Making Connections With Graphs

  • Relations DBs, joins, filtering, normal forms
  • Relational db - schema changes as your use cases evolve become painful
    • Every entity gets a table
    • Lots of many to many tables
    • Rigid structure
    • Going from one to many requires a migration and new data model
  • Solving problems with graph
    • Fundamentals
      • Vertex - a thing, example: Movie, Person
      • Edges - labeled, directional relationships

      JCVD – acted in –> Time cop – acted in –> Blood sport – directed –> Blood sport

    • Properties - similar to fields in a table
    • Power of graphs are relationships

    • Summary
      Graph fundamentals summary

    • Tinkerpop 3 & Gremlin - API for graph query

      g.V().has(“person”, “name”, “JCVD”)

  • RDF stores vs Graph DBs
    • RDF - great for inferencing capabilities, but tend to not scale very well
    • RDF stores can be considered to be specialist graph DBs
    • Good ref: https://www.quora.com/What-are-the-differences-between-a-Graph-database-and-a-Triple-store
  • Ref: datastax-enterprise-graph

Data modelling

  • Write path

      Data -> Memtable -> Commit log -> SSTable
  • SSTable compaction
    • Compaction strategies
      • Size tiered
      • Leveled
      • Date tiered (sort of not recommended - use with care)
      • Time tiered (not available yet, may come)
  • Data organisation
    • Partition key
    • Clustering key
    • Columns
    • Primary key = partition + clustering key
  • Data modelling
    • Always include partition key in where clause of query
      • User login scenario - customer login by email

    Login scenario - Problem Login scenario - Solution

    • User defined types and collections

    User defined types

    • Avoid client side joins from 2 or more tables
    • Customer registration problem
      • If an insert is done by 2 different clients at around the same time, the last write wins. This may be a problem in certain use-cases. Example: user registration by email
      • Solution User registration problem solution
      • IF NOT EXISTS - expensive, use with caution, when really required
    • Customer login problems
      • Customers, Customers_by_email
      • Materialized view w/ DSE 5.0

## Scaling DataStax in Docker

  • Key concepts
    • Images
    • Registries (example: Docker hub)
    • Containers - running instance of image
  • DSE processes
    • Core DSE JVM
    • Opscentre agent
    • Spark executor processes
    • Single spark workder process
    • etc
  • Things to consider
    • Host and DSE config
    • Cassandra data - where will you mount volumes etc
    • JVM heap size
    • Garbage colletor

  • Default networking not recommended in prod, instead use host networking

      docker run -net=host
  • Storage
    • commit log or anything else in /var/lib/data
  • Ref: https://github.com/joeljacobson/dse-docker