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System Design Masterclass · Data Governance Compliance
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Your e-commerce database has 10 million orders. A customer calls asking about their order from last Tuesday. Without filtering, you would need to scan all 10 million records. With filtering (and an index on the date column), the database jumps directly to that day's orders and returns a handful of results in 3ms.
Filtering is the most fundamental data operation. Every search bar, every dashboard filter, every API query parameter, every database WHERE clause is data filtering. You start with a large dataset and reduce it to the subset you care about.
The concept is simple. The engineering challenges are in making filtering fast (indexes, composite indexes, covering indexes), secure (parameterized queries, access control at the query level), and correct (handling NULL values, partial matches, timezone-aware date ranges).
Shopify processes over 4 billion database queries per day. Almost every single one includes at least one WHERE clause. At that scale, the difference between a filtered query that uses an index (3ms) and one that does a full table scan (800ms) is the difference between a responsive store and a store that times out.
| Filter Type | What It Does | Example |
|---|---|---|
| Equality | Exact match | WHERE status = 'active' |
| Range | Between two values | WHERE created_at BETWEEN '2026-01-01' AND '2026-03-31' |
| Pattern | Partial match | WHERE name LIKE '%smith%' or full-text search |
| Set membership | One of several values | WHERE role IN ('admin', 'editor') |
| Null check | Missing or present | WHERE deleted_at IS NULL |
| Compound | Multiple conditions combined | WHERE status = 'active' AND role = 'admin' |
| Negation | Exclude matches | WHERE status != 'deleted' (caution: can't use index efficiently) |
The NULL trap: WHERE status != 'deleted' does NOT return rows where status is NULL. In SQL, NULL is not equal to anything, including itself. You need WHERE (status != 'deleted' OR status IS NULL). This is one of the most common filtering bugs in production.
LIKE '%smith%' is a full table scan. A leading wildcard prevents index usage. If you need substring search on large tables, use a full-text search index (Postgres tsvector, , or Meilisearch) instead of LIKE.