Intro to Cassandra for Java Developers

Are you a developer tired of being let down by a database? Need scale and just can’t get it? Old solutions are just that, OLD! This online introductory talk is perfect for those looking to migrate to, or build cloud-native applications with NoSQL technology and embrace the future of data driven applications.

Session Details:
- Fundamental NoSQL concepts with Apache Cassandra™
- Why Cassandra and what it can do
- Data modeling concepts in NoSQL Apache Cassandra™ to make cloud-native application development a breeze
- Resources for Java developers
- Where to go next to continue your learning

Transcript

1. Intro to Cassandra for Java Developers
2. David Jones-Gilardi 🚀 DevRel @SonicDMG @SonicDMG ➢ Training ➢ Teaching ➢ Reference Applications ➢ Support ➢ Public Speaking ➢ Advanced User
3. Drivers & Stargate 04 Software Development Kit 05 Agenda WTH is Cassandra? 01 Tables, partitions, oh my! 02 The art of data modeling 03 Resources 06
4. NODE NODE NODE NODE NODE NODE NODE Apache Cassandra™ 1 Installation = 1 NODE ✔ Capacity = ~ 2-4TB ✔ Throughput = LOTS Tx/sec/core Communication: ✔ Gossiping DataCenter | Ring = NoSQL Distributed Database
5. - Big Data Ready - Highest Availability - Geographical Distribution - Read/Write Performance - Vendor Independent Apache Cassandra™= NoSQL Distributed Database
6. Country City Population USA New York 8.000.000 USA Los Angeles 4.000.000 FR Paris 2.230.000 DE Berlin 3.350.000 UK London 9.200.000 AU Sydney 4.900.000 FR Toulouse 1.100.000 JP Tokyo 37.430.000 IN Mumbai 20.200.000 DE Nuremberg 500.000 CA Montreal 4.200.000 CA Toronto 6.200.000 Partition Key Data is Distributed
7. Country City Population USA New York 8.000.000 USA Los Angeles 4.000.000 UK London 9.200.000 AU Sydney 4.900.000 JP Tokyo 37.430.000 IN Mumbai 20.200.000 DE Berlin 3.350.000 DE Nuremberg 500.000 CA Montreal 4.200.000 CA Toronto 6.200.000 FR Toulouse 1.100.000 FR Paris 2.230.000 Data is Distributed
8. RF = ? Replication Factor means the number of nodes used to store each partition 0 50 33 17 83 67 Replication Factor
9. RF = 1 Replication Factor 1 means that every partition is stored on 1 node 0 50 33 17 83 67 USA New York 8.000.000 4.000.000 Los Angeles USA Replication Factor
10. RF = 2 Replication Factor 2 means that every partition is stored on 2 nodes 0 50 33 17 83 67 USA New York 8.000.000 4.000.000 Los Angeles USA USA New York 8.000.000 4.000.000 Los Angeles USA Replication Factor
11. RF = 3 Replication Factor 3 means that every partition is stored on 3 nodes 0 50 33 17 83 67 USA New York 8.000.000 4.000.000 Los Angeles USA USA New York 8.000.000 4.000.000 Los Angeles USA USA New York 8.000.000 4.000.000 Los Angeles USA Replication Factor
12. 59 (data) RF = 3 0 50 33 17 83 67 USA New York 8.000.000 4.000.000 Los Angeles USA Replication Factor
13. RF = 3 0 50 33 17 83 67 59 (data) USA New York 8.000.000 4.000.000 Los Angeles USA 67 83 0 Replication Factor
14. RF = 3 0 50 33 17 83 67 59 (data) 59 (data) 59 (data) Replication Factor
15. RF = 3 0 50 33 17 83 67 59 (data) 59 (data) 59 (data) Hint Replication Factor
16. RF = 3 0 50 33 17 83 67 59 (data) 59 (data) 59 (data) Hint Replication Factor
17. RF = 3 0 50 33 17 83 67 59 (data) 59 (data) 59 (data) Replication Factor
18. Client Write CL = QUORUM Read CL = QUORUM Client Immediate Consistency
19. • Geographic Distribution • Hybrid-Cloud and Multi-Cloud Data Distributed Everywhere On-premise
20. High Throughput High Volume Heavy Writes Heavy Reads Event Streaming Log Analytics Internet of Things Other Time Series Mission-Critical No Data Loss Always-on Scalability Availability Distributed Cloud-native Caching Pricing Market Data Inventory Banking Retail Tracking / Logistics Customer Experience API Layer Hybrid-cloud Enterprise Data Layer Multi-cloud Modern Cloud Applications Global Presence Workload Mobility Compliance / GDPR Understanding Use Cases
21. Drivers & Stargate 04 Software Development Kit 05 Agenda WTH is Cassandra? 01 Tables, partitions, oh my! 02 The art of data modeling 03 Resources 06
22. Data Structure: a Cell An intersection of a row and a column, stores data.
23. Data Structure: a Row A single, structured data item in a table.
24. Data Structure: a Partition A group of rows having the same partition token, a base unit of access in Cassandra. IMPORTANT: stored together, all the rows are guaranteed to be neighbors. ID First Name Last Name Department 1 John Doe Wizardry 399 Marisha Chapez Wizardry 415 Maximus Flavius Wizardry
25. Data Structure: a Table A group of columns and rows storing partitions. ID First Name Last Name Department 1 John Doe Wizardry 2 Mary Smith Dark Magic 3 Patrick McFadin DevRel
26. • Tabular data model, with one twist • Tables are organized in rows and columns • Groups of related rows called partitions are stored together on the same node (or nodes) • Each row contains a partition key ◦ One or more columns that are hashed to determine which node(s) store that data Data Structure: Overall Keyspace Table - - - - x - - - - - - y - - - - - - - - - z - - - columns partitions rows Partition key
27. Example Data: Users organized by city Keyspace killrvideo Table users_by_city Clustering columns partitions rows Partition key column City Last Name First Name Address Email Phoenix Hellson Kevin 23 Jackson St. kevin@gmail.com Lastfall Norda 3 Stone St dino@gmail.com Smith Jana 3 Stone St wilm@gmail.com Seattle Franklin George 2 Star St gjet@gmail.com Jackson Jane 2 Star St jane@gmail.com Jasons Judy 2 StarSt judy@gmail.com Data columns
28. Creating a Table in CQL CREATE TABLE killrvideo.users_by_city ( city text, last_name text, first_name text, address text, email text, PRIMARY KEY ((city), last_name, first_name, email)); keyspace column definitions Partition key Primary key table Clustering columns
29. Primary Key CREATE TABLE killrvideo.users_by_city ( city text, last_name text, first_name text, address text, email text, PRIMARY KEY ((city), last_name, first_name, email)); An identifier for a row. Consists of at least one Partition Key and zero or more Clustering Columns. MUST ENSURE UNIQUENESS. MAY DEFINE SORTING. Partition key Clustering columns PRIMARY KEY ((city), last_name, first_name, email); PRIMARY KEY (user_id); Examples:
30. Partition Key CREATE TABLE killrvideo.users_by_city ( city text, last_name text, first_name text, address text, email text, PRIMARY KEY ((city), last_name, first_name, email)); An identifier for a partition. Consists of at least one column, may have more if needed PARTITIONS ROWS. Partition key Clustering columns PRIMARY KEY (user_id); PRIMARY KEY ((video_id), comment_id); Examples:
31. Clustering Column(s) CREATE TABLE killrvideo.users_by_city ( city text, last_name text, first_name text, address text, email text, PRIMARY KEY ((city), last_name, first_name, email)); Used to ensure uniqueness and sorting order. Optional. Partition key Clustering columns PRIMARY KEY ((video_id), comment_id); PRIMARY KEY ((city), last_name, first_name); PRIMARY KEY ((city), last_name, first_name, email); PRIMARY KEY ((video_id), created_at, comment_id); Not Unique Not Sorted
32. Rules of a Good Partition • Store together what you retrieve together • Avoid big partitions • Avoid hot partitions PRIMARY KEY ((video_id), created_at, comment_id); PRIMARY KEY ((comment_id), created_at); The Slide of the Year Award! Example: open a video? Get the comments in a single query!
33. Rules of a Good Partition PRIMARY KEY ((video_id), created_at, comment_id); The Slide of the Year Award! PRIMARY KEY ((country), user_id); • Up to 2 billion cells per partition • Up to ~100k rows in a partition • Up to ~100MB in a Partition • Store together what you retrieve together • Avoid big partitions • Avoid hot partitions
34. Rules of a Good Partition PRIMARY KEY ((sensor_id), reported_at); The Slide of the Year Award! • Store together what you retrieve together • Avoid big and constantly growing partitions! • Avoid hot partitions Example: a huge IoT infrastructure, hardware all over the world, different sensors reporting their state every 10 seconds. Every sensor reports its UUID, timestamp of the report, sensor’s value. • Sensor ID: UUID • Timestamp: Timestamp • Value: float
35. Rules of a Good Partition PRIMARY KEY ((sensor_id), reported_at); The Slide of the Year Award! BUCKETING PRIMARY KEY ((sensor_id, month_year), reported_at); • Sensor ID: UUID • MonthYear: Integer or String • Timestamp: Timestamp • Value: float • Store together what you retrieve together • Avoid big and constantly growing partitions! • Avoid hot partitions Example: a huge IoT infrastructure, hardware all over the world, different sensors reporting their state every 10 seconds. Every sensor reports its UUID, timestamp of the report, sensor’s value.
36. Rules of a Good Partition PRIMARY KEY ((video_id), created_at, comment_id); PRIMARY KEY (user_id); The Slide of the Year Award! PRIMARY KEY ((country), user_id); • Store together what you retrieve together • Avoid big partitions • Avoid hot partitions
37. Drivers & Stargate 04 Software Development Kit 05 Agenda WTH is Cassandra? 01 Tables, partitions, oh my! 02 The art of data modeling 03 Resources 06
38. Normalization “Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by Edgar F. Codd as part of his relational model.” PROS: Simple write, Data Integrity CONS: Slow read, Complex Queries userId deptId firstName lastName 1 1 Edgar Codd 2 1 Raymond Boyce departmentId department 1 Engineering 2 Math Employees Departments 38
39. Denormalization “Denormalization is a strategy used on a database to increase performance. In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data” PROS: Quick Read, Simple Queries CONS: Multiple Writes, Manual Integrity userId firstName lastName department 1 Edgar Codd Engineering 2 Raymond Boyce Engineering 3 Sage Lahja Math 4 Juniper Jones Botany Employees 39
40. Relational Data Modelling 1. Analyze raw data 2. Identify entities, their properties and relations 3. Design tables, using normalization and foreign keys. 4. Use JOIN when doing queries to join normalized data from multiple tables Data Models Application
41. NoSQL Data Modelling 1. Analyze user behaviour (customer first!) 2. Identify workflows, their dependencies and needs 3. Define Queries to fulfill these workflows 4. Knowing the queries, design tables, using denormalization. 5. Use BATCH when inserting or updating denormalized data of multiple tables Application Models Data
42. Designing Process: Step by Step Entities & Relationships Queries
43. Designing Process: Conceptual Data Model
44. Designing Process: Application Workflow Use-Case I: • A User opens a Profile WF2: Find comments related to target user using its identifier, get most recent first Use-Case II: • A User opens a Video Page WF1: Find comments related to target video using its identifier, most recent first
45. Designing Process: Mapping Query I: Find comments posted for a user with a known id (show most recent first) Query II: Find comments for a video with a known id (show most recent first) comments_by_video comments_by_user
46. Designing Process: Mapping SELECT * FROM comments_by_user WHERE userid = <some UUID> SELECT * FROM comments_by_video WHERE videoid = <some UUID> comments_by_video comments_by_user
47. Designing Process: Logical Data Model userid creationdate commentid videoid comment comments_by_user K C ↑ videoid creationdate commentid userid comment comments_by_video C ↑ K C ↑ ↑ C
48. Designing Process: Physical Data Model userid commentid videoid comment comments_by_user TIMEUUID K TEXT C UUID UUID ↑ videoid commentid userid comment comments_by_video TIMEUUID K TEXT C UUID UUID ↑
49. Designing Process: Schema DDL CREATE TABLE IF NOT EXISTS comments_by_user ( userid uuid, commentid timeuuid, videoid uuid, comment text, PRIMARY KEY ((userid), commentid) ) WITH CLUSTERING ORDER BY (commentid DESC); CREATE TABLE IF NOT EXISTS comments_by_video ( videoid uuid, commentid timeuuid, userid uuid, comment text, PRIMARY KEY ((videoid), commentid) ) WITH CLUSTERING ORDER BY (commentid DESC);
50. Drivers & Stargate 04 Software Development Kit 05 Agenda WTH is Cassandra? 01 Tables, partitions, oh my! 02 The art of data modeling 03 Resources 06
51. Datastax Drivers One of set drivers to connect them all - January 2020 Connectivity • Token & Datacenter Aware • Load Balancing Policies • Retry Policies • Reconnection Policies • Connection Pooling • Health Checks • Authentication | Authorization • SSL Query • CQL Support • Schema Management • Sync/Async/Reactive API • Query Builder • Compression • Paging Parsing Results • Lazy Load • Object Mapper • Spring Support • Paging
52. Data Gateway Rationale 52 Ubiquitous, API-based Consumption MICROSERVICES DEVELOPERS Developers want the option to use modern APIs and development gateways. Cassandra is a database designed for the new standard and the associated APIs that facilitate the first choice of developers.
53. Modules GRPC (incubating) CQL GraphQL Document API REST Cassandra 3.11 Cassandra 4.0 Cassandra DSE 6.8 Pulsar
54. Drivers & Stargate 04 Software Development Kit 05 Agenda WTH is Cassandra? 01 Tables, partitions, oh my! 02 The art of data modeling 03 Resources 06
55. c REST API DOC API GRAPHQL API CQL Interface Devops API
56. c REST API DOC API GRAPHQL API CQL Interface Devops API Astra Java SDK Drivers (CQL)
57. c REST API DOC API GRAPHQL API CQL Interface Devops API Astra Java SDK Drivers (CQL)
58. Drivers & Stargate 04 Software Development Kit 05 Agenda WTH is Cassandra? 01 Tables, partitions, oh my! 02 The art of data modeling 03 Resources 06
59. == Fully managed Cassandra Without the ops!
60. DataStax Astra Global Scale Put your data where you need it without compromising performance, availability, or accessibility. No Operations Eliminate the overhead to install, operate, and scale Cassandra. 5M writes, 30M reads, 40GB Free Tier Launch a database in the cloud with a few clicks, no credit card required.
61. MORE LEARNING!!!! Developer site: datastax.com/dev • Developer Stories • Hands-on learning scenarios with Katacoda • Cassandra Fundamentals • https://www.datastax.com/learn/cassandra-fundamentals • Data Modeling course https://www.datastax.com/dev/modeling • Java SDK and example app • https://github.com/datastax/astra-sdk-java • https://github.com/datastaxdevs/workshop-spring-stargate • Astra • https://astra.datastax.com/
62. ✔ Academy.datastax.com ✔ datastax.com/dev ✔ community.datastax.com ✔ Datastax Developers YouTube Channel 62
63. Weekly Workshops https://www.datastax.com/workshops 63
64. Join our 10k Discord Community The Fellowship of the RINGS https://bit.ly/cassandra-workshop 64
65. Thank you!