This document provides an introduction to data modeling with Apache Cassandra. It discusses how Cassandra data models are designed based on the queries an application will perform, unlike relational databases which are designed based on normalization rules. Key aspects covered include avoiding joins by denormalizing data, using a partition key to group related data on nodes, and controlling the clustering order of columns. The document provides examples of modeling time series and tag data in Cassandra.

1. ©2013 DataStax Confidential. Do not distribute without consent.
@PatrickMcFadin
Patrick McFadin
Chief Evangelist for Apache Cassandra
Introduction to Data Modeling
with Apache Cassandra
1

2. Relational Data Models
• 5 normal forms
• Foreign Keys
• Joins
deptId First Last
1 Edgar Codd
2 Raymond Boyce
id Dept
1 Engineering
2 Math
Employees
Department

4. Relational Modeling
CREATE TABLE users (
id number(12) NOT NULL ,
firstname nvarchar2(25) NOT NULL ,
lastname nvarchar2(25) NOT NULL,
email nvarchar2(50) NOT NULL,
password nvarchar2(255) NOT NULL,
created_date timestamp(6),
PRIMARY KEY (id),
CONSTRAINT email_uq UNIQUE (email)
);
-- Users by email address index
CREATE INDEX idx_users_email ON users (email);
• Create entity table
• Add constraints
• Index fields
• Foreign Key relationships
CREATE TABLE videos (
id number(12),
userid number(12) NOT NULL,
name nvarchar2(255),
description nvarchar2(500),
location nvarchar2(255),
location_type int,
added_date timestamp,
CONSTRAINT users_userid_fk
FOREIGN KEY (userid)
REFERENCES users (Id) ON DELETE CASCADE,
PRIMARY KEY (id)
);

5. Relational Modeling
Data
Models
Application

6. Cassandra Modeling
Data
Models
Application

7. • What are your application’s workflows?
• How will I access the data?
• Knowing your queries in advance is NOT optional
• Different from RDBMS because I can’t just JOIN or create a new
indexes to support new queries
7
Modeling Queries

8. Some Application Workflows in KillrVideo
8
User Logs
into site
Show basic
information
about user
Show videos
added by a
user
Show
comments
posted by a
user
Search for a
video by tag
Show latest
videos
added to the
site
Show
comments
for a video
Show
ratings for a
video
Show video
and its
details

9. Some Queries in KillrVideo to Support Workflows
9
Users
User Logs
into site
Find user by email
address
Show basic
information
about user
Find user by id
Comments
Show
comments
for a video
Find comments by
video (latest first)
Show
comments
posted by a
user
Find comments by
user (latest first)
Ratings
Show
ratings for a
video
Find ratings by
video

10. CQL vs SQL
• No joins
• Limited aggregations
deptId First Last
1 Edgar Codd
2 Raymond Boyce
id Dept
1 Engineering
2 Math
Employees
Department
SELECT e.First, e.Last, d.Dept
FROM Department d, Employees e
WHERE ‘Codd’ = e.Last
AND e.deptId = d.id

11. Denormalization
• Combine table columns into a single view
• Eliminate the need for joins
SELECT First, Last, Dept
FROM employees
WHERE id = ‘1’
id First Last Dept
1 Edgar Codd Engineering
2 Raymond Boyce Math
Employees

12. “Static” Table
CREATE 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)
);
Table Name
Column Name
Column CQL Type
Primary Key Designation Partition Key

13. Insert
INSERT INTO videos (videoid, name, userid, description, location, location_type, preview_thumbnails, tags, added_date, metadata)
VALUES (06049cbb-dfed-421f-b889-5f649a0de1ed,'The data model is dead. Long live the data model.',
9761d3d7-7fbd-4269-9988-6cfd4e188678,
'First in a three part series for Cassandra Data Modeling','http://www.youtube.com/watch?v=px6U2n74q3g',1,
{'YouTube':'http://www.youtube.com/watch?v=px6U2n74q3g'},{'cassandra','data model','relational','instruction'},
'2013-05-02 12:30:29');
Table Name
Fields
Values
Partition Key: Required

14. Partition keys
06049cbb-dfed-421f-b889-5f649a0de1ed Murmur3 Hash Token = 7224631062609997448
873ff430-9c23-4e60-be5f-278ea2bb21bd Murmur3 Hash Token = -6804302034103043898
Consistent hash. 128 bit number
between 2-63
and 264
INSERT INTO videos (videoid, name, userid, description)
VALUES (06049cbb-dfed-421f-b889-5f649a0de1ed,'The data model is dead. Long live the data model.’,
9761d3d7-7fbd-4269-9988-6cfd4e188678, 'First in a three part series for Cassandra Data Modeling');
INSERT INTO videos (videoid, name, userid, description)
VALUES (873ff430-9c23-4e60-be5f-278ea2bb21bd,'Become a Super Modeler’,
9761d3d7-7fbd-4269-9988-6cfd4e188678, 'Second in a three part series for Cassandra Data Modeling');

15. Select
name | description | added_date
---------------------------------------------------+----------------------------------------------------------+--------------------------
The data model is dead. Long live the data model. | First in a three part series for Cassandra Data Modeling | 2013-05-02 12:30:29-0700
SELECT name, description, added_date
FROM videos
WHERE videoid = 06049cbb-dfed-421f-b889-5f649a0de1ed;
Fields
Table Name
Primary Key: Partition Key Required

16. Locality
1000 Node Cluster
videoid = 06049cbb-dfed-421f-b889-5f649a0de1ed
SELECT name, description, added_date
FROM videos
WHERE videoid = 06049cbb-dfed-421f-b889-5f649a0de1ed;

17. No more sequences
• Great for auto-creation of Ids
• Guaranteed unique
• Needs ACID to work. (Sorry. No sharding)
INSERT INTO user (id, firstName, LastName)
VALUES (users_sequence.nextVal(), ‘Ted’, ‘Codd’)
CREATE SEQUENCE users_sequence
INCREMENT BY 1
START WITH 1
NOMAXVALUE
NOCYCLE
CACHE 10;

18. No sequences???
• Almost impossible in a distributed system
• Couple of great choices
• Natural Key - Unique values like email
• Surrogate Key - UUID
• Universal Unique ID
• 128 bit number represented in character form
• Easily generated on the client
• Same as GUID for the MS folks
99051fe9-6a9c-46c2-b949-38ef78858dd0

19. “Dynamic” Table
CREATE TABLE videos_by_tag (
tag text,
videoid uuid,
added_date timestamp,
name text,
preview_image_location text,
tagged_date timestamp,
PRIMARY KEY (tag, videoid)
);
Partition Key Clustering Column

20. Primary key relationship
PRIMARY KEY (tag,videoid)

21. Primary key relationship
Partition Key
PRIMARY KEY (tag,videoid)

22. Primary key relationship
Partition Key Clustering Column
PRIMARY KEY (tag,videoid)

23. Primary key relationship
Partition Key
data model
PRIMARY KEY (tag,videoid)
Clustering Column

24. -5.6
06049cbb-dfed-421f-b889-5f649a0de1ed
Primary key relationship
Partition Key
2013-05-16 16:50:002013-05-02 12:30:29
873ff430-9c23-4e60-be5f-278ea2bb21bd
PRIMARY KEY (tag,videoid)
Clustering Column
data model
49f64d40-7d89-4890-b910-dbf923563a33
2013-06-11 11:00:00

25. Row
Column
1
Partition
Key 1
Column
2
Column
3
Column
4

26. Partition with Clustering
Cluster
1
Partition
Key 1
Column
1
Column
2
Column
3
Cluster
2
Partition
Key 1
Column
1
Column
2
Column
3
Cluster
3
Partition
Key 1
Column
1
Column
2
Column
3
Cluster
4
Partition
Key 1
Column
1
Column
2
Column
3
Order By

27. Table Partition
Key 1
Partition
Key 1
Partition
Key 1
Partition
Key 1
Partition
Key 2
Partition
Key 2
Partition
Key 2
Partition
Key 2
Cluster
1
Column
1
Column
2
Column
3
Cluster
2
Column
1
Column
2
Column
3
Cluster
3
Column
1
Column
2
Column
3
Cluster
4
Column
1
Column
2
Column
3
Cluster
1
Column
1
Column
2
Column
3
Cluster
2
Column
1
Column
2
Column
3
Cluster
3
Column
1
Column
2
Column
3
Cluster
4
Column
1
Column
2
Column
3

28. Keyspace
Cluster
1
Partition
Key 1
Column
2
Column
3
Column
4
Partition
Key 2
Column
2
Column
3
Column
4
Cluster
2
Partition
Key 1
Column
2
Column
3
Column
4
Cluster
3
Partition
Key 1
Column
2
Column
3
Column
4
Cluster
4
Partition
Key 1
Column
2
Column
3
Column
4
Partition
Key 2
Column
2
Column
3
Column
4
Partition
Key 2
Column
2
Column
3
Column
4
Partition
Key 2
Column
2
Column
3
Column
4
Partition
Key 1
Column
2
Column
3
Column
4
Partition
Key 2
Column
2
Column
3
Column
4
Partition
Key 1
Column
2
Column
3
Column
4
Partition
Key 1
Column
2
Column
3
Column
4
Partition
Key 1
Column
2
Column
3
Column
4
Partition
Key 2
Column
2
Column
3
Column
4
Partition
Key 2
Column
2
Column
3
Column
4
Partition
Key 2
Column
2
Column
3
Column
4
Table 1 Table 2
Keyspace 1
Cluster
1
Cluster
2
Cluster
3
Cluster
4
Cluster
1
Cluster
2
Cluster
3
Cluster
4
Cluster
1
Cluster
2
Cluster
3
Cluster
4

29. Controlling Order
CREATE TABLE raw_weather_data (
wsid text,
year int,
month int,
day int,
hour int,
temperature double,
PRIMARY KEY ((wsid), year, month, day, hour)
) WITH CLUSTERING ORDER BY (year DESC, month DESC, day DESC, hour DESC);
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,10,-5.6);
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,9,-5.1);
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,8,-4.9);
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,7,-5.3);

30. Clustering Order
200510010:99999 12 1 10
200510010:99999 12 1 9
raw_weather_data
-5.6
-5.1
200510010:99999 12 1 8
200510010:99999 12 1 7
-4.9
-5.3
Order By
DESC

31. Write Path
Client
INSERT INTO raw_weather_data(wsid,year,month,day,hour,temperature)
VALUES (‘10010:99999’,2005,12,1,7,-5.3);
year 1wsid 1 month 1 day 1 hour 1
year 2wsid 2 month 2 day 2 hour 2
Memtable
SSTable
SSTable
SSTable
SSTable
Node
Commit Log Data
* Compaction *
Temp
Temp

32. Storage Model - Logical View
2005:12:1:10
-5.6
2005:12:1:9
-5.1
2005:12:1:8
-4.9
10010:99999
10010:99999
10010:99999
wsid hour temperature
2005:12:1:7
-5.3
10010:99999
SELECT wsid, hour, temperature
FROM raw_weather_data
WHERE wsid=‘10010:99999’
AND year = 2005 AND month = 12 AND day = 1;

33. 2005:12:1:10
-5.6 -5.3-4.9-5.1
Storage Model - Disk Layout
2005:12:1:9 2005:12:1:8
10010:99999
2005:12:1:7
Merged, Sorted and Stored Sequentially
SELECT wsid, hour, temperature
FROM raw_weather_data
WHERE wsid=‘10010:99999’
AND year = 2005 AND month = 12 AND day = 1;

34. 2005:12:1:10
-5.6
2005:12:1:11
-4.9 -5.3-4.9-5.1
Storage Model - Disk Layout
2005:12:1:9 2005:12:1:8
10010:99999
2005:12:1:7
Merged, Sorted and Stored Sequentially
SELECT wsid, hour, temperature
FROM raw_weather_data
WHERE wsid=‘10010:99999’
AND year = 2005 AND month = 12 AND day = 1;

35. 2005:12:1:10
-5.6
2005:12:1:11
-4.9 -5.3-4.9-5.1
Storage Model - Disk Layout
2005:12:1:9 2005:12:1:8
10010:99999
2005:12:1:7
Merged, Sorted and Stored Sequentially
SELECT wsid, hour, temperature
FROM raw_weather_data
WHERE wsid=‘10010:99999’
AND year = 2005 AND month = 12 AND day = 1;
2005:12:1:12
-5.4

36. Read Path
Client
SSTable
SSTable
SSTable
Node
Data
SELECT wsid,hour,temperature
FROM raw_weather_data
WHERE wsid='10010:99999'
AND year = 2005 AND month = 12 AND day = 1
AND hour >= 7 AND hour <= 10;
year 1wsid 1 month 1 day 1 hour 1
year 2wsid 2 month 2 day 2 hour 2
Memtable
Temp
Temp

37. Query patterns
• Range queries
• “Slice” operation on disk
Single seek on disk
10010:99999
Partition key for locality
SELECT wsid,hour,temperature
FROM raw_weather_data
WHERE wsid='10010:99999'
AND year = 2005 AND month = 12 AND day = 1
AND hour >= 7 AND hour <= 10;
2005:12:1:10
-5.6 -5.3-4.9-5.1
2005:12:1:9 2005:12:1:8 2005:12:1:7

38. Query patterns
• Range queries
• “Slice” operation on disk
Programmers like this
Sorted by event_time
2005:12:1:10
-5.6
2005:12:1:9
-5.1
2005:12:1:8
-4.9
10010:99999
10010:99999
10010:99999
weather_station hour temperature
2005:12:1:7
-5.3
10010:99999
SELECT weatherstation,hour,temperature
FROM temperature
WHERE weatherstation_id=‘10010:99999'
AND year = 2005 AND month = 12 AND day = 1
AND hour >= 7 AND hour <= 10;

39. CQL Collections

40. CQL Collections
• Meant to be dynamic part of table
• Update syntax is very different from insert
• Reads require all of collection to be read

41. CQL Set
• Set is sorted by CQL type comparator
INSERT INTO collections_example (id, set_example)
VALUES(1, {'1-one', '2-two'});
set_example set<text>
Collection name Collection type CQLType

42. CQL Set Operations
• Adding an element to the set
• After adding this element, it will sort to the beginning.
• Removing an element from the set
UPDATE collections_example
SET set_example = set_example + {'3-three'} WHERE id = 1;
UPDATE collections_example
SET set_example = set_example + {'0-zero'} WHERE id = 1;
UPDATE collections_example
SET set_example = set_example - {'3-three'} WHERE id = 1;

43. CQL List
• Ordered by insertion
• Use with caution
list_example list<text>
Collection name Collection type
INSERT INTO collections_example (id, list_example)
VALUES(1, ['1-one', '2-two']);
CQLType

44. CQL List Operations
• Adding an element to the end of a list
• Adding an element to the beginning of a list
• Deleting an element from a list
UPDATE collections_example
SET list_example = list_example + ['3-three']
WHERE id = 1;
UPDATE collections_example
SET list_example = ['0-zero'] + list_example
WHERE id = 1;
UPDATE collections_example
SET list_example = list_example - ['3-three'] WHERE id = 1;

45. CQL Map
• Key and value
• Key is sorted by CQL type comparator
INSERT INTO collections_example (id, map_example)
VALUES(1, { 1 : 'one', 2 : 'two' });
map_example map<int,text>
Collection name Collection type Value CQLTypeKey CQLType

46. CQL Map Operations
• Add an element to the map
• Update an existing element in the map
• Delete an element in the map
UPDATE collections_example
SET map_example[3] = 'three'
WHERE id = 1;
UPDATE collections_example
SET map_example[3] = 'tres'
WHERE id = 1;
DELETE map_example[3]
FROM collections_example
WHERE id = 1;

47. Entity with collections
• Same type of entity
• SET type for dynamic data
• tags for each video
// Videos by id
CREATE TABLE videos (
videoid uuid,
userid uuid,
name text,
description text,
location text,
location_type int,
preview_image_location text,
tags set<text>,
added_date timestamp,
PRIMARY KEY (videoid)
);

48. Index (or lookup) tables
• Table arranged to find data
• Denormalized for speed

49. Users – The Cassandra Way
User Logs
into site
Find user by email
address
Show basic
information
about user
Find user by id
CREATE TABLE user_credentials (
email text,
password text,
userid uuid,
PRIMARY KEY (email)
);
CREATE TABLE users (
userid uuid,
firstname text,
lastname text,
email text,
created_date timestamp,
PRIMARY KEY (userid)
);

50. 50
Show video
and its
details
Find video by id
Show videos
added by a
user
Find videos by user
(latest first)
CREATE TABLE videos (
videoid uuid,
userid uuid,
name text,
description text,
location text,
location_type int,
preview_image_location text,
tags set<text>,
added_date timestamp,
PRIMARY KEY (videoid)
);
CREATE TABLE user_videos (
userid uuid,
added_date timestamp,
videoid uuid,
name text,
preview_image_location text,
PRIMARY KEY (userid, added_date, videoid)
) WITH CLUSTERING
ORDER BY (added_date DESC, videoid ASC);
Views or indexes?
Denormalized data

51. Multiple Lookups
• Same data
• Different lookup pattern // Index for tag keywords
CREATE TABLE videos_by_tag (
tag text,
videoid uuid,
added_date timestamp,
name text,
preview_image_location text,
tagged_date timestamp,
PRIMARY KEY (tag, videoid)
);
// Index for tags by first letter in the tag
CREATE TABLE tags_by_letter (
first_letter text,
tag text,
PRIMARY KEY (first_letter, tag)
);

52. Many to Many Relationships
• Two views
• Different directions
• Insert data in a batch
// Comments for a given video
CREATE TABLE comments_by_video (
videoid uuid,
commentid timeuuid,
userid uuid,
comment text,
PRIMARY KEY (videoid, commentid)
) WITH CLUSTERING ORDER BY (commentid DESC);
// Comments for a given user
CREATE TABLE comments_by_user (
userid uuid,
commentid timeuuid,
videoid uuid,
comment text,
PRIMARY KEY (userid, commentid)
) WITH CLUSTERING ORDER BY (commentid DESC);

53. Deleting Data

54. Delete
DELETE FROM videos
WHERE id = 06049cbb-dfed-421f-b889-5f649a0de1ed;
Table Name
Primary Key: Required

55. Tombstones
-5.6
06049cbb-dfed-421f-b889-5f649a0de1ed
2013-05-16 16:50:002013-05-02 12:30:29
873ff430-9c23-4e60-be5f-278ea2bb21bd
data model
49f64d40-7d89-4890-b910-dbf923563a33
2013-06-11 11:00:00
DELETE FROM videos_by_tag
WHERE videioId = 06049cbb-dfed-421f-b889-5f649a0de1ed;
Deleted
2016-02-02 08:15:41

56. Expiring Data
Time To Live = TTL
INSERT INTO videos (videoid, name, userid, description, location, location_type, preview_thumbnails, tags, added_date, metadata)
VALUES (06049cbb-dfed-421f-b889-5f649a0de1ed,'The data model is dead. Long live the data model.',
9761d3d7-7fbd-4269-9988-6cfd4e188678,
'First in a three part series for Cassandra Data Modeling','http://www.youtube.com/watch?v=px6U2n74q3g',1,
{'YouTube':'http://www.youtube.com/watch?v=px6U2n74q3g'},{'cassandra','data model','relational','instruction'},
'2013-05-02 12:30:29’)
USING TTL = 2592000
Expire Data: 30 Days

57. Thank you!
Bring the questions
Follow me on twitter
@PatrickMcFadin