Top 7 PostgreSQL Migration Mistakes Developers Regret Later

Introduction: Why PostgreSQL Migrations Go Wrong So Often

Every time I help a team move to PostgreSQL from MySQL, MongoDB, SQL Server, or another engine, I see the same pattern: people underestimate how different PostgreSQL really is. On paper, it speaks SQL (most of the time), supports transactions, indexes, and joins, so the assumption is, “We’ll just tweak a few queries and we’re done.” That assumption is exactly where most PostgreSQL migration mistakes begin.

PostgreSQL is incredibly powerful, but it is also opinionated. Its type system, concurrency model, strictness around data integrity, and optimizer behavior all differ in subtle ways from other databases. When those differences collide with existing schemas and application code, you get painful surprises: slow queries in production, broken reports, mysterious deadlocks, or silent data issues that only surface months later.

In my experience, failed or painful migrations rarely come from PostgreSQL itself; they come from treating the migration as a “lift-and-shift” instead of a redesign where it actually matters. Teams copy schemas verbatim, ignore collation and encoding differences, assume identical date/time semantics, or port stored procedures line by line without rethinking logic. Everything looks fine in a small test environment, but under real load the cracks start to show.

This article walks through the PostgreSQL migration mistakes I see developers regret the most after go-live. My goal is to help you recognize these traps before you hit them, so you can plan your migration deliberately rather than firefighting it in production.

1. Treating PostgreSQL Like MySQL or SQL Server (Not Using Its Strengths)

The most painful PostgreSQL migration mistakes I see start with a simple assumption: “We’ll keep doing things the MySQL or SQL Server way, just on PostgreSQL.” When teams copy schemas, defaults, and habits unchanged, they end up paying for PostgreSQL’s complexity without getting its benefits: richer types, better constraints, smarter indexes, and powerful extensions. In my own migrations, the projects that turned out best were the ones where we consciously said, “We’re moving to PostgreSQL, not just moving off MySQL.”

Instead of a one-to-one port, I treat migrations as a chance to redesign the most painful parts of the model and queries. PostgreSQL rewards that effort: stricter data integrity, fewer application-level workarounds, simpler reporting, and less glue code. Ignoring those strengths usually leads to brittle schemas, weird performance surprises, and a database that feels harder than it should.

Copy-Pasting Schema Definitions Instead of Rethinking the Model

One recurring anti-pattern I encounter is a schema that looks exactly like the old database: same column types, same nullable fields, same loose constraints. You technically “finished” the migration, but you missed the chance to align the model with how PostgreSQL actually works. Over time, this shows up in subtle bugs: inconsistent enums, orphaned rows, and constraints that exist only in comments or application code.

Here are some areas I deliberately revisit during a migration:

Data types: Replace generic VARCHAR(255) or TEXT from MySQL with stronger native types: UUID, JSONB, INET, NUMERIC, TIMESTAMPTZ, and ENUM where appropriate.
Nullability and constraints: Use NOT NULL, CHECK, and proper foreign keys to encode rules that used to live only in application logic.
Surrogate vs. natural keys: Decide again whether everything really needs an auto-increment integer, or whether a natural key or UUID is more robust.

On one project, we took an old “status” VARCHAR and migrated it to a PostgreSQL enum. That tiny change eliminated a class of bugs caused by typos and made reporting much safer. PostgreSQL shines when you let it enforce rules for you.

Ignoring PostgreSQL-Specific Features That Simplify Your Application

Coming from MySQL or SQL Server, it’s easy to overlook the tools PostgreSQL gives you to push complex logic and structure down into the database. When I first started working with Postgres, I habitually tried to keep everything in the app layer; over time I realized I was re-implementing features Postgres already had—and usually doing it worse.

Some high-impact features I try to adopt early in a migration are:

JSONB: For semi-structured data, JSONB with GIN indexes often replaces awkward EAV tables or oversized VARCHAR blobs.
Powerful indexing: Partial indexes, expression indexes, and GIN/GiST indexes can rescue endpoints that used to rely on fragile composite indexes.
Window functions and CTEs: Reporting and analytics often become simpler and faster once you rewrite old procedural logic as window queries.
Extensions: PostGIS, pg_trgm, uuid-ossp, and others can remove thousands of lines of custom code.

Here’s a simple example where I lean on PostgreSQL instead of duplicating logic in the application. Say I need to ensure a case-insensitive unique username:

CREATE TABLE users (
    id          BIGSERIAL PRIMARY KEY,
    username    TEXT NOT NULL,
    email       TEXT NOT NULL,
    created_at  TIMESTAMPTZ NOT NULL DEFAULT NOW()
);

-- Enforce case-insensitive uniqueness of username
CREATE UNIQUE INDEX users_username_ci_idx
ON users (LOWER(username));

In MySQL-style thinking, I’d probably normalize and validate usernames entirely in the app. In PostgreSQL, I prefer this pattern because the database guarantees the rule globally, and the application code gets simpler.

Porting SQL and Stored Procedures Without Adopting PostgreSQL Conventions

Another place I see teams get burned is by copy-pasting SQL and stored procedures directly from SQL Server or MySQL. The syntax might run after a few tweaks, but it often fights PostgreSQL’s planner, locking model, and idioms. I’ve had to unpick migrations where developers faithfully translated every stored procedure into PL/pgSQL, only to discover later that half of them should have been rewritten as set-based queries or moved into application code.

When I review migrated SQL, I look for:

Cursor-heavy or row-by-row logic that should be expressed as a single set-based query or a window function.
Overuse of temp tables where Common Table Expressions (CTEs) or subqueries are clearer and often faster.
Transaction patterns that cause unnecessary locking or deadlocks under PostgreSQL’s MVCC model.

Here’s a small example of refactoring a cursor-style loop (common in SQL Server) into a more PostgreSQL-friendly window query. Instead of iterating over orders to compute running totals, I’d write:

SELECT
    customer_id,
    order_id,
    order_date,
    amount,
    SUM(amount) OVER (
        PARTITION BY customer_id
        ORDER BY order_date
        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
    ) AS running_total
FROM orders;

By leaning into PostgreSQL’s strengths—types, constraints, indexes, JSONB, window functions, and extensions—you turn the migration into an upgrade instead of a lateral move. Treating Postgres as “just another SQL database” is what turns migrations into long-term regrets.

PostgreSQL Documentation: Schemas

2. Mis-handling Data Types When Moving to PostgreSQL

Most of the ugliest PostgreSQL migration mistakes I’ve had to clean up came down to one root cause: sloppy data type choices. It’s tempting to preserve whatever types you had in MySQL, MongoDB, or SQL Server, but PostgreSQL has a richer, stricter type system. If you don’t respect that, you’ll see subtle bugs, performance regressions, and painful casting issues that pop up months after go-live.

When I plan a migration now, I spend real time on a type mapping strategy. That means looking at how the data is used, not just how it was stored before. The extra effort pays off in more reliable queries, smaller indexes, and far fewer surprises.

Common Type Mismatches from MySQL and SQL Server

Coming from MySQL or SQL Server, teams often assume integer, string, and date types are interchangeable. They are not. PostgreSQL will happily reject or truncate data that your old database accepted, especially around dates, enums, and booleans. I’ve seen production outages caused purely by an incorrect mapping of an INT flag to a PostgreSQL BOOLEAN column.

Here are some of the mismatches I watch for:

Booleans: MySQL often encodes flags as TINYINT(1). In PostgreSQL, use BOOLEAN and map 0/1 or 'Y'/'N' explicitly during migration.
Auto-increment keys: MySQL’s AUTO_INCREMENT and SQL Server’s IDENTITY become PostgreSQL’s SERIAL, BIGSERIAL, or, preferably, GENERATED … AS IDENTITY.
Dates and times: MySQL’s loose date handling (like '0000-00-00') can fail hard in PostgreSQL. Choose between DATE, TIMESTAMP, and TIMESTAMPTZ based on actual semantics.
Strings: Overusing VARCHAR(255) or NVARCHAR(MAX) from SQL Server can lead to bloated indexes. PostgreSQL is comfortable with TEXT and expression indexes where you need them.
Decimals: Money-like values mapped to FLOAT before should move to NUMERIC(p,s) to avoid rounding surprises.

Whenever I migrate a table, I sketch a small type mapping table between the old and new schema and then write a conversion script to make the transformation explicit and testable.

Date/Time, Time Zones, and Collation Pitfalls

Date/time and collation differences are where I’ve seen the most painful, silent bugs. Other engines are often surprisingly forgiving about invalid dates or time zone ambiguities; PostgreSQL is not. That’s good for data quality, but rough during migration if you’re not prepared.

Some patterns I check carefully:

Picking the right timestamp type: I almost always use TIMESTAMPTZ for event times (stored as UTC), and plain DATE for calendar-only data like birthdays.
Cleaning invalid dates: Values like '0000-00-00' or '1970-01-01' used as sentinels need explicit handling in ETL before loading into PostgreSQL.
Sorting and comparison: Differences in collation and case-sensitivity (especially from SQL Server) can change result ordering and uniqueness checks in subtle ways.

Here’s a minimal example of how I often migrate a legacy datetime column into a proper TIMESTAMPTZ, cleaning sentinel values on the way:

-- Legacy table (e.g. in staging after raw import)
CREATE TABLE legacy_orders (
    id               BIGINT,
    created_at_raw   TEXT  -- imported as-is from MySQL/SQL Server
);

-- New PostgreSQL-native table
CREATE TABLE orders (
    id          BIGINT PRIMARY KEY,
    created_at  TIMESTAMPTZ NOT NULL
);

-- Transform and load with explicit rules
INSERT INTO orders (id, created_at)
SELECT
    id,
    CASE
        WHEN created_at_raw IN ('0000-00-00 00:00:00', '1900-01-01 00:00:00') THEN
            NULL  -- or NOW(), or a custom fallback
        ELSE
            (created_at_raw || ' UTC')::timestamptz
    END
FROM legacy_orders;

By encoding your date/time assumptions in SQL like this, you document the migration logic and avoid “mystery” timezone bugs later.

JSON vs JSONB, Arrays, and Document Data from MongoDB

When moving from MongoDB, I often see teams either flatten everything into relational tables or stuff entire documents into a single TEXT column. Both extremes are painful. PostgreSQL’s JSONB and arrays give you a middle ground: you can keep flexible structures where it makes sense, but still query and index them efficiently.

In my own migrations from MongoDB, I tend to:

Move core, stable fields into normal columns with strong types (e.g., UUID, NUMERIC, TEXT, BOOLEAN).
Keep truly flexible or rarely queried parts of the document in a JSONB column.
Introduce GIN indexes only on JSONB paths that need fast lookup.

Here’s a simple pattern I’ve used to migrate a MongoDB-style user document:

CREATE TABLE users (
    id          UUID PRIMARY KEY,
    email       TEXT NOT NULL,
    is_active   BOOLEAN NOT NULL DEFAULT true,
    profile     JSONB  -- semi-structured, from MongoDB
);

-- Example query using JSONB operators and a GIN index
CREATE INDEX users_profile_gin_idx
ON users USING gin (profile);

SELECT id, email
FROM users
WHERE profile -> 'preferences' ->> 'language' = 'en';

Using JSONB instead of JSON also gives you better indexing and operators. One thing I learned the hard way was that ignoring JSONB and shoving everything into generic strings made future reporting almost impossible.

Designing a Type Mapping and Validation Strategy

The best way I’ve found to avoid data type headaches is to treat type mapping as a first-class design task, not an afterthought. Before I write any migration scripts, I create a simple mapping document that lists each source column, its original type, its intended PostgreSQL type, and any transformation rules.

Practically, that usually means:

Audit existing data: Sample real values to discover hidden sentinels, overflows, and mixed-type fields.
Define target types: Pick the narrowest correct PostgreSQL type that matches how the data is actually used.
Write conversion SQL: Use staging tables and explicit casts so that invalid rows fail in a controlled way.
Run validation queries: Compare row counts, min/max ranges, null ratios, and distinct counts before and after.

Even a lightweight version of this process has saved me from nasty surprises during cutover. A migration that respects PostgreSQL’s type system will be easier to maintain, easier to query, and far less likely to hide production data bugs.

MySQL Workbench Manual :: 10.6.4 PostgreSQL Type Mapping

3. Ignoring Transaction Semantics and Isolation Differences

Many of the most confusing PostgreSQL migration mistakes I’ve seen weren’t schema-related at all—they came from assuming transactions worked just like they did in MySQL or SQL Server. PostgreSQL has a very deliberate MVCC model, strict transaction boundaries, and different defaults around autocommit and isolation. If you carry over old habits without revisiting them, you can end up with phantom deadlocks, long-running transactions, unexpected rollbacks, and bloated tables from stuck row versions.

On one migration, we went live with an API that held transactions open across multiple HTTP requests (a pattern that had “worked” on MySQL). Under PostgreSQL, this led to massive bloat and blocking within days. Since then, I always treat transaction semantics as a first-class migration topic, not an afterthought.

Autocommit Assumptions and Long-Running Transactions

A classic trap is assuming PostgreSQL’s autocommit behavior is identical to your old database. Many ORMs and client drivers open a transaction and then forget to close it properly. In MySQL, this often goes unnoticed; in PostgreSQL, it can accumulate dead tuples, hold locks longer than expected, and even block VACUUM.

A few patterns I watch for during a migration:

Transactions spanning user interactions: Holding a transaction open across several service calls or UI steps is toxic in PostgreSQL. I now keep transactions as short and focused as possible.
Forgotten COMMIT/ROLLBACK: CLI tools, background workers, or custom scripts sometimes open a transaction and never close it, especially when ported quickly from MySQL tooling.
Implicit transactions in ORMs: Some ORMs change behavior when moving from one adapter (e.g., MySQL) to another (Postgres). I always check how autocommit is configured for the new driver.

Here’s a minimal example showing how I explicitly control a PostgreSQL transaction in an application migration script, instead of relying on implicit behavior:

import psycopg2

conn = psycopg2.connect(dsn)
conn.autocommit = False   # be explicit

try:
    with conn.cursor() as cur:
        cur.execute("UPDATE accounts SET balance = balance - %s WHERE id = %s", (100, 1))
        cur.execute("UPDATE accounts SET balance = balance + %s WHERE id = %s", (100, 2))
    conn.commit()
except Exception:
    conn.rollback()
    raise
finally:
    conn.close()

When I audit migrated code, I look for implicit assumptions like “every statement auto-commits” and replace them with explicit transaction blocks that match PostgreSQL’s expectations.

Isolation Levels, MVCC, and Lock Behavior

PostgreSQL’s implementation of MVCC (Multi-Version Concurrency Control) feels different if you’re coming from MySQL with InnoDB or from SQL Server. I’ve worked with teams who assumed that a REPEATABLE READ or SERIALIZABLE isolation level would behave exactly as before, and were surprised by serialization failures or missing updates.

Some key differences I make sure the team understands:

Default isolation level: PostgreSQL defaults to READ COMMITTED, which may not match your old engine’s behavior.
Serializable transactions: PostgreSQL’s SERIALIZABLE is implemented via predicate locking and can raise serialization errors that need retry logic in the application.
Row versioning: Readers don’t block writers and vice versa, but long-running transactions can prevent cleanup of old row versions, increasing bloat.

When porting critical code paths, I often rewrite them to expect and handle serialization errors explicitly. For example, a simple pattern for retrying a transactional block in PostgreSQL looks like this:

#!/usr/bin/env bash

# Pseudo-shell script using psql with a retry loop

for attempt in 1 2 3; do
  psql "$DSN" <<SQL && break || echo "attempt $attempt failed"
BEGIN ISOLATION LEVEL SERIALIZABLE;
  UPDATE inventory
    SET quantity = quantity - 1
  WHERE product_id = 42 AND quantity > 0;

  -- ensure exactly one row updated
  IF NOT FOUND THEN
    ROLLBACK;
    RAISE NOTICE 'out of stock';
  END IF;

COMMIT;
SQL
  sleep 1
done

In real code I’d use proper language constructs, but the idea is the same: under higher isolation levels in PostgreSQL, you design for retries instead of assuming every transaction will succeed on the first try.

Deadlocks, Explicit Locking, and Migration Surprises

Deadlocks show up in every database, but patterns that were “rare but tolerable” before can become constant pain under PostgreSQL’s stricter locking. I’ve debugged migrations where simple updates suddenly started throwing deadlock errors because two services were touching the same rows in opposite orders.

Common issues I look for when reviewing migrated SQL:

Inconsistent lock ordering: Services updating table A then table B in one flow, but table B then table A in another.
Overuse of SELECT … FOR UPDATE: Ported directly from another engine, this can cause more contention than necessary in PostgreSQL.
Table-level locks during migrations: DDL operations in production, especially without concurrent options, can block or be blocked by ordinary transactions.

One technique that has helped me a lot is introducing more targeted locking. Instead of grabbing broad locks just to “be safe,” I use row-level locks and consistent access patterns. For example, when I need to coordinate updates, I’ll do:

BEGIN;

-- Lock rows in a deterministic order
SELECT id
FROM accounts
WHERE id IN (1, 2)
ORDER BY id
FOR UPDATE;

UPDATE accounts
SET balance = balance - 100
WHERE id = 1;

UPDATE accounts
SET balance = balance + 100
WHERE id = 2;

COMMIT;

By always locking rows in a consistent order, I’ve avoided many of the deadlocks that showed up right after migrating to PostgreSQL. The big mindset shift is to treat PostgreSQL’s transaction and locking model as something you actively design for, not something you passively hope is “close enough” to what you had before.

4. Poor Indexing Strategy After Moving to PostgreSQL

One of the most expensive PostgreSQL migration mistakes I see is treating indexes as something you can just copy over from MySQL, SQL Server, or even MongoDB-style access patterns. On paper, the schema looks familiar and all the old indexes are present, so teams assume performance will be fine. Then real traffic hits, queries crawl, and everyone blames PostgreSQL. In reality, the problem is usually an indexing strategy that doesn’t match how PostgreSQL’s planner and index types actually work.

When I help with migrations now, I always carve out time to redesign indexes for PostgreSQL instead of doing a one-to-one port. The difference in performance and disk usage is huge, and it’s much easier to do this before the system is under production load.

Copying Legacy Indexes Without Understanding PostgreSQL’s Planner

Coming from MySQL or SQL Server, it’s easy to assume the same composite indexes will serve the same queries. But PostgreSQL’s cost-based planner, statistics, and index usage rules are different. I’ve seen schemas where every foreign key and every WHERE clause column was indexed individually “just in case,” leading to high write overhead and bloated tables, while critical combined filters were missing the right composite index.

Some legacy patterns I watch for:

Over-indexing single columns: Multiple overlapping indexes on the same column sets (e.g., (user_id), (user_id, created_at), (user_id, status)) where one well-designed composite index would do.
Wrong column order in composite indexes: Copying index orders from MySQL without checking how PostgreSQL actually filters and sorts.
Assuming LIKE patterns behave the same: Case-insensitive search and prefix matching often need different indexing strategies in PostgreSQL.

On one project, we dropped several redundant single-column indexes and replaced them with two carefully chosen composite indexes. Write performance improved noticeably, and the read queries involved actually got faster because the planner had better options.

Not Using PostgreSQL-Specific Index Types (GIN, GiST, BRIN, Expression)

PostgreSQL shines when you embrace its richer index types. If you copy only B-tree indexes from MySQL or SQL Server and ignore GIN, GiST, BRIN, and expression indexes, you’re leaving a lot of performance on the table. I’ve been able to turn “hopelessly slow” endpoints into snappy ones just by switching to the right index type.

Some of the PostgreSQL-specific tools I reach for during a migration:

GIN indexes: Perfect for JSONB, array membership, and full-text search. Great when migrating from MongoDB-style document structures or tag arrays.
GiST indexes: Useful for geometric types, ranges, and some advanced text search patterns.
BRIN indexes: Ideal for huge, append-only tables where data is naturally ordered (e.g., logs or events with a timestamp).
Expression indexes: Let you index on a computed value, such as LOWER(email) for case-insensitive lookups.

Here’s a concrete example of how I typically fix a case-insensitive search that was ported from MySQL:

-- Legacy pattern: simple B-tree index, case-sensitive search
CREATE INDEX idx_users_email ON users (email);

-- PostgreSQL-friendly pattern: expression index for case-insensitive lookup
CREATE INDEX idx_users_email_ci
    ON users (LOWER(email));

-- Query now matches the index
SELECT *
FROM users
WHERE LOWER(email) = LOWER('SomeUser@example.com');

In my experience, adding the right expression or GIN index often beats throwing more generic B-tree indexes at the problem.

Rebuilding Indexes Based on Real Query Patterns

The most effective indexing work I’ve done after a PostgreSQL migration started with query observation, not guesswork. Copied indexes reflect old access patterns, but once you’re on PostgreSQL, the application code tends to evolve. I always spend some time capturing slow queries and comparing them against the current index layout.

A practical approach that has worked well for me:

Enable query logging and pg_stat_statements: Identify the most frequent and slowest queries after cutover.
Use EXPLAIN (ANALYZE, BUFFERS): See whether PostgreSQL is using indexes as you expect, or falling back to sequential scans or inefficient plans.
Design targeted composite indexes: Index exactly the columns used together in WHERE, JOIN, and ORDER BY clauses for hot queries.
Trim redundant indexes: Drop overlapping or unused indexes to reduce write amplification and vacuum load.

As an example, suppose I see this query in pg_stat_statements after migration:

SELECT *
FROM orders
WHERE customer_id = $1
  AND status = 'PAID'
ORDER BY created_at DESC
LIMIT 50;

If the only existing indexes are (customer_id) and (created_at) from a previous engine, PostgreSQL might not have a great plan. I’d usually introduce a composite index tailored to this pattern:

CREATE INDEX idx_orders_customer_status_created_at
    ON orders (customer_id, status, created_at DESC);

After creating this index, I re-run EXPLAIN (ANALYZE) to confirm the planner is actually using it and that latency dropped. Doing this iteratively for the top queries has consistently given me better performance than blindly porting or auto-generating indexes.

If you treat PostgreSQL indexing as a fresh design problem instead of a copy-paste job, you’ll avoid a huge class of performance surprises and make the most of the engine you just migrated to.

5. Underestimating Migration Downtime, Cutover, and Rollback Plans

Most PostgreSQL migration mistakes I see in production aren’t about SQL syntax or data types—they’re about poor planning around downtime, cutover, and rollback. Teams spend weeks perfecting schemas and scripts, then treat the actual switchover as a single maintenance window with a vague “we’ll just flip it.” I’ve lived through cutovers like that, and they’re rarely fun: extended outages, data drift, and panicked attempts to roll back to a state that no longer exists.

These days, I treat cutover planning as seriously as schema design. A smooth migration to PostgreSQL means having a realistic downtime budget, a dual-write or sync strategy where it makes sense, and a rollback plan that’s been dry-run—not just talked about.

Unrealistic Downtime Windows and One-Shot Cutovers

A classic mistake is underestimating how long data copy and verification will take. I’ve seen teams plan a “30-minute” downtime window only to find their bulk load into PostgreSQL takes 90 minutes on its own, before any verification or warm-up queries. Users end up facing a maintenance page while everyone scrambles to finish index creation and sanity checks.

When I plan cutover windows now, I usually:

Measure full-load times on production-sized data in a staging environment, including index builds and constraints.
Pre-create heavy indexes where possible, or use CREATE INDEX CONCURRENTLY beforehand to reduce downtime work.
Add buffer time for validation queries, cache warm-up, and a final sanity check before opening traffic.

Even a simple dry-run over a realistic snapshot can reveal whether your planned window is optimistic or actually achievable.

Ignoring Dual-Writes and Data Drift During the Transition

For non-trivial systems, switching from the old database to PostgreSQL is rarely a single switch flip. There’s usually a period where both systems need to stay in sync while you test the new stack, migrate side services, or run read-only workloads from PostgreSQL. Underestimating this phase can lead to data drift: some writes hit the old system only, others hit both, and nobody’s quite sure which source of truth is correct.

What has worked well for me in practice:

Plan an explicit dual-write window: Application writes go to both the legacy database and PostgreSQL for a limited time, with careful logging.
Use comparison jobs: Periodic jobs compare row counts, checksums, or key business invariants between the two systems.
Define a clear cutover boundary: After a specific timestamp or version, PostgreSQL becomes the only write target, and the old system goes read-only or fully offline.

Here’s a simplified pseudo-implementation I’ve used in app code to make dual-writes explicit and traceable:

# Pseudo-code: dual write with metrics and error handling

def create_order(order_data):
    legacy_ok = False
    pg_ok = False

    try:
        legacy_ok = legacy_db.insert_order(order_data)
    finally:
        try:
            pg_ok = pg_db.insert_order(order_data)
        finally:
            metrics.emit_dual_write_status(legacy=legacy_ok, pg=pg_ok)

    if not (legacy_ok and pg_ok):
        # Decide on policy: fail fast, retry, or queue for reconciliation
        raise DualWriteError(legacy_ok, pg_ok)

It’s not glamorous, but planning this explicitly is far better than discovering later that half your writes never made it to PostgreSQL during the transition.

No Tested Rollback Path When Things Go Wrong

The other painful pattern I’ve seen is having no realistic way back if the migration blows up under real traffic. Teams say “we’ll just switch back” without thinking through what that means once PostgreSQL has accepted new writes that the old database hasn’t. When trouble hits, they’re stuck choosing between data loss and extended downtime while they improvise.

In my own migrations, I now insist on answering these questions beforehand:

What is the last known-good state of the old database? Is it snapshot-based, and how old is it at cutover time?
How are new writes handled during rollback? Will you replay from a queue, or accept a short write-freeze window to resync?
Have we actually rehearsed rollback? A dry-run rollback in staging uncovers missing scripts and assumptions fast.

Even a “partial” rollback plan—like being able to route only a subset of traffic back to the old system—can save a migration. The key is to treat rollback as an engineered path, not a vague hope. When you combine realistic downtime estimates, a deliberate dual-write or sync phase, and a tested rollback plan, the PostgreSQL cutover stops being a leap of faith and becomes a controlled change.

Migration and upgrades: achieving near zero-downtime in PostgreSQL

6. Neglecting PostgreSQL-Specific Operations: VACUUM, Autovacuum, and Maintenance

One of the most subtle PostgreSQL migration mistakes I see is operational, not architectural: teams move from MySQL or SQL Server and assume PostgreSQL will “just maintain itself.” They don’t think about MVCC, dead tuples, VACUUM, or autovacuum at all. Everything looks fine in the first weeks, then queries slow down, indexes bloat, and disk usage keeps creeping up. By the time someone says, “Should we look at VACUUM settings?”, the damage is already painful.

In my own projects, the migrations that aged well were the ones where we treated PostgreSQL’s maintenance story as a design concern from day one—especially around autovacuum, long-running transactions, and monitoring bloat.

Misunderstanding MVCC and Dead Tuples After Migration

PostgreSQL’s MVCC model means every UPDATE or DELETE leaves behind dead row versions that must be cleaned up later. On engines where this is handled differently, you can get away with ignoring it for a while; on PostgreSQL, pretending MVCC doesn’t exist shows up as bloat, slow sequential scans, and big indexes that barely fit in memory anymore.

Common patterns I’ve had to unwind post-migration include:

High-churn tables (sessions, jobs, events) with constant UPDATE/DELETE activity but no adjusted autovacuum settings.
Bulk operations imported from old maintenance scripts that delete or update millions of rows without planning for the resulting dead tuples.
Long-running idle transactions (often from ETL jobs or forgotten app connections) that prevent autovacuum from reclaiming space.

When planning a migration, I now flag high-write tables early and assume they’ll need special attention. The combination of MVCC and heavy churn is where “set it and forget it” really doesn’t work.

Relying Blindly on Default Autovacuum Settings

PostgreSQL ships with sane defaults, but they’re not tuned for every workload. Simply copying your schema from MySQL or SQL Server and leaving autovacuum untouched often leads to two extremes: either autovacuum runs too infrequently (bloat and slow queries) or it runs constantly and noisily on busy tables. I’ve walked into systems where autovacuum was blamed for poor performance, only to find it was reacting to a deeper configuration issue.

These days I treat autovacuum as something I deliberately configure, not a mysterious background task:

Per-table tuning: For hot tables, I lower thresholds like autovacuum_vacuum_scale_factor and autovacuum_analyze_scale_factor, so cleanup happens earlier.
Cost limits and delays: I adjust autovacuum_vacuum_cost_limit and autovacuum_vacuum_cost_delay so autovacuum doesn’t starve the system or fall behind.
Monitoring visibility: I regularly check pg_stat_user_tables and pg_stat_all_tables to see if dead tuples are accumulating faster than autovacuum clears them.

Here’s a small example of how I’ve overridden autovacuum settings for a particularly hot table after a migration:

ALTER TABLE events
  SET (autovacuum_vacuum_scale_factor = 0.05,  -- 5% of table
       autovacuum_analyze_scale_factor = 0.02,
       autovacuum_vacuum_cost_limit = 400);

That kind of tuning is usually unnecessary in development, but on real production workloads it can be the difference between a stable system and one that slowly grinds to a halt.

No Ongoing Maintenance Plan: REINDEX, ANALYZE, and Monitoring

Another operational trap I see after migrations is treating PostgreSQL like a “set and forget” service with no maintenance playbook. Indexes grow, statistics drift, and nobody notices until query plans get weird. I’ve been paged for “random latency spikes” that turned out to be missing ANALYZE or heavily bloated indexes that had never been rebuilt.

For migrations that I’m comfortable putting my name on, I always make sure we have at least a minimal maintenance routine:

Regular ANALYZE: Either rely on autovacuum’s ANALYZE or schedule manual ANALYZE for critical tables after big data changes so the planner has up-to-date stats.
REINDEX strategy: Plan periodic REINDEX CONCURRENTLY for heavily updated indexes or after major data cleanup, especially in older PostgreSQL versions or after known bloat issues.
Basic bloat and health monitoring: Use views like pg_stat_all_tables, pg_stat_all_indexes, and extensions/tools for bloat estimation so issues are caught early.

As a small example, here’s a quick query I’ve used as an early warning for tables that might need attention post-migration:

SELECT
    schemaname,
    relname,
    n_live_tup,
    n_dead_tup,
    ROUND( (n_dead_tup::numeric / GREATEST(n_live_tup,1)) * 100, 2 ) AS dead_pct
FROM pg_stat_all_tables
WHERE n_live_tup > 0
ORDER BY dead_pct DESC
LIMIT 20;

It’s not perfect, but it quickly surfaces tables where dead tuples are piling up. From there, I decide whether to tweak autovacuum, run a manual VACUUM, or revisit application patterns causing long transactions or heavy churn.

If you treat PostgreSQL as “just another SQL engine” operationally, you’ll miss these MVCC-aware maintenance needs and eventually pay for it in performance and outages. Building a lightweight but deliberate maintenance plan—VACUUM, autovacuum tuning, ANALYZE, REINDEX, and monitoring—turns PostgreSQL from a mysterious black box into a predictable part of your stack.

Routine Vacuuming – PostgreSQL Documentation

7. Skipping Performance Testing When Comparing PostgreSQL to Other Engines

One of the most avoidable PostgreSQL migration mistakes I’ve seen is assuming that PostgreSQL will automatically match or beat the performance of the old database without any realistic testing. Teams copy their schema, point the app to PostgreSQL, and then judge the database based on the first week of un-tuned, un-profiled production traffic. When throughput drops or latency spikes, PostgreSQL gets blamed—even though nobody has done a proper benchmark, query review, or configuration pass.

In my own migrations, the happiest outcomes always came when we treated performance as a separate workstream: we measured, tuned, and iterated before making PostgreSQL the primary engine.

Assuming “Same SQL” Means “Same Performance”

A common trap is thinking that because the SQL syntax looks similar between MySQL, SQL Server, or Oracle and PostgreSQL, the performance characteristics will also be similar. In reality, PostgreSQL’s planner, join strategies, index usage, and configuration defaults are all different. Queries that were accidentally optimized on your old engine can become slow here, and vice versa.

When I review a migration, I specifically look for:

Heavy JOIN chains and large IN lists that need different indexes or rewritten predicates.
Cursor-based or row-by-row patterns that need batching or set-based rewrites on PostgreSQL.
Complex reports or analytics queries that suddenly hit different execution plans due to changed statistics.

The mistake isn’t using the same queries; it’s assuming those queries don’t need to be revisited for a new engine.

No Realistic Workload Benchmarking Before Cutover

Another issue I’ve run into is teams “testing” PostgreSQL with tiny dev datasets, then acting surprised when production-sized workloads behave differently. PostgreSQL’s planner relies heavily on statistics and cost estimates; those behave very differently on 10,000 rows versus 100 million. Without a realistic dataset and workload, your confidence in performance is mostly wishful thinking.

These days, I try to include at least a basic performance test plan before cutover:

Load a production-sized subset (or full snapshot) into a staging PostgreSQL environment.
Replay representative queries or traffic using tools like pgbench or custom scripts.
Capture slow queries with pg_stat_statements and iterate on indexes, query plans, and configuration.

Even simple synthetic tests can reveal missing indexes, bad join orders, or configuration mistakes long before real users are affected.

Ignoring Postgres-Specific Tuning and Observability

Finally, I often see teams migrate to PostgreSQL and keep almost everything at default settings, relying on the same infrastructure assumptions they used for MySQL or SQL Server. Memory parameters, parallelism, and connection management all influence performance, but without basic tuning and observability you’re flying blind.

For any serious migration, I make sure we at least:

Review core parameters like shared_buffers, work_mem, maintenance_work_mem, and max_connections for the new workload.
Turn on pg_stat_statements and examine real query patterns instead of guessing.
Use EXPLAIN (ANALYZE, BUFFERS) to understand why a particular query is slow and whether it’s CPU, I/O, or plan choice.

Here’s an example of how I’ll quickly analyze a problematic query after a test run:

psql "$DSN" -c "EXPLAIN (ANALYZE, BUFFERS) \
SELECT o.id, o.created_at, c.name
FROM orders o
JOIN customers c ON c.id = o.customer_id
WHERE o.created_at > now() - interval '7 days'
ORDER BY o.created_at DESC
LIMIT 100;"

From that output, I can see whether I need a new composite index, more memory per sort, or perhaps a different query pattern entirely. Skipping this kind of testing and tuning is what turns an otherwise solid PostgreSQL migration into a performance regression story.

Conclusion: Turning PostgreSQL Migration Mistakes into Long-Term Wins

Every one of the PostgreSQL migration mistakes I’ve walked through—underestimating type differences, ignoring transactions, copying indexes blindly, skipping maintenance, or neglecting performance testing—has bitten real teams I’ve worked with, including my own. The good news is that each of these pitfalls points to a concrete practice: understand PostgreSQL’s data types and constraints, design transactions and locks intentionally, re-think indexing for Postgres, plan cutover and rollback like a real project, and treat VACUUM, autovacuum, and performance tuning as first-class citizens.

When I help teams compare engines now, I encourage a checklist mindset: for every major feature or workload, ask “How does PostgreSQL actually do this?” and “Have we tested it under realistic conditions?” If you take the time to answer those questions up front, you won’t just avoid painful outages—you’ll end up with a PostgreSQL deployment that’s faster, more predictable, and easier to live with over the long term.

Cary Huang

Hi, I’m Cary Huang — a tech enthusiast based in Canada. I’ve spent years working with complex production systems and open-source software. Through TechBuddies.io, my team and I share practical engineering insights, curate relevant tech news, and recommend useful tools and products to help developers learn and work more effectively.