PostgreSQL has no ADD COLUMN ... AFTER. The reason one little number in a system catalog turned a "simple" feature into a decade-long engineering saga, and what you can do about it today.

Why It's So Hard to Add a Column in the Middle of a PostgreSQL Table

What a database MCP server actually hands an AI agent, how a query runs through one, and why a raw connection-string server is fine on your laptop but risky against production.

What is a Database MCP Server?

A practical comparison of the Postgres MCP servers worth running in 2026 — Postgres MCP Pro, Supabase, Google MCP Toolbox, AWS, DBHub, and Bytebase. We look at vendor lock-in, database scope, access model, and governance to help you pick the right one.

Governed MCP vs. Raw MCP: How Agents Reach Your Database

AI agents are no longer just reading your database, they are authoring the migrations. The write path is a different risk class, and it needs its own gate.

When the Agent Writes the Migration, Who Approves It?

Generic PAM controls the database connection. Database PAM controls the SQL statement: per-query authorization and approval, SQL review, data masking, and statement-level audit. What the category means and why it exists.

What is Database PAM (Privileged Access Management)?

How to run SQL Server schema migrations safely in production, covering state-based vs migration-based workflows, online index operations, batched backfills, the expand-and-contract pattern, and CI/CD.

SQL Server Schema Migration and Change Management

CyberArk is a broad privileged access management (PAM) platform. Bytebase is a database-specific PAM for access control, masking, and SQL review. If you already run CyberArk, here is why you still need it for the database.

Bytebase vs. CyberArk: the database vertical in your PAM stack

How to store, rotate, and scope database credentials: secrets managers, dynamic credentials, the application path vs the human path, and the mistakes that leak passwords.

Managing Database Passwords and Secrets: Best Practices

The practical ways to audit MySQL activity, from the general query log to audit plugins, binlog CDC, and Bytebase, and where each one falls short.

MySQL Audit Logging Guide

Every critical asset has its system of record, except the database. Bytebase is the one. The standard for database development.

The Standard for Database Development

Dynamic data masking is chosen by access path, not product. Five approaches (engine-native, in-database, application, BI layer, and Bytebase) and which caller each one masks.

How to Implement Dynamic Data Masking: Five Approaches by Access Path

Static vs dynamic data masking, when each applies, how they differ on backups, performance, and reversibility, and when to run both.

Static vs Dynamic Data Masking: When to Use Which

The static data masking tools that produce safe non-production data, Delphix, Tonic.ai, and greenmask compared, plus how to roll your own when no tool fits.

Static Data Masking Tools (and How to Roll Your Own)

Compare BigQuery data masking options: native column-level dynamic data masking (built-in masking rules via policy tags and data policies, Dataplex catalog) and Bytebase fleet-wide masking.

BigQuery Dynamic Data Masking

Compare SQL Server data masking options, native Dynamic Data Masking (five mask types, SQL Server 2022 granular UNMASK) and Bytebase fleet-wide masking.

SQL Server Dynamic Data Masking

Compare Oracle data masking options. Native Data Redaction (five function types via DBMS_REDACT, EXEMPT REDACTION POLICY) and Bytebase fleet-wide masking.

Oracle Dynamic Data Masking

You write a one-line UPDATE against production. Here's the lane that keeps it safe: the four phases, the controls, and what each one prevents.

How to Handle Ad-hoc Database Data Changes?

Notes on the MySQL 9.7 LTS release: Hypergraph Optimizer in Community, JSON Duality View DML, Group Replication observability, and Dynamic Data Masking for Enterprise.

Notes on the MySQL 9.7 LTS release

Most DAC implementations stop at the connection. Real DAC operates at the statement and covers three vectors: machine, human, and AI agent.

What is Database Access Control (DAC)?

How to run dynamic data masking so it survives an audit: mask by role, go column-level, log unmask events, review policy changes in code, and keep environments in sync. Plus a vendor comparison and an end-to-end walkthrough.

Dynamic Data Masking Best Practices

How Bytebase audits the workflow layer: every SQL, schema change, approval, and export tied to a real user identity, with structured exports for any SIEM.

How Bytebase Handles Audit Logging

PostgreSQL 19 features I'm excited about: planner advisor framework, DDL extraction, online REPACK, pg_stat_recovery, parallel autovacuum, sequence sync, and 64-bit MultiXact.

PostgreSQL 19 features I'm excited about

Bytebase is HIPAA-compliant — independent verification that our administrative, physical, and technical safeguards meet the HIPAA Security Rule for handling Protected Health Information.

HIPAA Compliance

PAM tools gate the database connection. Databases also need SQL review, a schema change workflow, and per-statement approval. Here is where PAM stops and why most teams run it alongside Bytebase.

Bytebase vs. PAM: Why Database Teams Need More Than Privileged Access Management

What SOC 2 compliance audits actually want from your database audit log: not 'logging enabled' but evidence your auditor will accept. Lessons from going through our own audit.

SOC 2 Audit Log Requirements: Lessons From Our Own Audit

A side-by-side look at Bytebase and Atlas for database schema migration: how each one approaches migrations, their CI/CD and access control, supported databases, and what they cost. Updated for Atlas v1.1 and Bytebase 3.16.

Bytebase vs. Atlas: a side-by-side comparison for database schema migration

Database access control practices that hold up in a SOC 2 audit: least privilege, separation of duties, just-in-time access, statement-level enforcement, and the common mistakes that get flagged.

Database Access Control (DAC) Best Practices

A practical look at what SOX Section 404 expects from database changes: the four ITGC domains, the six questions auditors actually ask, and how to make compliance evidence a byproduct of your workflow.

Database Change Management for SOX Compliance

TRUNCATE and DELETE both remove rows, but they differ in speed, rollback behavior, and locking. Here is how each one behaves in PostgreSQL, MySQL, and SQL Server, and when to reach for which.

TRUNCATE vs DELETE in SQL: Differences Across PostgreSQL, MySQL, and SQL Server

A practical walk through six database design patterns you will actually meet in production: normalization, star schema, EAV, polymorphic associations, multi-tenancy, and the anti-patterns worth avoiding.

Database Design Patterns Every Developer Should Know

A practical walkthrough of pgvector: installing the extension, storing embeddings, writing similarity queries, building HNSW indexes, and knowing when plain PostgreSQL is enough.

pgvector: How to Add Vector Search to PostgreSQL

How upsert works across MySQL, PostgreSQL, and SQL Server, with the syntax for each, the edge cases worth knowing before you ship, and how upserts interact with migrations.

Upsert in SQL: INSERT ON DUPLICATE KEY vs ON CONFLICT Explained

AI agents need more than DDL. The full database lifecycle codified (schema, dependencies, ownership, history) gives LLMs the context to query correctly.

From Schema as Code to Schema as Context

How to find and fix slow MySQL queries: reading the slow query log, using EXPLAIN, tuning indexes, and rewriting queries, with a worked example for each.

How to Fix Slow MySQL Queries: A Practical Guide

Convex and Supabase are both backend-in-a-box platforms, but built on different foundations. This post compares their architecture, real-time model, data model, schema management, and pricing to help you decide.

Convex vs Supabase: Which Backend Platform Should You Choose?

Blue-green deployment is easy when your servers are stateless. The trouble starts at the database. This guide walks through why database blue-green is hard and how to do it without corrupting your data.

Database Blue-Green Deployment: A Practical Guide

A look at 10 data management tools developers and data engineers actually use in 2026: GUI clients, schema migration, pipelines, and analytics, with pricing for each.

Top 10 Data Management Tools in 2026

How to give AI agents governed access to your databases. Identity, authorization, query control, audit, and masking, applied to a new principal type.

How to Govern AI Agent Database Access

A look at three SQL editors built only for Postgres, with what each one is good at and who it fits, to help you pick the right one in 2026.

Enterprise Text-to-SQL: Context, Evaluation, and Governance

A practical look at the database deployment tools DevOps teams actually use in 2026, sorted by how each one defines, applies, and coordinates schema changes.

Database Deployment Tools for DevOps Teams in 2026

A walk-through of how renaming a single column can quietly break an analytics pipeline, why it happens, and how schema change detection and notification help you catch it early.

Schema Change Detection and Notification: Why a Simple Column Rename Can Break Your Data Pipeline

How to read a Postgres query plan, spot the common performance problems, and fix them without breaking other queries.

Postgres Query Plan: How to Read and Fix Performance Issues

A walk through every layer of the Postgres permission system that can raise ERROR 42501, why each one fires, and how the behavior differs from MySQL.

The Anatomy of Postgres Permission ERROR 42501

A practical look at Postgres logs: what they record, how the logging collector works, which parameters actually matter in production, and the mistakes that turn logs into expensive noise.

Postgres Logs Explained: A Practical Guide for Production

Postgres 19 widens MultiXactOffset to 64 bits, eliminating the hidden 4 billion member limit that caused catastrophic outages for high-concurrency workloads.

Postgres 19 Feature Preview: 64-bit MultiXactOffset

How to run MySQL schema migrations without blocking queries or causing downtime, covering the MySQL-specific behaviors that bite in production and the tools that work around them.

MySQL Schema Migration Best Practice

Postgres 19 introduces automatic sequence synchronization for logical replication, eliminating manual tracking and making major version upgrades and failover significantly easier.

Postgres 19 Feature Preview: Sequence Synchronization for Logical Replication

What multi-environment database deployment actually involves, the problems teams keep hitting, and the patterns (manual scripts vs GitOps) for handling them.

Database Multi-Environment Deployments - The Challenges and Patterns

A guide to open source sensitive data discovery tools, from NLP libraries like spaCy and Microsoft Presidio to multi-source scanners like Hawk-Eye and full metadata platforms like OpenMetadata.

Top Open Source Sensitive Data Discovery Tools in 2026

Database audit logging really has two layers: the infrastructure layer your cloud provider ships, and the workflow layer you have to build yourself. Here is what each one captures, and why an audit needs both.

Database Audit Logging: Two Layers, One Trail

A practical guide to HITRUST database compliance covering access control, audit logging, change management, and data protection.

Database Compliance for HITRUST: Controls, Risks, and Best Practices

What the "Require Primary Key" SQL review rule checks, the production problems a missing primary key tends to cause, and the SQL to fix tables that don't have one.

Require Primary Key: A SQL Review Rule Explained

What the "Enforce NOT VALID in CHECK" SQL Review rule does, why adding a CHECK constraint can lock your table, and how the two-step NOT VALID approach avoids it.

Enforce NOT VALID in CHECK: A SQL Review Rule Explained

The practical ways to audit a PostgreSQL database, from native logging to triggers, logical replication, pgAudit, and Bytebase, with the trade-offs of each.

Postgres Audit Logging Guide

Learn why CASCADE operations in SQL can lead to catastrophic data loss and how the "Prohibit CASCADE" review rule protects your production database.

Prohibit CASCADE: A SQL Review Rule Explained

A walkthrough of moving each Supabase piece (database, auth, storage, functions, realtime, networking) to its AWS counterpart, with a services-first order that keeps the final database cutover simple.

How to Migrate from Supabase to AWS

Six open-source PostgreSQL backup tools compared for 2026, with the language, license, interface, and best-fit use case for each, plus a note on which ones are still maintained.

What is Enterprise Database Management (EDM)?

A plain explanation of Postgres Transaction ID (XID) wraparound: what causes it, how to check how close you are, and what keeps it from taking your database offline.

Postgres Transaction ID (XID) Wraparound

How a CI/CD pipeline for database schema migration works end-to-end. What it gates, what it leaves outside, and the tools that fit.

How to Build a CI/CD Pipeline for Database Schema Migration

A practical guide to shipping features with PostgreSQL, covering my day-to-day development workflow, the tools I actually use, and real-world examples

How I Ship Features with Postgres

A practical look at what LGPD (Brazil's General Personal Data Protection Act) means for database operations, how it differs from GDPR, and the controls teams need to actually comply.

Database Compliance for LGPD: Implications and Best Practices

What a Postgres LATERAL JOIN does, when to reach for one, and the patterns and gotchas you'll run into using it.

What is Postgres LATERAL JOIN?

What MySQL Online DDL actually does, how its three algorithms (INSTANT, INPLACE, and COPY) differ, and the limitations to plan around before you run a schema change in production.

MySQL Online DDL: A Practical Guide

A practical look at the leading open-source Postgres connection poolers, with what each is good for and how to pick a pooling mode.

Open Source Postgres Connection Pooler

Should your tables be named user or users? This post walks through the trade-offs of singular vs. plural table naming, what the common stacks default to, and a set of conventions you can copy into your own handbook.

SQL Table Naming Dilemma: Singular vs. Plural

PostgreSQL 18 features that change how application developers write and ship code.

What's New in PostgreSQL 18: A Developer's Perspective

Compare Snowflake Dynamic Data Masking with database views and Bytebase fleet-wide masking on edition gate, policy management, and audit trail.

Snowflake Dynamic Data Masking (DDM) and Alternatives

A look at the open-source ways to add authentication and authorization to a Postgres app in 2025, from all-in-one platforms to libraries, identity servers, and the minimalist JWT-to-RLS pattern, with a "best for" note on each.

Postgres Case Sensitivity Explained

What pg_database_owner does in Postgres, why it was added, and how to use it without hitting the "cannot have explicit members" error.

What is pg_database_owner in Postgres

A reference for which Postgres DDL operations rewrite the whole table (and take heavy locks) versus which ones are metadata-only, plus how to test it yourself before you run it in production.

Which Postgres Operation causes a table rewrite

A practical rundown of the Postgres Row-Level Security traps that quietly break performance or leak data, with the fix for each.

Postgres Row-Level Security Footguns

A walk through the three ways to roll back changes in Postgres: transactions and SAVEPOINT, Point-in-Time Recovery, and compensating DML, and when each one fits.

Postgres Rollback Explained

Learn how Postgres locks affect schema migrations and which DDL operations conflict with DML to achieve zero-downtime deployments.

Postgres Schema Migration without Downtime

The limitations and gotchas of gh-ost for online MySQL schema changes, and when to reach for something else instead.

gh-ost Limitations You Need to Know Before Production

PostgreSQL best practices for application developers: connection naming, timeouts, online migrations, role separation, and UPSERT for reliable production apps.

Postgres best practices I wish every app developer knew

Learn how to use CREATE INDEX CONCURRENTLY in Postgres to build indexes without blocking writes. Pitfalls, safety checks, and performance tips.

How to Use Postgres CREATE INDEX CONCURRENTLY

Supabase and MongoDB take different paths to storing application data. This article compares them on data model, architecture, performance, pricing, and licensing to help you decide which one fits.

Supabase vs. MongoDB: a Complete Comparison in 2026

DataGrip is a SQL IDE built for individual developers; Bytebase is a web platform built for teams that need review and governance. This post compares their database support, features, and pricing to help you decide which fits.

Bytebase vs. DataGrip: a side-by-side comparison for database management

Supabase pricing starts free. We break down real costs for Pro ($25/mo) and Team ($599/mo) plans, explain overages, and compare each service head-to-head with AWS.

Supabase vs AWS: Feature and Pricing Comparison (2026)

Is Supabase free? Compare Supabase pricing vs AWS RDS and Aurora across free tier, Pro, and production plans. Includes Supabase free tier limits, storage costs, and compute pricing for 2026.

Supabase Pricing vs AWS RDS: Is Supabase Free? Full Cost Comparison (2026)

Learn how Bytebase enables organizations to implement comprehensive Database Security Lifecycle Management, unifying security and operations from development to production.

Database Security Lifecycle Management

Areas where MySQL shines and Postgres falls short.

Features I Wish Postgres Had but MySQL Already Has

Areas where PostgreSQL shines and MySQL falls short.

Features I Wish MySQL Had but Postgres Already Has

This guide focuses on HIPAA compliance at the database layer, providing healthcare organizations and database teams with practical guidance for implementing HIPAA Security Rule requirements.

HIPAA Data Security and Retention Requirements

Compare the online index creation difference between Postgres and MySQL

Postgres vs. MySQL: Online Index Creation

SOC 2 Data Security and Retention Requirements for Database Systems and How Bytebase Helps

SOC 2 Data Security and Retention Requirements

Compare the different indexing options between Postgres and MySQL

Postgres vs. MySQL: Indexing Options

Compare the JSON support difference between Postgres and MySQL

Postgres vs. MySQL: JSON Support

While PostgreSQL RLS is a powerful feature, it has inherent design limitations that hinder broader adoption. In this post, we’ll examine these limitations and explore alternatives.

Postgres Row-Level Security (RLS) Limitations and Alternatives

Evaluate several database schema diagram tools, and help you to choose the right one for 2025

Top 4 Free, Open Source Database Schema Diagram Tools to Visualize Database Easier in 2025

An overview of the most popular text-to-SQL AI tools in 2026, including DataGrip, TablePlus, SQL Chat, Vanna.ai, SQLAI.ai, Postgres.new, and DBHub MCP server. Compare features and capabilities to find the best SQL AI assistant for your workflow.

Top 5 Text-to-SQL Query Tools in 2026

Compare the DDL transaction difference between Postgres and MySQL

Postgres vs. MySQL: DDL Transaction Difference

Evaluate several TypeScript ORMs, and help you to choose the right one for 2025

Top TypeScript ORM 2025

Evaluate Prisma and TypeORM, and help you to choose the right TypeScript ORM for 2025

Prisma vs TypeORM: The Better TypeScript ORM in 2025

PostgreSQL 18 features and what they change for running production databases.

What's New in PostgreSQL 18: A DBA's Perspective

Explore alternatives to Amazon RDS, including 

RDS Alternatives in 2025


Bytebase helps teams manage schema migrations for MySQL and PostgreSQL, and one of the first things that trips people up when moving from MySQL is this: they try to add a column in the middle of a table, and Postgres flatly refuses. There is no `ADD COLUMN ... AFTER`. New columns go to the end, full stop.

It looks like a five-minute feature nobody got around to. It is not. The reason cuts straight to how Postgres physically stores your data.

## The innocent request

You have a `users` table:

```sql
CREATE TABLE users (
    id         bigint,
    name       text,
    created_at timestamptz
);
```

Product asks for an `email` column, and because you like a tidy schema, you want it right after `name`. Years of MySQL muscle memory type the answer:

```sql
-- MySQL: this just works
ALTER TABLE users ADD COLUMN email text AFTER name;
```

In PostgreSQL, you can't. `ADD COLUMN` always appends to the end, and there is no syntax to put it anywhere else. To see why, you have to meet one little number.

## Meet `attnum`, the number wearing three hats

Every column in Postgres is a row in the `pg_attribute` system catalog. The field that defines column order is `attnum`:

```sql
SELECT attname, attnum, atttypid::regtype, attisdropped
FROM   pg_attribute
WHERE  attrelid = 'users'::regclass
AND    attnum > 0
ORDER  BY attnum;
```

```text
attname    | attnum | atttypid    | attisdropped
-----------+--------+-------------+-------------
id         |      1 | bigint      | f
name       |      2 | text        | f
created_at |      3 | timestamptz | f
email      |      4 | text        | f
```

`attnum` is assigned when the column is created, and it never changes. `ADD COLUMN` simply grabs `max(attnum) + 1`. Here is the catch: that single integer is wearing three hats at once.

![attnum serves three jobs at once: identity (indexes, constraints, and rules reference it), physical position (its byte offset in the on-disk tuple), and logical position (its column order in SELECT * and psql \d)](/content/blog/why-its-hard-to-add-a-column-in-the-middle-of-postgres-table/attnum-three-jobs.svg)

- **Identity**: every index, constraint, stored rule, and statistic points at the column by this number, not by name.
- **Physical position**: where the column's bytes sit in each on-disk tuple.
- **Logical position**: the order you see in `SELECT *`, `psql`'s `\d`, and `COPY`.

One value, three jobs.

Now the problem writes itself. To move `email` after `name`, it has to become `attnum` 3. But that is `created_at`'s identity, and every index, constraint, and rule already refers to columns by number. You cannot renumber one without rewriting all of them. Move a column and you move all three hats at once.

## The holes that never fill

It gets worse. Drop a column and Postgres does not physically remove it or reclaim its `attnum`. It just marks it dead:

```sql
ALTER TABLE users DROP COLUMN created_at;

SELECT attname, attnum, attisdropped
FROM   pg_attribute
WHERE  attrelid = 'users'::regclass AND attnum > 0
ORDER  BY attnum;
```

```text
attname                      | attnum | attisdropped
-----------------------------+--------+-------------
id                           |      1 | f
name                         |      2 | f
........pg.dropped.3........ |      3 | t
email                        |      4 | f
```

The column is renamed to a tombstone, `attisdropped` flips to `true`, and `attnum` 3 is burned forever. The slot is never reused, and the dead column keeps taking space in every row until the table is rewritten. `attnum` is append-only by design, so Postgres cannot even compact it to make room. Ever seen a table report more columns in `pg_attribute` than in `\d`? This is why.

## Plot twist: column order is not just cosmetics

Tempting to shrug here. Who cares what order the columns print in? Here is the twist: column order changes how much disk your table uses.

Postgres stores each row as a packed tuple, and most data types carry an alignment requirement. An 8-byte `bigint` must begin at an offset divisible by 8, a 4-byte `int` at a multiple of 4, and so on. When the next column does not naturally land on its boundary, Postgres inserts padding bytes to push it there. Padding is pure waste, paid on every single row.

Look at this table:

```sql
CREATE TABLE events (
    is_ok    boolean,      -- 1 byte
    user_id  bigint,       -- 8 bytes, must start on an 8-byte boundary
    is_mobile boolean      -- 1 byte
);
```

A 1-byte `boolean` in front of an 8-byte `bigint` forces 7 bytes of padding to reach the boundary, and another stretch of padding at the end to align the whole row. Now reorder largest-alignment-first:

```sql
CREATE TABLE events (
    user_id  bigint,       -- 8 bytes
    is_ok    boolean,      -- 1 byte
    is_mobile boolean      -- 1 byte
);
```

Here is the same row on disk, before and after. Same three columns, same data, laid out byte for byte:

![Postgres row byte layout: naive column order wastes 14 bytes of padding for a 24-byte row, while the packed order needs only 6, shrinking the row to 16 bytes](/content/blog/why-its-hard-to-add-a-column-in-the-middle-of-postgres-table/column-padding.svg)

The internal gap is gone and the row drops from 24 bytes to 16, same data. Packing columns widest-alignment-first (8-byte, then 4-byte, 2-byte, 1-byte, with variable-length `text` and `bytea` last) is affectionately called [column tetris](https://stackoverflow.com/a/7431468), and the savings are real: on real schemas it routinely reclaims up to ~20% of table size. Eight bytes per row on a 100M-row table is about 800 MB off disk, plus fewer pages to scan and better cache behavior, basically for free. PayPal wrote up [exactly this win](https://medium.com/paypal-tech/postgresql-at-scale-saving-space-basically-for-free-d94483d9ed9a).

So the ability to place a column where you want it is not a vanity feature. It is the difference between a compact table and one quietly bleeding storage. Which is exactly why people keep asking for it.

## The decade-long attempt to fix it

To be fair, the Postgres developers are not against it. There is even a known, elegant design that just never got finished. The idea, [on pgsql-hackers since at least 2014](https://www.postgresql.org/message-id/20141209174146.GP1768@alvh.no-ip.org): stop making one number do three jobs, and split `attnum` into three.

| Field        | Meaning                                          | Changes? |
| ------------ | ------------------------------------------------ | -------- |
| `attnum`     | Permanent **identity** of the column             | Never    |
| `attphysnum` | **Physical** position in the on-disk tuple       | Yes      |
| `attlognum`  | **Logical** position (`SELECT *`, `\d`, COPY, …) | Yes      |

Give each number its own job: reorder columns logically without touching their identity, and let Postgres auto-tune the physical layout for alignment on its own. Álvaro Herrera even [sketched a phased rollout](https://www.postgresql.org/message-id/20220628085314.kgqrcniznaqc3nvs@alvherre.pgsql) to get there.

So why, a decade on, is it still not in? Because `attnum`-as-position is assumed in an astonishing number of places. The moment the three numbers can disagree, every one has to be found and fixed:

- tuple descriptors and `heap_form_tuple` / `heapam.c`, where the physical row is built;
- `SELECT *` and JOIN star-expansion in the planner;
- `COPY` import and export ordering;
- composite-type and set-returning-function result expansion;
- index column references;
- `psql`'s `\d`, `pg_dump`, and every other introspection path.

A textbook invasive change: simple in concept, enormous in surface area. As the [Postgres wiki](https://wiki.postgresql.org/wiki/Alter_column_position) puts it bluntly, the developers aren't opposed, but "no one has stepped forward to do the work."

## What to do today

Most of the time, reordering columns just to keep them tidy is not worth the trouble. Append order is ugly, not harmful, so the honest first answer is usually to leave it alone.

When you genuinely need the physical layout changed, to win at column tetris on a hot table, there is really one correct way: rebuild the table.

```sql
BEGIN;
CREATE TABLE users_new (
    id         bigint,
    name       text,
    email      text,
    created_at timestamptz
);
INSERT INTO users_new SELECT id, name, email, created_at FROM users;
-- recreate indexes, constraints, FKs, triggers, grants, sequences/defaults...
DROP TABLE users;
ALTER TABLE users_new RENAME TO users;
COMMIT;
```

It takes an `ACCESS EXCLUSIVE` lock, rewrites the whole table (roughly 2x disk during the copy), and you must reattach everything that pointed at the old one: indexes, foreign keys in both directions, check constraints, triggers, views, default and sequence ownership, privileges. The `INSERT` is the easy line. That one-line comment is where reorders go wrong.

BTW, this is exactly the kind of change worth putting through Bytebase. Automatic [SQL review](https://docs.bytebase.com/sql-review/review-policy) plus a human reviewer catches the pieces that are easy to drop on the floor.

## How MySQL implements instant `ADD COLUMN AFTER`

So why does MySQL shrug this off? Because it never welded a column's position to one immutable identity the way Postgres welds everything to `attnum`. For years MySQL still paid for `ADD COLUMN ... AFTER` with a full table rebuild under the hood, but it owned that rebuild: one statement, and the engine copied the table and reattached every index and constraint for you. You never met the `attnum` problem because MySQL never created it.

Then InnoDB went further. Since MySQL 8.0.29, an [instant ADD COLUMN](https://dev.mysql.com/blog-archive/mysql-8-0-instant-add-and-drop-columns/) can slot a column into any position without rewriting a single row, because InnoDB keeps the logical column order (in its data dictionary) separate from the physical order on disk. New columns land at the end of storage but appear wherever you asked. Sound familiar? That is exactly the `attlognum` / `attphysnum` split Postgres has been debating for years.

The split Postgres keeps on its wishlist has been shipping in MySQL for years, and it is a clean showcase of what decoupling logical from physical buys you: ask for a column in the middle and it just goes there, no rewrite. One of those everyday places where MySQL's usability quietly beats Postgres.

## Further reading

- [PostgreSQL Wiki: Alter column position](https://wiki.postgresql.org/wiki/Alter_column_position)
- pgsql-hackers discussion on logical column ordering: the [2014 thread](https://www.postgresql.org/message-id/20141209174146.GP1768@alvh.no-ip.org) and the [2022 phased rollout](https://www.postgresql.org/message-id/20220628085314.kgqrcniznaqc3nvs@alvherre.pgsql)
- [Stack Overflow: column tetris and table size](https://stackoverflow.com/a/7431468)
- [PayPal Engineering: PostgreSQL at scale, saving space for free](https://medium.com/paypal-tech/postgresql-at-scale-saving-space-basically-for-free-d94483d9ed9a)
- [MySQL: InnoDB instant ADD and DROP columns](https://dev.mysql.com/blog-archive/mysql-8-0-instant-add-and-drop-columns/)


The
Bytebase Blog

The Standard for Database Development

How to Implement Dynamic Data Masking: Five Approaches by Access Path

Static vs Dynamic Data Masking: When to Use Which

Explore the standard for database development

Solutions & Platform

Resources

Change Management

Access & Security

AI & Agents

Comparisons

Company

Legal

The Bytebase Blog

The Standard for Database Development

How to Implement Dynamic Data Masking: Five Approaches by Access Path

Static vs Dynamic Data Masking: When to Use Which

Explore the standard for database development

The
Bytebase Blog