Distribute PostgreSQL 18 with Citus 14
The Citus 14.0 release is out and includes PostgreSQL 18 support! We know you've been waiting, and we've been hard at work adding features we believe will take your experience to the next level, focusing on bringing the Postgres 18 exciting improvements to you at distributed scale. The Citus database is an open-source extension of Postgres that brings the power of Postgres to any scale, from a single node to a distributed database cluster. Since Citus is an extension, using Citus means you're also using Postgres, giving you direct access to the Postgres features. And the latest of such features came with Postgres 18 release! PostgreSQL 18 is a substantial release: asynchronous I/O (AIO), skip-scan for multicolumn B-tree indexes, uuidv7(), virtual generated columns by default, OAuth authentication, RETURNING OLD/NEW, and temporal constraints. For those of you who are interested in upgrading to Postgres 18 and scaling these new features of Postgres: you can upgrade to Citus 14.0! Let's take a closer look at what's new in Citus 14.0. Postgres 18 support in Citus 14.0 Citus 14.0 introduces support for PostgreSQL 18. This means that just by enabling PG18 in Citus 14.0, all the query performance improvements directly reflect on the Citus distributed queries, and several optimizer improvements benefit queries in Citus out of the box! Among the many new features in PG 18, the following capabilities enabled in Citus 14.0 are especially noteworthy for Citus users. To learn more about how you can use Citus 14.0 + PostgreSQL 18, as well as currently unsupported features and future work, you can consult the Citus 14.0 Updates page, which gives you detailed release notes. PostgreSQL 18 highlights that benefit Citus clusters Because Citus is implemented as a Postgres extension, the following PG18 improvements benefit your distributed cluster automatically, no Citus-specific changes needed. Faster scans and maintenance via AIO Postgres 18 adds an asynchronous I/O subsystem that can improve sequential scans, bitmap heap scans, and vacuuming—workloads that show up constantly in shard-heavy distributed clusters. This means your Citus cluster can benefit from faster table scans and more efficient maintenance operations without any code changes. You can control the I/O method via the new io_method GUC: -- Check the current I/O method SHOW io_method; Better index usage with skip-scan Postgres 18 expands when multicolumn B-tree indexes can be used via skip scan, helping common multi-tenant schemas where predicates don't always constrain the leading index column. This is particularly valuable for Citus users with multi-tenant applications where queries often filter by non-leading columns. -- Multi-tenant index: (tenant_id, created_at) -- PG18 skip-scan lets this query use the index even without tenant_id SELECT * FROM events WHERE created_at > now() - interval '1 day' ORDER BY created_at DESC LIMIT 100; uuidv7() for time-ordered UUIDs Time-ordered UUIDs can reduce index churn and improve locality; Postgres 18 adds uuidv7(). This is especially useful for distributed tables where you want predictable ordering and better index performance across shards. -- Use uuidv7() as a time-ordered primary key CREATE TABLE events ( id uuid DEFAULT uuidv7() PRIMARY KEY, tenant_id bigint, payload jsonb ); SELECT create_distributed_table('events', 'tenant_id'); OAuth authentication support Postgres 18 adds OAuth authentication, making it easier to plug database auth into modern SSO flows often a practical requirement in multi-node deployments. This simplifies authentication management across your Citus coordinator and worker nodes. What Citus 14 adds for PostgreSQL 18 compatibility While the highlights above work out of the box, PG18 also introduces new SQL syntax and behavior changes that require Citus-specific work parsing/deparsing, DDL propagation across coordinator + workers, and distributed execution correctness. Here's what we built to make these work end-to-end. JSON_TABLE() COLUMNS PG18 expands SQL/JSON JSON_TABLE() with a richer COLUMNS clause, making it easy to extract multiple fields from JSON documents in a single, typed table expression. Citus 14 ensures the syntax can be parsed/deparsed and executed consistently in distributed queries. CREATE TABLE pg18_json_test (id serial PRIMARY KEY, data JSON); SELECT jt.name, jt.age FROM pg18_json_test, JSON_TABLE( data, '$.user' COLUMNS ( age INT PATH '$.age', name TEXT PATH '$.name' ) ) AS jt WHERE jt.age BETWEEN 25 AND 35 ORDER BY jt.age, jt.name; Temporal constraints Postgres 18 adds temporal constraint syntax that Citus must propagate and preserve correctly: WITHOUT OVERLAPS for PRIMARY KEY / UNIQUE PERIOD for FOREIGN KEY CREATE TABLE temporal_rng ( id int4range, valid_at daterange, CONSTRAINT temporal_rng_pk PRIMARY KEY (id, valid_at WITHOUT OVERLAPS) ); SELECT create_distributed_table('temporal_rng', 'id'); CREATE FOREIGN TABLE ... LIKE Postgres 18 supports CREATE FOREIGN TABLE ... LIKE, letting you define a foreign table by copying the column layout (and optionally defaults/constraints/indexes) from an existing table. Citus 14 includes coverage so FDW workflows remain compatible in distributed environments. -- Copy column layout from an existing table CREATE FOREIGN TABLE my_ft (LIKE my_local_table EXCLUDING ALL) SERVER foreign_server OPTIONS (schema_name 'public', table_name 'my_local_table'); Generated columns (Virtual by Default) PostgreSQL 18 changes generated column behavior significantly: Virtual by default: Generated columns are now computed on read rather than stored, reducing write amplification Logical replication support: New publish_generated_columns publication option for replicating generated values CREATE TABLE events ( id bigint, payload jsonb, payload_hash text GENERATED ALWAYS AS (md5(payload::text)) VIRTUAL ); SELECT create_distributed_table('events', 'id'); VACUUM/ANALYZE ONLY semantics Postgres 18 introduces ONLY for VACUUM and ANALYZE so you can explicitly target only the parent of a partitioned/inheritance tree without automatically processing children. Citus 14 adapts distributed utility-command behavior so ONLY works as intended. -- Parent-only: do not recurse into partitions/children VACUUM (ANALYZE) ONLY metrics; ANALYZE ONLY metrics; Constraints: NOT ENFORCED + partitioned-table additions Postgres 18 expands constraint syntax in several ways that Citus must parse/deparse and propagate across coordinator + workers: CHECK constraints can be marked NOT ENFORCED FOREIGN KEY constraints can be marked NOT ENFORCED NOT VALID foreign keys on partitioned tables DROP CONSTRAINT ONLY on partitioned tables ALTER TABLE orders ADD CONSTRAINT orders_amount_positive CHECK (amount > 0) NOT ENFORCED; ALTER TABLE orders ADD CONSTRAINT orders_customer_fk FOREIGN KEY (customer_id) REFERENCES customers(id) NOT ENFORCED; DML: RETURNING OLD/NEW Postgres 18 lets RETURNING reference both the previous (old) and new (new) row values in INSERT/UPDATE/DELETE/MERGE. Citus 14 preserves these semantics in distributed execution. UPDATE t SET v = v + 1 WHERE id = 42 RETURNING old.v AS old_v, new.v AS new_v; COPY expansions PG18 adds two useful COPY improvements that Citus 14 supports in distributed queries: COPY ... REJECT_LIMIT: set a threshold for how many rows can be rejected before the COPY fails, useful for resilient bulk loading into sharded tables COPY table TO from materialized views: export data directly from materialized views -- Tolerate up to 10 bad rows during bulk load COPY my_distributed_table FROM '/data/import.csv' WITH (FORMAT csv, REJECT_LIMIT 10); MIN()/MAX() on arrays and composite types PG18 extends MIN() and MAX() aggregates to work on arrays and composite types. Citus 14 ensures these aggregates work correctly in distributed queries. CREATE TABLE sensor_data ( tenant_id bigint, readings int[] ); SELECT create_distributed_table('sensor_data', 'tenant_id'); -- Now works with array columns SELECT MIN(readings), MAX(readings) FROM sensor_data; Nondeterministic collations PG18 extends LIKE and text-position search functions to work with nondeterministic collations. Citus 14 verifies these work correctly across distributed queries. sslkeylogfile connection parameter PG18 adds the sslkeylogfile libpq connection parameter for dumping SSL key material, useful for debugging encrypted connections. Citus 14 allows configuring this via citus.node_conn_info so it works across inter-node connections. Planner fix: enable_self_join_elimination PG18 introduces the enable_self_join_elimination planner optimization. Citus 14 ensures this works correctly for joins between distributed and local tables, avoiding wrong results that could occur in early PG18 integration. Utility/Ops plumbing and observability Citus 14 adapts to PG18 interface/output changes that affect tooling and extension plumbing: New GUC file_copy_method for CREATE DATABASE ... STRATEGY=FILE_COPY EXPLAIN (WAL) adds a "WAL buffers full" field; Citus propagates it through distributed EXPLAIN output New extension macro PG_MODULE_MAGIC_EXT so extensions can report name/version metadata New libpq parameter sslkeylogfile support via citus.node_conn_info Diving deeper into Citus 14.0 and distributed Postgres To learn more about Citus 14.0, you can: Check out the 14.0 Updates page to get the detailed release notes. As of this release, Citus documentation is now hosted on Microsoft Learn. With Citus 14, elastic clusters will soon support PostgreSQL 18, now available in Azure Database for PostgreSQL. You can stay connected on the Citus Slack and visit the Citus open source GitHub repo to see recent developments as well. If there's something you'd like to see next in Citus, feel free to also open a feature request issue :)Exciting things on the horizon for PostgreSQL fans @ Ignite 2025
If you’re passionate about PostgreSQL or just curious about what’s new, you’ll want to join us at Microsoft Ignite 2025. We have a packed lineup, including sessions exploring cutting-edge features and exclusive giveaways at the PostgreSQL on Azure booth. Haven’t registered yet? Now’s the time – sign up for Microsoft Ignite and start building your schedule. Below are the must-see PostgreSQL on Azure activities, with highlights of what you’ll learn at each. Add these to your agenda today. Sessions can fill up fast! Theater sessions: get a first look, fast I know from experience that attention spans can start to wane after hours-long keynotes, content-rich sessions, and conference socializing. Luckily, we have a couple of theater sessions that offer snackable but substantial information in less time than it will take to grab lunch. And they’re located conveniently on the main conference floor. PostgreSQL on Azure: Your launchpad for intelligent apps and agents (THR705) - See how we’re making PostgreSQL AI-aware for developers to drive app and agent innovation. Includes a demo of vector similarity search, semantic operators baked into Postgres, and more! Simplifying scale-out of PostgreSQL for performant multi-tenant apps (THR706) - Discover a smarter, simpler way to scale PostgreSQL using the new Elastic Clusters feature. If your app or service is growing fast (or you want it to!), add this breakout to learn how Azure makes it easier to scale Postgres and keep it reliable. These talks are a great way to sample what’s new and decide where to dive deeper. Plus, they’re fun and demo-heavy, and who doesn’t love a good demo? Breakout sessions: a deep dive into Postgres innovations Led by Azure product leaders and executives from organizations driving innovation backed by PostgreSQL, these breakout sessions will dive into the coolest new capabilities and real-world use cases. If you want rich, technical content and more live demos, these are for you. Build mission-critical apps that scale with PostgreSQL on Azure (BRK127) - Get a closer look at the next generation of PostgreSQL on Azure. Add this session, if you’re curious about how we’re taking Postgres to the next level to support your mission-critical AI workloads. Modern data, modern apps: Innovation with Microsoft Databases (BRK134) - Gain insider knowledge on the latest innovations across open-source, SQL, and NoSQL databases, and understand how Microsoft’s integrated database portfolio supports next-gen innovation. Nasdaq Boardvantage: AI-driven governance on PostgreSQL and AI Foundry (BRK137) - Discover how a Fortune 100 merges trust with cutting-edge AI leveraging Azure’s AI-enriched and enterprise-ready solutions, including Azure Database for PostgreSQL, Azure Database for MySQL, Azure AI Foundry, Azure Kubernetes Service (AKS), and API Management. AI-assisted migration: The path to powerful performance on PostgreSQL (BRK123) - A before and after migration journey from Oracle to Azure Database for PostgreSQL. See how the new AI-assisted migration experience delivers conversion in a few clicks and minimal downtime. The blueprint for intelligent AI agents backed by PostgreSQL (BRK130) - If you’re into AI development, this session will spark ideas on bridging the gap between raw data and AI reasoning. You’ll leave with practical tips to turbocharge your AI agents with PostgreSQL. Each breakout session is 45 minutes with live demos and Q&A, so you’ll get plenty of detail and interaction with Postgres experts. Hands-on lab: experience coding with Azure superpowers Do you learn best by doing? Then our guided workshop, Build advanced AI agents with PostgreSQL (Lab515), is for you. In each 75-minute session, you’ll get to create a fully functional AI-powered application backed by PostgreSQL on Azure with step-by-step guidance and expert insight on the latest innovations enabling intelligent app development. All the tools and instructions you’ll need are provided. Labs have limited capacity, so be sure to reserve your seat for any of the four labs in advance. This lab is a great way to understand how all the pieces come together on Azure. And you’ll gain practical skills you can apply to your own projects, whether it’s customer support bots, intelligent search in your app, or any scenario where PostgreSQL + AI collide. Expert meet-up booth: meet the team, grab some swag If you still want more Postgres (or a little Postgres souvenir), you can stop by the PostgreSQL on Azure Expert Meetup booth in the Ignite Hub. This will be our homebase on the show floor, where you can: Meet the team: I’ll be there in person, along with engineers, program managers, cloud solution architects, and advocates from our team. Whether you have a burning technical question, want to share feedback, or need guidance for your specific use case, come chat with us. Get a quick demo re-run: Sometimes a 5-minute demo is worth a thousand words, especially after you’ve sat through all those words already in a keynote. The booth will have a monitor and a live environment so we can walk you through select use cases if you have questions - no appointment needed. Swag and giveaways: Ah yes, the goodies! We know conference swag is part of the fun, so we’ve got some special PostgreSQL-themed giveaways at the booth. I won’t spoil all the surprises, but rumor has it there are some limited-edition items up for grabs. Network with peers: The expert meet-up area is also a magnet for PostgreSQL enthusiasts. You might bump into other attendees at the booth who are tackling similar projects or challenges. Ignite is about community as much as content, so come by and spark up a conversation. Meet you there? Ignite is our largest event of the year. We love sharing what we’ve been working on and, most of all, hearing from you, the community. So, on behalf of the Azure for PostgreSQL team, thank you for your interest and support. We can’t wait to show you what’s new and to help you continue to succeed with Postgres. See you in San Francisco!PostgreSQL and the Power of Community
PGConf NYC 2025 is the premier event for the global PostgreSQL community, and Microsoft is proud to be a Platinum sponsor this year. The conference will also feature a keynote from Claire Giordano, Principal PM for PostgreSQL at Microsoft, who will share our vision for Postgres along with lessons from ten PostgreSQL hacker journeys.
