Apache Cassandra has been a driving force for applications that scale for over 10 years. This open-source database now powers 30% of the Fortune 100.Now is your chance to get an inside look, guided by the company that’s responsible for 85% of the code commits.You won’t want to miss this deep dive into the database that has become the power behind the moment — the force behind game-changing, scalable cloud applications - Patrick McFadin, VP Developer Relations at DataStax, is going behind the Cassandra curtain in an exclusive webinar. View recording: https://youtu.be/z8fLn8GL5as Explore all DataStax webinars: https://www.datastax.com/resources/webinars

1. Introduction to Apache
Cassandra™
1
Patrick McFadin
VP Developer Relations, DataStax
@PatrickMcFadin

2. Cloud Applications
2

3. 3
You may consider one of these …or one of these

4. 4
–Who knows
“The definition of insanity
is doing the same thing over and over and expecting
a different result”
“The definition of bad engineering

5. Sharding
5
shard 1 shard 2 shard 3 shard 4
App Server
client

6. Sharding
6
A-F G-M N-T U-Z
App Server
client
Customer Name

7. 2005 - It's broke!
7

8. June 29, 2007
8

9. Dynamo Paper(2007)
• How do we build a data store that is:
– Reliable
– Performant
– “Always On”
• Nothing new and shiny
• 24 papers cited
9
Evolutionary. Real. Computer Science
Also the basis for Riak and Voldemort

10. BigTable(2006)
• Richer data model
• 1 key. Lots of values
• Fast sequential access
• 38 Papers cited
10

11. Cassandra(2008)
• Distributed features of Dynamo
• Data Model and storage from
BigTable
• February 17, 2010 it graduated to a
top-level Apache project
11

12. Basic Architecture
12

13. Node
13
Server

14. Token
14
Server
•Each partition is a 64 bit value
•Consistent hash between -263 to
+263-1
•Each node owns a range of those
values
•The token is the beginning of that
range to the next node’s token value
•Virtual Nodes break these down
further
Data
Token Range
0 …

15. The cluster
15
Server
Token Range
0 0-100
0-100

16. The cluster
16
Server
Token Range
0 0-50
51 51-100
Server
0-50
51-100

17. The cluster
17
Server
Token Range
0 0-25
26 26-50
51 51-75
76 76-100
Server
ServerServer
0-25
76-100
26-5051-75

18. Replication
18
10.0.0.1
00-25
DC1
DC1: RF=1
Node Primary
10.0.0.1 00-25
10.0.0.2 26-50
10.0.0.3 51-75
10.0.0.4 76-100
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75

19. Replication
19
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
DC1
DC1: RF=2
Node Primary Replica
10.0.0.1 00-25 76-100
10.0.0.2 26-50 00-25
10.0.0.3 51-75 26-50
10.0.0.4 76-100 51-75
76-100
00-25
26-50
51-75

20. Replication
20
DC1
DC1: RF=3
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50

21. Consistency
21
DC1
DC1: RF=3
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
Client
Write to
partition 15

22. Consistency level
22
Consistency Level Number of Nodes Acknowledged
One One replica acknowledges read
One replica commits write
Quorum 51% nodes agree on read or commit
write
Local Quorum 51% in local DC

23. Consistency
23
DC1
DC1: RF=3
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
Client
Write to partition
15
CL= One

24. Consistency
24
DC1
DC1: RF=3
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
Client
Write to partition
15
CL= One

25. Consistency
25
DC1
DC1: RF=3
Node Primary Replica Replica
10.0.0.1 00-25 76-100 51-75
10.0.0.2 26-50 00-25 76-100
10.0.0.3 51-75 26-50 00-25
10.0.0.4 76-100 51-75 26-50
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
Client
Write to partition
15
CL= Quorum

26. Multi-datacenter
26
AWS
DC1: RF=3
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
Client
Write to
partition 15
GCP
10.1.0.1
00-25
10.1.0.4
76-100
10.1.0.2
26-50
10.1.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
DC2: RF=3

27. Multi-datacenter
27
AWS
DC1: RF=3
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
Client
Write to
partition 15
GCP
10.1.0.1
00-25
10.1.0.4
76-100
10.1.0.2
26-50
10.1.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
DC2: RF=3

28. Multi-datacenter
28
AWS
DC1: RF=3
10.0.0.1
00-25
10.0.0.4
76-100
10.0.0.2
26-50
10.0.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
Client
Write to
partition 15
GCP
10.1.0.1
00-25
10.1.0.4
76-100
10.1.0.2
26-50
10.1.0.3
51-75
76-100
51-75
00-25
76-100
26-50
00-25
51-75
26-50
DC2: RF=3

29. Data Modeling
29

30. Relational Data Models
• 5 normal forms
• Foreign Keys
• Joins
30
deptId First Last
1 Edgar Codd
2 Raymond Boyce
id Dept
1 Engineering
2 Math
Employees
Department

31. 31

32. Relational Modeling
32
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)
);

33. Relational Modeling
33
Data
Models
Application

34. Cassandra Modeling
34
Data
Models
Application

35. Modeling Queries
• 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
35

36. Some Application Workflows in KillrVideo
36
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

37. Some Queries in KillrVideo to Support Workflows
37
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

38. CQL vs SQL
• No joins
• Limited aggregations
38
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

39. Denormalization
• Combine table columns into a single view
• Eliminate the need for joins
39
SELECT First, Last, Dept
FROM employees
WHERE id = ‘1’
id First Last Dept
1 Edgar Codd Engineering
2 Raymond Boyce Math
Employees

40. “Static” Table
40
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

41. Insert
41
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

42. Partition keys
42
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');

43. Select
43
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

44. Locality
44
1000 Node Cluster
videoid = 06049cbb-dfed-421f-b889-5f649a0de1ed
SELECT name, description, added_date
FROM videos
WHERE videoid = 06049cbb-dfed-421f-b889-5f649a0de1ed;

45. No more sequences
• Great for auto-creation of Ids
• Guaranteed unique
• Needs ACID to work. (Sorry. No sharding)
45
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;

46. No sequences???
• Almost impossible in a distributed system
• Couple of great choices
– Natural Key - Unique values like email
– Surrogate Key - UUID
46
• 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

47. “Dynamic” Table
47
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

48. Primary key relationship
48
PRIMARY KEY (tag,videoid)

49. Primary key relationship
49
Partition Key
PRIMARY KEY (tag,videoid)

50. Primary key relationship
50
Partition Key Clustering Column
PRIMARY KEY (tag,videoid)

51. Primary key relationship
51
Partition Key
data model
PRIMARY KEY (tag,videoid)
Clustering Column

52. -5.6
06049cbb-dfed-421f-b889-5f649a0de1ed
Primary key relationship
52
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

53. Row
53
Column 1
Partition Key
1
Column 2 Column 3 Column 4

54. Partition with Clustering
54
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

55. Table
55
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

56. Keyspace
56
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

57. Controlling Order
57
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);

58. Clustering Order
58
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

59. Clustering Order
59
added_date 1userid 1 videoid 1
added_date 2userid 1 videoid 2
user_videos
added_date 3userid 1 videoid 3
added_date 4userid 1 videoid 4
Order By
ASC
name
name
name
name
preview_image
preview_image
preview_image
preview_image

60. Clustering Order
60
added_date 4userid 1 videoid 1
added_date 3userid 1 videoid 2
user_videos
added_date 2userid 1 videoid 3
added_date 1userid 1 videoid 4
Order By
DESC
name
name
name
name
preview_image
preview_image
preview_image
preview_image

61. Write Path
61
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

62. Storage Model - Logical View
62
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;

63. 2005:12:1:10
-5.6 -5.3-4.9-5.1
Storage Model - Disk Layout
63
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;

64. 2005:12:1:10
-5.6
2005:12:1:11
-4.9 -5.3-4.9-5.1
Storage Model - Disk Layout
64
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;

65. 2005:12:1:10
-5.6
2005:12:1:11
-4.9 -5.3-4.9-5.1
Storage Model - Disk Layout
65
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

66. Read Path
66
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

67. Query patterns
• Range queries
• “Slice” operation on disk
67
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

68. Query patterns
68
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;

69. What’s Next??
Cassandra 4.0
69

70. Cassandra 4.0
70
Massive Stability Release
Networking Changes
• Async internode communication
• 20% faster Streaming
Restart Conditions
• Gossip overhaul
• Nodes coordinate on restart
• Dead node detector
Queries
• Slow/Large query log
• Stop large queries killing cluster

71. Cassandra 4.0
71
Big Features
Pluggable Storage
Audit Logging
Virtual Tables
Management Sidecar

72. © DataStax, All Rights Reserved.
ZGC and the end of GC

73. Thank You!
Follow Me @PatrickMcFadin
73

74. 74 © DataStax, All Rights Reserved. Confidential
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