How to build a production-ready in-memory-based application in 1 hour
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This document provides an overview of the Hypi platform, which uses GraphQL and Apache Ignite to provide a serverless backend API that can integrate with any public or private cloud. It discusses key aspects of GraphQL and Apache Ignite, and demonstrates how to build a TODO application using the Hypi platform, focusing on creating, completing, commenting on, and searching TODO items. The document also provides details on how data is stored and queries are routed in Hypi's architecture.
![Instant CRUD API
type Item {
slug: String
summary: String!
comments: [Comment!]
}
findItem(arcql: String): [Item!]
createItem(values: [ItemInput!]!): [Item!]
updateItem(values: [ItemInputOpt!]!): [Item!]
deleteItem(arcql: String!): [Item!]
trashItem(arcql: String!): [Item!]](https://image.slidesharecdn.com/hypi-graphql-hack-2nd-may-190503180716/85/How-to-build-a-production-ready-in-memory-based-application-in-1-hour-14-320.jpg)
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How to build a production-ready in-memory-based application in 1 hour
- 1. GraphQL Serverless Platform 2019 Hypi May 2019
- 2. Courtney RobinsonFounder & CEO of Hypi; Jack of all trades and worse PhD student ever…so let’s skip the hard questions
- 3. A Little Bit About Hypi One API, any platform Hypi takes data model and in seconds turn it into a highly available, distributed, serverless backend API. Project development time goes from months to weeks. Includes serverless functions with built-in storage and, Identity and Access Management. Hypi Hyper Cloud enables development against a single API to integrate with any public or private cloud.
- 4. 4 GraphQL •Declarative, type based framework, language, standard…may be easier to say what it isn’t •Expressive, any model that can be expressed through an OOP object model can be expressed with GraphQL •Succinct, one of the points FB sells it on. Useful in low/expensive bandwidth situations •Flexible, use directives to add features/semantics •Growing adoption, can hardly be dismissed as a fad anymore
- 5. 5 Apache Ignite 101 1.SQL (including transactions) 2.Key value (put/get) 3.Machine learning 1. Some 20 built in sub-sections (K-Means, ANN, SVM…etc) 2.Deep learning - Tensorflow 4.Service Grid - infrastructure for micro- services 5.Distributed data structures (Queue/Set/ Locks/Counters) 6.Messaging & Events 7.File System 8.Compute Grid 9.Hadoop/Spark integrations Has many APIs, choose depending on your aim
- 6. 6 Apache Ignite 101 In other words…it’s huge!
- 7. 7 Apache Ignite 101 1.All those APIs are built on what Ignite terms as a Cache 2.Familiar with JCache? Then think of them as one and the same 3.APIs are an implementation of javax.cache.Cache<K, V> 4.Most usages will boil down to get(key), getAll(keys), put(key,val), putAll(kvSet) From Caching to Caches
- 8. 8 Apache Ignite 101 1.Data is store in memory by default 2.A single Cache can be portioned…or not 1.PARTIONED - Cache is split across the cluster, optionally replicated. 2.REPLICATED - All data for a cache is available on EVERY node in the cluster 3.LOCAL - No data is distributed but maintains other capabilities of partitioned or replicated caches Memory centric - Partitions & Replication
- 9. 9 Apache Ignite 101 1.Ignite stores all data in memory by default 2.A persistence store can optionally be configured to enable writes to disk as well for durability 3.Ignite native persistence is a distributed ACID and SQL- compliant disk store 4.Persistence is pluggable and can be backed off to other databases 1.Cassandra 2.MySQL/Postgres or another RDBMS 3.Implement your own 1.Hypi has an implementation based on RocksDB Persistent storage
- 10. 10 A layer above 1.Hypi is working to abstract all this away into one-click operations 2.Starts by taking a model centric view of the world 3.Centers around GraphQL Hypi platform
- 11. 11 Let’s build a todo app 1. Create todo item 2. Complete todo item 3. Add comments to todo items 4. Search for todo items 5.Paginate through todo items 6. Trash todo items 7. Add attachments to todo items 8. Create groups of todo items 9. Share individual todo items 10. Share groups 11.Delete todo items 12.Delete groups Possible features: For this talk we will focus on 1. Create todo item 2. Complete todo item 3. Add comments to todo items 4. Search for todo items 5.You can then implement the rest if you want…
- 12. 12 What does it look like? For this talk we will focus on 1. Create todo item 2. Complete todo item 3. Add comments to todo items 4. Search for todo items
- 13. 13 Some key points • Every GraphQL type results in one Ignite cache • Each Ignite cache has one lucene index and one RocksDB database • Each Ignite cache is shared if two tenants have the same GraphQL type name • Dedicated tenant caches are planned for Q4 2019 • Each RocksDB database is also shared • Each tenant gets a RocksDB Column Family • Relationship references are stored in the target Lucene index
- 14. Instant CRUD API type Item { slug: String summary: String! comments: [Comment!] } findItem(arcql: String): [Item!] createItem(values: [ItemInput!]!): [Item!] updateItem(values: [ItemInputOpt!]!): [Item!] deleteItem(arcql: String!): [Item!] trashItem(arcql: String!): [Item!]
- 15. Arc Query Language i.e. Arc QL Simple, intuitive, familiar! <query> <sort> <from> <limit> FROM ‘<pagination-cursor>’ SORT fieldName ASC | DESC LIMIT <N> Query types • Term - fieldName = ‘value’ • Phrase - fieldName ~ ‘some value’ • Prefix - fieldName ^ ‘music’ • Wildcard - fieldName * ‘mu?ic*’ • Fuzzy - ~fieldName~ ‘name’ • Range - fieldName IN [0, 100) • Match all - *
- 16. Storage Arc OS - Platform Architecture GraphQL Engine Query Tree Algebra Ignite Key value Cache API Arc Affinity Function Arc Cache Store RocksDB CacheStore Lucene CacheStore
- 17. Ignite Cluster Arc OS - Affinity Function & Query Routing Query & Data Routing • f : key => partition • Rendezvous hashing based on • Type of key • Node requirements • Cache name Lucene Lucene Lucene london ssd paris ssd berlin ssd RocksDB RocksDB RocksDB london ssd paris ssd berlin ssd put(key) Arc Affinity Function get(key) • Double query required to filter • Average <10ms to do both