![Fauna Query Language (FQL)
If all members of a class should share a property, we can add an index.
CreateIndex({
name: "office_by_id",
source: Class("office"),
terms: [{ field: [ "data", "id" ] }],
unique: true
})](https://image.slidesharecdn.com/faunadb-functionalnosql-190318214753/85/Fauna-DB-Functional-NoSQL-13-320.jpg)
Fauna DB - Functional NoSQL
- 1. FaunaDB - Functional NoSQL Conor Farrell, Lambda Lounge March 2019
- 2. Overview - What’s NoSQL? - Distributed DB basics - FaunaDB - Fauna Query Language (FQL) - Demo! - Questions
- 3. NoSQL - AKA Not Only SQL - Common usecases: real-time, big data - K-V, document store, graph… - Generally scale horizontally - Store data based on its access patterns, in contrast to relational
- 4. NoSQL compromises - Many do not provide ACID guarantees - BASE (basically available, soft state, eventually consistent) guarantees - Consistency (in the CAP sense) usually loses out, though not always - PACELC!
- 5. Distributed system consensus How can we reach agreement across nodes, given that they may fail or become unreachable? - Paxos - Google Spanner, Neo4J - Raft - Consul, FaunaDB Consensus algorithms formally proven (TLA+)
- 7. Calvin transactional protocol - 2012 paper by Thomson et al - Transaction scheduling and data replication layer - Separate ordering transactions from executing transactions - Deterministic ordering -> execute transactions in parallel across replicas - Linear scaling, no single point of failure - Clock skew doesn’t matter, so no need for atomic clocks (Spanner)
- 8. Calvin transactional protocol However, there’s a price! - Transactions can’t happen interactively - Transactions must be pure - Minimum latency as transaction window must finish
- 9. FaunaDB - Multiple paradigm NoSQL (document, relational, graph) - Each transaction is a single query expression - ACID guarantees, snapshot isolation to strict serializability - Serverless (generally) pricing model - Used by nVidia, Nextdoor & others
- 10. FaunaDB Transactions - Uses Calvin + Raft - Coordinator node chooses a snapshot time with which to make read requests. - If there are writes, a commit phase starts. Reads include last update timestamps -> optimistic lock acquisitions. - Read timestamps + writes committed to transaction log. - Transaction resolution in parallel across data partitions. - A transaction is committed and durable if no concurrent write to any read values has occurred.
- 11. Fauna Query Language (FQL) In SQL terms: Schema ~=> Database Table ~=> Class Row ~=> Instance
- 12. Fauna Query Language (FQL) Can do the usual things like creating a database (schema) and class (table) CreateDatabase({name: "employees"}) CreateClass({name: "office"}) Note that class structures are not defined at create time!
- 13. Fauna Query Language (FQL) If all members of a class should share a property, we can add an index. CreateIndex({ name: "office_by_id", source: Class("office"), terms: [{ field: [ "data", "id" ] }], unique: true })
- 14. Fauna Query Language (FQL) We can also use lambda functions inside an FQL expression Map(Arr("MCR","LON","NYC"), Lambda("office_id", Create(Class("office"), {data: "id", Var("office_id")}))) This will create three offices for us.
- 15. Fauna Query Language (FQL) Plus other ‘traditional’ programming concepts like: - Let-bindings - If statements - Filter/map/foreach/take Expressive enough to capture business logic in a database transaction.
- 16. FaunaDB Demo - Start a local cluster (can use cloud too) - fauna shell to interact with it