This document discusses Cassandra use cases and anti-patterns. It describes queue-like designs, intensive updates on the same column, and designing around a dynamic schema as anti-patterns that can lead to failures. Rate limiting, fraud prevention, and account validation are provided as examples of good use cases. Key-value modeling, clustering, compaction strategies, and time-to-live features are also overviewed.

1. Cassandra nice use-cases and worst anti-patterns
DuyHai DOAN, Technical Advocate
@doanduyhai

2. Shameless self-promotion!
@doanduyhai
2
Duy Hai DOAN
Cassandra technical advocate
• talks, meetups, confs
• open-source devs (Achilles, …)
• technical point of contact
☞ duy_hai.doan@datastax.com
• production troubleshooting

3. Agenda!
@doanduyhai
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Anti-patterns
• Queue-like designs
• CQL null values
• Intensive updates on same column
• Design around dynamic schema

4. Agenda!
@doanduyhai
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Nice use-cases
• Rate-limiting
• Anti Fraud
• Account validation
• Sensor data timeseries

5. Data Model Crash Course!

6. Last Write Win (LWW)!
INSERT INTO users(login, name, age) VALUES(‘jdoe’, ‘John DOE’, 33);
@doanduyhai
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jdoe
age
name
33 John DOE
#partition

7. Last Write Win (LWW)!
@doanduyhai
jdoe
age (t1) name (t1)
33 John DOE
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INSERT INTO users(login, name, age) VALUES(‘jdoe’, ‘John DOE’, 33);
auto-generated timestamp (μs)
.

8. Last Write Win (LWW)!
@doanduyhai
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UPDATE users SET age = 34 WHERE login = jdoe;
jdoe
SSTable1 SSTable2
age (t1) name (t1)
33 John DOE
jdoe
age (t2)
34

9. Last Write Win (LWW)!
@doanduyhai
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DELETE age FROM users WHERE login = jdoe;
tombstone
SSTable1 SSTable2 SSTable3
jdoe
age (t3)
ý
jdoe
age (t1) name (t1)
33 John DOE
jdoe
age (t2)
34

10. Last Write Win (LWW)!
@doanduyhai
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SELECT age FROM users WHERE login = jdoe;
? ? ?
SSTable1 SSTable2 SSTable3
jdoe
age (t3)
ý
jdoe
age (t1) name (t1)
33 John DOE
jdoe
age (t2)
34

11. Last Write Win (LWW)!
@doanduyhai
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SELECT age FROM users WHERE login = jdoe;
✕ ✕ ✓
SSTable1 SSTable2 SSTable3
jdoe
age (t3)
ý
jdoe
age (t1) name (t1)
33 John DOE
jdoe
age (t2)
34

12. Compaction!
@doanduyhai
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SSTable1 SSTable2 SSTable3
jdoe
age (t3)
ý
jdoe
age (t1) name (t1)
33 John DOE
jdoe
age (t2)
34
New SSTable
jdoe
age (t3) name (t1)
ý John DOE

13. Simple Table!
@doanduyhai
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CREATE TABLE users (
login text,
name text,
age int,
…
PRIMARY KEY(login));
partition key (#partition)

14. Clustered table (1 – N)!
@doanduyhai
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CREATE TABLE sensor_data (
sensor_id text,
date timestamp,
raw_data blob,
PRIMARY KEY((sensor_id), date));
partition key clustering column
(sorted)
unicity

15. Sorted on disk!
sensor_id
@doanduyhai
SSTable1
date1(t1) date2(t2) date3(t3) date4(t4) date5(t5) …
… … … … …

16. Worst anti-patterns!
Queue-like designs!
CQL null!
Intensive updates on same column!
Design around dynamic schema!
!

17. Failure level!
@doanduyhai
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☠
☠☠
☠☠☠
☠☠☠☠

18. Queue-like designs!
@doanduyhai
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Adding new message ☞ 1 physical insert

19. Queue-like designs!
@doanduyhai
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Adding new message ☞ 1 physical insert
Consuming message = deleting it ☞ 1 physical insert (tombstone)

20. Queue-like designs!
@doanduyhai
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Adding new message ☞ 1 physical insert
Consuming message = deleting it ☞ 1 physical insert (tombstone)
Transactional queue = re-inserting messages ☞ physical insert * <many>

21. Queue-like designs!
FIFO queue
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A
{ A }

22. Queue-like designs!
FIFO queue
@doanduyhai
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A B
{ A, B }

23. Queue-like designs!
FIFO queue
@doanduyhai
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A B C
{ A, B, C }

24. Queue-like designs!
FIFO queue
@doanduyhai
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A B C A
{ B, C }

25. Queue-like designs!
FIFO queue
@doanduyhai
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A B C A D
{ B, C, D }

26. Queue-like designs!
FIFO queue
@doanduyhai
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A B C A D B
{ C, D }

27. Queue-like designs!
FIFO queue
@doanduyhai
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A B C A D B C
{ D }

28. Queue-like designs!
FIFO queue, worst case
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A A A A A A A A A A
{ }

29. Failure level!
@doanduyhai
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☠☠☠

30. Queue-like designs!
Read cursor. Next read will give {A, E}
@doanduyhai
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Solution: event-sourcing
• write ahead, never delete
• read = move a cursor forward (or backward in time for history)
A B C D A E
Write cursor

31. CQL null semantics!
@doanduyhai
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Reading null value means
• value does not exist (has never bean created)
• value deleted (tombstone)
SELECT age FROM users WHERE login = jdoe; à NULL

32. CQL null semantics!
@doanduyhai
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Writing null means
• delete value (creating tombstone)
• even though it does not exist
UPDATE users SET age = NULL WHERE login = jdoe;

33. CQL null semantics!
@doanduyhai
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Seen in production: prepared statement
UPDATE users SET
age = ?,
…
geo_location = ?,
mood = ?,
…
WHERE login = ?;

34. CQL null semantics!
@doanduyhai
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Seen in production: bound statement
preparedStatement.bind(33, …, null, null, null, …);
null ☞ tombstone creation on each update …
jdoe
age name geo_loc mood status
33 John DOE ý ý ý

35. Failure level!
@doanduyhai
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☠

36. Intensive update!
@doanduyhai
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Context
• small start-up
• cloud-based video recording & alarm
• internet of things (sensor)
• 10 updates/sec for some sensors

37. Intensive update on same column!
@doanduyhai
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Data model
sensor_id
value
45.0034
CREATE TABLE sensor_data (
sensor_id long,
value double,
PRIMARY KEY(sensor_id));

38. Intensive update on same column!
UPDATE sensor_data SET value = 45.0034 WHERE sensor_id = …;
UPDATE sensor_data SET value = 47.4182 WHERE sensor_id = …;
UPDATE sensor_data SET value = 48.0300 WHERE sensor_id = …;
@doanduyhai
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Updates
sensor_id
value (t1)
45.0034
sensor_id
value (t13)
47.4182
sensor_id
value (t36)
48.0300

39. Intensive update on same column!
@doanduyhai
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Read
SELECT sensor_value from sensor_data WHERE sensor_id = …;
read N physical columns, only 1 useful … (until compaction)
sensor_id
value (t1)
45.0034
sensor_id
value (t13)
47.4182
sensor_id
value (t36)
48.0300

40. Failure level!
@doanduyhai
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☠☠

41. Intensive update on same column!
@doanduyhai
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Solution 1: leveled compaction! (if your I/O can keep up)
sensor_id
value (t1)
45.0034
sensor_id
value (t13)
47.4182
sensor_id
value (t36)
48.0300
sensor_id
value (t36)
48.0300

42. Intensive update on same column!
@doanduyhai
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Solution 2: reversed timeseries & DateTiered compaction strategy
CREATE TABLE sensor_data (
sensor_id long,
date timestamp,
value double,
PRIMARY KEY((sensor_id), date))
WITH CLUSTERING ORDER (date DESC);

43. Intensive update on same column!
SELECT sensor_value FROM sensor_data WHERE sensor_id = … LIMIT 1;
@doanduyhai
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sensor_id
date3(t3)
date2(t2)
date1(t1)
Data cleaning by configuration the strategy (base_time_seconds)
...
48.0300 47.4182 45.0034 …

44. Design around dynamic schema!
@doanduyhai
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Customer emergency call
• 3 nodes cluster almost full
• impossible to scale out
• 4th node in JOINING state for 1 week
• disk space is filling up, production at risk!

45. Design around dynamic schema!
@doanduyhai
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After investigation
• 4th node in JOINING state because streaming is stalled
• NPE in logs

46. Design around dynamic schema!
@doanduyhai
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After investigation
• 4th node in JOINING state because streaming is stalled
• NPE in logs
Cassandra source-code to the rescue

47. Design around dynamic schema!
@doanduyhai
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public class CompressedStreamReader extends StreamReader
{
…
@Override
public SSTableWriter read(ReadableByteChannel channel) throws IOException
{
…
Pair<String, String> kscf = Schema.instance.getCF(cfId);
ColumnFamilyStore cfs = Keyspace.open(kscf.left).getColumnFamilyStore(kscf.right);
NPE here

48. Design around dynamic schema!
@doanduyhai
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The truth is
• the devs dynamically drop & recreate table every day
• dynamic schema is in the core of their design
Example:
DROP TABLE catalog_127_20140613;
CREATE TABLE catalog_127_20140614( … );

49. Design around dynamic schema!
@doanduyhai
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Failure sequence
n1
n2
n4
n3
catalog_x_y
catalog_x_y
catalog_x_y
catalog_x_y
4 1
2
3
5
6

50. Design around dynamic schema!
@doanduyhai
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Failure sequence
n1
n2
n4
n3
catalog_x_y
catalog_x_y
catalog_x_y
catalog_x_y
4 1
2
3
5
6
catalog_x_z
catalog_x_z
catalog_x_z
catalog_x_z

51. Design around dynamic schema!
@doanduyhai
catalog_x_y ????
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Failure sequence
n1
n2
n4
n3
4 1
2
3
5
6
catalog_x_z
catalog_x_z
catalog_x_z
catalog_x_z

52. Design around dynamic schema!
@doanduyhai
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Nutshell
• dynamic schema change as normal prod operation is not
recommended
• schema AND topology change at the same time is an anti-pattern

53. Failure level!
@doanduyhai
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☠☠☠☠

54. ! "
!
Q & R

55. Nice Examples!
Rate limiting!
Anti Fraud!
Account Validation!

56. Rate limiting!
@doanduyhai
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Start-up company, reset password feature
1) /password/reset
2) SMS with token A0F83E63DB935465CE73DFE….
Phone number Random token
3) /password/new/<token>/<password>

57. Rate limiting!
@doanduyhai
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Problem 1
• account created with premium phone number

58. Rate limiting!
@doanduyhai
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Problem 1
• account created with premium phone number
• /password/reset x 100

59. Rate limiting!
@doanduyhai
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« money, money, money, give money, in the richman’s world » $$$

60. Rate limiting!
@doanduyhai
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Problem 2
• massive hack

61. Rate limiting!
@doanduyhai
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Problem 2
• massive hack
• 106 /password/reset calls from few accounts

62. Rate limiting!
@doanduyhai
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Problem 2
• massive hack
• 106 /password/reset calls from few accounts
• SMS messages are cheap

63. Rate limiting!
@doanduyhai
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Problem 2
• ☞ but not at the 106/per user/per day scale

64. Rate limiting!
@doanduyhai
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Solution
• premium phone number ☞ Google libphonenumber

65. Rate limiting!
@doanduyhai
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Solution
• premium phone number ☞ Google libphonenumber
• massive hack ☞ rate limiting with Cassandra

66. Cassandra Time To Live!
@doanduyhai
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Time to live
• built-in feature
• insert data with a TTL in sec
• expires server-side automatically
• ☞ use as sliding-window

67. Rate limiting in action!
@doanduyhai
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Implementation
• threshold = max 3 reset password per sliding 24h per
user

68. Rate limiting in action!
@doanduyhai
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Implementation
• when /password/reset called
• check threshold
• reached ☞ error message/ignore
• not reached ☞ log the attempt with TTL = 86400

69. Rate Limiting
Demo

70. Anti Fraud!
@doanduyhai
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Real story
• many special offers available
• 30 mins international calls (50 countries)
• unlimited land-line calls to 5 countries
• …

71. Anti Fraud!
@doanduyhai
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Real story
• each offer has a duration (week/month/year)
• only one offer active at a time

72. Anti Fraud!
@doanduyhai
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Cassandra TTL
• when granting new offer
INSERT INTO user_special_offer(login, offer_code, …)
VALUES(‘jdoe’, ’30_mins_international’,…)
IF NOT EXISTS
USING TTL <offer_duration>;

73. Anti Fraud Demo

74. Account Validation!
@doanduyhai
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Requirement
• user creates new account
• sends sms/email link with token to validate account
• 10 days to validate

75. Account Validation!
@doanduyhai
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How to ?
• create account with 10 days TTL
INSERT INTO users(login, name, age)
VALUES(‘jdoe’, ‘John DOE’, 33)
USING TTL 864000;

76. Account Validation!
@doanduyhai
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How to ?
• create random token for validation with 10 days TTL
INSERT INTO account_validation(token, login, name, age)
VALUES(‘A0F83E63DB935465CE73DFE…’, ‘jdoe’, ‘John DOE’, 33)
USING TTL 864000;

77. Account Validation!
@doanduyhai
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On token validation
• check token exist & retrieve user details
SELECT login, name, age FROM account_validation
WHERE token = ‘A0F83E63DB935465CE73DFE…’;
• re-insert durably user details without TTL
INSERT INTO users(login, name, age) VALUES(‘jdoe’, ‘John DOE’, 33);

78. ! "
!
Q & R

79. Thank You
@doanduyhai
duy_hai.doan@datastax.com
https://academy.datastax.com/