Introduction

PostgreSQL 18 is here (well, the beta is), and it’s one of the most developer-friendly releases we’ve seen in a while. If you’ve ever wished for faster data imports, cleaner UPSERTs, or saner replication setups, this update delivers.

No, it’s not rewriting how databases work. But it is solving long-standing headaches like treating NULL values as distinct in unique constraints (finally), letting you query JSON as if it were a normal table, and even syncing sequence values across replicated nodes.

Performance, clarity, and less duct-tape logic that’s the theme. And if you’re a DBA, backend engineer, or data-heavy app builder, you’re going to want to look under the hood.

This isn’t just another minor bump. Here are 10 features in PostgreSQL 18 that are actually worth caring about with real-world context, not just changelog bullet points.

1. Parallel COPY from file to table

If you’ve ever waited way too long for a huge CSV to import into PostgreSQL, you know the pain. PostgreSQL 18 introduces something deceptively simple but incredibly impactful: parallelized COPY FROM operations.

What changed

You can now use multiple workers to load data from a file into a table not just a single thread doing all the heavy lifting. This significantly speeds up bulk imports, especially for large datasets.

Why it matters

Before Postgres 18, COPY FROM was a bottleneck when ingesting large files. Now, with parallelization, it's finally able to take advantage of multi-core CPUs for this task closing a gap that’s existed for years in high-performance data pipelines.

Example

To enable parallel import:

COPY your_table FROM '/path/to/yourfile.csv' WITH (FORMAT csv, PARALLEL workers);

You can specify the number of workers (e.g., PARALLEL 4) or let PostgreSQL decide automatically.

Use case

Ideal for:

• Initial data loads during ETL jobs
• Refreshing materialized tables
• Backfilling large volumes of analytics data

Quick tip: Parallel COPY works best when the table has no triggers, foreign keys, or other overhead during insert. Disable constraints temporarily to maximize performance.

2. Logical replication gets full DDL support

This is one of the biggest quality-of-life wins in PostgreSQL 18: logical replication now supports DDL meaning schema changes like CREATE TABLE, ALTER TABLE, and even DROP can now be automatically replicated to subscribers.

What changed

Previously, logical replication only handled data not the structure. You had to manually apply schema changes on both publisher and subscriber. Miss a migration? Congrats, your replication is broken.

PostgreSQL 18 fixes that by allowing replication of DDL statements through the same logical stream. Finally.

Why it matters

Logical replication just went from “great but annoying” to “actually production-ready for evolving systems.”

You can now:

• Spin up read replicas without micromanaging schema changes
• Support zero-downtime deploys with versioned schema updates
• Make logical replication viable for multi-tenant or multi-region setups

Example

DDL changes like this will now replicate:

ALTER TABLE users ADD COLUMN last_seen_at TIMESTAMP;

Subscribers will auto-apply the change without manual intervention.

Use case

• Multi-region PostgreSQL clusters
• SaaS apps with dynamic schema changes
• Event-sourced systems that rely on logical decoding

Quick tip: You can still filter which DDL commands get replicated via settings, so you stay in control.

3. JSON TABLE support (finally here)

Ever stored deeply nested JSON in PostgreSQL and then tried to query it? Yeah. Same. It works, but it’s ugly, verbose, and often unreadable. Enter: JSON_TABLE, now officially supported in PostgreSQL 18.

What changed

PostgreSQL 18 adds native support for the SQL-standard JSON_TABLE function, which lets you treat JSON like a relational table columns and all. That means you can project keys, arrays, and nested values directly into structured, queryable rows.

Why it matters

Before this, you had to chain a mess of jsonb_extract_path_text(), jsonb_array_elements(), and lateral joins to get something halfway decent. Now? You just define the structure you want — and query JSON like a normal table.

Example

Let’s say you have a column payload storing this JSON:

{
  "user": {
    "id": 42,
    "name": "Ash",
    "roles": ["admin", "dev"]
  }
}

You can now do:

SELECT  FROM JSON_TABLE(
  payload,
  '$.user'
  COLUMNS (
    id INT PATH '$.id',
    name TEXT PATH '$.name'
  )
) AS jt;

No CTEs. No lateral joins. Just clean, readable output.

Use case

• Extracting structured data from APIs or webhooks
• Working with hybrid schemas (JSON + relational)
• Cleaning up data for analytics or exports

Quick tip: JSON_TABLE supports nested paths and arrays too. You can flatten pretty much anything.

4. UNIQUE NULLS DISTINCT constraint

Let’s talk about something that’s confused SQL devs for years: how PostgreSQL handles NULLs in unique constraints. Until now, if a column allowed NULLs, multiple rows with NULL were allowed because in SQL logic, NULL ≠ NULL.

PostgreSQL 18 finally gives us a fix: UNIQUE NULLS DISTINCT, a new constraint that treats NULLs as unique values.

What changed

By default, Postgres follows standard SQL, which treats NULLs as unknowns so two NULLs are not equal. This means a UNIQUE constraint doesn’t block duplicate NULLs:

-- This is allowed in PostgreSQL <18
INSERT INTO users (email) VALUES (NULL), (NULL);

Now, with UNIQUE NULLS DISTINCT, Postgres treats each NULL as a unique value and prevents duplicates:

CREATE TABLE users (
  email TEXT,
  CONSTRAINT email_unique UNIQUE NULLS DISTINCT (email)
);

Why it matters

You no longer have to write weird conditional partial indexes, triggers, or app-side workarounds to make sure optional fields (like emails, usernames, or promo codes) are unique if provided but optional if not.

Use case

• User email fields (nullable but must be unique if filled)
• Optional API keys, tokens, invite codes
• Unique metadata fields where NULL = “not provided”

Quick tip: This syntax aligns with other SQL databases like SQLite. It’s small, but your schema logic just got cleaner.

5. SQL and PL/pgSQL function optimizations

Not all performance wins make flashy headlines but this one might quietly speed up your app in all the right places. PostgreSQL 18 introduces significant planning and execution improvements for SQL and PL/pgSQL functions.

What changed

Functions written in SQL or PL/pgSQL now benefit from smarter inlining, execution plan reuse, and better integration with parallel and JIT execution. In simpler terms: PostgreSQL is getting more intelligent about when and how to execute functions especially when used in complex queries.

Why it matters

Many apps rely heavily on stored functions for business logic, permission checks, or computed columns. Until now, using them often meant sacrificing performance especially in tight loops or joins. That changes with 18.

Expect faster:

• Inline functions in large queries
• Complex SELECTs with nested function calls
• Reusable plans for SQL routines

Example

Say you have a function like:

CREATE FUNCTION active_user(id INT) RETURNS BOOLEAN AS $$
  SELECT is_active FROM users WHERE user_id = id;
$$ LANGUAGE SQL;

Previously, calling this repeatedly in a big join would bloat your plan. Now, PostgreSQL is more likely to inline or optimize this away depending on usage.

Use case

• Functions inside views
• Heavy reporting queries with embedded logic
• Performance tuning in analytics workflows

Quick tip: SQL functions are now more competitive with raw SQL in terms of performance. If you avoided them in the past due to overhead it’s time to revisit.

6. EXPLAIN VERBOSE improvements

You’ve seen it. You’ve probably copy-pasted it into a Slack thread with the caption: “Why is this so slow?”
Good news PostgreSQL 18 makes EXPLAIN VERBOSE actually helpful.

What changed

EXPLAIN VERBOSE now provides cleaner, more consistent, and more detailed output especially for internal operations like parallelism, JIT compilation, and inlined functions (shoutout to Feature #5).

It also surfaces how expressions are evaluated, gives better info for join types, and shows the actual behavior of SQL/PL functions in the plan.

Why it matters

Previously, EXPLAIN VERBOSE could feel like raw bytecode: dense, noisy, and impossible to parse unless you were a Postgres contributor. In 18, it’s far more human-readable so performance work becomes faster and less frustrating.

Example

Before:

Function Scan on foo_func

After:

Function Scan on foo_func
  Output: id, name, age
  Function Call: foo_func()
  Parallel Aware: true

Now you actually know what’s going on and where the time’s going.

Use case

• Debugging slow joins or subqueries
• Diagnosing JIT slowdowns or memory pressure
• Understanding planner decisions in hybrid JSON/relational workloads

Quick tip: Combine EXPLAIN (ANALYZE, VERBOSE, BUFFERS) to see timing, planner paths, memory usage, and I/O in one go.

7. pg_stat_io I/O monitoring like a boss

Performance tuning used to be 30% SQL tricks and 70% educated guesswork. With PostgreSQL 18’s new pg_stat_io view, you finally get native insight into your read and write I/O behavior without needing third-party extensions or hacks.

What changed

Postgres now exposes block-level I/O statistics across several layers:

• Buffers (shared, temp, local)
• Relation types (tables, indexes, sequences)
• Read/write timing, hits, and misses

It’s available through a new system view: pg_stat_io.

Why it matters

You can now answer questions like:

• Is this table scan hitting disk or memory?
• Are my indexes actually helping, or just taking up space?
• What’s eating up my I/O during peak traffic?

This is huge for diagnosing random slowdowns, understanding buffer cache behavior, and tuning your DB like a proper craftsman.

Example

SELECT  FROM pg_stat_io
WHERE backend_type = 'user backend'
AND io_object_type = 'relation';

You’ll get details like:

• Number of blocks read/written
• Cache hit ratios
• Time spent on I/Oops

Use case

• High-traffic systems with variable load
• Understanding query I/O patterns
• Tuning table layout, vacuum strategy, or index usage

Quick tip: Combine this with pg_stat_statements for a full picture what query is slow, and where it’s spending its time (CPU vs I/O).

8. Logical replication for sequences

You set up logical replication. You sync your tables. Everything looks great until your ID sequences start overlapping across nodes and your app breaks in the most confusing way possible.

PostgreSQL 18 finally solves this with native support for replicating sequences.

What changed

Before version 18, logical replication ignored sequences by default. That meant your SERIAL or GENERATED values could diverge silently between publisher and subscriber — and yes, cause conflicts, duplicates, or failed inserts.

Now, PostgreSQL can track, replicate, and sync sequence values just like table data. You no longer have to duct-tape your way through this.

Why it matters

• Safer replication with auto-increment IDs
• No more out-of-sync counters after failover
• Makes logical replication viable for more HA setups

Example

Once you enable replication for a table, its associated sequences can now be included in the publication:

CREATE PUBLICATION my_pub FOR TABLE users, SEQUENCE users_id_seq;

On the subscriber:

CREATE SUBSCRIPTION my_sub CONNECTION '...' PUBLICATION my_pub;

Boom. Synced IDs.

Use case

• High-availability clusters
• Blue/green deployments with logical replication
• Sharded setups where ID consistency is critical

Quick tip: If you’re using UUIDs or external IDs, you won’t care. But if you rely on sequences (especially with foreign keys), this is a must-have upgrade.

9. MERGE enhancements cleaner UPSERTs, less pain

PostgreSQL 15 introduced the long-awaited MERGE statement, giving SQL devs a proper way to handle conditional inserts and updates. In version 18, it gets even better with smarter syntax handling and improved standards compliance.

What changed

PostgreSQL 18 brings better support for SQL-standard MERGE behavior, with improvements in:

• Conditional logic resolution
• WHEN NOT MATCHED THEN INSERT enhancements
• Fewer weird edge cases around NULLs and data type mismatches

You can now write MERGE statements that are more declarative, less reliant on ON CONFLICT workarounds, and closer to what other SQL systems like Oracle or SQL Server expect.

Why it matters

Before MERGE, your UPSERT logic in Postgres often looked like this:

INSERT INTO users(id, name)
VALUES (1, 'Red')
ON CONFLICT(id) DO UPDATE
SET name = EXCLUDED.name;

That works but it’s limited when you need conditional behavior (e.g., update only if a column is NULL, or insert only if another table has a matching row).

Now:

MERGE INTO users AS u
USING new_users AS n
ON u.id = n.id
WHEN MATCHED THEN
  UPDATE SET name = n.name
WHEN NOT MATCHED THEN
  INSERT (id, name) VALUES (n.id, n.name);

That’s clearer. And more portable.

Use case

• Data sync pipelines
• Staging → production data merges
• Conditional inserts with fallback logic

Quick tip: If you’re migrating from other RDBMSs, PostgreSQL’s MERGE now plays much nicer with existing SQL:2023 logic.

10. Improved ICU collation and globalization support

Handling multilingual text in databases is tricky. Sorting strings in the right order, especially across different languages and locales, often leads to frustrating inconsistencies. PostgreSQL 18 brings major improvements here by upgrading its ICU (International Components for Unicode) integration.

What changed

PostgreSQL 18:

• Supports ICU version 73, bringing updated language rules and bug fixes
• Allows more flexibility in choosing and customizing collations
• Improves deterministic vs nondeterministic collation handling, especially for things like case folding and accent sensitivity

You can now define collations that more accurately reflect real-world language behavior, instead of relying on legacy system locales or guessing what “ORDER BY” will actually do.

Why it matters

• Better support for non-English alphabets (Arabic, Hindi, Korean, etc.)
• Accurate string comparisons in multi-language databases
• Safer ordering and equality checks for international text fields

Example

CREATE COLLATION german_ci (
  provider = icu,
  locale = 'de-u-ks-level2',
  deterministic = false
);

This creates a case- and accent-insensitive collation for German using the latest ICU definitions.

Use case

• E-commerce apps with multi-language product names
• User profiles with names in various scripts
• Search or sort operations sensitive to linguistic correctness

Quick tip: Use nondeterministic collations for fuzzy string comparisons, especially in user-facing search bars or filters.

Conclusion: PostgreSQL 18 is a developer-focused upgrade done right

PostgreSQL 18 isn’t some flashy, hype-driven overhaul and that’s exactly why it’s exciting. This release is packed with features that make developers’ lives easier, SQL logic cleaner, and production setups more reliable.

You get:

• Performance wins (parallel COPY, function planning
• Better replication and high availability (logical DDL + sequences)
• Cleaner standards (MERGE, UNIQUE NULLS DISTINCT)
• Powerful data handling (JSON_TABLE, ICU upgrades)
• Real observability (pg_stat_io)

This is the kind of upgrade that removes friction from schema design to debugging, from multilingual data to distributed systems.

The beta is out now, and if you’re running anything from a hobby app to an enterprise SaaS backend, this is a great time to test, tune, and prep your infra for a real upgrade.

Helpful resources

Keep learning, building, and tweaking:

• PostgreSQL 18 official release notes
• Beta 1 announcement
• pg_stat_io reference
• JSON_TABLE docs
• Logical replication overview
• MERGE statement reference
• Postgres Weekly weekly community news