16 Extensions worth knowing

We loaded two million events, shaped like the book’s, into a table with one extension installed. One alter table and a loop later, the same rows took 30 MB instead of 324 MB with their indexes, and a month of deploy durations read about 300 pages instead of about 3,050. On a different server, a “who’s within 50 km of Frankfurt” query went from reading every page of its table to reading 112.

Neither is core Postgres. Both are extensions, which is how Postgres gets what other databases sell as separate products: new types and index methods, loaded into the server you already run (You don’t need another database (yet)). They’re also code you didn’t write, running inside your database process. Here are the ones worth knowing as of October 2026, what they cost, and how to vet one.

An extension is a package the database tracks

An extension is files on the server’s disk (a control file, SQL scripts, often a compiled library) that create extension turns into objects in one database, tracked together so drop extension and pg_dump treat them as one. Three things follow, and each bites somebody: it installs only what the package manager put on disk (which is why a managed service hands you a list), it’s per database, and the SQL objects’ version can drift from the library’s.

Some must be loaded at server start

Extensions that need shared memory, background workers, or planner hooks must be in shared_preload_libraries, which takes a restart.

pg_stat_statements is the classic trap the other way round: it installs fine without the preload, then fails when you read it with must be loaded via "shared_preload_libraries".

create extension pg_stat_statements;
select lab.prove('pg_stat_statements installs without being preloaded',
  exists (select 1 from pg_extension where extname = 'pg_stat_statements'));
select lab.prove('but reading it fails until it is in shared_preload_libraries: 55000',
  lab.try('select count(*) from pg_stat_statements') like '55000:%');

Trusted extensions don’t need a superuser

Since PostgreSQL 13, an extension marked trusted, one that “cannot provide access to outside-the-database functionality”, can be installed by any role with CREATE on the database: citext, pg_trgm, btree_gist, ltree, yes; postgres_fdw, which opens connections, no. On a managed service there’s no superuser at all, so check the provider’s list before you design around anything here (Extensions come from an allowlist).

     name     | version | trusted | superuser
--------------+---------+---------+-----------
 citext       | 1.8     | t       | t
 pg_cron      | 1.6     | f       | t
 plpgsql      | 1.0     | t       | t
 plpython3u   | 1.0     | f       | t
 postgis      | 3.6.4   | f       | t
 postgres_fdw | 1.2     | f       | t

create role app login;
grant create on database lab to app;
grant create on schema public to app;
set role app;
select lab.prove('a role with CREATE on the database, not a superuser, can install a trusted extension (citext)',
  lab.try('create extension citext') = 'ok');
select lab.prove('the same role cannot install an untrusted one (postgres_fdw): 42501',
  lab.try('create extension postgres_fdw') like '42501:%');
select lab.prove('it can write functions in trusted plpgsql',
  lab.try($q$create function one_pg() returns int language plpgsql
                 as 'begin return 1; end'$q$) = 'ok');
select lab.prove('but not in untrusted plpython3u: 42501',
  lab.try($q$create function one_py() returns int language plpython3u
                 as 'return 1'$q$) like '42501:%');

Versions move with alter extension update

After every package, minor, or major upgrade, run alter extension ... update for each extension, in each database. Nothing does it for you, managed services included (The provider adds its own gate on CREATE EXTENSION). And a major upgrade waits until every extension has a build for the new major.

create extension hstore version '1.4';
select lab.prove('there is no 1.4-to-1.8 script; Postgres chains four update scripts',
  (select path from pg_extension_update_paths('hstore')
    where source = '1.4' and target = '1.8') = '1.4--1.5--1.6--1.7--1.8');
alter extension hstore update;
select lab.prove('alter extension hstore update walks it to the default version, 1.8',
  (select extversion from pg_extension where extname = 'hstore') = '1.8');

Contrib is the safest shelf, though some of it is obsolete

About fifty extensions ship in contrib, on Postgres’s own release cycle and license. They’re the safest there are, and a few are obsolete:

Core has gen_random_uuid(), uuidv7(), and sha256(). For case-insensitive text, our default is a unique index on lower(), and citext’s own docs point to nondeterministic collations as the other alternative (For emails, make lower() the unique key). If you use pgcrypto’s PGP functions, be on 18.6 or later: before it, a cipher OpenSSL refused (under FIPS, say) silently produced trivially breakable data (18.6 release notes).

PostGIS is why many teams run Postgres at all

PostGIS adds spatial types, functions, and GiST spatial indexing. It’s mature and widely offered: 3.6.4 (June 2026), for PostgreSQL 12 through 18, under the GPL v2 or later, which attaches to distributing it, not running it behind your app.

The type decides what distance means

PostGIS has two families of types, and the choice decides what “distance” means. geometry works on a flat plane, in the units of its spatial reference system (SRID): degrees for longitude and latitude (4326), meters for a projection. geography works on the curved Earth, in meters. Frankfurt to Amsterdam, four ways:

 geometry_4326 | geography | mercator_3857 | laea_3035
---------------+-----------+---------------+-----------
         4.404 |    364465 |        581921 |    364479

The real answer is about 364.5 km. geometry in 4326 says 4.4 “degrees”, meaningless. Web Mercator, the projection of most web maps, says 582 km, 60 percent too long. Use geography for points spread across the world, or geometry in a projected SRID for regional data. Never compute distances in degrees.

select st_distance('SRID=4326;POINT(8.68 50.11)'::geometry,
                   'SRID=4326;POINT(4.90 52.37)'::geometry)   as geometry_4326,
       st_distance('SRID=4326;POINT(8.68 50.11)'::geography,
                   'SRID=4326;POINT(4.90 52.37)'::geography)  as geography,
       st_distance(st_transform('SRID=4326;POINT(8.68 50.11)'::geometry, 3857),
                   st_transform('SRID=4326;POINT(4.90 52.37)'::geometry, 3857))
                   as mercator_3857,
       st_distance(st_transform('SRID=4326;POINT(8.68 50.11)'::geometry, 3035),
                   st_transform('SRID=4326;POINT(4.90 52.37)'::geometry, 3035))
                   as laea_3035;

select lab.prove('as geography: about 364.5 km, in meters, on the spheroid',
  st_distance('SRID=4326;POINT(8.68 50.11)'::geography,
              'SRID=4326;POINT(4.90 52.37)'::geography) between 364000 and 365000);
select lab.prove('in Web Mercator (3857): about 582 km, 60 percent too long',
  st_distance(st_transform('SRID=4326;POINT(8.68 50.11)'::geometry, 3857),
              st_transform('SRID=4326;POINT(4.90 52.37)'::geometry, 3857))
    between 575000 and 590000);

ST_DWithin uses the index; ST_Distance doesn’t

Give each of the book’s 100,000 users a last-seen geography point around one of the five seed regions, with a GiST index on it. “Users within 50 km of Frankfurt”:

select count(*) from user_locations
where st_dwithin(last_seen, 'SRID=4326;POINT(8.68 50.11)'::geography, 50000);

 Aggregate (actual rows=1.00 loops=1)
   Buffers: shared hit=773
   ->  Bitmap Heap Scan on user_locations (actual rows=955.00 loops=1)
         Rows Removed by Filter: 660
         Heap Blocks: exact=725
         ->  Bitmap Index Scan on user_locations_gist (actual rows=1615.00 loops=1)
               Index Cond: (last_seen && _st_expand('0101...'::geography,
                                                    '50000'::double precision))
               Buffers: shared hit=48

Read it from the bottom: a bounding-box test (&& against a box grown by 50 km) found 1,615 candidates in 48 index pages, then the exact distance kept 955.

Now the version everybody writes first, st_distance(last_seen, ...) < 50000: same 955 rows, and a sequential scan of every page, because nothing tells the planner that “under 50 km” implies “inside this box”. Intersecting a st_buffer circle uses the index but draws the circle with 32 sides, and missed 6 users. Use ST_DWithin. Always.

The index finds rows; physical order decides pages

Look at the buffers again: 773 pages for 955 rows, from a table of 834. Nearby users were inserted in id order, so they sit on 725 different pages. cluster user_locations using user_locations_gist rewrote the table in spatial order, and the same query read 112 pages. cluster locks the table for the rewrite and the order decays as rows change, so it suits tables loaded in bulk.

Nearest neighbors, the other query spatial apps live on, is order by last_seen <-> point limit 5, which walked the index in distance order and read 12 pages.

PostGIS’s own bill comes due at upgrade time: it links against the GEOS and PROJ libraries, and a major Postgres upgrade waits until PostGIS is built for the new major.

\set fra '''SRID=4326;POINT(8.68 50.11)''::geography'
\set within 'select count(*) from user_locations where st_dwithin(last_seen, ' :fra ', 50000)'
\set dist   'select count(*) from user_locations where st_distance(last_seen, ' :fra ') < 50000'
\set buf    'select count(*) from user_locations where st_intersects(last_seen, st_buffer(' :fra ', 50000))'

select lab.prove('the box finds 1,615 candidates; the exact distance check keeps 955',
  (select (p -> 'Plan' -> 'Plans' -> 0 -> 'Plans' -> 0 ->> 'Actual Rows')::numeric = 1615
          and (p -> 'Plan' -> 'Plans' -> 0 ->> 'Rows Removed by Filter')::numeric = 660
     from lab.plan(:'within') p));
select lab.prove('ST_Distance(...) < 50000 gives the same answer and never uses the index',
  (:dist) = 955 and 'Seq Scan' = any (lab.nodes(:'dist'))
  and not ('Bitmap Index Scan' = any (lab.nodes(:'dist'))));
select lab.prove('a 50 km ST_Buffer is a 32-sided polygon, so intersecting it misses 6 of the 955',
  st_npoints(st_buffer(:fra, 50000)::geometry) = 33 and (:buf) = 949);

 Aggregate (actual rows=1.00 loops=1)
   Buffers: shared read=112
   ->  Bitmap Heap Scan on user_locations (actual rows=955.00 loops=1)
         Heap Blocks: exact=62

 Limit (actual rows=5.00 loops=1)
   Buffers: shared hit=12
   ->  Index Scan using user_locations_gist
         on user_locations (actual rows=5.00 loops=1)
         Order By: (last_seen <-> '0101...'::geography)

\set knn 'select user_id from user_locations order by last_seen <-> ' :fra ' limit 5'
cluster user_locations using user_locations_gist;
analyze user_locations;
select lab.prove('after CLUSTER on the GiST index, the same query reads under 150 pages',
  (:within) = 955 and lab.buffers(:'within') < 150);
select lab.prove('nearest neighbors with <-> walk the index in distance order and read under 20 pages',
  lab.nodes(:'knn') = '{Limit,"Index Scan"}' and lab.buffers(:'knn') < 20);

TimescaleDB is partitioning with opinions

TimescaleDB turns a table into a hypertable that creates child tables (chunks) by time on the fly, with a columnar format for older chunks and incrementally maintained rollups. We ran 2.30.2, the current release, on PostgreSQL 18; its company, formerly Timescale, is now Tiger Data.

A hypertable is automatic partitioning

Native partitioning (Partition for the delete) makes you create every partition before rows arrive. A hypertable makes chunks as needed: create table events (...) with (tsdb.hypertable, tsdb.partition_column = 'created_at', tsdb.chunk_interval = '7 days'), and inserting the book’s year of events created 53 weekly chunks with no further DDL.

The columnstore: 324 MB to 30 MB

The columnstore rewrites a chunk into batches of up to 1,000 rows, each column of a batch stored as one compressed array, grouped by a segmentby column and sorted by an orderby:

alter table events set (timescaledb.enable_columnstore,
                        timescaledb.segmentby = 'kind',
                        timescaledb.orderby   = 'created_at desc');
-- then call convert_to_columnstore() on each chunk, or let a policy do it

 before | after | ratio
--------+-------+-------
 324 MB | 30 MB |  10.8

A month of deploy durations read about 3,050 pages from row chunks and about 300 from the columnstore, which skipped batches outside the month and decompressed only the columns the query names. Our time-ordered data is the shape the format is built for; yours may compress less. The cost moves to writes: updating compressed rows decompresses and recompresses batches, so it pays off for data that’s append-mostly once it’s a few days old.

-- :q is the month-of-deploys query; :rowstore_buffers is what it read before conversion
select lab.prove('the columnstore shrinks the events from about 324 MB to about 30 MB: a ratio over 9',
  (select before_compression_total_bytes::numeric / after_compression_total_bytes > 9
          and before_compression_total_bytes between 300e6 and 360e6
     from hypertable_columnstore_stats('events')));
select lab.prove('the same query now reads under a tenth of the pages',
  lab.buffers(:'q') * 10 < :rowstore_buffers);

do $$ declare c regclass;
begin
  for c in select show_chunks('events') loop
    call convert_to_columnstore(c);
  end loop;
end $$;

 ->  Custom Scan (ColumnarScan) on _hyper_1_48_chunk (actual rows=2820.00 loops=1)
       Vectorized Filter:
         (created_at >= '2026-09-01 00:00:00+00'::timestamp with time zone)
       Buffers: shared hit=42
       ->  Index Scan using
             _hyper_1_48_chunk_compressed_kind__ts_meta_v2_first_created_idx
             on _hyper_1_48_chunk_compressed (actual rows=3.00 loops=1)

Continuous aggregates lag until a refresh

A continuous aggregate is a materialized view over a hypertable that TimescaleDB refreshes incrementally, recomputing only the time buckets whose rows changed. The default since 2.13 surprises people: they’re materialized only. An event inserted for October 5 didn’t show up in our daily rollup until a refresh covered that day. Set timescaledb.materialized_only = false to union in fresh rows, or schedule refreshes with a policy.

create materialized view events_daily with (timescaledb.continuous) as
select time_bucket('1 day', created_at) as day, kind, count(*) as n
from events group by 1, 2
with no data;
call refresh_continuous_aggregate('events_daily', null, null);

insert into events values (1, 1, 'deploy', '{}', '2026-10-05 12:00');
select lab.prove('continuous aggregates are materialized-only by default: a new event is invisible until a refresh',
  (select materialized_only from timescaledb_information.continuous_aggregates
    where view_name = 'events_daily')
  and (select n from events_daily where day = '2026-10-05' and kind = 'deploy') + 1
    = (select count(*) from events where kind = 'deploy'
         and created_at >= '2026-10-05' and created_at < '2026-10-06'));

Off Tiger Data’s cloud, expect hypertables at most

Read this before you build on it. The LICENSE splits the repository: code outside the tsl directory is Apache 2.0, code inside it is under the source-available Timescale License (TSL). Use it in your own product all you like; you can’t sell it as a hosted database. The columnstore, continuous aggregates, retention policies, and the job scheduler are TSL (editions), so on a managed service other than Tiger Data’s own, expect hypertables at most.

TimescaleDB earns its place where the columnstore changes the size of the machine you need. If your time series is one events table and retention is “drop old months”, native partitioning with pg_partman and BRIN is less machinery and no license question.

pg_cron and pg_partman keep time on your side

pg_cron (PostgreSQL license; 1.6.8) runs SQL on a cron schedule from a preloaded background worker, down to one-second intervals. Jobs fail over with the database, with no scheduler host to run. Every run lands in cron.job_run_details, which nothing cleans, so schedule a delete for it too.

pg_partman (PostgreSQL license; 5.5.0) manages native partitions: it creates them ahead of time and drops what falls out of retention each time run_maintenance() runs, from pg_cron or its own worker (Create partitions before you need them).

Rebuild bloated tables without locking them

vacuum makes dead space reusable but rarely gives it back; vacuum full gives it back by rewriting the table under an ACCESS EXCLUSIVE lock (VACUUM FULL is an outage on a big table). Two extensions do the same rewrite while you keep using the table.

We bloated three identical tables to 85 MB, about three times their live data, and rebuilt each one while using it. vacuum full shrank its copy to a third, and while it ran, even a plain select hit its lock_timeout. pg_repack (BSD; 1.5.3) shrank its copy the same amount while an insert went straight through. pg_squeeze (PostgreSQL-style license; 1.9.4) did the same.

Both write the whole table to WAL, which replicas and backups will feel. Fix whatever caused the bloat first.

written=$(pg -c "set lock_timeout = '1s'" \
  -c "insert into rp values (999999, 0, 'written during the repack') returning 'written'")
prove "while pg_repack rebuilds the table, a write goes through within a 1 s lock_timeout" \
  test "$written" = "written"

select lab.prove('while VACUUM FULL runs, even a plain select waits: lock_timeout fires (55P03)',
  (select outcome from seen) = 'blocked');
select lab.prove('pg_repack also shrank it to about a third, and kept the row written mid-copy',
  pg_table_size('rp') * 2.5 < (select bloated from sizes where t = 'rp')
  and (select pad from rp where id = 999999) = 'written during the repack'
  and (select count(*) from rp) = 200001);

pgaudit logs who did what, at the cost of log volume

pgaudit (PostgreSQL license; 18.0) logs statements by class (read, write, ddl, role, and more) in the fixed, parseable format auditors ask for, or narrows to particular tables through a role’s grants. It must be preloaded. The cost is log volume: audit writes and DDL broadly, reads narrowly (Start auditing with the server log; it’s free).

Ask the planner hypothetical questions

HypoPG (PostgreSQL license; 1.4.3) creates hypothetical indexes that plain explain sees and no disk holds. A lookup on users.email read all 1,126 pages; with a hypothetical index, explain picked it before we built anything. Test a column order or a partial index’s predicate in seconds. pg_hint_plan (BSD; 1.8.0) goes the other way and dictates plans through comments; keep it for incidents (pg_hint_plan is for the night a plan flips).

select * from hypopg_create_index('create index on users (email)');
explain select * from users where email = 'user4242@example.com';

 Index Scan using "<13640>btree_users_email" on users  (cost=0.04..8.06 rows=1 ...)

\set q 'select * from users where email = ''user4242@example.com'''
select lab.prove('without an index on email, the lookup reads all 1,126 pages of users',
  lab.nodes(:'q') = '{"Seq Scan"}' and lab.buffers(:'q') = 1126
  and pg_relation_size('users') / 8192 = 1126);
select indexrelid as hypo, hypopg_relation_size(indexrelid) as hypo_bytes
from hypopg_create_index('create index on users (email)') \gset
select lab.prove('with a hypothetical index, plain EXPLAIN picks it',
  lab.plan_text(:'q') like '%btree_users_email%');
select lab.prove('EXPLAIN ANALYZE ignores it: the query still reads all 1,126 pages',
  lab.nodes(:'q') = '{"Seq Scan"}' and lab.buffers(:'q') = 1126);

PGroonga searches text the built-in parser can’t split

pgvector and the BM25 search extensions have their own chapter, with each one’s license and where it runs (Search extensions put BM25 in the index). One more belongs here: PGroonga (PostgreSQL license; 4.0.9) searches Japanese and Chinese text, which the built-in parser, finding words by spaces, can’t.

Apache AGE is a nicer language for the same joins

Apache AGE (Apache 2.0; 1.8.0) adds openCypher, the graph query language, over ordinary tables. A match like -[:PARENT_OF*1..]-> is the recursive CTE in a nicer language; each hop is still a join, so the performance argument stands. Every session must load 'age', which fights poolers, and the project went quiet from September 2024 to May 2025 (board reports).

create extension age;
load 'age';
set search_path = ag_catalog, "$user", public;
select create_graph('org');
select * from cypher('org', $$
  create (:Team {name: 'Engineering'})-[:PARENT_OF]->(:Team {name: 'Platform'})
         -[:PARENT_OF]->(:Team {name: 'Databases'})
         -[:PARENT_OF]->(:Team {name: 'Postgres'})
$$) as (v agtype);
select * from cypher('org', $$
  match (:Team {name: 'Engineering'})-[:PARENT_OF*1..]->(t:Team) return t.name
$$) as (name agtype);

Run an analytics engine on a replica, never the primary

Row storage reads whole rows, which is why wide scans are where Postgres loses (Rollups and a column extension close much of the gap). pg_duckdb (MIT; 1.1.1) embeds DuckDB’s columnar engine and reads Parquet and Iceberg; pg_lake (Apache 2.0) puts lake files behind tables. Some fade: ParadeDB archived pg_analytics, and pg_mooncake’s last published release was in February 2025 (a v0.1.3 tag followed that December). Each is a second engine in your process; run it on a replica, never your OLTP primary.

pgmq is still a Postgres table

pgmq (PostgreSQL license; 1.13.0) wraps the SKIP LOCKED queue (A Postgres queue commits with the business write) in a cloud-queue API: send(), read() with a visibility timeout, archive(). It’s still a Postgres table, with the same vacuum needs.

Untrusted languages need a superuser

PL/pgSQL is trusted: it can’t do anything SQL can’t (Code in the database). Untrusted languages (plpython3u, plperlu) run as the server’s OS user, so only superusers write them, and managed services leave them out. Trusted JavaScript exists in PL/v8 and PLJS. Python in the database usually belongs in the application.

A crashing extension drops every connection, so vet it

An extension’s code runs inside the backend, and when a backend crashes, the server ends every other session: one bad function call, and every connection drops. Before you depend on one, ask:

1. Is it maintained? Release dates, how fast it supported the last major version, how many people commit.
2. What license, for which parts? Read the whole LICENSE: some projects split by directory.
3. Will it be there for the next major version? Your upgrade waits for every extension’s build.
4. Can you run it where you run Postgres? The provider’s list, and whether it needs preloading or a superuser.
5. What does it do to your process? Workers, shared memory, hooks on every statement, WAL.
6. What’s the exit? An extension that owns your data’s type (PostGIS, pgvector) is a long-term commitment.

Then test its upgrade path in staging.

The short version

• An extension is files on the server plus SQL objects in one database. Some need shared_preload_libraries and a restart; all need alter extension ... update after upgrades.
• Drop uuid-ossp and most of pgcrypto and citext: core functions and lower() indexes cover them.
• In PostGIS, use geography or a projected SRID, and filter with ST_DWithin, which used the index, never ST_Distance, which read every page.
• TimescaleDB’s columnstore took our events from 324 MB to 30 MB and a month’s query from about 3,050 pages to about 300. It’s Timescale License code: free to self-host, not to resell.
• pg_repack and pg_squeeze rebuild bloated tables online where vacuum full blocks even reads.
• Test an index with HypoPG before you build it.
• Vet every extension for maintenance, license, next-major support, availability, and crash risk.

Further reading

• Packaging Related Objects into an Extension, PostgreSQL documentation. Control files, trusted extensions, update scripts, and how to write your own.
• Additional Supplied Modules and Extensions, PostgreSQL documentation. Every contrib module, with which ones are trusted.
• PostGIS manual: spatial queries, PostGIS project. Index use, ST_DWithin, and nearest-neighbor search, with the reasons behind each.
• TimescaleDB editions, Tiger Data. Which features are Apache 2.0 and which are under the Timescale License.
• pg_repack documentation, reorg project. Requirements, locks, and options for online rebuilds.
• HypoPG documentation, HypoPG project. Hypothetical indexes and hidden indexes, with their limits.