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MPP

Learn massively parallel processing.

MPP

MPP (Massively Parallel Processing) splits a large query across many cores or nodes and runs it in parallel. Each node scans its own data; results are merged.

SET max_parallel_workers_per_gather = 4;
EXPLAIN SELECT COUNT(*) FROM events;

MPP splits a query in parallel.

MPP — details

In MPP each node works independently; this calls for scale-out design. A shared-nothing architecture cuts inter-node dependency.

Practice MPP

Set the parallel worker count to 4: SET max_parallel_workers_per_gather = 4.

Starter code

query.sql
-- MPP setting
SET max_parallel_workers_per_gather

Sample solution

query.sql
SET max_parallel_workers_per_gather = 4;

Expected output

SET

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 Parallel setting

Question 
SET max_parallel_workers_per_gather = ____;
Answer 
SET max_parallel_workers_per_gather = 4;

Explanation: max_parallel_workers_per_gather = 4 lets a query use up to 4 parallel workers.

2 How does MPP run a query?

  • In parallel Correct answer
  • On a single core
  • Sequentially
  • Only on disk

Explanation: MPP runs a query in parallel.

3 MPP: match the terms

Term Meaning
MPP MPP
parallel parallel
node node
gather gather

All lessons in Expert

1 Database design

  1. ER model
  2. Cardinality
  3. Surrogate keys
  4. Natural keys
  5. Denormalization
  6. Summary - Design

2 Index tuning

  1. B-tree structure
  2. Covering index
  3. Partial index
  4. Index selectivity
  5. Index maintenance
  6. Summary - Index tuning

3 Query optimizer

  1. What is an optimizer?
  2. Statistics
  3. Cost model
  4. Join strategies
  5. Plan cache
  6. Summary - Optimizer

4 Project: Performance tuning

  1. Scenario design
  2. Reading plans
  3. Adding indexes
  4. Rewriting queries
  5. Comparing results

5 Replication and sharding

  1. What is replication?
  2. Master-slave
  3. Sync vs async
  4. What is sharding?
  5. Shard key
  6. Summary - Replication and sharding

6 Distributed and big data

  1. Distributed queries
  2. MPP
  3. Columnar storage
  4. SQL on Hadoop
  5. SQL vs NoSQL
  6. Summary - Distributed and big data

7 Security and backup

  1. SQL injection
  2. Parameterised queries
  3. Authorisation
  4. Backup
  5. Recovery
  6. Summary - Security and backup

8 Expert final

  1. Design challenge
  2. Optimization challenge
  3. Security challenge
  4. Backup challenge