VoxxedDays Luxembourg 2019
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This document discusses different API options for databases: REST, gRPC, and GraphQL. It begins with an overview of Apache Cassandra and its key features as a distributed database. It then covers an API design methodology, including conceptual and logical data modeling, mapping queries to tables, and creating the physical schema. The document presents criteria for evaluating API choices and provides pros and cons of REST, gRPC, and GraphQL. It concludes that REST is best for CRUD operations, gRPC for high performance services, and GraphQL for discoverability and flexible payloads.
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VoxxedDays Luxembourg 2019
- 1. Designing API for Databases: Which one should you pick ? GraphQL @clunven | #voxxed_lu | @voxxed_lu Casino 2000, Luxembourg
- 2. @clunven | @voxxed_lu | #voxxed_lu About me Cedrick Lunven Developer Advocate Creator Contributor
- 3. © DataStax, All Rights Reserved.Confidential © DataStax, All Rights Reserved. Agenda 2 3 4 DECISION TREE DEMONSTRATION AND CODE REVIEW API DESIGN METHODOLOGY 1 APACHE CASSANDRA™ OVERVIEW
- 5. @clunven | @voxxed_lu | #voxxed_lu This is a DISTRIBUTED Database Node • Up to 1TB • 3000 Tx/s/core
- 6. @clunven | @voxxed_lu | #voxxed_lu …which scale linearly
- 7. @clunven | @voxxed_lu | #voxxed_lu Data is distributed 0 13 25 38 50 63 75 88 59 (data) RF=2 59 (data) RF=3 replicated
- 8. @clunven | @voxxed_lu | #voxxed_lu Cassandra is « AP » Consistency Partition Tolerance Availability
- 9. @clunven | @voxxed_lu | #voxxed_lu Tuneable Consistency RF=3 Client CL=ONECL=QUORUMCL=ALL
- 10. @clunven | @voxxed_lu | #voxxed_lu …anywhere Cluster Datacenter (Ring)
- 11. @clunven | @voxxed_lu | #voxxed_lu Sweet spots 1. High Throughput (because we can keep up) 2. High Volume (because we scale linearly and still OLTP) 3. High Availability (replication, masterless) 4. Data distribution (read/write around the globe)
- 12. @clunven | @voxxed_lu | #voxxed_lu • Le KEYSPACE est comme un schéma dans Oracle, une isolation des données 12 Projet_X Keyspace Une table contient une CLEF PRIMAIRE contenant 2 parties : La partition key et le reste (clustering columns). Chaque valeur de partition key est hashée sous la forme d’un token. Plusieurs lignes avec la même partition key constitue une partition. Data Modelling
- 13. @clunven | @voxxed_lu | #voxxed_lu • Syntaxe proche du SQL pour les bases relationnelles • Création des objets avec le DDL : • CREATE, INSERT, UPDATE, DELETE, GRANT, REVOKE, SELECT, WHERE 13 Exemple CREATE TABLE market_prices ( symbol TEXT, date TIMESTAMP, price DECIMAL, side INT, PRIMARY KEY (symbol, date) ) WITH CLUSTERING ORDER BY(date DESC); Cassandra Query Language
- 14. LIST, SET, MAP, UDT
- 15. API DESIGN METHODOLOGY From zero to hero
- 16. @clunven | @voxxed_lu | #voxxed_lu Reference Application http://killrvideo.github.io
- 17. @clunven | @voxxed_lu | #voxxed_lu Api Design Methodology 1 killrvideo-dse Drivers DAO3 killrvideo-api-rest killrvideo-api-grpc killrvideo-api-graphql 2
- 18. @clunven | @voxxed_lu | #voxxed_lu Designing Data Model Entities & Relationships Queries
- 19. @clunven | @voxxed_lu | #voxxed_lu Conceptual Data Model
- 20. @clunven | @voxxed_lu | #voxxed_lu Application Workflow R1: Find comments related to target video using its identifier • Get most recent first • Implement Paging R2: Find comments related to target user using its identifier • Get most recent first • Implement Paging R3: Implement CRUD operations
- 21. @clunven | @voxxed_lu | #voxxed_lu Mapping Q2: Find comments posted for a user with a known id (show most recent first) comments_by_video comments_by_user Q1: Find comments for a video with a known id (show most recent first) Q3: CRUD Operations
- 22. @clunven | @voxxed_lu | #voxxed_lu 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
- 23. @clunven | @voxxed_lu | #voxxed_lu 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 ↑
- 24. @clunven | @voxxed_lu | #voxxed_lu 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);
- 25. @clunven | @voxxed_lu | #voxxed_lu How? Conceptual Data Model (Entities, Relations) Application Workflow (Queries) Database Family (Technos +Table)
- 27. @clunven | @voxxed_lu | #voxxed_lu
- 28. @clunven | @voxxed_lu | #voxxed_lu
- 29. DECISION TREE Because we are serious
- 30. @clunven | @voxxed_lu | #voxxed_lu Analysis Criteria 📋 Conceptual Data Model Application Workflow (Queries) Database Family (Technos +Table) Caching Syncvs AsyncReactive SLA (Volume) Data Integrity Filters Paging Sort Latencies Throughput Versionning Confidentiality Atomicity Cardinality Developers 👤 Users/Consumers Language CodeFirst/ Vs SchemaFirst Documentation Test Build Packaging Api Catalog Internalvs Public Techno Profile XP
- 31. @clunven | @voxxed_lu | #voxxed_lu Analysis Matrix
- 32. @clunven | @voxxed_lu | #voxxed_lu Decision Tree
- 33. @clunven | @voxxed_lu | #voxxed_lu Decoupling Client / Server (Schema on read) Flexibility: Sync, Async, Reactive + Multi payload Api Lifecycle (Versioning) Tooling (API Management, Serverless) Verbose payloads (json, xml) No discoverability Not best fit for command-like (functions) API (RPC) CRUD superstar Relevant for OLTP mutations and statuses Public and web APIs
- 34. @clunven | @voxxed_lu | #voxxed_lu High Performances (http/2 – binary serialisation) Multiple stubs : Sync, Async, Streaming Multi languages (Interoperability) Strongly coupled (schema with proto files) No discoverability Protobuf serialization format Distributed network of services (no waits) High throughput & streaming use cases Command-like, RPC
- 35. @clunven | @voxxed_lu | #voxxed_lu Discoverability, documentation Custom payloads Match standards (Json | Http) Single endpoint (versioning, monitoring, security) Complex implementation (tooling, still young) Nice for customers nasty for DB (N+1 select) BFF : Backend for frontend Service aggregation | composition (joins) When bandwidth matters (mobile phones) GraphQL
- 36. RESOURCES Because COPY/PASTE is the most important skill for developers
- 37. @clunven | @voxxed_lu | #voxxed_lu References https://github.com/clun/api_Rest_Grpc_GraphQL
- 38. THANK YOU
Editor's Notes
- One of Cassandra's fault-tolerance strategies is replication. Replication is a matter of duplicating data across nodes. The number of replicas is called the Replication Factor. Let’s look at some examples… <click> (RF=1 appears) Let’s start with the simplest example: a replication factor of 1 – only a single copy It’s not something you would likely do in production, but it's a good place to start the discussion. <click> (data appears) Here we’re showing a write request Some data with a partition token of 59 <click> (data moves to node) The top-right node will serve as the coordinator <click> (data turns purple) Notice 59 falls in the purple range <click> (data move to the node) So the coordinator forwards the data to the purple node <click> (data clears) <click> (RF=2 appears) Let's increase the replication factor to 2 <click> (ring colors double) This doubles the range that each node is responsible for. For example, node 75 becomes responsible for the red range and the purple range <click> (data appears) Again our request to write partition with token 59 arrives. But this time the coordinator sends it to two nodes. <click> (data moves) <click> (data fades) Let's increase the replication factor to 3 <click> (RF=3 appears) This means that each node is responsible for 3 ranges <click> (3 ranges appear) Once again, the data arrives at the coordinator Where will the coordinator send the data this time? <click> (data moves) We see the data replicated to all three nodes <click> (data fades) So, in a nutshell, that's how replication works
- Consistency level is different than replication factor. On a read, consistency level is how many replicas you read Each replica has a time stamp. The most recent replica wins In this example, imagine we have a replication factor of 3. <click> (shows write arrows) So when we write, we write 3 replicas of the data. <click> (shows CL=ONE) Now let's say we want to read from this cluster with a consistency level of 1. In this case, we only need to read from a single node to resolve the data. <click> (shows read arrows) Now, we can change the consistency level to quorum, <click> (CL=QUORUM appears) Which means we want to read a majority of the replicas. Since the replication factor is 3, quorum implies reading 2 replicas <click> (second read line appears) Notice, if the replicas disagree, the coordinator returns the data with the most recent time stamp <click> (CL clears) We can even specify a consistency level of ALL, <click> (CL=ALL appears) Which means we will read all replicas <click> (third read line appears) (pause to let people absorb)
- the default data model in DSE is tabular. It is similar to an RDBMS table but more flexible/dynamic Table is partitioned by one or more columns enabling fast lookups by partition keys A keyspace is the outermost container containing data corresponding to applications, keyspace is similar to database a relational database. each row in a column family is indexed by its key and contains ordered columns. Such unique data model allow DSE to deal with unstructured, schemaless data.
- Cassandra Query Language (CQL) is the primary language for communicating with DSE data management platform. CQL is a SQL-like language and allows you to create keyspace and tables, insert and query tables plus other activities using the language you are already familiar with.
- ALEXANDRE Few logos: As we already told we are using Java why not using the last Java 12 Services are implemented and connected with Spring Everything is wrapped into a Spring boot 2.1 application Services are exposed as REST with Spring MVC Did you see our gray hairs and beards here, we do Java, we are serious people and do not play with the teenager language JavaScript
- ALEXANDRE Show repository, stress its simplicity and blocking calls Show controller, same thing Show controller unit test, run tests