. 2
CC BY 2.0, by Blake Patterson on Flickr
100000 transactions
per second
200000 transactions
per second

Application
Replication Factor 3
Eventual Consistency
© DataStax, All Rights Reserved. 3
… is not hopefully consistent
Some data
Some dataSome data
Consistency Level:
ONE

Application
UP
Replication Factor 3
Quorum Consistency
Some data
Some dataSome data
Consistency Level:
QUORUM
DOWN

DSE / Cassandra NodeApplication
Write Path
Memtable
Commit
Log
Files
SSTable
Some data
Some data
SSTable
SSTable SSTable
SSTable SSTable
Some data
Some data
Some data
Some data
Some data

Compaction
SSTable SSTable SSTable SSTable
SSTable

Compaction Strategies
• Size Tiered
• Leveled
• Date Tiered

Data Organization in DSE / Cassandra
Partition
Device ID Timestamp Temperature Humidity
01-32483-17383 2016-04-19 14:00 22 70
01-32483-17383 2016-04-19 15:00 21.5 65
01-32483-17383 2016-04-19 16:00 23.0 70
Partition
Key
Clustering
Key
Columns
Primary Key
Device ID Timestamp
01-32483-17383 2016-04-19 14:00
01-32483-17383 2016-04-19 15:00
01-32483-17383 2016-04-19 16:00
Device ID
01-32483-17383
01-32483-17383
01-32483-17383

Data Modeling 101
1. Understand your data
Conceptual data modeling
2. Collect queries
Understand your application
3. Model according to queries
Logical data modeling
4. Apply optimizations
Physical data modeling

Query driven modeling
1. Collect your use cases
2. Extract queries
3. Model your tables

Queries, yes
SELECT
timestamp, temperature, humidity
FROM
sensor_data
WHERE
sensor_id = ’01-32483-17383’
Always include the
Partition Key

Some standard use-cases
• Customer registration
• Customer login
• Delivery addresses

Customer registration
1. Check if customer exists
query by username
CREATE TABLE customers (
username text PRIMARY KEY,
password_hash text,
first_name text,
last_name text,
email text
);
SELECT username FROM customers WHERE username = ?

Customer login by username
1. Check if user exists and password matches
query by username
password_hash text,
first_name text,
last_name text,
email text
);
SELECT password_hash FROM customers WHERE username = ?

Customer login by email
query by email
password_hash text,
first_name text,
last_name text,
email text
);
SELECT password_hash FROM customers WHERE email = ?
InvalidRequest: code=2200 [Invalid query]
message="Cannot execute this query as it might
involve data filtering and
thus may have unpredictable performance.

Customer login by email
query by email
CREATE TABLE customers_by_email (
email text PRIMARY KEY,
password_hash text,
first_name text,
last_name text,
username text
);
SELECT password_hash FROM customers_by_email WHERE email = ?
This works

Modeling delivery addresses
CREATE TABLE customer_addresses (
username text,
address_type text,
street text,
zip text,
city text,
PRIMARY KEY ( username, address_type )
);
SELECT street,zip,city FROM customer_addresses WHERE username = ?;
SELECT street,zip,city FROM customer_addresses
WHERE username = ? AND address_type = ?;

1. Print delivery address label
query by user by user name
query delivery address by user and type
SELECT first_name, last_name FROM customers WHERE username = ?;
SELECT street,zip,city FROM customer_addresses
WHERE username = ? AND address_type = ?;
This works,
But it’s not great.

CREATE TYPE delivery_address (
street text,
zip text,
city text);
Just 1 read
password_hash text,
first_name text,
last_name text,
email text,
delivery_addrs map < text, frozen < delivery_address > >
);
SELECT first_name, last_name, delivery_addrs
FROM customers WHERE username = ?;

Customer registration – the problem
SELECT username FROM customers
WHERE username = ?
(no results)
WHERE username = ?
(no results)
INSERT INTO customers
(username, first_name, last_name)
VALUES
(‘snazy’, ‘Robert’, ‘Stupp’)
(success) INSERT INTO customers
(username, first_name, last_name)
VALUES
(‘snazy’, ‘Not’, ‘Robert’)
(success)
This one wins
This one gets
overwritten

Customer registration – the solution
WHERE username = ?
(no results)
WHERE username = ?
(no results)
INSERT INTO customers …
IF NOT EXISTS
 [applied] = true
IF NOT EXISTS
 [applied] = false
Sorry, dude
OK

Customer registration – the even better
solution
IF NOT EXISTS
 [applied] = true
IF NOT EXISTS
 [applied] = false
Sorry, dude
OK

Customer login by email – w/ DSE 5.0
query by email
password_hash text,
first_name text,
last_name text,
email text
);
CREATE MATERIALIZED VIEW customers_by_email AS
SELECT email, username, first_name, last_name, password_hash
FROM customers
WHERE email IS NOT NULL
PRIMARY KEY ( email, username );
SELECT password_hash FROM customers_by_email WHERE email = ?;

May the node
be with you!
Robert Stupp Solutions Architect @ DataStax
robert.stupp@datastax.com Committer to Apache Cassandra
@snazy

Data Modeling Basics for the Cloud with DataStax

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