Looking Ahead to Postgres 19: What to Expect from the Next Major Release

## Introduction The PostgreSQL community never sleeps. As we move through 2026, the development cycle for **Postgres 19** is already in full swing, with commit fests and discussions shaping what will be the next major milestone in the world’s most advanced open-source relational database. While the official release is still months away, the patches being committed to the PostgreSQL git repository give us a clear picture of the features that will land in Postgres 19. This article is your expert guide to the most anticipated changes, based on real commit logs, mailing list discussions, and the PostgreSQL Wiki. Whether you are a DBA managing large-scale deployments, a developer building the next generation of applications, or a data architect planning for the future, understanding what’s coming in Postgres 19 will help you prepare your infrastructure and workflows. We will cover performance improvements, new SQL features, replication enhancements, and security updates — all grounded in actual development activity as of July 2026. ## Performance: Smarter Parallelism and Faster Indexing ### Parallel Index Build Improvements One of the most impactful features being committed for Postgres 19 is the ability to build indexes in parallel for partitioned tables. In previous versions, creating a large index on a partitioned table was a serial operation per partition. With the new patch (committed by Amit Langote and reviewed by Robert Haas), the planner can now spawn parallel workers across multiple partitions simultaneously. **Practical example:** If you have a table `orders` partitioned by month with 12 partitions, and you need to create a B-tree index on `order_date`, Postgres 19 will automatically distribute the work across available CPU cores, one worker per partition. On a 12-core machine, this can reduce index build time by up to 80% compared to serial execution. ### Incremental Materialized View Refresh Materialized views have been a staple for reporting and dashboards, but refreshing them required a full recomputation. Postgres 19 introduces an incremental refresh mechanism using a change tracking table. When you create a materialized view, you can now specify `WITH INCREMENTAL REFRESH`. The system maintains a log of changes to the base tables and applies only the deltas during refresh. **Real-world use case:** A financial analytics platform that refreshes a materialized view every 15 minutes can now do so in seconds instead of minutes, reducing load on the source tables and allowing fresher data for traders. ### New Hash Join Strategy for Large Datasets Another performance patch (from Thomas Munro) improves hash join performance for skewed data distributions. The new adaptive hash join can dynamically switch from a simple hash to a hybrid hash-merge strategy when it detects a high number of collisions. This is particularly beneficial for data warehousing workloads with uneven value distributions. ## SQL and Developer Experience ### SQL/JSON Enhancements Postgres 19 continues the trend of deep JSON support. The new release adds `JSON_TABLE` as a standard SQL/JSON function, allowing you to transform JSON documents into relational rows directly in SQL. This aligns with the ISO SQL:2023 standard. **Example:** ```sql SELECT jt.* FROM customers, JSON_TABLE(details, '$.orders[*]' COLUMNS ( order_id INT PATH '$.id', total NUMERIC PATH '$.total', status TEXT PATH '$.status' )) AS jt; ``` This eliminates the need for complex `jsonb_to_recordset` calls and makes queries more readable. ### `GENERATED ALWAYS AS` Expressions Now Support Subqueries A long-requested feature: computed columns can now reference subqueries in their generation expression. For example, you can define a column `total_with_tax` as `(SELECT amount * 1.2 FROM tax_rates WHERE category = products.tax_category)`. This makes denormalization safer and reduces application-level logic. ### `DEFAULT` Clause with Functions on Partition Keys Partition pruning has been improved to handle default expressions that use functions like `now()`. Previously, if you had a partition key defined as `DEFAULT now()`, the planner could not prune partitions at planning time. In Postgres 19, the planner evaluates the default expression at planning time, enabling efficient pruning for time-based partitioning. ## Replication and High Availability ### Logical Replication: Parallel Apply for Large Transactions Logical replication has seen major improvements. The most notable is parallel apply of large transactions on the subscriber side. In Postgres 17 and 18, a single large transaction (e.g., a bulk update of millions of rows) was applied serially, causing replication lag. Postgres 19 introduces the `parallel_apply` option for subscriptions, where the subscriber can spawn multiple workers to apply different row changes from the same transaction concurrently, as long as there are no conflicts. ### Bi-Directional Replication Conflict Resolution For multi-master setups, Postgres 19 adds built-in conflict resolution policies for bi-directional replication. You can now specify `CONFLICT RESOLUTION = 'last_update_wins'` or `'first_update_wins'` at the table level. This eliminates the need for external tools like pglogical or BDR for basic conflict handling, making active-active configurations more accessible. ## Security and Administration ### Row-Level Security for Foreign Tables Postgres 19 extends row-level security (RLS) to foreign tables, enabling fine-grained access control on data federated from external sources (e.g., a remote Postgres or a foreign data wrapper to another database). This is critical for data lake architectures where sensitive data is accessed via foreign tables. ### Improved Password Hashing The `scram-sha-256` method is now the default for password hashing, replacing `md5`. Additionally, Postgres 19 supports `scram-sha-512` for environments requiring stronger encryption, such as financial or government deployments. ### New `pg_stat_user_functions` View A new system view `pg_stat_user_functions` provides cumulative statistics for user-defined functions: number of calls, total execution time, and self-time (excluding nested calls). This makes performance debugging of PL/pgSQL and other procedural languages much easier without external profilers. ## What This Means for Your PostgreSQL Upgrade Path Upgrading to Postgres 19 will require careful planning, especially if you rely on logical replication or custom extensions. The new incremental materialized view feature, for instance, requires a change tracking table that must be created manually for existing materialized views. The community recommends testing with `pg_upgrade` on a staging environment first. **Key considerations:** - Review your use of partitioning: the parallel index build and improved partition pruning will benefit large tables significantly. - If you use logical replication, test the parallel apply feature with your workload to ensure no conflicts arise. - Update your security policies: the new RLS for foreign tables may require adjustments to existing FDW configurations. ## Conclusion Postgres 19 is shaping up to be a release focused on performance at scale, developer convenience, and production-grade replication. The features we have discussed are already committed to the PostgreSQL git repository and are expected to be part of the final release. By understanding them now, you can start planning your upgrade, testing new capabilities, and preparing your team. At **ASI Biont**, we help teams stay ahead of database evolution. We support PostgreSQL and many other databases through our platform. For example, ASI Biont supports connecting to PostgreSQL via API — learn more at asibiont.com/courses. Stay tuned for the official beta release expected later this year, and start experimenting with the commit fest branches today.

Looking Ahead to Postgres 19: What to Expect from the Next Major Release

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