The document discusses modeling data in Cassandra using the Chebotko method. It begins by explaining the conceptual, logical, and physical modeling stages of the Chebotko method. It then provides an example of modeling user data in a music database, showing the conceptual model, identifying access patterns, and designing the logical model with tables to satisfy each query. The logical model example shows how to design Cassandra tables for queries about performers, albums, tracks, users and their activities.

1. CassandraData
Advanced
Modeling

2. Let Us
Assume:
• You%know%your%way%around%a%cluster
(at%least%theore4cally)%
• You%have%seen%some%CQL

3. Let Us
Explore:
• Some%use%cases%
• The%Chebotko%Method%
• Some%Cassandra%2.1%features

4. Use Cases

5. Top Gamer Scores

6. Top
Scores

7. Top
Scores

8. Top
Scores

9. Top
Scores

10. Top
Scores Daily Top 10 Users
handle | score
-----------------+-------
subsonic | 66.2
neo | 55.2
bennybaru | 49.2
tigger | 46.2
velvetfog | 45.2
flashberg | 43.6
jbellis | 43.4
cafruitbat | 43.2
groovemerchant | 41.2
rustyrazorblade | 39.2

11. Top
Scores
CREATE TABLE userScores
(userId uuid,
handle text static,
gameId uuid,
score_timestamp timestamp,
score double,
PRIMARY KEY ((userId, gameId), score_timestamp))
WITH CLUSTERING ORDER BY (score_timestamp DESC);

12. Top
Scores
CREATE TABLE TopTen (
gameId uuid,
process_timestamp timestamp,
score double,
userId uuid,
handle text,
PRIMARY KEY (gameId, process_timestamp, score))
WITH CLUSTERING ORDER BY (process_timestamp DESC, score DESC)
AND default_time_to_live = '259200'
AND COMPACTION = {'class': 'DateTieredCompactionStrategy', 'enabled': 'TRUE'};

13. Top
Scores
SELECT gameId, process_timestamp, score, handle, userId
FROM TopTen
WHERE gameid = 99051fe9-6a9c-46c2-b949-38ef78858dd0
AND process_timestamp <= '2014-12-31 14:00:00'
LIMIT 1;

14. Top
Scores
gameid | process_timestamp | score | handle | userid
--------------------------------------+ --------------------------+-------+-----------------+--------------------------------------
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 66.2 | trinity |
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 55.2 | neo |
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 49.2 | bennbaru |
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 46.2 | tigger |
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 45.2 | velvetfog |
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 43.6 | flashberg |
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 43.4 | jbellis |
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 43.2 | catfruitbat |
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 41.2 | groovemerchant | 99051fe9-6a9c-46c2-b949-38ef78858d03
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 39.2 | rustyrazorblade | 99051fe9-6a9c-46c2-b949-38ef78858d01
99051fe9-6a9c-46c2-b949-38ef78858dd0 | 2014-12-31 13:42:40-0800 | 20.2 | driftx | 99051fe9-6a9c-46c2-b949-38ef78858d08

15. Yay!

16. Spark?

17. File Storage

18. File
Storage

19. File
Storage

20. Stories(In(Brief
• User%creates%an%account%%
• User%uploads%image%
• Image%is%distributed%worldwide%%
• User%can%check%access%paHerns%
File
Storage

21. • Recall%a%single%image%
• Recall%all%images%in%a%given%4me%range%
• Recall%specific%images%over%a%given%4me%range%
• Recall%the%4mes%each%image%was%accessed
File
Storage Access(Pa1erns

22. CREATE TABLE user (
username text,
firstname text,
lastname text,
emails list<text>,
PRIMARY KEY (username));
File
Storage
User%Crea(on

23. INSERT INTO user
(username, firstname, lastname, emails)
VALUES
('tlberglund',
'Tim'
'Berglund'
['tim.berglund@datastax.com', 'tlberglund@gmail.com'])
IF NOT EXISTS;
File
Storage
User%Crea(on

24. CREATE TABLE image (
image_id,
username,
created_at,
image_name,
image_description text,
tags list<text>,
images map<text, uuid>,
PRIMARY KEY (image_id));
File
Storage
Image%Model

25. CREATE TABLE images_timeseries (
username text,
bucket int,
sequence timestamp,
image_id uuid,
image_name text,
image_description text,
images map<text, uuid>,
PRIMARY KEY ((username, bucket), sequence)
) WITH CLUSTERING ORDER BY (sequence DESC);
File
Storage
Image%Accesses

26. CREATE TABLE bucket_index (
username text,
bucket int,
PRIMARY KEY(username, bucket))
WITH CLUSTERING ORDER BY (bucket DESC);
File
Storage
Image%Buckets

27. CREATE TABLE blob (
object_id uuid, // unique identifier
chunk_count int, // total number of chunks
size int, // total size (bytes)
chunk_size int, // max chunk size
checksum text,
attributes text, // json-encoded metadata
PRIMARY KEY (object_id));
File
Storage
Chunked%Blobs

28. CREATE TABLE blob_chunk (
object_id uuid,
chunk_id int,
chunk_size int,
data blob,
PRIMARY KEY ((object_id, chunk_id)));
File
Storage
Chunked%Blobs

29. CREATE TABLE access_log (
object_id uuid,
access_date text,
access_time timestamp,
ip_address inet,
PRIMARY KEY ((object_id, access_date),
access_time, ip_address));
File
Storage
Access%Log

30. User Registration

31. User
Registration LWT

32. SELECT *
FROM users
WHERE userName = 'tlberglund'
Coordinator%1 Coordinator%2

33. SELECT *
FROM users
WHERE userName = 'tlberglund' SELECT *
FROM users
WHERE userName = 'pmcfadin'
Coordinator%1 Coordinator%2

34. SELECT *
FROM users
WHERE userName = 'tlberglund' SELECT *
FROM users
WHERE userName = 'pmcfadin'
INSERT INTO users (username, ...)
VALUES ('tlberglund',...);
Coordinator%1 Coordinator%2

35. SELECT *
FROM users
WHERE userName = 'tlberglund' SELECT *
FROM users
WHERE userName = 'pmcfadin'
INSERT INTO users (username, ...)
VALUES ('tlberglund',...);
INSERT INTO users (username, ...)
VALUES ('pmcfadin',...);
Coordinator%1 Coordinator%2

36. INSERT INTO users (username, ...)
VALUES ('tlberglund',...); INSERT INTO users (username, ...)
VALUES ('pmcfadin',...);
LWT
Which user wins?
User
Registration

37. LWT
• Lightweight)transac/ons)
• Uses)the)Paxos)algorithm)
• Hard)to)understand)
• So$easy$to$use
User
Registration

38. Coordinator%1 Coordinator%2

39. INSERT INTO users (username, ...)
VALUES (‘tlberglund',...)
IF NOT EXISTS;
Coordinator%1 Coordinator%2

40. INSERT INTO users (username, ...)
VALUES (‘tlberglund',...)
IF NOT EXISTS;
INSERT INTO users (username, ...)
VALUES (‘pmcfadin',...)
IF NOT EXISTS;
Coordinator%1 Coordinator%2

41. Paxos%Write:%Happy%Path
NODE
NODE
NODE
NODE

42. Paxos%Write:%Happy%Path
NODE
NODE
NODE
NODE
FLAT%WHITE
1. Prepare

43. Paxos%Write:%Happy%Path
PROPOSER
FLAT%WHITE
Proposer generates
a sequence number
1. Prepare

44. Paxos%Write:%Happy%Path
PROPOSER
1:FLAT%WHITE
1:FLAT%WHITE
1:FLAT%WHITE
1:FLAT%WHITE
1. Prepare

45. Paxos%Write:%Happy%Path
ACCEPTOR
ACCEPTOR
ACCEPTOR
PROPOSER
Acceptors
compare
sequence
numbers1:FLAT%WHITE
1:FLAT%WHITE
1:FLAT%WHITE
2 . Promise

46. Paxos%Write:%Happy%Path
ACCEPTOR
ACCEPTOR
ACCEPTOR
PROPOSER
Proposer receives
quorum, “makes
progress”
3. Accept Request
1:FLAT%WHITE
1:FLAT%WHITE
1:FLAT%WHITE

47. Paxos%Write:%Happy%Path
PROPOSER
FLAT%WHITE
4. Acceptance
1:FLAT%WHITE
1:FLAT%WHITE
1:FLAT%WHITE
ACCEPTOR
ACCEPTOR
ACCEPTOR
Acceptors check
sequence numbers
one more time

48. Paxos%Write:%BeLer%Offer
ACCEPTOR
ACCEPTOR
ACCEPTOR
PROPOSER
5:CAFÉ%CUBANO
5:CAFÉ%CUBANO
5:CAFÉ%CUBANO
5:CAFÉ%CUBANO
1. Prepare

49. Paxos%Write:%BeLer%Offer
ACCEPTOR
1. Prepare
8:FRENCH%PRESS
Meanwhile, this node had
gotten another proposal
when we weren’t looking…

50. Paxos%Write:%BeLer%Offer
ACCEPTOR
ACCEPTOR
ACCEPTOR
PROPOSER
8:FRENCH%PRESS
5:CAFÉ%CUBANO
5:CAFÉ%CUBANO
2 . Promise

51. Paxos%Write:%BeLer%Offer
ACCEPTOR
ACCEPTOR
ACCEPTOR
PROPOSER
Proposer changes
its mind!
3. Accept Request
8:FRENCH%PRESS
8:FRENCH%PRESS
8:FRENCH%PRESS
8:FRENCH%PRESS

52. Paxos%Write:%BeLer%Offer
PROPOSER
FRENCH%PRESS
4. Acceptance
8:FRENCH%PRESS
8:FRENCH%PRESS
8:FRENCH%PRESS
ACCEPTOR
ACCEPTOR
ACCEPTOR
Two acceptors are
surprised about this,
but the sequence
numbers work out…

53. Lightweight
Transactions
• Good)solu/on)for)distributed)race)
condi/ons)
• At)some)cost)in)latency)
• Run)your)own)load)tests!)
• Now…why)were)you)using)ZooKeeper?

54. When Complex
Domains
Attack

55. Dr . Chebotko’s
Data Modeling
Emporium

56. • Conceptual)model)
• Logical)model)
• Physical)model
Chebotko
Method

57. • Abstract,)implementa/onHindependent)model)
• Tradi/onally)built)in)Chen)ER)nota/on)
• Describes)en//es,)rela/onships,)roles,)keys,)
and)cardinali/es
Chebotko
Method
Conceptual%Model

58. Chebotko
Method
Conceptual%Model
Album
title
year genre
releasesPerformername
founded
country
1 n
style
IsA
ArtistBand
disjoint5
covering
born
died
has3
member
n m
period
format
cover5image
number
title
1
n
Track
has
User
id
email
name
preferences
performs
m
1
involvedIn
1
n
IsA
RatePlay
disjoint5
not5covering
Activity
id
timestamp
rating

59. Chebotko
Method
Logical%Model
• A)diagram)showing)queries)and)tables)
• Ensures)that)each)query)“fits”)in)a)par//on)
• Tends)to)produce)one)table)per)query

60. Chebotko
Method
Logical%Model
1. Iden/fy)access)paTerns)(“queries”))
2. Find)a)subset)of)the)conceptual)model)that)
sa/sfies)a)query)
3. Determine)key)
4. Verify)maximum)par//on)size

61. Chebotko
Method
Logical%Model
Q1
ACCESS%PATTERNS
Q1:$Find$performers$for$a$specified$style;$order$by$performer$(ASC).
Q2:$Find$information$for$a$specified$performer$(artist$or$band).
Q3:$Find$information$for$a$specified$album$(title$and$year).
Q4:$Find$albums$for$a$specified$performer;$order$by$album$release$year$(DESC)$and$title$(ASC).
Q5:$Find$albums$for$a$specified$genre;$order$by$performer$(ASC),$year$(DESC),$and$title$(ASC).
Q6:$Find$albums$and$performers$for$a$specified$track$title;$order$by$performer$(ASC),$year$(DESC),$and$title$(ASC).
Q7:$Find$tracks$for$a$specified$album$(title$and$year);$order$by$track$number$(ASC).
Q8:$Find$information$for$a$specified$user.
Q9:$Find$activities$for$a$specified$user;$order$by$activity$time$(DESC).
Q10:$Find$statistics$for$a$specified$track.
Q11:$Find$user$activities$for$a$specified$track;$order$by$activity$time$(DESC).
Q12:$Find$user$activities$for$a$specified$activity$type.
…
Performer
name K
type
country
style
founded
born
died
Performers_by_style
style K
name C
Albums_by_performer
performer $$$$K
year $$$$C
title $$$$C
genre
Albums_by_genre
genre K
performer$ C
year C
title C
Tracks_by_album
album K
year K
number $ C
performer$ S
genre S
title
Albums_by_track
track K
performer$ C
year C
title C
Album
title K
year K
performer
genre
tracks$(map)
Q2
Q2
Q4
Q3
Q3
Q4
Q5
Q5
Q6
Q1
Q3
Q3
Q7
Q7
Q7
User
id K
name$
email
preferences$(set)
Q8
Activities_by_user
user K
activity (timeuuid) C
type IDX
album_title
album_year
track_title
rating
Activities_by_track
album_title K
album_year K
track_title K
activity$(timeuuid) C
user
type
rating
Track_stats
album_title K
album_year K
track_title K
num_ratings$(counter)
sum_ratings$(counter)
num_plays$(counter)
Q9
Q8
Q10
Q11
Q12

62. Q1
ACCESS%PATTERNS
Q1:$Find$performers$for$a$specified$style;$order$by$performer$(ASC).
Q2:$Find$information$for$a$specified$performer$(artist$or$band).
Q3:$Find$information$for$a$specified$album$(title$and$year).
Q4:$Find$albums$for$a$specified$performer;$order$by$album$release$year$(DESC)$and$title$(ASC).
Q5:$Find$albums$for$a$specified$genre;$order$by$performer$(ASC),$year$(DESC),$and$title$(ASC).
Q6:$Find$albums$and$performers$for$a$specified$track$title;$order$by$performer$(ASC),$year$(DESC),$and$title$(ASC).
Q7:$Find$tracks$for$a$specified$album$(title$and$year);$order$by$track$number$(ASC).
Q8:$Find$information$for$a$specified$user.
Q9:$Find$activities$for$a$specified$user;$order$by$activity$time$(DESC).
Q10:$Find$statistics$for$a$specified$track.
Q11:$Find$user$activities$for$a$specified$track;$order$by$activity$time$(DESC).
Q12:$Find$user$activities$for$a$specified$activity$type.
…
Performer
name K
type
country
style
founded
born
died
Performers_by_style
style K
name C
Albums_by_performer
performer $$$$K
year $$$$C
title $$$$C
genre
Albums_by_genre
genre K
performer$ C
year C
title C
Tracks_by_album
album K
year K
number $ C
performer$ S
genre S
title
Albums_by_track
track K
performer$ C
year C
title C
Album
title K
year K
performer
genre
tracks$(map)
Q2
Q2
Q4
Q3
Q3
Q4
Q5
Q5
Q6
Q1
Q3
Q3
Q7
Q7
Q7
User
id K
name$
email
preferences$(set)
Q8
Activities_by_user
user K
activity (timeuuid) C
type IDX
album_title
album_year
track_title
rating
Activities_by_track
album_title K
album_year K
track_title K
activity$(timeuuid) C
user
type
rating
Track_stats
album_title K
album_year K
track_title K
num_ratings$(counter)
sum_ratings$(counter)
num_plays$(counter)
Q9
Q8
Q10
Q11
Q12

63. ACCESS%PATTERNS
Q1:$Find$performers$for$a$specified$style;$order$by$performer$(ASC).
Q2:$Find$information$for$a$specified$performer$(artist$or$band).
Q3:$Find$information$for$a$specified$album$(title$and$year).
Q4:$Find$albums$for$a$specified$performer;$order$by$album$release$year$(DESC)$and$title$(ASC).
Q5:$Find$albums$for$a$specified$genre;$order$by$performer$(ASC),$year$(DESC),$and$title$(ASC).
Q6:$Find$albums$and$performers$for$a$specified$track$title;$order$by$performer$(ASC),$year$(DESC),$and$title$(ASC).
Q7:$Find$tracks$for$a$specified$album$(title$and$year);$order$by$track$number$(ASC).
Q8:$Find$information$for$a$specified$user.
Q9:$Find$activities$for$a$specified$user;$order$by$activity$time$(DESC).
Q10:$Find$statistics$for$a$specified$track.
Q11:$Find$user$activities$for$a$specified$track;$order$by$activity$time$(DESC).
Q12:$Find$user$activities$for$a$specified$activity$type.
…
performer $$$$K
year $$$$C
title $$$$C
genre
performer$
year
title
Q3
Q7

64. K
$(set)
Q8
Activities_by_user
user K
activity (timeuuid) C
type IDX
album_title
album_year
track_title
rating
Q9
Q12

65. Q1
ACCESS%PATTERNS
Q1:$Find$performers$for$a$specified$style;$order$by$performer$(ASC).
Q2:$Find$information$for$a$specified$performer$(artist$or$band).
Q3:$Find$information$for$a$specified$album$(title$and$year).
Q4:$Find$albums$for$a$specified$performer;$order$by$album$release$year$(DESC)$and$title$(ASC).
Q5:$Find$albums$for$a$specified$genre;$order$by$performer$(ASC),$year$(DESC),$and$title$(ASC).
Q6:$Find$albums$and$performers$for$a$specified$track$title;$order$by$performer$(ASC),$year$(DESC),$and$title$(ASC).
Q7:$Find$tracks$for$a$specified$album$(title$and$year);$order$by$track$number$(ASC).
Q8:$Find$information$for$a$specified$user.
Q9:$Find$activities$for$a$specified$user;$order$by$activity$time$(DESC).
Q10:$Find$statistics$for$a$specified$track.
Q11:$Find$user$activities$for$a$specified$track;$order$by$activity$time$(DESC).
Q12:$Find$user$activities$for$a$specified$activity$type.
…
Performer
name K
type
country
style
founded
born
died
Performers_by_style
style K
name C
Albums_by_performer
performer $$$$K
year $$$$C
title $$$$C
genre
Albums_by_genre
genre K
performer$ C
year C
title C
Tracks_by_album
album K
year K
number $ C
performer$ S
genre S
title
Albums_by_track
track K
performer$ C
year C
title C
Album
title K
year K
performer
genre
tracks$(map)
Q2
Q2
Q4
Q3
Q3
Q4
Q5
Q5
Q6
Q1
Q3
Q3
Q7
Q7
Q7
User
id K
name$
email
preferences$(set)
Q8
Activities_by_user
user K
activity (timeuuid) C
type IDX
album_title
album_year
track_title
rating
Activities_by_track
album_title K
album_year K
track_title K
activity$(timeuuid) C
user
type
rating
Track_stats
album_title K
album_year K
track_title K
num_ratings$(counter)
sum_ratings$(counter)
num_plays$(counter)
Q9
Q8
Q10
Q11
Q12

66. Chebotko
Method
Logical%Model%Analysis
• Natural)or)surrogate)keys?)
• Are)write)conflicts)(overwrites))possible?)
• What)data)types)to)use?)
• How)large)are)par//ons?)
• How)much)data)duplica/on)is)required?)
• Are)clientHside)joins)required)and)at)what)cost?)
• Are)data)consistency)anomalies)possible?)
• How)to)enable)transac/ons?

67. Chebotko
Method
Physical%Model
• Not)a)diagram!)
• Just)the)CQL)version)of)the)logical)
tables

68. C* 2 .1 Features
Bonus!

69. User-Defined
Types

70. UDTs
• Good)for)modeling)nested)
“value)objects”)
• Eliminates)extra)queries,)inH
app)joins)
• Mechanism)for)
denormaliza/on
CREATE TYPE address (
street text,
city text,
zip_code int,
country text,
cross_streets set<text>
);

71. UDTs
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));
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;
InQapp%%
Join

72. CREATE TYPE video_metadata (
height int,
width int,
video_bit_rate set<text>,
encoding text
);
UDTs
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));

73. Thank
You!