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You can't fix what you don't understand, and \u003Ccode\u003EEXPLAIN\u003C/code\u003E is how Postgres tells you exactly what it's doing with your query — and why it's slow.\u003C/p\u003E\n","\u003Cp\u003EThere are two forms, and the difference matters:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Estimated plan only (does NOT execute the query)\nEXPLAIN SELECT * FROM orders WHERE customer_id = 42;\n\n-- Actually executes and shows real timing (use this!)\nEXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 42;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003EEXPLAIN\u003C/code\u003E shows what the planner \u003Cem\u003Ethinks\u003C/em\u003E will happen. \u003Ccode\u003EEXPLAIN ANALYZE\u003C/code\u003E shows what \u003Cem\u003Eactually\u003C/em\u003E happened. When they disagree, that's a clue — the planner is working with bad estimates.\u003C/p\u003E\n","\u003Cp\u003EHere's an annotated plan from a real query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E                                                          QUERY PLAN\n------------------------------------------------------------------------------------------------------------------------------\n Hash Join  (cost=1204.52..5765.83 rows=1248 width=164) (actual time=8.412..42.891 rows=1195 loops=1)\n   Hash Cond: (o.customer_id = c.id)\n   ->  Seq Scan on orders o  (cost=0.00..4230.00 rows=50000 width=96) (actual time=0.015..18.234 rows=50000 loops=1)\n         Filter: (created_at > '2025-01-01'::date)\n         Rows Removed by Filter: 150000\n   ->  Hash  (cost=1102.00..1102.00 rows=8202 width=68) (actual time=8.102..8.102 rows=8200 loops=1)\n         Buckets: 16384  Batches: 1  Memory Usage: 892kB\n         ->  Seq Scan on customers c  (cost=0.00..1102.00 rows=8202 width=68) (actual time=0.012..4.521 rows=8200 loops=1)\n               Filter: (region = 'us-west')\n               Rows Removed by Filter: 41800\n Planning Time: 0.285 ms\n Execution Time: 43.124 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe key fields to understand:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Ecost\u003C/strong\u003E (e.g., \u003Ccode\u003E0.00..4230.00\u003C/code\u003E): Startup cost and total cost in arbitrary planner units. The first number is the cost to return the first row; the second is total cost for all rows.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Erows\u003C/strong\u003E: Estimated (in the parenthetical) vs actual row count. Large differences signal stale statistics.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eactual time\u003C/strong\u003E: Real milliseconds. First number is time to first row; second is total time.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eloops\u003C/strong\u003E: How many times this node executed. Multiply \u003Ccode\u003Eactual time × loops\u003C/code\u003E for true cost.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERows Removed by Filter\u003C/strong\u003E: Rows read but discarded — a high number means wasted work.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EJoin Types in Plans\u003C/h3\u003E\n","\u003Cp\u003EPostgres chooses between three join strategies:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Nested Loop: best for small inner tables or index-driven lookups\nEXPLAIN ANALYZE\nSELECT o.id, c.name\nFROM orders o\nJOIN customers c ON c.id = o.customer_id\nWHERE o.id = 12345;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E Nested Loop  (cost=0.71..16.75 rows=1 width=42) (actual time=0.038..0.041 rows=1 loops=1)\n   ->  Index Scan using orders_pkey on orders o  (cost=0.42..8.44 rows=1 width=8) (actual time=0.021..0.022 rows=1 loops=1)\n         Index Cond: (id = 12345)\n   ->  Index Scan using customers_pkey on customers c  (cost=0.29..8.31 rows=1 width=42) (actual time=0.012..0.013 rows=1 loops=1)\n         Index Cond: (id = o.customer_id)\n Planning Time: 0.195 ms\n Execution Time: 0.068 ms\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENested Loop\u003C/strong\u003E: Iterates outer rows, looks up each in the inner table. Fast when inner table has an index or few rows.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHash Join\u003C/strong\u003E: Builds a hash table from the smaller relation, probes it with the larger. Good for joining large sets without indexes.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMerge Join\u003C/strong\u003E: Sorts both inputs and merges. Efficient when both inputs are already sorted or when the join is very large.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESpotting Planner Misestimates\u003C/h3\u003E\n","\u003Cp\u003EWhen "rows" (estimated) and "actual rows" differ by 10x or more, the planner is working with bad data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E Index Scan using idx_orders_status on orders  (cost=0.42..85.10 rows=12 width=96) (actual time=0.025..145.882 rows=48500 loops=1)\n   Index Cond: (status = 'pending')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe planner expected 12 rows but got 48,500. This usually means:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EStatistics are stale — run \u003Ccode\u003EANALYZE orders;\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECorrelation between columns that the planner can't model\u003C/li\u003E\u003Cli\u003EA recently loaded batch that changed the data distribution\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUnderstanding Scan Types\u003C/h2\u003E\n","\u003Cp\u003EThe scan type in your \u003Ccode\u003EEXPLAIN\u003C/code\u003E output tells you how Postgres is accessing the table data. Each type has a sweet spot, and understanding when Postgres chooses each one helps you know whether you need to intervene.\u003C/p\u003E\n","\u003Ch3\u003ESequential Scan (Seq Scan)\u003C/h3\u003E\n","\u003Cp\u003EReads the entire table from start to finish. This is the baseline — no index needed, no random I/O, just read everything.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE SELECT count(*) FROM events WHERE event_type = 'page_view';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E Aggregate  (cost=28450.00..28450.01 rows=1 width=8) (actual time=187.234..187.235 rows=1 loops=1)\n   ->  Seq Scan on events  (cost=0.00..26200.00 rows=900000 width=0) (actual time=0.021..142.891 rows=892410 loops=1)\n         Filter: (event_type = 'page_view')\n         Rows Removed by Filter: 107590\n Execution Time: 187.301 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESeq Scan is the \u003Cem\u003Ecorrect\u003C/em\u003E choice when:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe table is small (a few thousand rows)\u003C/li\u003E\u003Cli\u003EYou're reading most of the table anyway (low selectivity)\u003C/li\u003E\u003Cli\u003ENo useful index exists\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESeq Scan is a \u003Cem\u003Eproblem\u003C/em\u003E when:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EYou're selecting a tiny fraction of a large table\u003C/li\u003E\u003Cli\u003EIt's in a hot path that runs thousands of times per second\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EIndex Scan\u003C/h3\u003E\n","\u003Cp\u003EUses an index to find matching rows, then fetches full row data from the heap (table) pages.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 8842;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E Index Scan using idx_orders_customer_id on orders  (cost=0.42..52.18 rows=45 width=96) (actual time=0.028..0.185 rows=42 loops=1)\n   Index Cond: (customer_id = 8842)\n Planning Time: 0.082 ms\n Execution Time: 0.214 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIndex Scan involves two steps: traverse the index B-tree, then jump to heap pages. It's fast for highly selective queries but involves random I/O to the heap.\u003C/p\u003E\n","\u003Ch3\u003EIndex Only Scan\u003C/h3\u003E\n","\u003Cp\u003EThe holy grail — Postgres satisfies the query entirely from the index without touching the heap table at all.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- With a covering index: CREATE INDEX idx_orders_cust_covering ON orders(customer_id) INCLUDE (status, total);\nEXPLAIN ANALYZE SELECT status, total FROM orders WHERE customer_id = 8842;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E Index Only Scan using idx_orders_cust_covering on orders  (cost=0.42..48.12 rows=45 width=18) (actual time=0.022..0.078 rows=42 loops=1)\n   Index Cond: (customer_id = 8842)\n   Heap Fetches: 0\n Planning Time: 0.075 ms\n Execution Time: 0.102 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E"Heap Fetches: 0" means zero trips to the table. This only works when:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAll columns in SELECT, WHERE, and ORDER BY are in the index (via INCLUDE)\u003C/li\u003E\u003Cli\u003EThe visibility map shows all pages are all-visible (meaning VACUUM has run recently)\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EBitmap Index Scan + Bitmap Heap Scan\u003C/h3\u003E\n","\u003Cp\u003EBitmap scans bridge the gap between seq scan and index scan. They're chosen for \u003Cstrong\u003Emedium selectivity\u003C/strong\u003E — too many rows for an index scan (random I/O gets expensive), but too few for a seq scan to be optimal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE SELECT * FROM orders WHERE created_at BETWEEN '2025-03-01' AND '2025-03-31';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E Bitmap Heap Scan on orders  (cost=285.42..8924.15 rows=15200 width=96) (actual time=2.841..28.412 rows=14892 loops=1)\n   Recheck Cond: ((created_at >= '2025-03-01') AND (created_at <= '2025-03-31'))\n   Heap Blocks: exact=4218\n   ->  Bitmap Index Scan on idx_orders_created_at  (cost=0.00..281.62 rows=15200 width=0) (actual time=2.124..2.124 rows=14892 loops=1)\n         Index Cond: ((created_at >= '2025-03-01') AND (created_at <= '2025-03-31'))\n Planning Time: 0.108 ms\n Execution Time: 29.891 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe two-phase approach:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EBitmap Index Scan\u003C/strong\u003E: Walks the index and builds an in-memory bitmap of which heap pages contain matching rows\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBitmap Heap Scan\u003C/strong\u003E: Reads those heap pages sequentially (converting random I/O into sequential I/O)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis is especially powerful when combining multiple indexes with BitmapAnd/BitmapOr:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE SELECT * FROM orders WHERE status = 'pending' AND region = 'eu-west';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E Bitmap Heap Scan on orders  (cost=412.85..2180.42 rows=820 width=96) (actual time=1.892..5.421 rows=795 loops=1)\n   Recheck Cond: ((status = 'pending') AND (region = 'eu-west'))\n   ->  BitmapAnd\n         ->  Bitmap Index Scan on idx_orders_status  (cost=0.00..205.12 rows=12500 width=0) (actual time=0.845..0.845 rows=12500 loops=1)\n         ->  Bitmap Index Scan on idx_orders_region  (cost=0.00..198.42 rows=8200 width=0) (actual time=0.712..0.712 rows=8200 loops=1)\n Execution Time: 5.612 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhen Does Postgres Choose Each?\u003C/h3\u003E\n","\u003Cp\u003EThe decision comes down to selectivity — what fraction of the table you're reading:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESelectivity\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETypical Scan\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EReasoning\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E< 1-2% of rows\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIndex Scan\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFew rows; random I/O is acceptable\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E2-15% of rows\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBitmap Scan\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EToo many for index, too few for seq scan\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E> 15-20% of rows\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESeq Scan\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESequential I/O wins for large fractions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll indexed columns available\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIndex Only Scan\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAvoids heap entirely\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThese thresholds shift depending on table size, correlation, and whether data is cached in \u003Ccode\u003Eshared_buffers\u003C/code\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMemory and I/O Configuration\u003C/h2\u003E\n","\u003Cp\u003EPostgres ships with conservative defaults designed to run on a 1990s-era shared hosting server with 128 MB of RAM. If you haven't tuned these settings, you're leaving enormous performance on the table. The good news: a handful of parameters cover 90% of what matters.\u003C/p\u003E\n","\u003Ch3\u003Eshared_buffers\u003C/h3\u003E\n","\u003Cp\u003EThis is Postgres's own cache — the pool of 8KB pages kept in shared memory. Every read goes through this cache first before hitting the OS page cache or disk.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERule of thumb: 25% of total RAM\u003C/strong\u003E, but with diminishing returns past about 8 GB on most workloads.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check current setting\nSHOW shared_buffers;\n\n-- Recommended: 25% of RAM\n-- 16 GB server → set shared_buffers = '4GB'\n-- 64 GB server → set shared_buffers = '16GB'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eeffective_cache_size\u003C/h3\u003E\n","\u003Cp\u003EThis doesn't allocate memory — it tells the planner how much data is likely cached between \u003Ccode\u003Eshared_buffers\u003C/code\u003E and the OS page cache. A higher value makes the planner more willing to choose index scans (since it expects the data is already in memory).\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERule of thumb: 50-75% of total RAM.\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW effective_cache_size;\n-- 64 GB server → set effective_cache_size = '48GB'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Ework_mem\u003C/h3\u003E\n","\u003Cp\u003EMemory available for each sort, hash, or merge operation within a query. A single complex query might use \u003Ccode\u003Ework_mem\u003C/code\u003E multiple times (once per sort node, once per hash join). Set this too high and a burst of concurrent queries can exhaust RAM.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERule of thumb: Start at 16-64 MB\u003C/strong\u003E, increase for specific sessions running analytical queries.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check current setting\nSHOW work_mem;\n\n-- For an analytical session that needs large sorts:\nSET work_mem = '256MB';\n-- Run your big query...\nRESET work_mem;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESigns that \u003Ccode\u003Ework_mem\u003C/code\u003E is too low: you see "Sort Method: external merge Disk" or "Batches: 4" (multiple batches) in hash joins in your \u003Ccode\u003EEXPLAIN ANALYZE\u003C/code\u003E output.\u003C/p\u003E\n","\u003Ch3\u003Emaintenance_work_mem\u003C/h3\u003E\n","\u003Cp\u003EUsed by VACUUM, CREATE INDEX, and ALTER TABLE operations. These run single-threaded so you can safely set this much higher than \u003Ccode\u003Ework_mem\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW maintenance_work_mem;\n-- Set to 1-2 GB for faster index builds and vacuums\n-- ALTER SYSTEM SET maintenance_work_mem = '1GB';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Erandom_page_cost\u003C/h3\u003E\n","\u003Cp\u003EThe planner's estimate of how expensive a random page read is relative to a sequential read. The default of 4.0 assumes spinning disks. On SSDs, random reads are nearly as fast as sequential reads.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFor SSDs: set to 1.1 - 1.5.\u003C/strong\u003E This makes the planner more willing to use index scans.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW random_page_cost;\n-- ALTER SYSTEM SET random_page_cost = 1.1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMonitoring Your Cache Hit Ratio\u003C/h3\u003E\n","\u003Cp\u003EThis is the single most important metric for memory tuning. You want shared_buffers cache hit ratio above 99%:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  sum(heap_blks_read) AS heap_read,\n  sum(heap_blks_hit) AS heap_hit,\n  round(\n    sum(heap_blks_hit)::numeric /\n    nullif(sum(heap_blks_hit) + sum(heap_blks_read), 0) * 100, 2\n  ) AS cache_hit_ratio\nFROM pg_statio_user_tables;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E heap_read | heap_hit   | cache_hit_ratio\n-----------+------------+-----------------\n    285412 | 89421058   |           99.68\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf your cache hit ratio drops below 99%, you're hitting disk too often. Either increase \u003Ccode\u003Eshared_buffers\u003C/code\u003E or reduce working set size (better indexes, partitioning, archiving old data).\u003C/p\u003E\n","\u003Ch3\u003ECheckpoint Tuning\u003C/h3\u003E\n","\u003Cp\u003ECheckpoints flush dirty pages from \u003Ccode\u003Eshared_buffers\u003C/code\u003E to disk. Poorly configured checkpoints cause I/O spikes that stall queries.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check current checkpoint settings\nSELECT name, setting, unit FROM pg_settings\nWHERE name IN ('max_wal_size', 'checkpoint_completion_target', 'checkpoint_timeout');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E           name            | setting | unit\n---------------------------+---------+------\n checkpoint_completion_target | 0.9    |\n checkpoint_timeout           | 300    | s\n max_wal_size                 | 2048   | MB\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey principles:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Emax_wal_size\u003C/strong\u003E: Controls how much WAL accumulates before forcing a checkpoint. Higher values = less frequent checkpoints = smoother I/O. Set to 4-16 GB for write-heavy workloads.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Echeckpoint_completion_target\u003C/strong\u003E: Spread checkpoint I/O over this fraction of the checkpoint interval. Default 0.9 is good (spreads over 90% of the interval).\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EUnderstanding Your Workload Profile\u003C/h3\u003E\n","\u003Cp\u003EBefore tuning, know whether you're read-heavy or write-heavy — the strategies differ:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  datname,\n  tup_returned + tup_fetched AS reads,\n  tup_inserted + tup_updated + tup_deleted AS writes,\n  round(\n    (tup_returned + tup_fetched)::numeric /\n    nullif(tup_inserted + tup_updated + tup_deleted, 1), 1\n  ) AS read_write_ratio\nFROM pg_stat_database\nWHERE datname = current_database();\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E  datname  |   reads    |  writes  | read_write_ratio\n-----------+------------+----------+------------------\n myapp     | 284521000  | 28120000 |             10.1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA 10:1 ratio is starting to get write-heavy. For context:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ERead-heavy\u003C/strong\u003E (100:1+): Focus on caching, \u003Ccode\u003Eeffective_cache_size\u003C/code\u003E, covering indexes, read replicas\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBalanced\u003C/strong\u003E (10:1 to 100:1): Focus on shared_buffers, index optimization, connection pooling\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWrite-heavy\u003C/strong\u003E (<10:1): Focus on WAL tuning, checkpoint spreading, \u003Ccode\u003Efillfactor\u003C/code\u003E, HOT updates\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EHOT Updates and Fill Factor\u003C/h3\u003E\n","\u003Cp\u003EWhen Postgres updates a row, it normally must update every index on the table (because the new row version lives at a new heap location). HOT (Heap Only Tuple) updates avoid this by storing the new version on the same heap page — but only when there's room.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Reserve 20% of each page for HOT updates on a frequently-updated table\nALTER TABLE sessions SET (fillfactor = 80);\n\n-- After changing fillfactor, VACUUM FULL to rewrite the table (or wait for natural turnover)\nVACUUM FULL sessions;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EMonitor HOT update effectiveness:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  relname,\n  n_tup_upd,\n  n_tup_hot_upd,\n  round(n_tup_hot_upd::numeric / nullif(n_tup_upd, 0) * 100, 1) AS hot_update_pct\nFROM pg_stat_user_tables\nWHERE n_tup_upd > 1000\nORDER BY n_tup_upd DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E   relname   | n_tup_upd | n_tup_hot_upd | hot_update_pct\n-------------+-----------+---------------+----------------\n sessions    |    892410 |        845100 |           94.7\n orders      |    245000 |         42100 |           17.2\n user_prefs  |     85200 |         81400 |           95.6\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Eorders\u003C/code\u003E table at 17.2% HOT updates is a candidate for a lower fillfactor — something is preventing in-page updates (likely an indexed column being updated, or pages already full).\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nSnowflake Postgres automatically tunes shared_buffers, work_mem, and checkpoint settings based on your instance size and workload patterns. For advanced tuning, Snowflake Postgres Insights surfaces your top queries by execution time and recommends index improvements.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Epg_stat_statements and Query Analysis\u003C/h2\u003E\n","\u003Cp\u003EIf you only install one Postgres extension for performance, make it \u003Ccode\u003Epg_stat_statements\u003C/code\u003E. It tracks execution statistics for every normalized query — how many times it ran, total time, mean time, rows returned, and buffer usage. This is how you find the queries that actually matter.\u003C/p\u003E\n","\u003Ch3\u003EEnabling pg_stat_statements\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Requires adding to shared_preload_libraries (needs restart)\nALTER SYSTEM SET shared_preload_libraries = 'pg_stat_statements';\n-- After restart:\nCREATE EXTENSION IF NOT EXISTS pg_stat_statements;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFinding Your Most Expensive Queries\u003C/h3\u003E\n","\u003Cp\u003EThe queries that consume the most total execution time are your highest-impact optimization targets:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  substring(query, 1, 80) AS short_query,\n  calls,\n  round(total_exec_time::numeric, 1) AS total_time_ms,\n  round(mean_exec_time::numeric, 1) AS mean_time_ms,\n  rows,\n  round((shared_blks_hit::numeric / nullif(shared_blks_hit + shared_blks_read, 0)) * 100, 1) AS cache_hit_pct\nFROM pg_stat_statements\nORDER BY total_exec_time DESC\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E                    short_query                     |  calls  | total_time_ms | mean_time_ms |   rows   | cache_hit_pct\n----------------------------------------------------+---------+---------------+--------------+----------+---------------\n SELECT o.*, c.name FROM orders o JOIN customers c  |  284521 |     892451.2  |         3.1  | 14218050 |          99.8\n UPDATE sessions SET last_active = $1 WHERE id = $2 | 1842100 |     645210.8  |         0.4  |  1842100 |          99.9\n SELECT * FROM events WHERE user_id = $1 AND crea   |   42810 |     428100.5  |        10.0  |  4185200 |          87.2\n INSERT INTO audit_log (action, user_id, details)   |  892410 |     215420.1  |         0.2  |   892410 |          99.9\n SELECT count(*) FROM orders WHERE status = $1      |   28450 |     185210.4  |         6.5  |    28450 |          92.1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey insight: that \u003Ccode\u003EUPDATE sessions\u003C/code\u003E query is individually cheap (0.4ms) but runs 1.8 million times — making it the second-most expensive overall. The \u003Ccode\u003Eevents\u003C/code\u003E query at 87.2% cache hit rate is thrashing the buffer pool.\u003C/p\u003E\n","\u003Ch3\u003ETotal Time vs Mean Time\u003C/h3\u003E\n","\u003Cp\u003EDon't just look at mean time. A query running 0.5ms × 2 million calls consumes more resources than one running 500ms × 100 calls:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Frequent but individually fast (death by a thousand cuts)\nSELECT\n  substring(query, 1, 60) AS query,\n  calls,\n  round(mean_exec_time::numeric, 2) AS mean_ms,\n  round(total_exec_time::numeric / 1000, 1) AS total_seconds\nFROM pg_stat_statements\nWHERE calls > 10000\nORDER BY total_exec_time DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Infrequent but individually slow (long-running reports)\nSELECT\n  substring(query, 1, 60) AS query,\n  calls,\n  round(mean_exec_time::numeric, 1) AS mean_ms,\n  round(total_exec_time::numeric / 1000, 1) AS total_seconds\nFROM pg_stat_statements\nWHERE mean_exec_time > 1000\nORDER BY mean_exec_time DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoth need attention, but through different approaches — high-frequency queries benefit from micro-optimizations (covering indexes, connection pooling), while slow queries need plan analysis.\u003C/p\u003E\n","\u003Ch3\u003EBuffer Hit Ratio Per Query\u003C/h3\u003E\n","\u003Cp\u003EThe per-query buffer hit ratio reveals which specific queries are causing disk reads:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  substring(query, 1, 60) AS query,\n  calls,\n  shared_blks_hit,\n  shared_blks_read,\n  round(\n    shared_blks_hit::numeric /\n    nullif(shared_blks_hit + shared_blks_read, 0) * 100, 1\n  ) AS buffer_hit_pct\nFROM pg_stat_statements\nWHERE shared_blks_read > 1000\nORDER BY shared_blks_read DESC\nLIMIT 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQueries with buffer hit rates below 95% are candidates for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBetter indexes to reduce data scanned\u003C/li\u003E\u003Cli\u003EIncreased \u003Ccode\u003Eshared_buffers\u003C/code\u003E if the working set doesn't fit\u003C/li\u003E\u003Cli\u003EQuery rewrites to access less data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResetting Statistics\u003C/h3\u003E\n","\u003Cp\u003EReset periodically to get fresh data — stats are cumulative since the last reset:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Reset all pg_stat_statements data\nSELECT pg_stat_statements_reset();\n\n-- Check when stats were last reset (useful for context)\nSELECT stats_reset FROM pg_stat_bgwriter;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA good practice: reset after deployments or configuration changes so you can see the impact clearly.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EParallel Queries\u003C/h2\u003E\n","\u003Cp\u003EPostgres can split expensive operations across multiple CPU cores. When a single query needs to read millions of rows, parallel execution can cut wall-clock time dramatically — but it's not magic, and not every query benefits.\u003C/p\u003E\n","\u003Ch3\u003EWhen Postgres Goes Parallel\u003C/h3\u003E\n","\u003Cp\u003EThe planner considers parallel execution for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELarge sequential scans\u003C/strong\u003E (reading millions of rows)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHash joins\u003C/strong\u003E on large datasets\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAggregates\u003C/strong\u003E (COUNT, SUM, AVG) over large tables\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMerge joins\u003C/strong\u003E with sorted inputs\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXPLAIN ANALYZE SELECT count(*), avg(total) FROM orders WHERE created_at > '2025-01-01';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E Finalize Aggregate  (cost=15842.52..15842.53 rows=1 width=40) (actual time=48.125..50.412 rows=1 loops=1)\n   ->  Gather  (cost=15842.10..15842.51 rows=2 width=40) (actual time=45.812..50.385 rows=3 loops=1)\n         Workers Planned: 2\n         Workers Launched: 2\n         ->  Partial Aggregate  (cost=14842.10..14842.11 rows=1 width=40) (actual time=42.125..42.126 rows=1 loops=3)\n               ->  Parallel Seq Scan on orders  (cost=0.00..13592.10 rows=250000 width=8) (actual time=0.024..28.412 rows=166420 loops=3)\n                     Filter: (created_at > '2025-01-01'::date)\n                     Rows Removed by Filter: 33580\n Planning Time: 0.125 ms\n Execution Time: 50.512 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKey indicators: "Workers Planned: 2", "Workers Launched: 2", and "loops=3" (the leader process plus 2 workers). The "Parallel Seq Scan" distributes the table scan across all workers.\u003C/p\u003E\n","\u003Ch3\u003EConfiguration Parameters\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Maximum parallel workers available system-wide\nSHOW max_parallel_workers;           -- Default: 8\n\n-- Maximum workers per single query operation (Gather node)\nSHOW max_parallel_workers_per_gather; -- Default: 2\n\n-- Minimum table size before parallel is considered\nSHOW min_parallel_table_scan_size;   -- Default: 8MB\n\n-- Cost thresholds that affect planner decisions\nSHOW parallel_setup_cost;            -- Default: 1000\nSHOW parallel_tuple_cost;            -- Default: 0.1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo allow more parallelism for analytical workloads:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Allow up to 4 workers per query for heavy analytical work\nSET max_parallel_workers_per_gather = 4;\n\nEXPLAIN ANALYZE\nSELECT region, count(*), avg(total)\nFROM orders\nWHERE created_at > '2024-01-01'\nGROUP BY region;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E Finalize GroupAggregate  (cost=18425.12..18512.84 rows=12 width=48) (actual time=32.412..32.845 rows=12 loops=1)\n   Group Key: region\n   ->  Gather Merge  (cost=18425.12..18510.84 rows=48 width=48) (actual time=32.125..32.712 rows=60 loops=1)\n         Workers Planned: 4\n         Workers Launched: 4\n         ->  Sort  (cost=17425.06..17425.09 rows=12 width=48) (actual time=28.412..28.415 rows=12 loops=5)\n               Sort Key: region\n               ->  Partial HashAggregate  (cost=17424.50..17424.62 rows=12 width=48) (actual time=28.125..28.132 rows=12 loops=5)\n                     ->  Parallel Seq Scan on orders  (cost=0.00..14925.00 rows=499900 width=16) (actual time=0.018..15.421 rows=399920 loops=5)\n Execution Time: 33.012 ms\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith 4 workers, a 2-million row aggregation completes in 33ms instead of the ~130ms it would take single-threaded.\u003C/p\u003E\n","\u003Ch3\u003EWhen Parallel Doesn't Help\u003C/h3\u003E\n","\u003Cp\u003ENot everything benefits from parallelism:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Index scans are already fast — parallel doesn't help\nEXPLAIN SELECT * FROM orders WHERE id = 12345;\n-- Result: plain Index Scan, no parallelism (correct!)\n\n-- Small tables don't trigger parallel (below min_parallel_table_scan_size)\nEXPLAIN SELECT count(*) FROM status_codes;\n-- Result: plain Seq Scan, no parallelism (table is tiny)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EParallel execution is also avoided when:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe function called in the query is marked \u003Ccode\u003EPARALLEL UNSAFE\u003C/code\u003E (e.g., writes data, uses cursors)\u003C/li\u003E\u003Cli\u003EThe query is inside a transaction that has written data\u003C/li\u003E\u003Cli\u003EThe query uses \u003Ccode\u003EFOR UPDATE\u003C/code\u003E/\u003Ccode\u003EFOR SHARE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe table is below \u003Ccode\u003Emin_parallel_table_scan_size\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EWorkers aren't available (all \u003Ccode\u003Emax_parallel_workers\u003C/code\u003E slots are in use)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EParallel-Safe vs Parallel-Unsafe Functions\u003C/h3\u003E\n","\u003Cp\u003ECustom functions must be marked as parallel-safe for the planner to parallelize queries that use them:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- This function CAN run in parallel workers\nCREATE FUNCTION calculate_discount(price numeric, tier text)\nRETURNS numeric\nLANGUAGE sql\nPARALLEL SAFE\nAS $$\n  SELECT CASE tier\n    WHEN 'gold' THEN price * 0.20\n    WHEN 'silver' THEN price * 0.10\n    ELSE price * 0.05\n  END;\n$$;\n\n-- This function CANNOT (it writes data)\nCREATE FUNCTION log_access(user_id int)\nRETURNS void\nLANGUAGE sql\nPARALLEL UNSAFE\nAS $$\n  INSERT INTO access_log(user_id, accessed_at) VALUES (user_id, now());\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf a query uses a \u003Ccode\u003EPARALLEL UNSAFE\u003C/code\u003E function, the entire query runs single-threaded — even parts that don't involve that function.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003EPerformance optimization is an iterative process: measure with \u003Ccode\u003EEXPLAIN ANALYZE\u003C/code\u003E and \u003Ccode\u003Epg_stat_statements\u003C/code\u003E, identify the bottleneck (bad plan? missing index? insufficient memory? I/O bound?), fix it, and measure again. Resist the urge to tune everything at once — start with the highest-impact query from \u003Ccode\u003Epg_stat_statements\u003C/code\u003E, understand its plan, and work from there.\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-scan-types-in-explain-plans\"\u003ECrunchy Data Blog — Postgres Scan Types in EXPLAIN Plans\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EVisual guide to understanding scan types and when they appear.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.crunchydata.com/blog/is-postgres-read-heavy-or-write-heavy-and-why-should-you-care\"\u003ECrunchy Data Blog — Is Postgres Read Heavy or Write Heavy?\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EUnderstand your workload profile to guide tuning decisions.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/engineering-blog/postgres-jsonb-columns-and-toast/\"\u003ESnowflake Engineering Blog — Postgres JSONB and TOAST Performance\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EPerformance implications of large JSONB columns and TOAST.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<button>\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.crunchydata.com/blog/postgres-performance-boost-hot-updates-and-fill-factor\"\u003ECrunchy Data Blog — HOT Updates and Fill Factor\u003C/a\u003E\n</button>\u003C/p\u003E\n","\u003Cp\u003EReduce index maintenance overhead with HOT updates.\u003C/p\u003E"],"description":"","title":"Performance Optimization",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Reading EXPLAIN Plans\n\nPerformance tuning in Postgres always starts in the same place: reading the query plan. You can't fix what you don't understand, and `EXPLAIN` is how Postgres tells you exactly what it's doing with your query — and why it's slow.\n\nThere are two forms, and the difference matters:\n\n```sql\n-- Estimated plan only (does NOT execute the query)\nEXPLAIN SELECT * FROM orders WHERE customer_id = 42;\n\n-- Actually executes and shows real timing (use this!)\nEXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 42;\n```\n\n`EXPLAIN` shows what the planner *thinks* will happen. `EXPLAIN ANALYZE` shows what *actually* happened. When they disagree, that's a clue — the planner is working with bad estimates.\n\nHere's an annotated plan from a real query:\n\n```\n                                                          QUERY PLAN\n------------------------------------------------------------------------------------------------------------------------------\n Hash Join  (cost=1204.52..5765.83 rows=1248 width=164) (actual time=8.412..42.891 rows=1195 loops=1)\n   Hash Cond: (o.customer_id = c.id)\n   -\u003E  Seq Scan on orders o  (cost=0.00..4230.00 rows=50000 width=96) (actual time=0.015..18.234 rows=50000 loops=1)\n         Filter: (created_at \u003E '2025-01-01'::date)\n         Rows Removed by Filter: 150000\n   -\u003E  Hash  (cost=1102.00..1102.00 rows=8202 width=68) (actual time=8.102..8.102 rows=8200 loops=1)\n         Buckets: 16384  Batches: 1  Memory Usage: 892kB\n         -\u003E  Seq Scan on customers c  (cost=0.00..1102.00 rows=8202 width=68) (actual time=0.012..4.521 rows=8200 loops=1)\n               Filter: (region = 'us-west')\n               Rows Removed by Filter: 41800\n Planning Time: 0.285 ms\n Execution Time: 43.124 ms\n```\n\nThe key fields to understand:\n\n- **cost** (e.g., `0.00..4230.00`): Startup cost and total cost in arbitrary planner units. The first number is the cost to return the first row; the second is total cost for all rows.\n- **rows**: Estimated (in the parenthetical) vs actual row count. Large differences signal stale statistics.\n- **actual time**: Real milliseconds. First number is time to first row; second is total time.\n- **loops**: How many times this node executed. Multiply `actual time × loops` for true cost.\n- **Rows Removed by Filter**: Rows read but discarded — a high number means wasted work.\n\n### Join Types in Plans\n\nPostgres chooses between three join strategies:\n\n```sql\n-- Nested Loop: best for small inner tables or index-driven lookups\nEXPLAIN ANALYZE\nSELECT o.id, c.name\nFROM orders o\nJOIN customers c ON c.id = o.customer_id\nWHERE o.id = 12345;\n```\n\n```\n Nested Loop  (cost=0.71..16.75 rows=1 width=42) (actual time=0.038..0.041 rows=1 loops=1)\n   -\u003E  Index Scan using orders_pkey on orders o  (cost=0.42..8.44 rows=1 width=8) (actual time=0.021..0.022 rows=1 loops=1)\n         Index Cond: (id = 12345)\n   -\u003E  Index Scan using customers_pkey on customers c  (cost=0.29..8.31 rows=1 width=42) (actual time=0.012..0.013 rows=1 loops=1)\n         Index Cond: (id = o.customer_id)\n Planning Time: 0.195 ms\n Execution Time: 0.068 ms\n```\n\n- **Nested Loop**: Iterates outer rows, looks up each in the inner table. Fast when inner table has an index or few rows.\n- **Hash Join**: Builds a hash table from the smaller relation, probes it with the larger. Good for joining large sets without indexes.\n- **Merge Join**: Sorts both inputs and merges. Efficient when both inputs are already sorted or when the join is very large.\n\n### Spotting Planner Misestimates\n\nWhen \"rows\" (estimated) and \"actual rows\" differ by 10x or more, the planner is working with bad data:\n\n```\n Index Scan using idx_orders_status on orders  (cost=0.42..85.10 rows=12 width=96) (actual time=0.025..145.882 rows=48500 loops=1)\n   Index Cond: (status = 'pending')\n```\n\nThe planner expected 12 rows but got 48,500. This usually means:\n\n1. Statistics are stale — run `ANALYZE orders;`\n2. Correlation between columns that the planner can't model\n3. A recently loaded batch that changed the data distribution\n\n\u003C!-- ------------------------ --\u003E\n## Understanding Scan Types\n\nThe scan type in your `EXPLAIN` output tells you how Postgres is accessing the table data. Each type has a sweet spot, and understanding when Postgres chooses each one helps you know whether you need to intervene.\n\n### Sequential Scan (Seq Scan)\n\nReads the entire table from start to finish. This is the baseline — no index needed, no random I/O, just read everything.\n\n```sql\nEXPLAIN ANALYZE SELECT count(*) FROM events WHERE event_type = 'page_view';\n```\n\n```\n Aggregate  (cost=28450.00..28450.01 rows=1 width=8) (actual time=187.234..187.235 rows=1 loops=1)\n   -\u003E  Seq Scan on events  (cost=0.00..26200.00 rows=900000 width=0) (actual time=0.021..142.891 rows=892410 loops=1)\n         Filter: (event_type = 'page_view')\n         Rows Removed by Filter: 107590\n Execution Time: 187.301 ms\n```\n\nSeq Scan is the *correct* choice when:\n- The table is small (a few thousand rows)\n- You're reading most of the table anyway (low selectivity)\n- No useful index exists\n\nSeq Scan is a *problem* when:\n- You're selecting a tiny fraction of a large table\n- It's in a hot path that runs thousands of times per second\n\n### Index Scan\n\nUses an index to find matching rows, then fetches full row data from the heap (table) pages.\n\n```sql\nEXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 8842;\n```\n\n```\n Index Scan using idx_orders_customer_id on orders  (cost=0.42..52.18 rows=45 width=96) (actual time=0.028..0.185 rows=42 loops=1)\n   Index Cond: (customer_id = 8842)\n Planning Time: 0.082 ms\n Execution Time: 0.214 ms\n```\n\nIndex Scan involves two steps: traverse the index B-tree, then jump to heap pages. It's fast for highly selective queries but involves random I/O to the heap.\n\n### Index Only Scan\n\nThe holy grail — Postgres satisfies the query entirely from the index without touching the heap table at all.\n\n```sql\n-- With a covering index: CREATE INDEX idx_orders_cust_covering ON orders(customer_id) INCLUDE (status, total);\nEXPLAIN ANALYZE SELECT status, total FROM orders WHERE customer_id = 8842;\n```\n\n```\n Index Only Scan using idx_orders_cust_covering on orders  (cost=0.42..48.12 rows=45 width=18) (actual time=0.022..0.078 rows=42 loops=1)\n   Index Cond: (customer_id = 8842)\n   Heap Fetches: 0\n Planning Time: 0.075 ms\n Execution Time: 0.102 ms\n```\n\n\"Heap Fetches: 0\" means zero trips to the table. This only works when:\n1. All columns in SELECT, WHERE, and ORDER BY are in the index (via INCLUDE)\n2. The visibility map shows all pages are all-visible (meaning VACUUM has run recently)\n\n### Bitmap Index Scan + Bitmap Heap Scan\n\nBitmap scans bridge the gap between seq scan and index scan. They're chosen for **medium selectivity** — too many rows for an index scan (random I/O gets expensive), but too few for a seq scan to be optimal.\n\n```sql\nEXPLAIN ANALYZE SELECT * FROM orders WHERE created_at BETWEEN '2025-03-01' AND '2025-03-31';\n```\n\n```\n Bitmap Heap Scan on orders  (cost=285.42..8924.15 rows=15200 width=96) (actual time=2.841..28.412 rows=14892 loops=1)\n   Recheck Cond: ((created_at \u003E= '2025-03-01') AND (created_at \u003C= '2025-03-31'))\n   Heap Blocks: exact=4218\n   -\u003E  Bitmap Index Scan on idx_orders_created_at  (cost=0.00..281.62 rows=15200 width=0) (actual time=2.124..2.124 rows=14892 loops=1)\n         Index Cond: ((created_at \u003E= '2025-03-01') AND (created_at \u003C= '2025-03-31'))\n Planning Time: 0.108 ms\n Execution Time: 29.891 ms\n```\n\nThe two-phase approach:\n1. **Bitmap Index Scan**: Walks the index and builds an in-memory bitmap of which heap pages contain matching rows\n2. **Bitmap Heap Scan**: Reads those heap pages sequentially (converting random I/O into sequential I/O)\n\nThis is especially powerful when combining multiple indexes with BitmapAnd/BitmapOr:\n\n```sql\nEXPLAIN ANALYZE SELECT * FROM orders WHERE status = 'pending' AND region = 'eu-west';\n```\n\n```\n Bitmap Heap Scan on orders  (cost=412.85..2180.42 rows=820 width=96) (actual time=1.892..5.421 rows=795 loops=1)\n   Recheck Cond: ((status = 'pending') AND (region = 'eu-west'))\n   -\u003E  BitmapAnd\n         -\u003E  Bitmap Index Scan on idx_orders_status  (cost=0.00..205.12 rows=12500 width=0) (actual time=0.845..0.845 rows=12500 loops=1)\n         -\u003E  Bitmap Index Scan on idx_orders_region  (cost=0.00..198.42 rows=8200 width=0) (actual time=0.712..0.712 rows=8200 loops=1)\n Execution Time: 5.612 ms\n```\n\n### When Does Postgres Choose Each?\n\nThe decision comes down to selectivity — what fraction of the table you're reading:\n\n| Selectivity | Typical Scan | Reasoning |\n|---|---|---|\n| \u003C 1-2% of rows | Index Scan | Few rows; random I/O is acceptable |\n| 2-15% of rows | Bitmap Scan | Too many for index, too few for seq scan |\n| \u003E 15-20% of rows | Seq Scan | Sequential I/O wins for large fractions |\n| All indexed columns available | Index Only Scan | Avoids heap entirely |\n\nThese thresholds shift depending on table size, correlation, and whether data is cached in `shared_buffers`.\n\n\u003C!-- ------------------------ --\u003E\n## Memory and I/O Configuration\n\nPostgres ships with conservative defaults designed to run on a 1990s-era shared hosting server with 128 MB of RAM. If you haven't tuned these settings, you're leaving enormous performance on the table. The good news: a handful of parameters cover 90% of what matters.\n\n### shared_buffers\n\nThis is Postgres's own cache — the pool of 8KB pages kept in shared memory. Every read goes through this cache first before hitting the OS page cache or disk.\n\n**Rule of thumb: 25% of total RAM**, but with diminishing returns past about 8 GB on most workloads.\n\n```sql\n-- Check current setting\nSHOW shared_buffers;\n\n-- Recommended: 25% of RAM\n-- 16 GB server → set shared_buffers = '4GB'\n-- 64 GB server → set shared_buffers = '16GB'\n```\n\n### effective_cache_size\n\nThis doesn't allocate memory — it tells the planner how much data is likely cached between `shared_buffers` and the OS page cache. A higher value makes the planner more willing to choose index scans (since it expects the data is already in memory).\n\n**Rule of thumb: 50-75% of total RAM.**\n\n```sql\nSHOW effective_cache_size;\n-- 64 GB server → set effective_cache_size = '48GB'\n```\n\n### work_mem\n\nMemory available for each sort, hash, or merge operation within a query. A single complex query might use `work_mem` multiple times (once per sort node, once per hash join). Set this too high and a burst of concurrent queries can exhaust RAM.\n\n**Rule of thumb: Start at 16-64 MB**, increase for specific sessions running analytical queries.\n\n```sql\n-- Check current setting\nSHOW work_mem;\n\n-- For an analytical session that needs large sorts:\nSET work_mem = '256MB';\n-- Run your big query...\nRESET work_mem;\n```\n\nSigns that `work_mem` is too low: you see \"Sort Method: external merge Disk\" or \"Batches: 4\" (multiple batches) in hash joins in your `EXPLAIN ANALYZE` output.\n\n### maintenance_work_mem\n\nUsed by VACUUM, CREATE INDEX, and ALTER TABLE operations. These run single-threaded so you can safely set this much higher than `work_mem`.\n\n```sql\nSHOW maintenance_work_mem;\n-- Set to 1-2 GB for faster index builds and vacuums\n-- ALTER SYSTEM SET maintenance_work_mem = '1GB';\n```\n\n### random_page_cost\n\nThe planner's estimate of how expensive a random page read is relative to a sequential read. The default of 4.0 assumes spinning disks. On SSDs, random reads are nearly as fast as sequential reads.\n\n**For SSDs: set to 1.1 - 1.5.** This makes the planner more willing to use index scans.\n\n```sql\nSHOW random_page_cost;\n-- ALTER SYSTEM SET random_page_cost = 1.1;\n```\n\n### Monitoring Your Cache Hit Ratio\n\nThis is the single most important metric for memory tuning. You want shared_buffers cache hit ratio above 99%:\n\n```sql\nSELECT\n  sum(heap_blks_read) AS heap_read,\n  sum(heap_blks_hit) AS heap_hit,\n  round(\n    sum(heap_blks_hit)::numeric /\n    nullif(sum(heap_blks_hit) + sum(heap_blks_read), 0) * 100, 2\n  ) AS cache_hit_ratio\nFROM pg_statio_user_tables;\n```\n\n```\n heap_read | heap_hit   | cache_hit_ratio\n-----------+------------+-----------------\n    285412 | 89421058   |           99.68\n```\n\nIf your cache hit ratio drops below 99%, you're hitting disk too often. Either increase `shared_buffers` or reduce working set size (better indexes, partitioning, archiving old data).\n\n### Checkpoint Tuning\n\nCheckpoints flush dirty pages from `shared_buffers` to disk. Poorly configured checkpoints cause I/O spikes that stall queries.\n\n```sql\n-- Check current checkpoint settings\nSELECT name, setting, unit FROM pg_settings\nWHERE name IN ('max_wal_size', 'checkpoint_completion_target', 'checkpoint_timeout');\n```\n\n```\n           name            | setting | unit\n---------------------------+---------+------\n checkpoint_completion_target | 0.9    |\n checkpoint_timeout           | 300    | s\n max_wal_size                 | 2048   | MB\n```\n\nKey principles:\n- **max_wal_size**: Controls how much WAL accumulates before forcing a checkpoint. Higher values = less frequent checkpoints = smoother I/O. Set to 4-16 GB for write-heavy workloads.\n- **checkpoint_completion_target**: Spread checkpoint I/O over this fraction of the checkpoint interval. Default 0.9 is good (spreads over 90% of the interval).\n\n### Understanding Your Workload Profile\n\nBefore tuning, know whether you're read-heavy or write-heavy — the strategies differ:\n\n```sql\nSELECT\n  datname,\n  tup_returned + tup_fetched AS reads,\n  tup_inserted + tup_updated + tup_deleted AS writes,\n  round(\n    (tup_returned + tup_fetched)::numeric /\n    nullif(tup_inserted + tup_updated + tup_deleted, 1), 1\n  ) AS read_write_ratio\nFROM pg_stat_database\nWHERE datname = current_database();\n```\n\n```\n  datname  |   reads    |  writes  | read_write_ratio\n-----------+------------+----------+------------------\n myapp     | 284521000  | 28120000 |             10.1\n```\n\nA 10:1 ratio is starting to get write-heavy. For context:\n- **Read-heavy** (100:1+): Focus on caching, `effective_cache_size`, covering indexes, read replicas\n- **Balanced** (10:1 to 100:1): Focus on shared_buffers, index optimization, connection pooling\n- **Write-heavy** (\u003C10:1): Focus on WAL tuning, checkpoint spreading, `fillfactor`, HOT updates\n\n### HOT Updates and Fill Factor\n\nWhen Postgres updates a row, it normally must update every index on the table (because the new row version lives at a new heap location). HOT (Heap Only Tuple) updates avoid this by storing the new version on the same heap page — but only when there's room.\n\n```sql\n-- Reserve 20% of each page for HOT updates on a frequently-updated table\nALTER TABLE sessions SET (fillfactor = 80);\n\n-- After changing fillfactor, VACUUM FULL to rewrite the table (or wait for natural turnover)\nVACUUM FULL sessions;\n```\n\nMonitor HOT update effectiveness:\n\n```sql\nSELECT\n  relname,\n  n_tup_upd,\n  n_tup_hot_upd,\n  round(n_tup_hot_upd::numeric / nullif(n_tup_upd, 0) * 100, 1) AS hot_update_pct\nFROM pg_stat_user_tables\nWHERE n_tup_upd \u003E 1000\nORDER BY n_tup_upd DESC;\n```\n\n```\n   relname   | n_tup_upd | n_tup_hot_upd | hot_update_pct\n-------------+-----------+---------------+----------------\n sessions    |    892410 |        845100 |           94.7\n orders      |    245000 |         42100 |           17.2\n user_prefs  |     85200 |         81400 |           95.6\n```\n\nThe `orders` table at 17.2% HOT updates is a candidate for a lower fillfactor — something is preventing in-page updates (likely an indexed column being updated, or pages already full).\n\n\u003E aside positive\n\u003E Snowflake Postgres automatically tunes shared_buffers, work_mem, and checkpoint settings based on your instance size and workload patterns. For advanced tuning, Snowflake Postgres Insights surfaces your top queries by execution time and recommends index improvements.\n\n\u003C!-- ------------------------ --\u003E\n## pg_stat_statements and Query Analysis\n\nIf you only install one Postgres extension for performance, make it `pg_stat_statements`. It tracks execution statistics for every normalized query — how many times it ran, total time, mean time, rows returned, and buffer usage. This is how you find the queries that actually matter.\n\n### Enabling pg_stat_statements\n\n```sql\n-- Requires adding to shared_preload_libraries (needs restart)\nALTER SYSTEM SET shared_preload_libraries = 'pg_stat_statements';\n-- After restart:\nCREATE EXTENSION IF NOT EXISTS pg_stat_statements;\n```\n\n### Finding Your Most Expensive Queries\n\nThe queries that consume the most total execution time are your highest-impact optimization targets:\n\n```sql\nSELECT\n  substring(query, 1, 80) AS short_query,\n  calls,\n  round(total_exec_time::numeric, 1) AS total_time_ms,\n  round(mean_exec_time::numeric, 1) AS mean_time_ms,\n  rows,\n  round((shared_blks_hit::numeric / nullif(shared_blks_hit + shared_blks_read, 0)) * 100, 1) AS cache_hit_pct\nFROM pg_stat_statements\nORDER BY total_exec_time DESC\nLIMIT 10;\n```\n\n```\n                    short_query                     |  calls  | total_time_ms | mean_time_ms |   rows   | cache_hit_pct\n----------------------------------------------------+---------+---------------+--------------+----------+---------------\n SELECT o.*, c.name FROM orders o JOIN customers c  |  284521 |     892451.2  |         3.1  | 14218050 |          99.8\n UPDATE sessions SET last_active = $1 WHERE id = $2 | 1842100 |     645210.8  |         0.4  |  1842100 |          99.9\n SELECT * FROM events WHERE user_id = $1 AND crea   |   42810 |     428100.5  |        10.0  |  4185200 |          87.2\n INSERT INTO audit_log (action, user_id, details)   |  892410 |     215420.1  |         0.2  |   892410 |          99.9\n SELECT count(*) FROM orders WHERE status = $1      |   28450 |     185210.4  |         6.5  |    28450 |          92.1\n```\n\nKey insight: that `UPDATE sessions` query is individually cheap (0.4ms) but runs 1.8 million times — making it the second-most expensive overall. The `events` query at 87.2% cache hit rate is thrashing the buffer pool.\n\n### Total Time vs Mean Time\n\nDon't just look at mean time. A query running 0.5ms × 2 million calls consumes more resources than one running 500ms × 100 calls:\n\n```sql\n-- Frequent but individually fast (death by a thousand cuts)\nSELECT\n  substring(query, 1, 60) AS query,\n  calls,\n  round(mean_exec_time::numeric, 2) AS mean_ms,\n  round(total_exec_time::numeric / 1000, 1) AS total_seconds\nFROM pg_stat_statements\nWHERE calls \u003E 10000\nORDER BY total_exec_time DESC\nLIMIT 5;\n```\n\n```sql\n-- Infrequent but individually slow (long-running reports)\nSELECT\n  substring(query, 1, 60) AS query,\n  calls,\n  round(mean_exec_time::numeric, 1) AS mean_ms,\n  round(total_exec_time::numeric / 1000, 1) AS total_seconds\nFROM pg_stat_statements\nWHERE mean_exec_time \u003E 1000\nORDER BY mean_exec_time DESC\nLIMIT 5;\n```\n\nBoth need attention, but through different approaches — high-frequency queries benefit from micro-optimizations (covering indexes, connection pooling), while slow queries need plan analysis.\n\n### Buffer Hit Ratio Per Query\n\nThe per-query buffer hit ratio reveals which specific queries are causing disk reads:\n\n```sql\nSELECT\n  substring(query, 1, 60) AS query,\n  calls,\n  shared_blks_hit,\n  shared_blks_read,\n  round(\n    shared_blks_hit::numeric /\n    nullif(shared_blks_hit + shared_blks_read, 0) * 100, 1\n  ) AS buffer_hit_pct\nFROM pg_stat_statements\nWHERE shared_blks_read \u003E 1000\nORDER BY shared_blks_read DESC\nLIMIT 5;\n```\n\nQueries with buffer hit rates below 95% are candidates for:\n- Better indexes to reduce data scanned\n- Increased `shared_buffers` if the working set doesn't fit\n- Query rewrites to access less data\n\n### Resetting Statistics\n\nReset periodically to get fresh data — stats are cumulative since the last reset:\n\n```sql\n-- Reset all pg_stat_statements data\nSELECT pg_stat_statements_reset();\n\n-- Check when stats were last reset (useful for context)\nSELECT stats_reset FROM pg_stat_bgwriter;\n```\n\nA good practice: reset after deployments or configuration changes so you can see the impact clearly.\n\n\u003C!-- ------------------------ --\u003E\n## Parallel Queries\n\nPostgres can split expensive operations across multiple CPU cores. When a single query needs to read millions of rows, parallel execution can cut wall-clock time dramatically — but it's not magic, and not every query benefits.\n\n### When Postgres Goes Parallel\n\nThe planner considers parallel execution for:\n- **Large sequential scans** (reading millions of rows)\n- **Hash joins** on large datasets\n- **Aggregates** (COUNT, SUM, AVG) over large tables\n- **Merge joins** with sorted inputs\n\n```sql\nEXPLAIN ANALYZE SELECT count(*), avg(total) FROM orders WHERE created_at \u003E '2025-01-01';\n```\n\n```\n Finalize Aggregate  (cost=15842.52..15842.53 rows=1 width=40) (actual time=48.125..50.412 rows=1 loops=1)\n   -\u003E  Gather  (cost=15842.10..15842.51 rows=2 width=40) (actual time=45.812..50.385 rows=3 loops=1)\n         Workers Planned: 2\n         Workers Launched: 2\n         -\u003E  Partial Aggregate  (cost=14842.10..14842.11 rows=1 width=40) (actual time=42.125..42.126 rows=1 loops=3)\n               -\u003E  Parallel Seq Scan on orders  (cost=0.00..13592.10 rows=250000 width=8) (actual time=0.024..28.412 rows=166420 loops=3)\n                     Filter: (created_at \u003E '2025-01-01'::date)\n                     Rows Removed by Filter: 33580\n Planning Time: 0.125 ms\n Execution Time: 50.512 ms\n```\n\nKey indicators: \"Workers Planned: 2\", \"Workers Launched: 2\", and \"loops=3\" (the leader process plus 2 workers). The \"Parallel Seq Scan\" distributes the table scan across all workers.\n\n### Configuration Parameters\n\n```sql\n-- Maximum parallel workers available system-wide\nSHOW max_parallel_workers;           -- Default: 8\n\n-- Maximum workers per single query operation (Gather node)\nSHOW max_parallel_workers_per_gather; -- Default: 2\n\n-- Minimum table size before parallel is considered\nSHOW min_parallel_table_scan_size;   -- Default: 8MB\n\n-- Cost thresholds that affect planner decisions\nSHOW parallel_setup_cost;            -- Default: 1000\nSHOW parallel_tuple_cost;            -- Default: 0.1\n```\n\nTo allow more parallelism for analytical workloads:\n\n```sql\n-- Allow up to 4 workers per query for heavy analytical work\nSET max_parallel_workers_per_gather = 4;\n\nEXPLAIN ANALYZE\nSELECT region, count(*), avg(total)\nFROM orders\nWHERE created_at \u003E '2024-01-01'\nGROUP BY region;\n```\n\n```\n Finalize GroupAggregate  (cost=18425.12..18512.84 rows=12 width=48) (actual time=32.412..32.845 rows=12 loops=1)\n   Group Key: region\n   -\u003E  Gather Merge  (cost=18425.12..18510.84 rows=48 width=48) (actual time=32.125..32.712 rows=60 loops=1)\n         Workers Planned: 4\n         Workers Launched: 4\n         -\u003E  Sort  (cost=17425.06..17425.09 rows=12 width=48) (actual time=28.412..28.415 rows=12 loops=5)\n               Sort Key: region\n               -\u003E  Partial HashAggregate  (cost=17424.50..17424.62 rows=12 width=48) (actual time=28.125..28.132 rows=12 loops=5)\n                     -\u003E  Parallel Seq Scan on orders  (cost=0.00..14925.00 rows=499900 width=16) (actual time=0.018..15.421 rows=399920 loops=5)\n Execution Time: 33.012 ms\n```\n\nWith 4 workers, a 2-million row aggregation completes in 33ms instead of the ~130ms it would take single-threaded.\n\n### When Parallel Doesn't Help\n\nNot everything benefits from parallelism:\n\n```sql\n-- Index scans are already fast — parallel doesn't help\nEXPLAIN SELECT * FROM orders WHERE id = 12345;\n-- Result: plain Index Scan, no parallelism (correct!)\n\n-- Small tables don't trigger parallel (below min_parallel_table_scan_size)\nEXPLAIN SELECT count(*) FROM status_codes;\n-- Result: plain Seq Scan, no parallelism (table is tiny)\n```\n\nParallel execution is also avoided when:\n- The function called in the query is marked `PARALLEL UNSAFE` (e.g., writes data, uses cursors)\n- The query is inside a transaction that has written data\n- The query uses `FOR UPDATE`/`FOR SHARE`\n- The table is below `min_parallel_table_scan_size`\n- Workers aren't available (all `max_parallel_workers` slots are in use)\n\n### Parallel-Safe vs Parallel-Unsafe Functions\n\nCustom functions must be marked as parallel-safe for the planner to parallelize queries that use them:\n\n```sql\n-- This function CAN run in parallel workers\nCREATE FUNCTION calculate_discount(price numeric, tier text)\nRETURNS numeric\nLANGUAGE sql\nPARALLEL SAFE\nAS $$\n  SELECT CASE tier\n    WHEN 'gold' THEN price * 0.20\n    WHEN 'silver' THEN price * 0.10\n    ELSE price * 0.05\n  END;\n$$;\n\n-- This function CANNOT (it writes data)\nCREATE FUNCTION log_access(user_id int)\nRETURNS void\nLANGUAGE sql\nPARALLEL UNSAFE\nAS $$\n  INSERT INTO access_log(user_id, accessed_at) VALUES (user_id, now());\n$$;\n```\n\nIf a query uses a `PARALLEL UNSAFE` function, the entire query runs single-threaded — even parts that don't involve that function.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n\nPerformance optimization is an iterative process: measure with `EXPLAIN ANALYZE` and `pg_stat_statements`, identify the bottleneck (bad plan? missing index? insufficient memory? I/O bound?), fix it, and measure again. Resist the urge to tune everything at once — start with the highest-impact query from `pg_stat_statements`, understand its plan, and work from there.\n\n### Related Resources\n\n\u003Cbutton\u003E\n\n[Crunchy Data Blog — Postgres Scan Types in EXPLAIN Plans](https://www.crunchydata.com/blog/postgres-scan-types-in-explain-plans)\n\u003C/button\u003E\n\nVisual guide to understanding scan types and when they appear.\n\n---\n\n\u003Cbutton\u003E\n\n[Crunchy Data Blog — Is Postgres Read Heavy or Write Heavy?](https://www.crunchydata.com/blog/is-postgres-read-heavy-or-write-heavy-and-why-should-you-care)\n\u003C/button\u003E\n\nUnderstand your workload profile to guide tuning decisions.\n\n---\n\n\u003Cbutton\u003E\n\n[Snowflake Engineering Blog — Postgres JSONB and TOAST Performance](https://www.snowflake.com/en/engineering-blog/postgres-jsonb-columns-and-toast/)\n\u003C/button\u003E\n\nPerformance implications of large JSONB columns and TOAST.\n\n---\n\n\u003Cbutton\u003E\n\n[Crunchy Data Blog — HOT Updates and Fill Factor](https://www.crunchydata.com/blog/postgres-performance-boost-hot-updates-and-fill-factor)\n\u003C/button\u003E\n\nReduce index maintenance overhead with HOT updates.\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"isDeveloperGuidesPage":true,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-89a983ec3d","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-195a754174",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ec5dc04199","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-047c537f58","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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