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They're the single biggest lever you have for Postgres performance. In our experience, when someone says "my queries are slow," the answer comes down to missing or unused indexes the vast majority of the time. The good news? Indexing in Postgres is straightforward once you understand a few core concepts.\u003C/p\u003E\n","\u003Cp\u003EThis guide covers the main index types (B-tree, GIN, GiST, Hash, BRIN), how to read execution plans with \u003Ccode\u003EEXPLAIN ANALYZE\u003C/code\u003E, and how to monitor index usage in production so you can find what's missing and drop what's dead weight.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EHow Postgres Scans Tables\u003C/h2\u003E\n","\u003Cp\u003EUnderstanding scan types is essential to understanding how indexes help. When Postgres executes a query, it chooses a scan strategy based on table size, available indexes, and the selectivity of your WHERE clause.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESequential Scan\u003C/strong\u003E — reads every row in the table from start to finish. This is the default when no useful index exists or when most of the table will be returned anyway.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIndex Scan\u003C/strong\u003E — traverses the index tree to find matching entries, then follows pointers back to the heap (table) to fetch full rows. Best for highly selective queries returning a small percentage of rows.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBitmap Index Scan\u003C/strong\u003E — scans the index to build a bitmap of matching pages, then does a single pass over the heap reading only those pages. Efficient for medium-selectivity queries that return too many rows for a plain index scan but too few for a sequential scan.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIndex Only Scan\u003C/strong\u003E — satisfies the query entirely from the index without touching the heap. Requires a covering index that includes all columns the query needs.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EParallel Scan\u003C/strong\u003E — distributes the scan across multiple worker processes. Postgres can parallelize sequential scans, index scans, and bitmap scans for large tables.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EParallel Index Scan\u003C/strong\u003E — multiple workers traverse the index concurrently, with results gathered by the leader process.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://publish-p57963-e462109.adobeaemcloud.com/content/dam/snowflake-site/developers/postgres/indexing/postgres_scan_types.png?v=03111311\" alt=\"postgres-scan-types\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe query planner automatically picks the best strategy. Use \u003Ccode\u003EEXPLAIN\u003C/code\u003E to see which scan type a query uses — and read on to learn how to create the indexes that unlock the faster scan types.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EB-tree Indexes for Equality and Range Queries\u003C/h2\u003E\n","\u003Cp\u003EA B-tree index is the default — when you run \u003Ccode\u003ECREATE INDEX\u003C/code\u003E without specifying a type, you get a B-tree. It covers about 95% of indexing needs and supports equality (\u003Ccode\u003E=\u003C/code\u003E), range (\u003Ccode\u003E<\u003C/code\u003E, \u003Ccode\u003E>\u003C/code\u003E, \u003Ccode\u003E<=\u003C/code\u003E, \u003Ccode\u003E>=\u003C/code\u003E, \u003Ccode\u003EBETWEEN\u003C/code\u003E), \u003Ccode\u003EIN\u003C/code\u003E, \u003Ccode\u003EIS NULL\u003C/code\u003E, and sorting.\u003C/p\u003E\n","\u003Cp\u003ELet's start with the basics:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a simple B-tree index\nCREATE INDEX idx_orders_customer_id ON orders(customer_id);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's see what this actually does for performance. Here's a query without the index:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 12345;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E                                                    QUERY PLAN\n-------------------------------------------------------------------------------------------------------------------\n Seq Scan on orders  (cost=0.00..18450.00 rows=52 width=96) (actual time=12.451..89.234 rows=48 loops=1)\n   Filter: (customer_id = 12345)\n   Rows Removed by Filter: 999952\n Planning Time: 0.085 ms\n Execution Time: 89.301 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThat sequential scan is reading every single row in the table — about a million rows — just to find 48 matches. After creating the index:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 12345;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E                                                          QUERY PLAN\n------------------------------------------------------------------------------------------------------------------------------\n Index Scan using idx_orders_customer_id on orders  (cost=0.42..56.84 rows=52 width=96) (actual time=0.028..0.142 rows=48 loops=1)\n   Index Cond: (customer_id = 12345)\n Planning Time: 0.091 ms\n Execution Time: 0.168 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFrom 89ms to 0.17ms. That's a 500x improvement — and this is a relatively small table. On tables with tens of millions of rows, the difference between a seq scan and an index scan can be seconds versus microseconds.\u003C/p\u003E\n","\u003Ch3\u003EMulti-Column Indexes\u003C/h3\u003E\n","\u003Cp\u003EWhen your queries filter on multiple columns, a multi-column index can be dramatically more efficient than separate single-column indexes. But column order matters — Postgres can use the index for queries that filter on a \u003Cstrong\u003Eleftmost prefix\u003C/strong\u003E of the indexed columns.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Multi-column index\nCREATE INDEX idx_orders_status_created ON orders(status, created_at);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis index helps these queries:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Uses the index (leftmost prefix: status)\nSELECT * FROM orders WHERE status = 'pending';\n\n-- Uses the index (both columns)\nSELECT * FROM orders WHERE status = 'shipped' AND created_at > '2025-01-01';\n\n-- Uses the index (leftmost prefix for filter, second column for range)\nSELECT * FROM orders WHERE status = 'pending' AND created_at BETWEEN '2025-01-01' AND '2025-06-01';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBut this query \u003Cstrong\u003Ecannot\u003C/strong\u003E use the index:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Does NOT use the index (missing leftmost column)\nSELECT * FROM orders WHERE created_at > '2025-01-01';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPut the most selective column first — the column that narrows down the most rows. If \u003Ccode\u003Estatus\u003C/code\u003E has 5 distinct values and \u003Ccode\u003Ecreated_at\u003C/code\u003E has millions, leading with \u003Ccode\u003Estatus\u003C/code\u003E makes sense when you're always filtering by status.\u003C/p\u003E\n","\u003Ch3\u003EUNIQUE Indexes and Covering Indexes\u003C/h3\u003E\n","\u003Cp\u003EA \u003Ccode\u003EUNIQUE\u003C/code\u003E constraint automatically creates a B-tree index — you get both data integrity and query performance:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- This creates a unique B-tree index automatically\nALTER TABLE users ADD CONSTRAINT users_email_unique UNIQUE (email);\n\n-- Equivalent to:\nCREATE UNIQUE INDEX users_email_unique ON users(email);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECovering indexes use \u003Ccode\u003EINCLUDE\u003C/code\u003E to store additional columns in the index leaf pages. This enables \u003Cstrong\u003Eindex-only scans\u003C/strong\u003E — Postgres can answer the query entirely from the index without touching the heap table at all:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Covering index: include columns needed by SELECT\nCREATE INDEX idx_orders_covering ON orders(customer_id) INCLUDE (status, total_amount);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE SELECT status, total_amount FROM orders WHERE customer_id = 12345;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E                                                             QUERY PLAN\n-------------------------------------------------------------------------------------------------------------------------------------\n Index Only Scan using idx_orders_covering on orders  (cost=0.42..52.10 rows=48 width=18) (actual time=0.024..0.089 rows=48 loops=1)\n   Index Cond: (customer_id = 12345)\n   Heap Fetches: 0\n Planning Time: 0.078 ms\n Execution Time: 0.112 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E"Heap Fetches: 0" — Postgres answered the entire query from the index. This is the fastest possible path.\u003C/p\u003E\n","\u003Ch3\u003EThe Write Overhead Tradeoff\u003C/h3\u003E\n","\u003Cp\u003EEvery index adds overhead to writes. Each \u003Ccode\u003EINSERT\u003C/code\u003E, \u003Ccode\u003EUPDATE\u003C/code\u003E (of an indexed column), and \u003Ccode\u003EDELETE\u003C/code\u003E must update every index on the table. For read-heavy workloads, more indexes are almost always worth it. For write-heavy tables, be more deliberate — each index you add slows down every write operation.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGIN, GiST, and Specialized Index Types\u003C/h2\u003E\n","\u003Cp\u003EB-tree handles scalar comparisons beautifully, but what about searching inside complex data types — JSONB documents, arrays, full-text search vectors? That's where specialized index types come in.\u003C/p\u003E\n","\u003Ch3\u003EGIN — Generalized Inverted Index\u003C/h3\u003E\n","\u003Cp\u003EGIN indexes are built for values that contain multiple elements. Think of them like the index at the back of a book — they map each element to the rows that contain it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest for:\u003C/strong\u003E JSONB containment (\u003Ccode\u003E@>\u003C/code\u003E), key existence (\u003Ccode\u003E?\u003C/code\u003E), array overlap (\u003Ccode\u003E&&\u003C/code\u003E), full-text search (\u003Ccode\u003E@@\u003C/code\u003E)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- GIN index for JSONB containment queries\nCREATE INDEX idx_products_metadata ON products USING gin(metadata);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow JSONB containment queries become fast:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Find products with specific attributes (uses GIN index)\nEXPLAIN ANALYZE\nSELECT name, price FROM products\nWHERE metadata @> '{"color": "blue", "size": "large"}';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E                                                           QUERY PLAN\n---------------------------------------------------------------------------------------------------------------------------------\n Bitmap Heap Scan on products  (cost=24.01..1205.42 rows=312 width=38) (actual time=0.198..1.245 rows=287 loops=1)\n   Recheck Cond: (metadata @> '{"color": "blue", "size": "large"}'::jsonb)\n   Heap Blocks: exact=215\n   ->  Bitmap Index Scan on idx_products_metadata  (cost=0.00..23.93 rows=312 width=0) (actual time=0.152..0.152 rows=287 loops=1)\n         Index Cond: (metadata @> '{"color": "blue", "size": "large"}'::jsonb)\n Planning Time: 0.112 ms\n Execution Time: 1.398 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor JSONB, you can also use \u003Ccode\u003Ejsonb_path_ops\u003C/code\u003E — a more compact operator class that only supports the \u003Ccode\u003E@>\u003C/code\u003E containment operator but is smaller and faster for that specific use case:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Smaller, faster — but only supports @> containment\nCREATE INDEX idx_products_metadata_pathops ON products USING gin(metadata jsonb_path_ops);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGIN for arrays works the same way:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Index an array column\nCREATE INDEX idx_posts_tags ON posts USING gin(tags);\n\n-- Query: find posts tagged with both 'postgres' and 'performance'\nSELECT title FROM posts WHERE tags @> ARRAY['postgres', 'performance'];\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGIN tradeoffs:\u003C/strong\u003E GIN indexes are larger than B-tree indexes and slower to update (inserts are batched into a "pending list" and merged later). But for query speed on complex data types, they're unmatched.\u003C/p\u003E\n","\u003Ch3\u003EGiST — Generalized Search Tree\u003C/h3\u003E\n","\u003Cp\u003EGiST indexes handle geometric and range data — things where "overlaps" and "contains" and "nearest neighbor" are the important operations.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest for:\u003C/strong\u003E PostGIS spatial queries, range types (\u003Ccode\u003Etsrange\u003C/code\u003E, \u003Ccode\u003Eint4range\u003C/code\u003E), full-text search (alternative to GIN), nearest-neighbor searches\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- GiST for range type queries\nCREATE INDEX idx_reservations_during ON reservations USING gist(during);\n\n-- Find overlapping reservations\nSELECT * FROM reservations\nWHERE during && tsrange('2025-03-01', '2025-03-07', '[]');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGiST supports nearest-neighbor ordering with the \u003Ccode\u003E<->\u003C/code\u003E operator, which GIN cannot do:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- PostGIS: find the 5 nearest coffee shops\nSELECT name, ST_Distance(location, ST_MakePoint(-122.4194, 37.7749)) AS dist\nFROM shops\nORDER BY location <-> ST_MakePoint(-122.4194, 37.7749)\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EHash Indexes\u003C/h3\u003E\n","\u003Cp\u003EHash indexes support only equality (\u003Ccode\u003E=\u003C/code\u003E) comparisons. Since B-tree also handles equality (plus ranges and sorting), hash indexes are rarely needed. The one niche advantage: hash indexes can be slightly smaller for very long keys (like UUIDs or long strings) where only equality matching is needed.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Hash index — rarely needed, B-tree usually better\nCREATE INDEX idx_sessions_token ON sessions USING hash(session_token);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EChoosing the Right Index Type\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EIndex Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBest For\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EOperators\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EB-tree\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEquality, range, sorting, most queries\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E=\u003C/code\u003E, \u003Ccode\u003E<\u003C/code\u003E, \u003Ccode\u003E>\u003C/code\u003E, \u003Ccode\u003E<=\u003C/code\u003E, \u003Ccode\u003E>=\u003C/code\u003E, \u003Ccode\u003EBETWEEN\u003C/code\u003E, \u003Ccode\u003EIN\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGIN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMulti-element values (JSONB, arrays, text search)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E@>\u003C/code\u003E, \u003Ccode\u003E?\u003C/code\u003E, \u003Ccode\u003E?&\u003C/code\u003E, \u003Ccode\u003E?|\u003C/code\u003E, \u003Ccode\u003E&&\u003C/code\u003E, \u003Ccode\u003E@@\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGiST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGeometric, range, nearest-neighbor\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E&&\u003C/code\u003E, \u003Ccode\u003E@>\u003C/code\u003E, \u003Ccode\u003E<@\u003C/code\u003E, \u003Ccode\u003E<->\u003C/code\u003E, \u003Ccode\u003E<<\u003C/code\u003E, \u003Ccode\u003E>>\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHash\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEquality only (large keys)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E=\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUsing pg_stat_statements to Find Your Slowest Queries\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003Epg_stat_statements\u003C/code\u003E is the best tool for figuring out where to add indexes. Instead of guessing which queries are slow, it gives you hard data — total execution time, call count, rows scanned, and buffer usage for every normalized query your application runs.\u003C/p\u003E\n","\u003Ch3\u003EEnabling pg_stat_statements\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Epg_stat_statements\u003C/code\u003E must be loaded via \u003Ccode\u003Eshared_preload_libraries\u003C/code\u003E in \u003Ccode\u003Epostgresql.conf\u003C/code\u003E — this requires a server restart since it allocates shared memory at startup:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E# postgresql.conf\nshared_preload_libraries = 'pg_stat_statements'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter restarting Postgres, create the extension:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE EXTENSION IF NOT EXISTS pg_stat_statements;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOn Snowflake Postgres, \u003Ccode\u003Epg_stat_statements\u003C/code\u003E is pre-loaded and ready to use — just run \u003Ccode\u003ECREATE EXTENSION\u003C/code\u003E and you're set.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nIf you're running self-managed Postgres, adding to \u003Ccode\u003Eshared_preload_libraries\u003C/code\u003E requires a full server restart (\u003Ccode\u003Epg_ctl restart\u003C/code\u003E or \u003Ccode\u003Esystemctl restart postgresql\u003C/code\u003E). A reload is not sufficient. Plan this during a maintenance window.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EFinding Queries That Need Indexes\u003C/h3\u003E\n","\u003Cp\u003EThe highest-value queries to index are those consuming the most total server time. These are your candidates:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Top 10 queries by total execution time\nSELECT\n  left(query, 80) AS query_preview,\n  calls,\n  round(total_exec_time::numeric, 1) AS total_ms,\n  round(mean_exec_time::numeric, 1) AS avg_ms,\n  rows,\n  round((shared_blks_read::numeric / nullif(shared_blks_hit + shared_blks_read, 0)) * 100, 1) AS cache_miss_pct\nFROM pg_stat_statements\nORDER BY total_exec_time DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Ecache_miss_pct\u003C/code\u003E column is key — a high value means the query is reading from disk rather than shared buffers, which is exactly what a good index fixes.\u003C/p\u003E\n","\u003Ch3\u003EIdentifying Sequential Scans in Your Heaviest Queries\u003C/h3\u003E\n","\u003Cp\u003EOnce you've found your slowest queries, run them through \u003Ccode\u003EEXPLAIN ANALYZE\u003C/code\u003E to confirm they're doing sequential scans:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN (ANALYZE, BUFFERS) SELECT ... ; -- paste the slow query here\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELook for \u003Ccode\u003ESeq Scan\u003C/code\u003E nodes with high \u003Ccode\u003Eactual time\u003C/code\u003E and \u003Ccode\u003ERows Removed by Filter\u003C/code\u003E. A seq scan that filters out 99% of rows is screaming for an index on the filter column.\u003C/p\u003E\n","\u003Ch3\u003EQueries With High Buffer Reads\u003C/h3\u003E\n","\u003Cp\u003EQueries that read large amounts of data are prime indexing candidates — even if they're individually fast, they burn I/O:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Queries reading the most data (I/O heavy)\nSELECT\n  left(query, 80) AS query_preview,\n  calls,\n  shared_blks_read + shared_blks_hit AS total_buffers,\n  round((shared_blks_read + shared_blks_hit)::numeric / nullif(calls, 0), 0) AS buffers_per_call,\n  round(mean_exec_time::numeric, 1) AS avg_ms\nFROM pg_stat_statements\nWHERE calls > 100\nORDER BY (shared_blks_read + shared_blks_hit) DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA query that reads 50,000 buffers per call is almost certainly doing a sequential scan on a large table. Add an index on the columns in its WHERE clause.\u003C/p\u003E\n","\u003Ch3\u003EResetting Statistics\u003C/h3\u003E\n","\u003Cp\u003EAfter adding indexes, reset the statistics to measure the improvement with a clean slate:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Reset pg_stat_statements (requires superuser)\nSELECT pg_stat_statements_reset();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWait for normal traffic to accumulate, then check again — your previously slow queries should show dramatically lower \u003Ccode\u003Etotal_exec_time\u003C/code\u003E and \u003Ccode\u003Eshared_blks_read\u003C/code\u003E values.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPartial Indexes, Expression Indexes, and BRIN\u003C/h2\u003E\n","\u003Cp\u003EThese three index variants let you build smarter, smaller, more targeted indexes. I wanted to share these because they're incredibly powerful but often overlooked.\u003C/p\u003E\n","\u003Ch3\u003EPartial Indexes\u003C/h3\u003E\n","\u003Cp\u003EA partial index only includes rows that match a \u003Ccode\u003EWHERE\u003C/code\u003E condition. The result is a smaller, faster index that's tailored to your actual query patterns.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Only index active orders (not the 90% that are completed)\nCREATE INDEX idx_orders_active ON orders(created_at)\nWHERE status IN ('pending', 'processing', 'shipped');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis index is dramatically smaller than indexing all rows — if only 10% of orders are active, the index is roughly 10% the size. Queries that include the matching condition use it automatically:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE\nSELECT * FROM orders\nWHERE status = 'pending' AND created_at > '2025-06-01';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E                                                            QUERY PLAN\n-----------------------------------------------------------------------------------------------------------------------------------\n Index Scan using idx_orders_active on orders  (cost=0.29..42.15 rows=18 width=96) (actual time=0.021..0.098 rows=15 loops=1)\n   Index Cond: (created_at > '2025-06-01 00:00:00'::timestamp)\n   Filter: (status = 'pending')\n Planning Time: 0.095 ms\n Execution Time: 0.128 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOther great use cases for partial indexes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Soft deletes: only index non-deleted rows\nCREATE INDEX idx_users_active_email ON users(email) WHERE deleted_at IS NULL;\n\n-- Only index rows that need processing\nCREATE INDEX idx_jobs_unprocessed ON jobs(priority, created_at) WHERE processed_at IS NULL;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExpression Indexes\u003C/h3\u003E\n","\u003Cp\u003EWhen your queries apply a function to a column, a regular index on that column won't help — the planner can't match \u003Ccode\u003Elower(email)\u003C/code\u003E to an index on \u003Ccode\u003Eemail\u003C/code\u003E. You need an expression index:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Expression index for case-insensitive email lookups\nCREATE INDEX idx_users_email_lower ON users(lower(email));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow this query uses the index:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- The expression in the query must match the index expression exactly\nSELECT * FROM users WHERE lower(email) = 'alice@example.com';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOther practical expression indexes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Index on a date extraction (for queries that group by month)\nCREATE INDEX idx_orders_month ON orders(date_trunc('month', created_at));\n\n-- Index on a JSONB field extracted as text\nCREATE INDEX idx_products_brand ON products((metadata->>'brand'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe query must use the exact same expression. \u003Ccode\u003EWHERE lower(email) = 'x'\u003C/code\u003E uses \u003Ccode\u003Eidx_users_email_lower\u003C/code\u003E, but \u003Ccode\u003EWHERE email = 'X'\u003C/code\u003E does not.\u003C/p\u003E\n","\u003Ch3\u003EBRIN — Block Range INdex\u003C/h3\u003E\n","\u003Cp\u003EBRIN indexes are tiny — they store summary information (min/max values) about ranges of physical table blocks rather than indexing individual rows. They work beautifully for columns whose values are naturally correlated with the physical order of the table.\u003C/p\u003E\n","\u003Cp\u003EThe classic example: timestamps in an append-only table. Since rows are inserted chronologically, nearby rows on disk have similar timestamps.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- BRIN index: tiny index for physically ordered data\nCREATE INDEX idx_events_created_brin ON events USING brin(created_at);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHow small is "tiny"? For a table with 100 million rows, a B-tree index on a timestamp might be 2 GB. The equivalent BRIN index? About 100 KB — roughly \u003Cstrong\u003E20,000x smaller\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Compare index sizes\nSELECT\n  indexname,\n  pg_size_pretty(pg_relation_size(indexname::regclass)) AS index_size\nFROM pg_indexes\nWHERE tablename = 'events' AND indexname LIKE '%created%';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E          indexname           | index_size\n-----------------------------+------------\n idx_events_created_btree    | 2142 MB\n idx_events_created_brin     | 104 kB\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBRIN works well when:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EData is inserted in order (timestamps, auto-incrementing IDs)\u003C/li\u003E\u003Cli\u003EThe table is large (millions of rows)\u003C/li\u003E\u003Cli\u003EQueries filter on ranges rather than exact values\u003C/li\u003E\u003Cli\u003EYou can tolerate slightly less precision (BRIN may scan a few extra blocks)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EBRIN does \u003Cstrong\u003Enot\u003C/strong\u003E work well for columns with random physical ordering — if \u003Ccode\u003Ecustomer_id\u003C/code\u003E values are scattered randomly across the table, a BRIN index provides almost no filtering benefit.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWhen Postgres Won't Use Your Index\u003C/h2\u003E\n","\u003Cp\u003EYou've created the index. The query should be fast. But \u003Ccode\u003EEXPLAIN\u003C/code\u003E shows a sequential scan anyway. What gives?\u003C/p\u003E\n","\u003Cp\u003EThis is one of the most common frustrations in Postgres, and the answer is almost always rational — the query planner made a cost-based decision. Let's walk through the reasons.\u003C/p\u003E\n","\u003Ch3\u003EThe Table Is Too Small\u003C/h3\u003E\n","\u003Cp\u003EFor tables that fit in a handful of pages, a sequential scan is actually faster than an index scan. An index scan requires reading the index pages, then randomly seeking to heap pages. For a tiny table, just reading everything sequentially is cheaper.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- A table with 200 rows: seq scan is correct behavior\nEXPLAIN SELECT * FROM status_codes WHERE code = 'ACTIVE';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E                          QUERY PLAN\n--------------------------------------------------------------\n Seq Scan on status_codes  (cost=0.00..1.25 rows=1 width=24)\n   Filter: (code = 'ACTIVE'::text)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is fine. Don't worry about it.\u003C/p\u003E\n","\u003Ch3\u003ELow Selectivity — Too Many Rows Match\u003C/h3\u003E\n","\u003Cp\u003EIf the planner estimates your query will return more than about 5-10% of the table, it often prefers a sequential scan. Random I/O (jumping between heap pages via index pointers) is expensive compared to sequential reads.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- If 40% of orders have status = 'completed', the planner skips the index\nEXPLAIN SELECT * FROM orders WHERE status = 'completed';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E                             QUERY PLAN\n---------------------------------------------------------------------\n Seq Scan on orders  (cost=0.00..18450.00 rows=412000 width=96)\n   Filter: (status = 'completed'::text)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe planner is right here — reading 40% of the table via random index lookups would be slower than just scanning the whole thing.\u003C/p\u003E\n","\u003Ch3\u003EStale Statistics\u003C/h3\u003E\n","\u003Cp\u003EThe planner relies on table statistics (row counts, value distributions, most common values) to estimate selectivity. If those statistics are outdated, it makes bad choices.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- After bulk loading data, update statistics\nANALYZE orders;\n\n-- Check when statistics were last updated\nSELECT\n  schemaname || '.' || relname AS table_name,\n  last_analyze,\n  last_autoanalyze,\n  n_live_tup\nFROM pg_stat_user_tables\nWHERE relname = 'orders';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you've recently loaded or deleted a large portion of a table, run \u003Ccode\u003EANALYZE\u003C/code\u003E before worrying about missing index usage.\u003C/p\u003E\n","\u003Ch3\u003EType Mismatch\u003C/h3\u003E\n","\u003Cp\u003EThis is a subtle one. If the column is \u003Ccode\u003Einteger\u003C/code\u003E but you're comparing to a \u003Ccode\u003Etext\u003C/code\u003E value, Postgres may not be able to use the index:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Column is integer, but comparing to text — implicit cast may prevent index use\nSELECT * FROM orders WHERE customer_id = '12345';\n\n-- Fix: use the correct type\nSELECT * FROM orders WHERE customer_id = 12345;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFunctions on Indexed Columns\u003C/h3\u003E\n","\u003Cp\u003EA plain index on \u003Ccode\u003Eemail\u003C/code\u003E doesn't help a query that applies \u003Ccode\u003Elower()\u003C/code\u003E to the column. The index stores raw email values — Postgres can't match a transformed expression against them.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- This does NOT use an index on the email column\nSELECT * FROM users WHERE lower(email) = 'alice@example.com';\n\n-- Solution: create an expression index (covered in the previous section)\nCREATE INDEX idx_users_email_lower ON users(lower(email));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETesting with enable_seqscan (Development Only)\u003C/h3\u003E\n","\u003Cp\u003EWhen debugging, you can force Postgres to avoid sequential scans to see if an index \u003Cem\u003Ecould\u003C/em\u003E be used:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- NEVER use this in production — testing only\nSET enable_seqscan = off;\nEXPLAIN ANALYZE SELECT * FROM orders WHERE status = 'completed';\nSET enable_seqscan = on;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf the plan changes to an index scan with this setting, it means the index exists but the planner calculated that a seq scan was cheaper for that query. The planner is usually right.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMonitoring Index Usage\u003C/h2\u003E\n","\u003Cp\u003ECreating indexes is only half the job. In production, you need to know which indexes are being used (and earning their keep) and which are just consuming disk space and slowing down writes.\u003C/p\u003E\n","\u003Ch3\u003EFinding Unused Indexes\u003C/h3\u003E\n","\u003Cp\u003EEvery index imposes a write penalty — it must be updated on every INSERT, UPDATE, and DELETE. An index that never gets scanned is pure overhead:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Find indexes that have never (or rarely) been used since last stats reset\nSELECT\n  schemaname || '.' || relname AS table_name,\n  indexrelname AS index_name,\n  pg_size_pretty(pg_relation_size(indexrelid)) AS index_size,\n  idx_scan AS times_used,\n  idx_tup_read AS tuples_read,\n  idx_tup_fetch AS tuples_fetched\nFROM pg_stat_user_indexes\nWHERE idx_scan = 0\n  AND indexrelname NOT LIKE '%_pkey'\n  AND indexrelname NOT LIKE '%_unique%'\nORDER BY pg_relation_size(indexrelid) DESC\nLIMIT 15;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E     table_name     |        index_name         | index_size | times_used | tuples_read | tuples_fetched\n--------------------+---------------------------+------------+------------+-------------+----------------\n public.events      | idx_events_source         | 892 MB     |          0 |           0 |              0\n public.orders      | idx_orders_old_status     | 214 MB     |          0 |           0 |              0\n public.audit_log   | idx_audit_deprecated_col  | 156 MB     |          0 |           0 |              0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThose indexes are costing you over 1 GB of storage and slowing down every write — with zero read benefit. Candidates for removal (after confirming no scheduled batch jobs use them).\u003C/p\u003E\n","\u003Ch3\u003EFinding Missing Indexes\u003C/h3\u003E\n","\u003Cp\u003EPostgres tracks sequential scans on tables. A table with many sequential scans and many rows is probably missing an index:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Tables that might benefit from additional indexes\nSELECT\n  schemaname || '.' || relname AS table_name,\n  seq_scan,\n  seq_tup_read,\n  idx_scan,\n  n_live_tup,\n  pg_size_pretty(pg_total_relation_size(relid)) AS total_size,\n  round(seq_scan::numeric / nullif(seq_scan + idx_scan, 0) * 100, 1) AS seq_scan_pct\nFROM pg_stat_user_tables\nWHERE n_live_tup > 10000\n  AND seq_scan > 100\nORDER BY seq_tup_read DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E     table_name     | seq_scan | seq_tup_read | idx_scan | n_live_tup | total_size | seq_scan_pct\n--------------------+----------+--------------+----------+------------+------------+--------------\n public.events      |     8412 |  42058412000 |     1204 |    5012000 | 2.8 GB     |         87.5\n public.sessions    |     3201 |   1921600000 |    89421 |     600500 | 412 MB     |          3.5\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThat \u003Ccode\u003Eevents\u003C/code\u003E table has 87.5% sequential scans — a strong signal that queries against it would benefit from better indexing. Look at \u003Ccode\u003Epg_stat_statements\u003C/code\u003E or query logs to find the specific queries driving those scans.\u003C/p\u003E\n","\u003Ch3\u003EIndex Health Overview\u003C/h3\u003E\n","\u003Cp\u003EA combined view that shows which indexes are working hard and which are dead weight:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Overall index health: what's being used, what's wasted\nSELECT\n  schemaname || '.' || relname AS table_name,\n  indexrelname AS index_name,\n  pg_size_pretty(pg_relation_size(indexrelid)) AS index_size,\n  idx_scan AS scans,\n  CASE\n    WHEN idx_scan = 0 THEN 'UNUSED'\n    WHEN idx_scan < 10 THEN 'RARELY_USED'\n    ELSE 'ACTIVE'\n  END AS status\nFROM pg_stat_user_indexes\nWHERE schemaname = 'public'\nORDER BY pg_relation_size(indexrelid) DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERemember to check when statistics were last reset — an "unused" index might just be unused since the last \u003Ccode\u003Epg_stat_reset()\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- When were stats last reset?\nSELECT stats_reset FROM pg_stat_bgwriter;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nSnowflake Postgres Insights surfaces index health automatically — including unused indexes that waste write performance and missing indexes suggested by query patterns. Monitor through the Insights dashboard or query the pg_stat_user_indexes view directly.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003EIndexing is the highest-leverage optimization tool in Postgres. Start with B-tree indexes on your most-queried columns, use EXPLAIN ANALYZE to verify they're working, and monitor \u003Ccode\u003Epg_stat_user_indexes\u003C/code\u003E to catch unused indexes before they accumulate. Remember: every index speeds up reads but slows down writes — the goal is having exactly the indexes your queries need, no more, no less.\u003C/p\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.crunchydata.com/blog/postgres-indexes-for-newbies\"\u003E🦛 Crunchy Data Blog — Postgres Indexes for Newbies\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EBeginner-friendly guide to Postgres indexing fundamentals.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.crunchydata.com/blog/indexing-jsonb-in-postgres\"\u003E🦛 Crunchy Data Blog — Indexing JSONB in Postgres\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EDeep dive into GIN indexes for JSONB data.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.crunchydata.com/blog/indexing-materialized-views-in-postgres\"\u003E🦛 Crunchy Data Blog — Indexing Materialized Views\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EStrategies for indexing precomputed query results.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.postgresql.org/docs/current/indexes.html\"\u003E🐘 PostgreSQL Documentation — Indexes\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EComplete reference for all index types and options.\u003C/p\u003E"],"description":"","title":"Indexing",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n\nIndexes! They're the single biggest lever you have for Postgres performance. In our experience, when someone says \"my queries are slow,\" the answer comes down to missing or unused indexes the vast majority of the time. The good news? Indexing in Postgres is straightforward once you understand a few core concepts.\n\nThis guide covers the main index types (B-tree, GIN, GiST, Hash, BRIN), how to read execution plans with `EXPLAIN ANALYZE`, and how to monitor index usage in production so you can find what's missing and drop what's dead weight.\n\n\u003C!-- ------------------------ --\u003E\n## How Postgres Scans Tables\n\nUnderstanding scan types is essential to understanding how indexes help. When Postgres executes a query, it chooses a scan strategy based on table size, available indexes, and the selectivity of your WHERE clause.\n\n- **Sequential Scan** — reads every row in the table from start to finish. This is the default when no useful index exists or when most of the table will be returned anyway.\n- **Index Scan** — traverses the index tree to find matching entries, then follows pointers back to the heap (table) to fetch full rows. Best for highly selective queries returning a small percentage of rows.\n- **Bitmap Index Scan** — scans the index to build a bitmap of matching pages, then does a single pass over the heap reading only those pages. Efficient for medium-selectivity queries that return too many rows for a plain index scan but too few for a sequential scan.\n- **Index Only Scan** — satisfies the query entirely from the index without touching the heap. Requires a covering index that includes all columns the query needs.\n- **Parallel Scan** — distributes the scan across multiple worker processes. Postgres can parallelize sequential scans, index scans, and bitmap scans for large tables.\n- **Parallel Index Scan** — multiple workers traverse the index concurrently, with results gathered by the leader process.\n\n![postgres-scan-types](https://publish-p57963-e462109.adobeaemcloud.com/content/dam/snowflake-site/developers/postgres/indexing/postgres_scan_types.png?v=03111311)\n\nThe query planner automatically picks the best strategy. Use `EXPLAIN` to see which scan type a query uses — and read on to learn how to create the indexes that unlock the faster scan types.\n\n\u003C!-- ------------------------ --\u003E\n## B-tree Indexes for Equality and Range Queries\n\nA B-tree index is the default — when you run `CREATE INDEX` without specifying a type, you get a B-tree. It covers about 95% of indexing needs and supports equality (`=`), range (`\u003C`, `\u003E`, `\u003C=`, `\u003E=`, `BETWEEN`), `IN`, `IS NULL`, and sorting.\n\nLet's start with the basics:\n\n```sql\n-- Create a simple B-tree index\nCREATE INDEX idx_orders_customer_id ON orders(customer_id);\n```\n\nNow let's see what this actually does for performance. Here's a query without the index:\n\n```sql\nEXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 12345;\n```\n\n```\n                                                    QUERY PLAN\n-------------------------------------------------------------------------------------------------------------------\n Seq Scan on orders  (cost=0.00..18450.00 rows=52 width=96) (actual time=12.451..89.234 rows=48 loops=1)\n   Filter: (customer_id = 12345)\n   Rows Removed by Filter: 999952\n Planning Time: 0.085 ms\n Execution Time: 89.301 ms\n```\n\nThat sequential scan is reading every single row in the table — about a million rows — just to find 48 matches. After creating the index:\n\n```sql\nEXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 12345;\n```\n\n```\n                                                          QUERY PLAN\n------------------------------------------------------------------------------------------------------------------------------\n Index Scan using idx_orders_customer_id on orders  (cost=0.42..56.84 rows=52 width=96) (actual time=0.028..0.142 rows=48 loops=1)\n   Index Cond: (customer_id = 12345)\n Planning Time: 0.091 ms\n Execution Time: 0.168 ms\n```\n\nFrom 89ms to 0.17ms. That's a 500x improvement — and this is a relatively small table. On tables with tens of millions of rows, the difference between a seq scan and an index scan can be seconds versus microseconds.\n\n### Multi-Column Indexes\n\nWhen your queries filter on multiple columns, a multi-column index can be dramatically more efficient than separate single-column indexes. But column order matters — Postgres can use the index for queries that filter on a **leftmost prefix** of the indexed columns.\n\n```sql\n-- Multi-column index\nCREATE INDEX idx_orders_status_created ON orders(status, created_at);\n```\n\nThis index helps these queries:\n\n```sql\n-- Uses the index (leftmost prefix: status)\nSELECT * FROM orders WHERE status = 'pending';\n\n-- Uses the index (both columns)\nSELECT * FROM orders WHERE status = 'shipped' AND created_at \u003E '2025-01-01';\n\n-- Uses the index (leftmost prefix for filter, second column for range)\nSELECT * FROM orders WHERE status = 'pending' AND created_at BETWEEN '2025-01-01' AND '2025-06-01';\n```\n\nBut this query **cannot** use the index:\n\n```sql\n-- Does NOT use the index (missing leftmost column)\nSELECT * FROM orders WHERE created_at \u003E '2025-01-01';\n```\n\nPut the most selective column first — the column that narrows down the most rows. If `status` has 5 distinct values and `created_at` has millions, leading with `status` makes sense when you're always filtering by status.\n\n### UNIQUE Indexes and Covering Indexes\n\nA `UNIQUE` constraint automatically creates a B-tree index — you get both data integrity and query performance:\n\n```sql\n-- This creates a unique B-tree index automatically\nALTER TABLE users ADD CONSTRAINT users_email_unique UNIQUE (email);\n\n-- Equivalent to:\nCREATE UNIQUE INDEX users_email_unique ON users(email);\n```\n\nCovering indexes use `INCLUDE` to store additional columns in the index leaf pages. This enables **index-only scans** — Postgres can answer the query entirely from the index without touching the heap table at all:\n\n```sql\n-- Covering index: include columns needed by SELECT\nCREATE INDEX idx_orders_covering ON orders(customer_id) INCLUDE (status, total_amount);\n```\n\n```sql\nEXPLAIN ANALYZE SELECT status, total_amount FROM orders WHERE customer_id = 12345;\n```\n\n```\n                                                             QUERY PLAN\n-------------------------------------------------------------------------------------------------------------------------------------\n Index Only Scan using idx_orders_covering on orders  (cost=0.42..52.10 rows=48 width=18) (actual time=0.024..0.089 rows=48 loops=1)\n   Index Cond: (customer_id = 12345)\n   Heap Fetches: 0\n Planning Time: 0.078 ms\n Execution Time: 0.112 ms\n```\n\n\"Heap Fetches: 0\" — Postgres answered the entire query from the index. This is the fastest possible path.\n\n### The Write Overhead Tradeoff\n\nEvery index adds overhead to writes. Each `INSERT`, `UPDATE` (of an indexed column), and `DELETE` must update every index on the table. For read-heavy workloads, more indexes are almost always worth it. For write-heavy tables, be more deliberate — each index you add slows down every write operation.\n\n\u003C!-- ------------------------ --\u003E\n## GIN, GiST, and Specialized Index Types\n\nB-tree handles scalar comparisons beautifully, but what about searching inside complex data types — JSONB documents, arrays, full-text search vectors? That's where specialized index types come in.\n\n### GIN — Generalized Inverted Index\n\nGIN indexes are built for values that contain multiple elements. Think of them like the index at the back of a book — they map each element to the rows that contain it.\n\n**Best for:** JSONB containment (`@\u003E`), key existence (`?`), array overlap (`&&`), full-text search (`@@`)\n\n```sql\n-- GIN index for JSONB containment queries\nCREATE INDEX idx_products_metadata ON products USING gin(metadata);\n```\n\nNow JSONB containment queries become fast:\n\n```sql\n-- Find products with specific attributes (uses GIN index)\nEXPLAIN ANALYZE\nSELECT name, price FROM products\nWHERE metadata @\u003E '{\"color\": \"blue\", \"size\": \"large\"}';\n```\n\n```\n                                                           QUERY PLAN\n---------------------------------------------------------------------------------------------------------------------------------\n Bitmap Heap Scan on products  (cost=24.01..1205.42 rows=312 width=38) (actual time=0.198..1.245 rows=287 loops=1)\n   Recheck Cond: (metadata @\u003E '{\"color\": \"blue\", \"size\": \"large\"}'::jsonb)\n   Heap Blocks: exact=215\n   -\u003E  Bitmap Index Scan on idx_products_metadata  (cost=0.00..23.93 rows=312 width=0) (actual time=0.152..0.152 rows=287 loops=1)\n         Index Cond: (metadata @\u003E '{\"color\": \"blue\", \"size\": \"large\"}'::jsonb)\n Planning Time: 0.112 ms\n Execution Time: 1.398 ms\n```\n\nFor JSONB, you can also use `jsonb_path_ops` — a more compact operator class that only supports the `@\u003E` containment operator but is smaller and faster for that specific use case:\n\n```sql\n-- Smaller, faster — but only supports @\u003E containment\nCREATE INDEX idx_products_metadata_pathops ON products USING gin(metadata jsonb_path_ops);\n```\n\nGIN for arrays works the same way:\n\n```sql\n-- Index an array column\nCREATE INDEX idx_posts_tags ON posts USING gin(tags);\n\n-- Query: find posts tagged with both 'postgres' and 'performance'\nSELECT title FROM posts WHERE tags @\u003E ARRAY['postgres', 'performance'];\n```\n\n**GIN tradeoffs:** GIN indexes are larger than B-tree indexes and slower to update (inserts are batched into a \"pending list\" and merged later). But for query speed on complex data types, they're unmatched.\n\n### GiST — Generalized Search Tree\n\nGiST indexes handle geometric and range data — things where \"overlaps\" and \"contains\" and \"nearest neighbor\" are the important operations.\n\n**Best for:** PostGIS spatial queries, range types (`tsrange`, `int4range`), full-text search (alternative to GIN), nearest-neighbor searches\n\n```sql\n-- GiST for range type queries\nCREATE INDEX idx_reservations_during ON reservations USING gist(during);\n\n-- Find overlapping reservations\nSELECT * FROM reservations\nWHERE during && tsrange('2025-03-01', '2025-03-07', '[]');\n```\n\nGiST supports nearest-neighbor ordering with the `\u003C-\u003E` operator, which GIN cannot do:\n\n```sql\n-- PostGIS: find the 5 nearest coffee shops\nSELECT name, ST_Distance(location, ST_MakePoint(-122.4194, 37.7749)) AS dist\nFROM shops\nORDER BY location \u003C-\u003E ST_MakePoint(-122.4194, 37.7749)\nLIMIT 5;\n```\n\n### Hash Indexes\n\nHash indexes support only equality (`=`) comparisons. Since B-tree also handles equality (plus ranges and sorting), hash indexes are rarely needed. The one niche advantage: hash indexes can be slightly smaller for very long keys (like UUIDs or long strings) where only equality matching is needed.\n\n```sql\n-- Hash index — rarely needed, B-tree usually better\nCREATE INDEX idx_sessions_token ON sessions USING hash(session_token);\n```\n\n### Choosing the Right Index Type\n\n| Index Type | Best For | Operators |\n|---|---|---|\n| B-tree | Equality, range, sorting, most queries | `=`, `\u003C`, `\u003E`, `\u003C=`, `\u003E=`, `BETWEEN`, `IN` |\n| GIN | Multi-element values (JSONB, arrays, text search) | `@\u003E`, `?`, `?&`, `?|`, `&&`, `@@` |\n| GiST | Geometric, range, nearest-neighbor | `&&`, `@\u003E`, `\u003C@`, `\u003C-\u003E`, `\u003C\u003C`, `\u003E\u003E` |\n| Hash | Equality only (large keys) | `=` |\n\n\u003C!-- ------------------------ --\u003E\n## Using pg_stat_statements to Find Your Slowest Queries\n\n`pg_stat_statements` is the best tool for figuring out where to add indexes. Instead of guessing which queries are slow, it gives you hard data — total execution time, call count, rows scanned, and buffer usage for every normalized query your application runs.\n\n### Enabling pg_stat_statements\n\n`pg_stat_statements` must be loaded via `shared_preload_libraries` in `postgresql.conf` — this requires a server restart since it allocates shared memory at startup:\n\n```\n# postgresql.conf\nshared_preload_libraries = 'pg_stat_statements'\n```\n\nAfter restarting Postgres, create the extension:\n\n```sql\nCREATE EXTENSION IF NOT EXISTS pg_stat_statements;\n```\n\nOn Snowflake Postgres, `pg_stat_statements` is pre-loaded and ready to use — just run `CREATE EXTENSION` and you're set.\n\n\u003E aside positive\n\u003E If you're running self-managed Postgres, adding to `shared_preload_libraries` requires a full server restart (`pg_ctl restart` or `systemctl restart postgresql`). A reload is not sufficient. Plan this during a maintenance window.\n\n### Finding Queries That Need Indexes\n\nThe highest-value queries to index are those consuming the most total server time. These are your candidates:\n\n```sql\n-- Top 10 queries by total execution time\nSELECT\n  left(query, 80) AS query_preview,\n  calls,\n  round(total_exec_time::numeric, 1) AS total_ms,\n  round(mean_exec_time::numeric, 1) AS avg_ms,\n  rows,\n  round((shared_blks_read::numeric / nullif(shared_blks_hit + shared_blks_read, 0)) * 100, 1) AS cache_miss_pct\nFROM pg_stat_statements\nORDER BY total_exec_time DESC\nLIMIT 10;\n```\n\nThe `cache_miss_pct` column is key — a high value means the query is reading from disk rather than shared buffers, which is exactly what a good index fixes.\n\n### Identifying Sequential Scans in Your Heaviest Queries\n\nOnce you've found your slowest queries, run them through `EXPLAIN ANALYZE` to confirm they're doing sequential scans:\n\n```sql\nEXPLAIN (ANALYZE, BUFFERS) SELECT ... ; -- paste the slow query here\n```\n\nLook for `Seq Scan` nodes with high `actual time` and `Rows Removed by Filter`. A seq scan that filters out 99% of rows is screaming for an index on the filter column.\n\n### Queries With High Buffer Reads\n\nQueries that read large amounts of data are prime indexing candidates — even if they're individually fast, they burn I/O:\n\n```sql\n-- Queries reading the most data (I/O heavy)\nSELECT\n  left(query, 80) AS query_preview,\n  calls,\n  shared_blks_read + shared_blks_hit AS total_buffers,\n  round((shared_blks_read + shared_blks_hit)::numeric / nullif(calls, 0), 0) AS buffers_per_call,\n  round(mean_exec_time::numeric, 1) AS avg_ms\nFROM pg_stat_statements\nWHERE calls \u003E 100\nORDER BY (shared_blks_read + shared_blks_hit) DESC\nLIMIT 10;\n```\n\nA query that reads 50,000 buffers per call is almost certainly doing a sequential scan on a large table. Add an index on the columns in its WHERE clause.\n\n### Resetting Statistics\n\nAfter adding indexes, reset the statistics to measure the improvement with a clean slate:\n\n```sql\n-- Reset pg_stat_statements (requires superuser)\nSELECT pg_stat_statements_reset();\n```\n\nWait for normal traffic to accumulate, then check again — your previously slow queries should show dramatically lower `total_exec_time` and `shared_blks_read` values.\n\n\u003C!-- ------------------------ --\u003E\n## Partial Indexes, Expression Indexes, and BRIN\n\nThese three index variants let you build smarter, smaller, more targeted indexes. I wanted to share these because they're incredibly powerful but often overlooked.\n\n### Partial Indexes\n\nA partial index only includes rows that match a `WHERE` condition. The result is a smaller, faster index that's tailored to your actual query patterns.\n\n```sql\n-- Only index active orders (not the 90% that are completed)\nCREATE INDEX idx_orders_active ON orders(created_at)\nWHERE status IN ('pending', 'processing', 'shipped');\n```\n\nThis index is dramatically smaller than indexing all rows — if only 10% of orders are active, the index is roughly 10% the size. Queries that include the matching condition use it automatically:\n\n```sql\nEXPLAIN ANALYZE\nSELECT * FROM orders\nWHERE status = 'pending' AND created_at \u003E '2025-06-01';\n```\n\n```\n                                                            QUERY PLAN\n-----------------------------------------------------------------------------------------------------------------------------------\n Index Scan using idx_orders_active on orders  (cost=0.29..42.15 rows=18 width=96) (actual time=0.021..0.098 rows=15 loops=1)\n   Index Cond: (created_at \u003E '2025-06-01 00:00:00'::timestamp)\n   Filter: (status = 'pending')\n Planning Time: 0.095 ms\n Execution Time: 0.128 ms\n```\n\nOther great use cases for partial indexes:\n\n```sql\n-- Soft deletes: only index non-deleted rows\nCREATE INDEX idx_users_active_email ON users(email) WHERE deleted_at IS NULL;\n\n-- Only index rows that need processing\nCREATE INDEX idx_jobs_unprocessed ON jobs(priority, created_at) WHERE processed_at IS NULL;\n```\n\n### Expression Indexes\n\nWhen your queries apply a function to a column, a regular index on that column won't help — the planner can't match `lower(email)` to an index on `email`. You need an expression index:\n\n```sql\n-- Expression index for case-insensitive email lookups\nCREATE INDEX idx_users_email_lower ON users(lower(email));\n```\n\nNow this query uses the index:\n\n```sql\n-- The expression in the query must match the index expression exactly\nSELECT * FROM users WHERE lower(email) = 'alice@example.com';\n```\n\nOther practical expression indexes:\n\n```sql\n-- Index on a date extraction (for queries that group by month)\nCREATE INDEX idx_orders_month ON orders(date_trunc('month', created_at));\n\n-- Index on a JSONB field extracted as text\nCREATE INDEX idx_products_brand ON products((metadata-\u003E\u003E'brand'));\n```\n\nThe query must use the exact same expression. `WHERE lower(email) = 'x'` uses `idx_users_email_lower`, but `WHERE email = 'X'` does not.\n\n### BRIN — Block Range INdex\n\nBRIN indexes are tiny — they store summary information (min/max values) about ranges of physical table blocks rather than indexing individual rows. They work beautifully for columns whose values are naturally correlated with the physical order of the table.\n\nThe classic example: timestamps in an append-only table. Since rows are inserted chronologically, nearby rows on disk have similar timestamps.\n\n```sql\n-- BRIN index: tiny index for physically ordered data\nCREATE INDEX idx_events_created_brin ON events USING brin(created_at);\n```\n\nHow small is \"tiny\"? For a table with 100 million rows, a B-tree index on a timestamp might be 2 GB. The equivalent BRIN index? About 100 KB — roughly **20,000x smaller**.\n\n```sql\n-- Compare index sizes\nSELECT\n  indexname,\n  pg_size_pretty(pg_relation_size(indexname::regclass)) AS index_size\nFROM pg_indexes\nWHERE tablename = 'events' AND indexname LIKE '%created%';\n```\n\n```\n          indexname           | index_size\n-----------------------------+------------\n idx_events_created_btree    | 2142 MB\n idx_events_created_brin     | 104 kB\n```\n\nBRIN works well when:\n- Data is inserted in order (timestamps, auto-incrementing IDs)\n- The table is large (millions of rows)\n- Queries filter on ranges rather than exact values\n- You can tolerate slightly less precision (BRIN may scan a few extra blocks)\n\nBRIN does **not** work well for columns with random physical ordering — if `customer_id` values are scattered randomly across the table, a BRIN index provides almost no filtering benefit.\n\n\u003C!-- ------------------------ --\u003E\n## When Postgres Won't Use Your Index\n\nYou've created the index. The query should be fast. But `EXPLAIN` shows a sequential scan anyway. What gives?\n\nThis is one of the most common frustrations in Postgres, and the answer is almost always rational — the query planner made a cost-based decision. Let's walk through the reasons.\n\n### The Table Is Too Small\n\nFor tables that fit in a handful of pages, a sequential scan is actually faster than an index scan. An index scan requires reading the index pages, then randomly seeking to heap pages. For a tiny table, just reading everything sequentially is cheaper.\n\n```sql\n-- A table with 200 rows: seq scan is correct behavior\nEXPLAIN SELECT * FROM status_codes WHERE code = 'ACTIVE';\n```\n\n```\n                          QUERY PLAN\n--------------------------------------------------------------\n Seq Scan on status_codes  (cost=0.00..1.25 rows=1 width=24)\n   Filter: (code = 'ACTIVE'::text)\n```\n\nThis is fine. Don't worry about it.\n\n### Low Selectivity — Too Many Rows Match\n\nIf the planner estimates your query will return more than about 5-10% of the table, it often prefers a sequential scan. Random I/O (jumping between heap pages via index pointers) is expensive compared to sequential reads.\n\n```sql\n-- If 40% of orders have status = 'completed', the planner skips the index\nEXPLAIN SELECT * FROM orders WHERE status = 'completed';\n```\n\n```\n                             QUERY PLAN\n---------------------------------------------------------------------\n Seq Scan on orders  (cost=0.00..18450.00 rows=412000 width=96)\n   Filter: (status = 'completed'::text)\n```\n\nThe planner is right here — reading 40% of the table via random index lookups would be slower than just scanning the whole thing.\n\n### Stale Statistics\n\nThe planner relies on table statistics (row counts, value distributions, most common values) to estimate selectivity. If those statistics are outdated, it makes bad choices.\n\n```sql\n-- After bulk loading data, update statistics\nANALYZE orders;\n\n-- Check when statistics were last updated\nSELECT\n  schemaname || '.' || relname AS table_name,\n  last_analyze,\n  last_autoanalyze,\n  n_live_tup\nFROM pg_stat_user_tables\nWHERE relname = 'orders';\n```\n\nIf you've recently loaded or deleted a large portion of a table, run `ANALYZE` before worrying about missing index usage.\n\n### Type Mismatch\n\nThis is a subtle one. If the column is `integer` but you're comparing to a `text` value, Postgres may not be able to use the index:\n\n```sql\n-- Column is integer, but comparing to text — implicit cast may prevent index use\nSELECT * FROM orders WHERE customer_id = '12345';\n\n-- Fix: use the correct type\nSELECT * FROM orders WHERE customer_id = 12345;\n```\n\n### Functions on Indexed Columns\n\nA plain index on `email` doesn't help a query that applies `lower()` to the column. The index stores raw email values — Postgres can't match a transformed expression against them.\n\n```sql\n-- This does NOT use an index on the email column\nSELECT * FROM users WHERE lower(email) = 'alice@example.com';\n\n-- Solution: create an expression index (covered in the previous section)\nCREATE INDEX idx_users_email_lower ON users(lower(email));\n```\n\n### Testing with enable_seqscan (Development Only)\n\nWhen debugging, you can force Postgres to avoid sequential scans to see if an index *could* be used:\n\n```sql\n-- NEVER use this in production — testing only\nSET enable_seqscan = off;\nEXPLAIN ANALYZE SELECT * FROM orders WHERE status = 'completed';\nSET enable_seqscan = on;\n```\n\nIf the plan changes to an index scan with this setting, it means the index exists but the planner calculated that a seq scan was cheaper for that query. The planner is usually right.\n\n\u003C!-- ------------------------ --\u003E\n## Monitoring Index Usage\n\nCreating indexes is only half the job. In production, you need to know which indexes are being used (and earning their keep) and which are just consuming disk space and slowing down writes.\n\n### Finding Unused Indexes\n\nEvery index imposes a write penalty — it must be updated on every INSERT, UPDATE, and DELETE. An index that never gets scanned is pure overhead:\n\n```sql\n-- Find indexes that have never (or rarely) been used since last stats reset\nSELECT\n  schemaname || '.' || relname AS table_name,\n  indexrelname AS index_name,\n  pg_size_pretty(pg_relation_size(indexrelid)) AS index_size,\n  idx_scan AS times_used,\n  idx_tup_read AS tuples_read,\n  idx_tup_fetch AS tuples_fetched\nFROM pg_stat_user_indexes\nWHERE idx_scan = 0\n  AND indexrelname NOT LIKE '%_pkey'\n  AND indexrelname NOT LIKE '%_unique%'\nORDER BY pg_relation_size(indexrelid) DESC\nLIMIT 15;\n```\n\n```\n     table_name     |        index_name         | index_size | times_used | tuples_read | tuples_fetched\n--------------------+---------------------------+------------+------------+-------------+----------------\n public.events      | idx_events_source         | 892 MB     |          0 |           0 |              0\n public.orders      | idx_orders_old_status     | 214 MB     |          0 |           0 |              0\n public.audit_log   | idx_audit_deprecated_col  | 156 MB     |          0 |           0 |              0\n```\n\nThose indexes are costing you over 1 GB of storage and slowing down every write — with zero read benefit. Candidates for removal (after confirming no scheduled batch jobs use them).\n\n### Finding Missing Indexes\n\nPostgres tracks sequential scans on tables. A table with many sequential scans and many rows is probably missing an index:\n\n```sql\n-- Tables that might benefit from additional indexes\nSELECT\n  schemaname || '.' || relname AS table_name,\n  seq_scan,\n  seq_tup_read,\n  idx_scan,\n  n_live_tup,\n  pg_size_pretty(pg_total_relation_size(relid)) AS total_size,\n  round(seq_scan::numeric / nullif(seq_scan + idx_scan, 0) * 100, 1) AS seq_scan_pct\nFROM pg_stat_user_tables\nWHERE n_live_tup \u003E 10000\n  AND seq_scan \u003E 100\nORDER BY seq_tup_read DESC\nLIMIT 10;\n```\n\n```\n     table_name     | seq_scan | seq_tup_read | idx_scan | n_live_tup | total_size | seq_scan_pct\n--------------------+----------+--------------+----------+------------+------------+--------------\n public.events      |     8412 |  42058412000 |     1204 |    5012000 | 2.8 GB     |         87.5\n public.sessions    |     3201 |   1921600000 |    89421 |     600500 | 412 MB     |          3.5\n```\n\nThat `events` table has 87.5% sequential scans — a strong signal that queries against it would benefit from better indexing. Look at `pg_stat_statements` or query logs to find the specific queries driving those scans.\n\n### Index Health Overview\n\nA combined view that shows which indexes are working hard and which are dead weight:\n\n```sql\n-- Overall index health: what's being used, what's wasted\nSELECT\n  schemaname || '.' || relname AS table_name,\n  indexrelname AS index_name,\n  pg_size_pretty(pg_relation_size(indexrelid)) AS index_size,\n  idx_scan AS scans,\n  CASE\n    WHEN idx_scan = 0 THEN 'UNUSED'\n    WHEN idx_scan \u003C 10 THEN 'RARELY_USED'\n    ELSE 'ACTIVE'\n  END AS status\nFROM pg_stat_user_indexes\nWHERE schemaname = 'public'\nORDER BY pg_relation_size(indexrelid) DESC\nLIMIT 20;\n```\n\nRemember to check when statistics were last reset — an \"unused\" index might just be unused since the last `pg_stat_reset()`:\n\n```sql\n-- When were stats last reset?\nSELECT stats_reset FROM pg_stat_bgwriter;\n```\n\n\u003E aside positive\n\u003E Snowflake Postgres Insights surfaces index health automatically — including unused indexes that waste write performance and missing indexes suggested by query patterns. Monitor through the Insights dashboard or query the pg_stat_user_indexes view directly.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n\nIndexing is the highest-leverage optimization tool in Postgres. Start with B-tree indexes on your most-queried columns, use EXPLAIN ANALYZE to verify they're working, and monitor `pg_stat_user_indexes` to catch unused indexes before they accumulate. Remember: every index speeds up reads but slows down writes — the goal is having exactly the indexes your queries need, no more, no less.\n\n### Related Resources\n\n\u003Cbutton\u003E\n\n[🦛 Crunchy Data Blog — Postgres Indexes for Newbies](https://www.crunchydata.com/blog/postgres-indexes-for-newbies)\n\u003C/button\u003E\n\nBeginner-friendly guide to Postgres indexing fundamentals.\n\n---\n\n\u003Cbutton\u003E\n\n[🦛 Crunchy Data Blog — Indexing JSONB in Postgres](https://www.crunchydata.com/blog/indexing-jsonb-in-postgres)\n\u003C/button\u003E\n\nDeep dive into GIN indexes for JSONB data.\n\n---\n\n\u003Cbutton\u003E\n\n[🦛 Crunchy Data Blog — Indexing Materialized Views](https://www.crunchydata.com/blog/indexing-materialized-views-in-postgres)\n\u003C/button\u003E\n\nStrategies for indexing precomputed query results.\n\n---\n\n\u003Cbutton\u003E\n\n[🐘 PostgreSQL Documentation — Indexes](https://www.postgresql.org/docs/current/indexes.html)\n\u003C/button\u003E\n\nComplete reference for all index types and options.\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":true,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-59ee5d81b0","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-f1863a008f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-f63ea1e53c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-41e81c856d","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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