Module Review: CQL

You’ve mastered the query language that powers the world’s most scalable databases. It looks like SQL, but requires a completely different mindset.

1. Key Takeaways

  • Schema is Deployment: A Keyspace defines replication strategies (NetworkTopologyStrategy vs SimpleStrategy). A Table defines data distribution (Partition Key).
  • Primary Key Anatomy: PRIMARY KEY ((Partition Key), Clustering Key).
  • Partition Key: Determines WHERE data lives (which node).
  • Clustering Key: Determines ORDER of data on disk.
  • The Golden Rule: Always provide the Partition Key in your WHERE clause.
  • ALLOW FILTERING: The keyword of death. It causes a full cluster scan. Never use in production.
  • Writes are Cheap: INSERT/UPDATE/DELETE are all appended to the Commit Log and MemTable. No read-before-write.
  • Collections: Use Set, List, Map for nested data. Use frozen<> for performance or Primary Key usage.

2. Interactive Flashcards

Test your recall.

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3. Cheat Sheet

Concept Command / Syntax Notes
Create Keyspace CREATE KEYSPACE k WITH replication = {'class': 'NetworkTopologyStrategy', 'dc1': 3}; Always use NTS in prod.
Create Table CREATE TABLE t (id uuid, val text, PRIMARY KEY(id)); Simple PK.
Composite PK PRIMARY KEY ((part_key), clust_key) Groups by part_key, sorts by clust_key.
Insert / Update INSERT INTO t (id, val) VALUES (...) Same as Update (Upsert).
TTL USING TTL 86400 Expires data after N seconds.
Select SELECT * FROM t WHERE id = ? Must allow coordinator to find the node.
Frozen UDT frozen<my_type> Serialized as one blob. Required for PKs.
Batch BEGIN BATCH ... APPLY BATCH Use for atomicity across tables, NOT performance.
  1. Glossary Link: Cassandra Glossary

4. Next Steps

Now that you can model and query data, it’s time to understand Consistency Levels.