Billion Records from SQL to Cassandra, lessons learned

30min talk at the #CassandraSummit London 2014 with @BriceDutheil and @DoanDuyHai

At Libon, we process a lot of data. A lot means billions of messages/calls and billions of backed up contacts data. All the messaging backup is done in Cassandra with read & write latency below 10ms. Since we are happy with the level of performance achieved with Cassandra, we decided to migrate the contacts backup as well into Cassandra. During this session, we'll see how to tackle a migration project, from the SQL and Hibernate couple to Cassandra and Achilles. We'll highlight some mistakes to avoid and some tricks to make the migration smoother and more resilient.

Presentation video : https://youtu.be/U6oa3Tsdtp4

Transcript

1. Billion Records from SQL to Cassandra, lessons learned DuyHai Doan Brice Dutheil
2. #CassandraSummit @doanduyhai @BriceDutheil Who are we ? Brice Dutheil Mockito Java Track Lead @ Devoxx France Independant contractor @ Libon (Orange-Vallée) DuyHai Doan Achilles Cassandra Technical Advocate Former Java Developer @ Libon 2
3. #CassandraSummit @doanduyhai @BriceDutheil Agenda •  Libon context •  Migration strategy •  Business code migration •  Data Modeling •  Take Away 3
4. #CassandraSummit @doanduyhai @BriceDutheil Libon Context
5. #CassandraSummit @doanduyhai @BriceDutheil What is Libon ? •  Messaging app •  VOIP (out) •  Custom voicemail & greetings •  SMS/chat/file transfer •  Contacts matching 5
6. #CassandraSummit @doanduyhai @BriceDutheil Contact Matching 6 Libon User
7. #CassandraSummit @doanduyhai @BriceDutheil Contact Matching 7 Libon User Friend
8. #CassandraSummit @doanduyhai @BriceDutheil Contact Matching 8 Libon User Friend Contact matching
9. #CassandraSummit @doanduyhai @BriceDutheil Contact Matching 9 Libon User Friend Accept link
10. #CassandraSummit @doanduyhai @BriceDutheil Project Context •  Application grew over the years 10
11. #CassandraSummit @doanduyhai @BriceDutheil Project Context •  Application grew over the years •  Already using Cassandra to handle events •  messaging / file sharing / SMS / notifications •  Cassandra R/W latencies ≈ 0,4 ms •  server response time under 10 ms 11
12. #CassandraSummit @doanduyhai @BriceDutheil Project Context •  About contacts … 12
13. #CassandraSummit @doanduyhai @BriceDutheil Project Context •  About contacts … •  stored as relational model in RDBMS (Oracle) 13
14. #CassandraSummit @doanduyhai @BriceDutheil Project Context •  About contacts … •  stored as relational model in RDBMS (Oracle) •  1 user ≈ 300 contacts 14
15. #CassandraSummit @doanduyhai @BriceDutheil Project Context •  About contacts … •  stored as relational model in RDBMS (Oracle) •  1 user ≈ 300 contacts •  with millions users ‛ billions of contacts to handle 15
16. #CassandraSummit @doanduyhai @BriceDutheil Project Context •  About contacts … •  stored as relational model in RDBMS (Oracle) •  1 user ≈ 300 contacts •  with millions users ‛ billions of contacts to handle •  query latency unpredictable 16
17. #CassandraSummit @doanduyhai @BriceDutheil 17
18. #CassandraSummit @doanduyhai @BriceDutheil Fixing the problem •  Tune the RDBMS 18
19. #CassandraSummit @doanduyhai @BriceDutheil Fixing the problem •  Tune the RDBMS •  indices 19
20. #CassandraSummit @doanduyhai @BriceDutheil Fixing the problem •  Tune the RDBMS •  indices •  partitioning 20
21. #CassandraSummit @doanduyhai @BriceDutheil Fixing the problem •  Tune the RDBMS •  indices •  partitioning •  less joins, simplified relational model 21
22. #CassandraSummit @doanduyhai @BriceDutheil Fixing the problem •  Tune the RDBMS •  indices •  partitioning •  less joins, simplified relational model •  hardware capacity increased 22
23. #CassandraSummit @doanduyhai @BriceDutheil Fixing the problem •  Tune the RDBMS •  indices •  partitioning •  less joins, simplified relational model •  hardware capacity increased That worked 23
24. #CassandraSummit @doanduyhai @BriceDutheil Fixing the problem •  Tune the RDBMS •  indices •  partitioning •  less joins, simplified relational model •  hardware capacity increased That worked but … 24
25. #CassandraSummit @doanduyhai @BriceDutheil Back-end application RDBMS Cassandra 25
26. #CassandraSummit @doanduyhai @BriceDutheil Next Challenges •  High Availability (DB failure, site failure …) 26
27. #CassandraSummit @doanduyhai @BriceDutheil Next Challenges •  High Availability (DB failure, site failure …) •  Predictable performance at scale 27
28. #CassandraSummit @doanduyhai @BriceDutheil Next Challenges •  High Availability (DB failure, site failure …) •  Predictable performance at scale •  Going to multi data-centers 28
29. #CassandraSummit @doanduyhai @BriceDutheil Going for Cassandra •  Denormalize (if possible …) 29
30. #CassandraSummit @doanduyhai @BriceDutheil Going for Cassandra •  Denormalize (if possible …) •  Know your business ‛ know your queries 30
31. #CassandraSummit @doanduyhai @BriceDutheil Going for Cassandra •  Denormalize (if possible …) •  Know your business ‛ know your queries •  Linear scaling out 31
32. #CassandraSummit @doanduyhai @BriceDutheil Going for Cassandra •  Denormalize (if possible …) •  Know your business ‛ know your queries •  Linear scaling out •  Consistent performance 32
33. #CassandraSummit @doanduyhai @BriceDutheil Data Migration Strategy
34. #CassandraSummit @doanduyhai @BriceDutheil Objectives •  No downtime 34
35. #CassandraSummit @doanduyhai @BriceDutheil Objectives •  No downtime •  No concurrency corner-cases 35
36. #CassandraSummit @doanduyhai @BriceDutheil Objectives •  No downtime •  No concurrency corner-cases •  Safe rollback possible 36
37. #CassandraSummit @doanduyhai @BriceDutheil Objectives •  No downtime •  No concurrency corner-cases •  Safe rollback possible •  Replay-ability & resume-ability 37
38. #CassandraSummit @doanduyhai @BriceDutheil Strategy •  3 phases 38
39. #CassandraSummit @doanduyhai @BriceDutheil Strategy •  3 phases •  Write contacts to both data stores 39
40. #CassandraSummit @doanduyhai @BriceDutheil Strategy •  3 phases •  Write contacts to both data stores •  Old contacts migration 40
41. #CassandraSummit @doanduyhai @BriceDutheil Strategy •  3 phases •  Write contacts to both data stores •  Old contacts migration •  Switch to Cassandra … •  … and deprecate SQL 41
42. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 1 Back end server · · · SQL SQL SQL C* C* C* C* C* Write contactUUID 42 contactId … contactUUID 129363 123e4567- e89b-12d3… 834849 contacId(long) + contactUUID
43. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 1 Back end server · · · SQL SQL SQL C* C* C* C* C* Read 43
44. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 SQL SQL SQL C* C* C* C* C* For each batch of users SELECT * FROM contacts WHERE user_id = … AND contact_uuid IS NULL •  On live production, migrate old contacts 44 Old contacts created before phase 1
45. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 SQL SQL SQL C* C* C* C* C* For each batch of users SELECT * FROM contacts WHERE user_id = … AND contact_uuid IS NULL Logged batches of INSERT INTO contacts(..) VALUES(…) USING TIMESTAMP now() - 1 week •  On live production, migrate old contacts 45 Old contacts created before phase 1
46. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 USING TIMESTAMP now() - 1 week 46
47. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 •  During data migration … 47
48. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 •  During data migration … •  … concurrent writes from the migration batch … 48
49. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 •  During data migration … •  … concurrent writes from the migration batch … •  … and updates from production for the same contact 49
50. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 contact_uuid name (now -1 week) … name (now) … Johny … Johnny … Insert from batch (to the past) Update from production 50
51. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 contact_uuid name (now -1 week) … name (now) … Johny … Johnny … Future reads pick the most up-to-date value 51
52. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 2 "Write to the Past… to save the Future" Libon – 2014/10/08 52
53. #CassandraSummit @doanduyhai @BriceDutheil Migration Phase 3 Back end server · · · SQL SQL SQL C* C* C* C* C* Write ❌ 53
54. #CassandraSummit @doanduyhai @BriceDutheil Business Code Refactoring
55. #CassandraSummit @doanduyhai @BriceDutheil Code Inventory •  Written for RDBMS 55
56. #CassandraSummit @doanduyhai @BriceDutheil Code Inventory •  Written for RDBMS •  Lots of joins (no surprise) 56
57. #CassandraSummit @doanduyhai @BriceDutheil Code Inventory •  Written for RDBMS •  Lots of joins (no surprise) •  Designed around transactions 57
58. #CassandraSummit @doanduyhai @BriceDutheil Code Inventory •  Written for RDBMS •  Lots of joins (no surprise) •  Designed around transactions •  Spring @Transactional everywhere 58
59. #CassandraSummit @doanduyhai @BriceDutheil Code Inventory cont. •  Entities go through Services & Repositories 59 Repositories Services ContactEntity
60. #CassandraSummit @doanduyhai @BriceDutheil Code Inventory cont. •  Hibernate is auto-magic 60
61. #CassandraSummit @doanduyhai @BriceDutheil Code Inventory cont. •  Hibernate is auto-magic •  lazy loading •  1st level cache •  N+1 select 61 Repositories Services ContactEntity
62. #CassandraSummit @doanduyhai @BriceDutheil Which options ? •  Throw existing code … •  … and re-design from scratch for Cassandra 62
63. #CassandraSummit @doanduyhai @BriceDutheil Which options ? •  Throw existing code … •  … and re-design from scratch for Cassandra No way ! 63
64. #CassandraSummit @doanduyhai @BriceDutheil Code Quality •  Existing business code has… •  … ≈ 3500 unit tests 64
65. #CassandraSummit @doanduyhai @BriceDutheil Code Quality •  Existing business code has… •  … ≈ 3500 unit tests •  and ≈600+ integration tests 65
66. #CassandraSummit @doanduyhai @BriceDutheil Code Quality •  We are TDD aficionados … 66
67. #CassandraSummit @doanduyhai @BriceDutheil Code Quality •  We are TDD aficionados … •  … and we love our code coverage 67
68. #CassandraSummit @doanduyhai @BriceDutheil Code Quality "The code coverage is one of your most valuable technical asset" Libon – since beginning 68
69. #CassandraSummit @doanduyhai @BriceDutheil Repositories Services Refactoring Strategy ContactMatchingService ContactService ContactSync ContactEntity n 1 n n 69
70. #CassandraSummit @doanduyhai @BriceDutheil Repositories Services Refactoring Strategy ContactMatchingService ContactService ContactNoSQLEntity ContactSync ContactEntity n 1 n n 70 Proxy
71. #CassandraSummit @doanduyhai @BriceDutheil Repositories Services Refactoring Strategy ContactMatchingService ContactService ContactNoSQLEntity ContactSync ContactEntity n 1 n n Denorm2 … DenormN Denorm1 71 Proxy
72. #CassandraSummit @doanduyhai @BriceDutheil Refactoring Strategy •  Use CQRS •  ContactReadRepository •  ContactWriteRepository •  ContactUpdateRepository •  ContactDeleteRepository 72
73. #CassandraSummit @doanduyhai @BriceDutheil Refactoring Strategy •  ContactReadRepository •  direct sequential read •  no joins •  1 read ≈ 1 SELECT 73
74. #CassandraSummit @doanduyhai @BriceDutheil Refactoring Strategy •  ContactWriteRepository •  write to all denormalized tables •  using CQL logged batches •  use TTLs 74
75. #CassandraSummit @doanduyhai @BriceDutheil Refactoring Strategy •  ContactUpdateRepository •  read-before-write most of the time •  rare updates ‛ acceptable perf penalty 75
76. #CassandraSummit @doanduyhai @BriceDutheil Refactoring Strategy •  ContactDeleteRepository •  delete •  update contact modification date 76
77. #CassandraSummit @doanduyhai @BriceDutheil Outcome •  5 months of 2 men work 77
78. #CassandraSummit @doanduyhai @BriceDutheil Outcome •  5 months of 2 men work •  Many iterations to fix bugs (thanks to IT) 78
79. #CassandraSummit @doanduyhai @BriceDutheil Outcome •  5 months of 2 men work •  Many iterations to fix bugs (thanks to IT) •  Lots of performance benchmarks using Gatling 79
80. #CassandraSummit @doanduyhai @BriceDutheil Gatling Output 80
81. #CassandraSummit @doanduyhai @BriceDutheil Outcome •  5 months of 2 men work •  Many iterations to fix bugs (thanks to IT) •  Lots of performance benchmarks using Gatling ‛ data model & code validation 81
82. #CassandraSummit @doanduyhai @BriceDutheil Outcome •  5 months of 2 men work •  Many iterations to fix bugs (thanks to IT) •  Lots of performance benchmarks using Gatling ‛ data model & code validation •  … we are almost there for production 82
83. #CassandraSummit @doanduyhai @BriceDutheil Data Model
84. #CassandraSummit @doanduyhai @BriceDutheil Denormalization, the good •  Support fast reads •  1 read ≈ 1 SELECT •  Worthy because mostly read, few updates 84
85. #CassandraSummit @doanduyhai @BriceDutheil Denormalization, the bad •  Updating mutable data can be nightmare •  Data model bound by existing client-facing API •  Update paths very error-prone without tests 85
86. #CassandraSummit @doanduyhai @BriceDutheil Data model in detail Contacts_by_id Contacts_by_identifiers Contacts_in_profiles Contacts_by_modification_date Contacts_by_firstname_lastname Contacts_linked_user 86
87. #CassandraSummit @doanduyhai @BriceDutheil Data model in detail Contacts_by_id Contacts_by_identifiers Contacts_in_profiles Contacts_by_modification_date Contacts_by_firstname_lastname Contacts_linked_user 87 user_id always component of partition key
88. #CassandraSummit @doanduyhai @BriceDutheil Scalable design 88 n1 n2 n3 n4 n5 n6 n7 n8 A B C D E F G H user_id1 user_id2 user_id3 user_id4 user_id5
89. #CassandraSummit @doanduyhai @BriceDutheil Scalable design 89 n1 n2 n3 n4 n5 n6 n7 n8 A B C D E F G H user_id1 user_id2 user_id3 user_id4 user_id5
90. #CassandraSummit @doanduyhai @BriceDutheil Bloom filters in action 90 •  For some tables, partition key = (user_id, contact_id) ‛ fast look-up, leverages Bloom filters ‛ touches 1 SSTable most of the time
91. #CassandraSummit @doanduyhai @BriceDutheil Data model in detail Contacts_by_id Contacts_by_identifiers Contacts_in_profiles Contacts_by_modification_date Contacts_by_firstname_lastname Contacts_linked_user 91 Wide partition Bucketed
92. #CassandraSummit @doanduyhai @BriceDutheil A "queue" story 92 •  contacts_by_modification_date •  queue-like pattern
93. #CassandraSummit @doanduyhai @BriceDutheil A "queue" story 93 •  contacts_by_modification_date •  queue-like pattern ‛ buckets to the rescue user_id:2014-12 date35 date12 … … date47 … … … … user_id:2014-11 date11 date12 … … date34 … … … …
94. #CassandraSummit @doanduyhai @BriceDutheil Data model summary •  7 tables for denormalization 94
95. #CassandraSummit @doanduyhai @BriceDutheil Data model summary •  7 tables for denormalization •  Normalize some tables because rare access 95
96. #CassandraSummit @doanduyhai @BriceDutheil Data model summary •  7 tables for denormalization •  Normalize some tables because rare access •  Read-before write in most update scenarios 96
97. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  In SQL, auto-generated long using sequence •  In Cassandra, auto-generated timeuuid 97
98. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  How to store both types ? 98
99. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  How to store both types ? •  As text ? ‛ easy solution … 99
100. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  How to store both types ? •  As text ? ‛ easy solution … •  … but waste of space ! •  because encoded as UTF-8 or ASCII in Cassandra 100
101. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  Long ‛ 8 bytes •  Long as text(UTF-8: 1 byte) ‛ "digits count" bytes 101
102. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  UUID ‛ 16 bytes •  32 hex chars + 4 hyphens = 36 chars •  UUID as text(UTF-8: 1 byte) ‛ 36 bytes •  Bytes overhead = 36 – 16 = 20 bytes 102
103. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  20 bytes wasted per contact uuid 103
104. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  20 bytes wasted per contact uuid •  × 7 denormalizations = 140 bytes per contact uuid 104
105. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  20 bytes wasted per contact uuid •  × 7 denormalizations = 140 bytes per contact uuid •  × 109 contacts = 140 GB wasted 105 not even counting replication factor …
106. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  ‛ just save contact id as byte[ ] 106
107. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  ‛ just save contact id as byte[ ] •  Achilles @TypeTransformer for automatic conversion (see later) 107
108. #CassandraSummit @doanduyhai @BriceDutheil Notes on contact_id •  ‛ just save contact id as byte[ ] •  Achilles @TypeTransformer for automatic conversion (see later) •  Use blobAsBigInt( ) or blobAsUUID( ) to view data 108
109. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Advanced "object mapper" •  Fluent API •  Tons of features •  TDD friendly 109
110. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Dirty checking, why is it important ? 110
111. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Dirty checking, why is it important ? •  1 contact ≈ 8 mutable fields 111
112. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Dirty checking, why is it important ? •  1 contact ≈ 8 mutable fields •  × 7 denormalizations = 56 update combinations … 112
113. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Dirty checking, why is it important ? •  1 contact ≈ 8 mutable fields •  × 7 denormalizations = 56 update combinations … •  and not even counting multiple fields updates … 113
114. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Are you going to manually generate 56+ prepared statements for all possible updates ? 114
115. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Are you going to manually generate 56+ prepared statements for all possible updates ? •  Or just use dynamic plain string statements and get some perf penalty ? 115
116. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Dirty check in action //No read-before-write ContactEntity proxy = manager.forUpdate(ContactEntity.class, contactId); proxy.setFirstName(…); proxy.setLastName(…); //type-safe updates proxy.setAddress(…); manager.update(proxy); 116
117. #CassandraSummit @doanduyhai @BriceDutheil Achilles 117 Empty Entity DirtyMap Proxy Setters interception PrimaryKey
118. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Dynamic statements generation UPDATE contacts SET firstname=?, lastname=?,address=? WHERE contact_id=? 118 prepared statements are cached, of course
119. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Insert strategy, what is it ? 119
120. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Simple INSERT prepared statement INSERT INTO contacts(contact_id,name,age,address,gender,avatar,…) VALUES(?, ?, ?, ? … ?); 120
121. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Runtime values binding •  some columns are optional preparedStatement.bind(49374,’John DOE’,33, null, null, …, null); 121
122. #CassandraSummit @doanduyhai @BriceDutheil Achilles Wait … are you saying inserting null in CQL??? 122
123. #CassandraSummit @doanduyhai @BriceDutheil Achilles Inserting null 㲇 creating tombstones 123
124. #CassandraSummit @doanduyhai @BriceDutheil Achilles Inserting null 㲇 creating tombstones × 7 denormalizations 124
125. #CassandraSummit @doanduyhai @BriceDutheil Achilles Inserting null 㲇 creating tombstones × 7 denormalizations × billions of contacts created 125 not even counting replication factor …
126. #CassandraSummit @doanduyhai @BriceDutheil Achilles @Entity(table = "contacts_by_id ») @Strategy(insert = InsertStrategy.NOT_NULL_FIELDS) public class ContactById { } 126 •  Simple annotation
127. #CassandraSummit @doanduyhai @BriceDutheil Achilles •  Runtime dynamic INSERT statement INSERT INTO contacts(contact_id, name, age, address,) VALUES(:contact_id, :name, :age, :address); 127 prepared statements are cached, of course
128. #CassandraSummit @doanduyhai @BriceDutheil Achilles @PartitionKey @Column(name = "contact_id") @TypeTransformer(valueCodecClass = ContactIdToBytes.class) private ContactId contactId; 128 •  Remember the contactId ⁶ byte[ ] conversion ? BYOC ‛ Bring Your Own Codec
129. #CassandraSummit @doanduyhai @BriceDutheil Achilles public interface Codec<FROM, TO> { Class<FROM> sourceType(); Class<TO> targetType(); TO encode(FROM fromJava) FROM decode(TO fromCassandra); } 129
130. #CassandraSummit @doanduyhai @BriceDutheil Achilles 130 2014-12-01 14:25:20,554 Bound statement : [INSERT INTO contacts.contacts_by_modification_date(user_id,month_bucket,modification_date,...) VALUES (:user_id,:month_bucket,:modification_date,...) USING TTL :ttl;] with CONSISTENCY LEVEL [LOCAL_QUORUM] 2014-12-01 14:25:20,554 bound values : [222130151, 2014-12, e13d0d50-7965-11e4-af38-90b11c2549e0, ...] 2014-12-01 14:25:20,701 Bound statement : [SELECT birthday,middlename,avatar_size,... FROM contacts.contacts_by_modification_date WHERE user_id=:user_id AND month_bucket=:month_bucket AND (modification_date)>=(:modification_date) ORDER BY modification_date ASC;] with CONSISTENCY LEVEL [LOCAL_QUORUM] 2014-12-01 14:25:20,701 bound values : [222130151, 2014-10, be6bc010-6109-11e4-b385-000038377ead] •  Dynamic logging in action
131. #CassandraSummit @doanduyhai @BriceDutheil Achilles 131 •  Dynamic logging •  runtime activation •  no need to recompile/re-deploy •  save us hours of debugging •  TRACE log level ‛ query tracing
132. #CassandraSummit @doanduyhai @BriceDutheil Take Away
133. #CassandraSummit @doanduyhai @BriceDutheil Conditions for success •  Data modeling is crucial 133
134. #CassandraSummit @doanduyhai @BriceDutheil Conditions for success •  Data modeling is crucial •  Double-run strategy & timestamp trick FTW 134
135. #CassandraSummit @doanduyhai @BriceDutheil Conditions for success •  Data modeling is crucial •  Double-run strategy & timestamp trick FTW •  Data type conversion can be tricky 135
136. #CassandraSummit @doanduyhai @BriceDutheil Conditions for success •  Data modeling is crucial •  Double-run strategy & timestamp trick FTW •  Data type conversion can be tricky •  Benchmark ! 136
137. #CassandraSummit @doanduyhai @BriceDutheil Conditions for success •  Data modeling is crucial •  Double-run strategy & timestamp trick FTW •  Data type conversion can be tricky •  Benchmark ! •  Mindset shifts for the team 137
138. Thank You