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Database Indexing and Query Optimization for Python Developers

Introduction Fixing N+1 queries with select_related / prefetch_related or selectinload (see the previous post ) gets you down to a small, sane number of queries per request. The next bottleneck is what each query costs once the table has millions of rows — and that is almost always about indexing. An index turns "scan every row" into "look it up directly." Skip it, and a query that's instant in development takes seconds once real data volume shows up in production. How Indexes Work: The B-Tree Intuition Without an index, a WHERE clause forces a sequential scan : the database reads every row and checks the condition — O(n) , cost grows linearly with table size. An index is a separate, sorted structure (almost always a B-tree ) mapping column values to row locations. Because it's sorted and balanced, finding a value is a tree walk: O(log n) . On a 10-million-row table, that's the difference between reading 10 million rows and roughly 23 tree nodes. This isn't free: Writes get slower — every INSERT / UPDATE / DELETE on an indexed column also updates the index. Storage grows — each index is a sorted copy of (part of) the data. An index trades write cost and storage for read speed. Indexing a column you rarely filter or sort on is pure cost, no benefit. Reading Query Plans: EXPLAIN ANALYZE Postgres' EXPLAIN ANALYZE shows what the planner actually did, not an estimate. Before an index , filtering orders by customer_id : EXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 48291 ; Seq Scan on orders (cost=0.00..21453.00 rows=42 width=96) (actual time=0.021..118.442 rows=41 loops=1) Filter: (customer_id = 48291) Rows Removed by Filter: 1199959 Planning Time: 0.112 ms Execution Time: 118.471 ms Seq Scan means Postgres read all ~1.2 million rows and discarded all but 41. actual time is real elapsed time — 118ms for one lookup. After CREATE INDEX idx_orders_customer_id ON orders (customer_id); : Index Scan using idx_orders_customer_id on orders (cost=0.42..8.53 rows=42 wid

2026-07-04 原文 →
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Database Indexing and Query Optimization for Java Developers

Introduction Fixing N+1 queries (see the previous post ) gets your Hibernate app down to a handful of queries per request. The next bottleneck is what each of those queries costs once your tables have millions of rows — and that is almost always a question of indexing. An index turns "scan every row" into "look it up directly." Get the index wrong — or skip it — and a query that took 2ms in development takes 4 seconds in production once real data volume shows up. How Indexes Work: The B-Tree Intuition Without an index, a WHERE clause forces a sequential scan : the database reads every row and checks the condition. That's O(n) — cost grows linearly with table size. An index is a separate, sorted data structure (almost always a B-tree ) that maps column values to row locations. Because it's sorted and balanced, finding a value is a tree walk: O(log n) . On a 10-million-row table, that's the difference between reading 10 million rows and reading roughly 23 tree nodes. The cost is not free: Writes get slower. Every INSERT / UPDATE / DELETE on an indexed column must also update the index structure. Storage grows. Each index is a copy of (part of) the data, sorted differently. An index is a trade: you pay on every write so that specific reads become fast. Indexing a column you rarely filter or sort on is pure cost with no benefit. Reading Query Plans: EXPLAIN ANALYZE Postgres' EXPLAIN ANALYZE shows what the planner actually did — not what you hope it did. Before an index , filtering orders by customer_id : EXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 48291 ; Seq Scan on orders (cost=0.00..21453.00 rows=42 width=96) (actual time=0.021..118.442 rows=41 loops=1) Filter: (customer_id = 48291) Rows Removed by Filter: 1199959 Planning Time: 0.112 ms Execution Time: 118.471 ms Seq Scan means Postgres read all ~1.2 million rows and threw away all but 41 of them. actual time is the real elapsed time, not an estimate — 118ms for one lookup. After CREATE INDEX idx_orders

2026-07-04 原文 →
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Testing Best Practices in Python

Introduction Python's testing tools are lightweight enough that it's easy to write a lot of tests without writing good ones. A suite that mocks every collaborator, duplicates the same assertion ten times with different inputs pasted in by hand, or chases a coverage number will pass in CI and still miss real bugs. pytest gives you fixtures, parametrize , and monkeypatch — the tools that make it just as easy to write the right tests as the wrong ones. This post covers how to use them well. Test at the Right Level: the Pyramid Not every test should look the same. The test pyramid is a rough guide to where your effort should go: Unit tests — the bulk of the suite. Pure functions and classes, no I/O, no real database. Milliseconds each. Integration tests — fewer of these. Verify the seams : does your ORM query actually produce correct SQL against a real database, does your HTTP client actually parse a real response. End-to-end tests — a handful. Cover the critical flows through the whole stack, accepting they're slower and more brittle. # Unit — pure logic, no database, no framework def test_applies_ten_percent_discount_for_orders_over_100 (): calculator = DiscountCalculator () total = calculator . apply ( order_total = 150.0 ) assert total == pytest . approx ( 135.0 ) # Integration — the seam that matters: our query against a real database import pytest @pytest.fixture def db_session ( postgres_container ): # real Postgres in a test container, not mocked with postgres_container . session () as session : yield session def test_finds_orders_placed_in_the_last_week ( db_session ): db_session . add ( Order ( id = " ord-1 " , placed_at = datetime . now ( UTC ))) db_session . commit () recent = order_repository . find_recent ( db_session , within = timedelta ( days = 7 )) assert len ( recent ) == 1 A unit suite that never touches a database runs in seconds and catches most logic bugs. A handful of integration tests catch what only shows up at the boundary — the query that's s

2026-07-04 原文 →
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