Cassandra data modeling

1. C a s s a n d r a
QUERY DRIVEN
DEVELOPMENT
How Cassandra influences the way we build applications.

2. AGENDA
2

3. AGENDA
• Relational Databases
• Column Family Databases
• Cassandra Concepts
• Architecture
• CQL
• Tables
• Primary Keys
• Partition Keys
• Clustering Columns
• Data Modeling
• Conceptual Model
• Application Workflow
• Logical Model
• Physical Model
• Anti-Patterns
3

4. RELATIONAL
DATABASES
4

5. RELATIONAL DATABASES
5

6. RELATIONAL DATABASES
A relational database is in general…
Suited for:
• Relational data (well defined domain model)
• Structured data (tabular data)
• "Small" data volume
• ACID operations (transactions)
• Ad-hoc and/or complex queries (joins)
Not suited for:
• Unstructured data
• Large data volume
• Geographically distributed users
• Scaling
• Reliability
6

7. COLUMN FAMILY
DATABASES
7

8. COLUMN FAMILY DATABASES
8

9. COLUMN FAMILY DATABASES
9

10. COLUMN FAMILY DATABASES
A column family model is in general…
Suited for:
• Structured and unstructured data
• Large data volumes
• Geographically distributed users
• Scaling
• Reliability
Not suited for:
• ACID operations (transactions)
• Ad-hoc and/or complex queries (joins)
10

11. CASSANDRA’S
ARCHITECTURE
11

12. CASSANDRA’S ARCHITECTURE
12

13. CASSANDRA’S ARCHITECTURE
A Cassandra cluster…
• Is a distributed system.
• Does not have a leader or master node (all nodes are equal).
• Replicates data across nodes (allows fault tolerance).
• Partitions data across nodes (allows fast writes/reads).
• Is fault tolerant.
• Is eventually consistent.
• Can be distributed across data centers.
• Is usually called a Ring.
13

14. CQL
14

15. CQL
15

16. CQL
CQL…
• Stands for Cassandra Query Language.
• Is similar to SQL.
• Does not support joins.
• Supports user defined types.
• Supports user defined functions.
• Supports materialized views.
16

17. TABLES
17

18. TABLES
18

19. TABLES
Tables in Cassandra…
• Are an abstraction.
• Are actually implemented as column families.
19

20. PRIMARY
KEYS
20

21. PRIMARY KEYS
21

22. PRIMARY KEYS
A primary key fulfill the following purposes…
• Uniqueness of a record within a table
• Search criteria (what columns can be used to filter the table)
22

23. PARTITION
KEYS
23

24. PARTITION KEYS
24

25. PARTITION KEYS
Partition keys define…
• How the data is going to be partitioned across the cluster
• Which columns require an equality search criteria
• Columns defined in the partition key must be present in the where clause of a query
25

26. CLUSTERING
COLUMNS
26

27. CLUSTERING COLUMNS
27

28. CLUSTERING COLUMNS
Clustering columns define…
• How the data is grouped within a partition
• The ordering of the data within the table
Clustering columns also…
• Can be used both as equality and inequality search criteria
• Are not required in a where clause.
28

29. DATA MODELING
29

30. DATA MODELING
30

31. DATA MODELING
Data modeling in Cassandra is composed by the following steps…
• Definition of the Conceptual Model
• Definition of the Application Workflow
• Derivation and selection of the Logical Model
• Derivation of the Physical Model
• Translation of the Physical Model into CQL scripts
Cassandra data modeling principles:
• Know your data
• Know your queries
• Nest data
• Duplicate data (denormalize)
31

32. CONCEPTUAL
MODEL
32

33. CONCEPTUAL MODEL
33

34. CONCEPTUAL MODEL
A conceptual model…
• Is usually described as an Entity Relationship diagram.
• Describes domain entities, its attributes and relationships.
• Enables collaboration between business and technical people.
• Allows abstraction of the implementation details.
• Facilitates understanding of the domain.
34

35. APPLICATION
WORKFLOW
35

36. APPLICATION WORKFLOW
36

37. APPLICATION WORKFLOW
37

38. APPLICATION WORKFLOW
The application workflow…
• Helps identifying tasks and its dependencies.
• Defines which queries are required by the application.
• Helps identifying which queries need to run first.
38

39. LOGICAL MODEL
39

40. LOGICAL MODEL
40

41. LOGICAL MODEL
A logical model…
• It’s an abstraction of the physical schema
• Identifies tables along with its columns, partition keys, clustering columns and other
metadata
• Allows us to think about the data model, partitioning and ordering, without worrying
about the implementation details.
41

42. LOGICAL MODEL
42

43. LOGICAL MODEL
Mapping Rules
• Rule 1: Entities and relationships (becomes tables)
• Rule 2: Equality search and attributes (becomes PKs)
• Rule 3: Inequality search and attributes (becomes clustering columns)
• Rule 4: Ordering attributes (defines ordering: ASC, DESC)
• Rule 5: Key attributes (guarantees record uniqueness)
43

44. LOGICAL MODEL
Mapping Patterns
• Entity mapping (query attribs = key attribs; query attribs != key attribs)
• 1:1 relationship mapping (query attribs = key attribs; query attributes != key attribs)
• 1:n relationship mapping
• m:n relationship mapping
• Hierarchical mapping
44

45. LOGICAL MODEL
45

46. PHYSICAL MODEL
46

47. PHYSICAL MODEL
47

48. PHYSICAL MODEL
48

49. PHYSICAL MODEL
The physical model…
• Is the actual implementation of the logical model
• Defines column types
• It’s where we think about optimizations
49

50. ANTI-PATTERNS
50

51. ANTI-PATTERNS
Things we should not do in Cassandra…
• Table scan (too expensive, searches the entire cluster)
• Secondary indexes (stored locally in a node, too expensive, searches the entire cluster)
• Multi-table (requires multiple look ups and joins in the client side)
• Reads before writes
51

52. QUESTIONS?
52

53. For questions or suggestions:
Contact
jsilva@thoughtworks.com
joao.dasilva@equifax.com
THANK YOU