Helpful Postgres Techniques from 15 Years Experience

[ryanmaynard]

Helpful Postgres Techniques from 15 Years Experience

In no way do I claim mastery or expertise, but I've put together a few things that could be helpful for anyone learning Postgresql - my personal preference of db for most things. Feel free to add to the list, question, or correct anything needing correction.

1. Advanced Query Optimization

• Use of CTEs and Window Functions: Mastering Common Table Expressions (CTEs) and window functions can lead to cleaner, more efficient queries.
• Indexing Strategies: Beyond basic indexing, understanding partial indexes, covering indexes, and BRIN indexes can drastically improve performance for large datasets.
• Execution Plan Analysis: Regularly use

  EXPLAIN

  and

  EXPLAIN ANALYZE

  to understand the performance characteristics of your queries.

2. Database Maintenance and Performance Tuning

• Autovacuum Tuning: Fine-tuning the autovacuum settings for your workload can prevent table bloat and ensure consistent performance.
• Partitioning: Implementing table partitioning can improve performance and manageability for large tables.
• Monitoring Tools: Use advanced monitoring tools like

  pg_stat_statements

  ,

  pgBadger

  , and

  pgCluu

  to gain insights into database performance.

3. Advanced Data Modeling

• Normalization vs. Denormalization: Knowing when to use each approach based on performance considerations and query patterns.
• Advanced Constraints: Using exclusion constraints and deferrable constraints to enforce complex business rules at the database level.
• Temporal Data Management: Implementing temporal tables and using PostgreSQL's

  period

  data type for historical data tracking.

4. Security Best Practices

• Row-Level Security: Implementing row-level security policies for fine-grained access control.
• Encryption: Using Transparent Data Encryption (TDE) and column-level encryption for sensitive data.
• Audit Logging: Setting up comprehensive audit logging to track all database activities using

  pgaudit

  or custom triggers.

5. Replication and High Availability

• Logical Replication: Using logical replication for selective data replication and cross-version upgrades.
• Hot Standby and Streaming Replication: Setting up and managing hot standby servers for high availability and disaster recovery.
• PgBouncer and Connection Pooling: Using PgBouncer for connection pooling to manage high concurrency workloads.

6. Advanced SQL Techniques

• Recursive Queries: Leveraging recursive CTEs for hierarchical data and graph-based queries.
• Advanced JSON Handling: Utilizing PostgreSQL's powerful JSON functions and operators for semi-structured data.
• Full-Text Search: Implementing full-text search using

  tsvector

  and

  tsquery

  for advanced text search capabilities.

7. Custom Extensions and Functions

• PL/pgSQL Scripting: Writing complex business logic using PL/pgSQL and other procedural languages supported by PostgreSQL.
• Custom Extensions: Developing and deploying custom PostgreSQL extensions to extend the database's capabilities.
• Foreign Data Wrappers: Using Foreign Data Wrappers (FDWs) to integrate PostgreSQL with other data sources.

8. Resource Management and Scaling

• Resource Queues: Implementing resource queues to manage query concurrency and resource usage.
• Horizontal Scaling: Strategies for sharding and distributed databases to handle massive data volumes.
• Cloud Deployments: Best practices for deploying and managing PostgreSQL in cloud environments like AWS RDS, Google Cloud SQL, and Azure Database for PostgreSQL.

Resources

• Books: "PostgreSQL: Up and Running" by Regina Obe and Leo Hsu, "Mastering PostgreSQL in Application Development" by Dimitri Fontaine
• Courses: Online courses from platforms like Udemy, Coursera, and edX that cover advanced PostgreSQL topics.
• Communities: Engage with the PostgreSQL community through mailing lists, forums, and attending conferences like PGConf and PGDay.
• Documentation: Regularly consult the official PostgreSQL documentation and keep up with the release notes for the latest features and improvements.

Hopefully one or more of these were helpful to someone. I encourage anyone reading to share their own in thread.

 

[ryanmaynard]

Masterclass: Grokking `EXPLAIN` in PostgreSQL

In the corresponding Slack of this group, someone suggested a masterclass on EXPLAIN in response to my prior post. This is my attempt at that.

Understanding the output of `EXPLAIN` is like reading a treasure map. Each part of the map gives you clues about where to dig for performance gold. Let’s break down the key elements using a treasure hunt metaphor.

1. Understanding the Basics

Imagine you’re a pirate with a map. The map (`EXPLAIN` output) shows different routes to the treasure (query result). Each route (plan) has landmarks (operations) that you need to navigate.

2. Key Elements of `EXPLAIN` Output

a. Nodes and Sub-Nodes

• Node: Think of nodes as checkpoints on your map. Each node represents an operation PostgreSQL performs to retrieve data.
• Sub-Nodes: Some nodes have sub-nodes, like forks in the path. They show further steps needed within a larger operation.

b. Cost

• Startup Cost: This is the initial effort to reach the first checkpoint. It’s like the preparation needed before setting off on your journey.
• Total Cost: This is the total effort to complete the journey. It includes all steps from start to finish. Lower total costs are better, just like in a real treasure hunt where less effort means faster treasure retrieval.

c. Rows

• Rows: This indicates the number of treasure chests (rows) you expect to find at each checkpoint. It’s an estimate of how many pieces of data PostgreSQL thinks it will process.

d. Width

• Width: This tells you the size of each piece of treasure (data row). Larger widths mean more data per row, which can affect performance.

3. Common Node Types

a. Seq Scan (Sequential Scan)

• Description: This is like walking through the entire island, checking every single spot for treasure. It’s exhaustive but can be slow if the island (table) is large.
• Indicator: Use this when you don’t know where the treasure is hidden or if the map (index) doesn’t help.

b. Index Scan

• Description: This is like having clues that lead directly to the treasure. The map has markers (indexes) that point to specific spots.
• Indicator: Faster than Seq Scan when indexes are available. Great for pinpointing exact locations without unnecessary searching.

c. Index Only Scan

• Description: This is like having a spyglass that shows exactly where the treasure is without even digging. You get the data directly from the index.
• Indicator: Fastest method when the index contains all needed data.

d. Bitmap Index Scan

• Description: Imagine having a magic compass that narrows down the search area. It groups nearby treasures into a smaller search zone.
• Indicator: Useful for large datasets with multiple search criteria.

e. Nested Loop

• Description: This is like searching one island for clues that lead to another island. You go back and forth, combining treasures from each location.
• Indicator: Efficient for small datasets but can become slow with larger ones if not optimized.

f. Hash Join

• Description: Think of this as using a magical map that highlights matching treasures from different islands. It combines them quickly by matching attributes.
• Indicator: Efficient for large datasets with equi-joins.

g. Merge Join

• Description: This is like sorting treasures from two islands and then walking through them in order, merging as you go.
• Indicator: Useful for sorted datasets, especially large ones.

4. Practical Examples

Let’s apply these concepts to a sample `EXPLAIN` output.

EXPLAIN ANALYZE
SELECT * FROM pirates
JOIN treasures ON pirates.id = treasures.pirate_id
WHERE pirates.age > 30;

Sample Output:

Nested Loop  (cost=0.43..289.76 rows=10 width=48) (actual time=0.029..0.054 rows=3 loops=1)
  -> Index Scan using idx_pirates_age on pirates  (cost=0.29..8.42 rows=5 width=24) (actual time=0.013..0.015 rows=2 loops=1)
        Index Cond: (age > 30)
  -> Index Scan using idx_treasures_pirate_id on treasures  (cost=0.14..56.58 rows=2 width=24) (actual time=0.005..0.018 rows=2 loops=2)
        Index Cond: (pirate_id = pirates.id)

Breakdown:

• Nested Loop: We’re combining data from two islands (tables) by checking each pirate against the treasures.
• Index Scan (pirates): Using a clue (index on age) to quickly find pirates older than 30.
• Index Scan (treasures): Using another clue (index on pirate_id) to find treasures belonging to the found pirates.

5. Mnemonics to Remember

SCANS: Sequential (Search all), Index (Informed), Index-Only (Informed directly), Bitmap (Broad)

JOINS: Nested Loop (Nested search), Hash Join (Hashing match), Merge Join (Merging sorted)

As always, additional comments, questions, or clearer explanations are greatly welcomed.