Data Systems
PostgreSQL Deep Dive
PostgreSQL is the most advanced open-source relational database. Master its advanced features: JSONB, partitioning, full-text search, CTEs, window functions, and extension ecosystem.
- Extensibility â Custom types, functions, and extensions
- Standards Compliance â Full SQL standard support
- Advanced Features â Window functions, CTEs, JSONB, geospatial
PostgreSQL is the database that grows with you.
PostgreSQL Architecture
Process Architecture
Advanced SQL Features
Window Functions
| Function | Purpose | Example |
|---|---|---|
| ROW_NUMBER() | Sequential numbering | Rank rows within a partition |
| RANK() | Ranking with gaps | Ties get same rank |
| DENSE_RANK() | Ranking without gaps | No gaps after ties |
| LAG/LEAD() | Access previous/next rows | Compare with adjacent rows |
| SUM/AVG() OVER | Running aggregates | Running total, moving average |
| NTILE() | Divide into buckets | Quartiles, deciles |
JSONB
| Operator | Description | Example |
|---|---|---|
| -> | Get JSON field | data->'name' |
| ->> | Get JSON field as text | data->>'name' |
| #> | Get JSON path | data#>'{address,city}' |
| @> | Contains | data @> '{"status":"active"}' |
| ? | Key exists | data ? 'name' |
| jsonb_path_query | SQL/JSON path | jsonb_path_query(data, '$.orders[*].price') |
Table Partitioning
| Partition Type | Strategy | Use Case |
|---|---|---|
| Range | Value ranges (e.g., dates) | Time-series data |
| List | Discrete values (e.g., regions) | Multi-tenant systems |
| Hash | Hash of partition key | Even distribution |
Extension Ecosystem
| Extension | Purpose |
|---|---|
| PostGIS | Geospatial data and queries |
| TimescaleDB | Time-series data optimization |
| pgvector | Vector similarity search (AI/ML) |
| pg_trgm | Trigram similarity for fuzzy search |
| uuid-ossp | UUID generation |
| pg_stat_statements | Query performance analysis |
Practice Exercises
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Window Functions: Write a SQL query to calculate the 7-day moving average of daily revenue, including day-over-day percentage change.
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JSONB Design: Design a PostgreSQL schema for an e-commerce order system using JSONB for flexible product attributes. What indexes would you create?
-
Partitioning Strategy: Design the partitioning strategy for a logging table that receives 10M rows per day and needs to retain 1 year of data.
-
Performance Analysis: Given a slow query, write the steps you would take to diagnose and optimize it using EXPLAIN ANALYZE, pg_stat_statements, and index analysis.
What to Learn Next
-> SQL Deep Dive PostgreSQL, MySQL, indexing strategies, and query optimization.
-> MongoDB Deep Dive Advanced MongoDB features, aggregation pipeline, and sharding.
-> Spanner and CockroachDB Deep dive into specific NewSQL implementations.
-> Database Indexing B-trees, hash indexes, and indexing strategies.
-> Data Partitioning Sharding strategies, consistent hashing, and partition keys.
-> Data Replication Sync vs async replication, leader election, and consistency.