Data Engineering 102: Understanding Transactions, ACID, and Isolation in PostgreSQL – ██FR█████ █INTELL███████████

This content originally appeared on DEV Community and was authored by Sajjad Rahman

The Power of Transactions & ACID . Before a Data Engineer can design reliable data systems or move petabytes through ETL pipelines, one question always echoes:

How does a database keep data accurate and safe — even when hundreds of things happen at the same time?

The answer lies in transactions and the ACID properties — the unshakable pillars that make relational databases like PostgreSQL so reliable.

What Is a Transaction?

A transaction is a sequence of one or more database operations (INSERT, UPDATE, DELETE, etc.) that act as a single logical unit of work.

Rule: A transaction must fully succeed or fully fail — there’s no halfway.

Think of it as a sealed envelope — you either deliver it completely or destroy it; you never send half a letter.

Why It Matters

Without transactions, systems would constantly fall into inconsistent states:

Money deducted from one account but never credited to another.
Inventory reduced but the order never created.
User deleted but their related records still exist elsewhere.

Transactions keep your system trustworthy and predictable.

Example: Money Transfer Scenario

You send 1000 BDT to your friend:
1⃣ Your balance decreases by 1000.
2⃣ Your friend’s balance increases by 1000.

If step 1 succeeds but step 2 fails → inconsistent state (you lose money).

A transaction ensures:
Either both succeed, or
both roll back (cancelled)

That’s Atomicity in action — all or nothing.

Transaction Lifecycle in PostgreSQL

Every transaction goes through five predictable stages:

Stage	Description	Example
Active	Transaction is running; statements executing.	You start transferring money.
Partially Committed	All commands run but not yet saved.	Balance reduced, waiting to finalize.
Committed	Changes are permanently stored.	Both accounts updated successfully.
Failed	An error occurred before commit.	Network error or insufficient funds.
Terminated	Transaction ends (success or rollback).	Process closed.

This lifecycle guarantees data safety even under heavy system load.

ACID — The Pillars of Reliable Databases

What Is ACID?

ACID stands for Atomicity, Consistency, Isolation, and Durability —
four principles that make transactions predictable, recoverable, and safe.

Let’s break them down

A — Atomicity (All or Nothing)

Concept	Explanation
Meaning	Treat every transaction as a single unit — it either completes fully or doesn’t happen at all.
Why It Matters	Prevents partial changes that corrupt data.
Example	Debit from A, credit to B. If credit fails, debit is rolled back.
Under the Hood	Databases use logs and checkpoints to undo incomplete transactions.

No half-done operations.

C — Consistency (Follow the Rules)

Concept	Explanation
Meaning	The database must always move from one valid state to another.
Why It Matters	Ensures that rules, constraints, and relationships stay true.
Example	Age can’t be negative. Email must be unique. Foreign keys must match.
Under the Hood	Constraints are verified before commit; invalid data = rollback.

Data always obeys the rules.

I — Isolation (Don’t Disturb Others)

Concept	Explanation
Meaning	Each transaction behaves as if it’s running alone.
Why It Matters	Prevents interference between concurrent users.
Example	Two people buy the last item — isolation ensures only one succeeds.
Under the Hood	Controlled through isolation levels: Read Uncommitted → Serializable. Higher isolation = more safety (and more cost).

Transactions run independently — like private sessions.

D — Durability (It Stays Forever)

Concept	Explanation
Meaning	Once committed, data is safe — even after a crash or restart.
Why It Matters	Guarantees persistence of committed changes.
Example	PostgreSQL’s Write-Ahead Log (WAL) ensures recovery after system failure.
Under the Hood	Uses logs, checkpoints, and replication to preserve data.

Committed = permanent.

Why ACID Matters

Without ACID, your database becomes chaos in disguise:

Data could vanish midway through a transaction.
Tables might drift into inconsistent states.
Queries could return half-truths or outdated values.
Under heavy load, transactions might overwrite or corrupt each other.

ACID transforms chaos into order.
It’s what allows banks, hospitals, and analytics systems to trust every single bit of data.

Remember: every failed constraint or rejected transaction isn’t a problem — it’s your database defending its integrity. When a rule fails, Consistency and Atomicity are doing their job.

Consistency Models — Immediate vs Eventual

Model	Definition	Example Use Case	Explanation
Immediate Consistency	Once committed, all users instantly see the new data.	Banking, OLTP, payment systems.	After sending 1000 BDT, your friend’s balance updates immediately.
Eventual Consistency	Temporary differences allowed across replicas; all copies sync eventually.	Social media, NoSQL, caches.	Post a photo — visible to some users right away, others after sync delay.

Choose the model that fits your workload:

Need accuracy + safety → Immediate Consistency
Need speed + scalability → Eventual Consistency

Read Anomalies — When Isolation Breaks Down

When multiple transactions run at once, weak isolation can cause strange behavior:

Phenomenon	Definition	Scenario	Explanation
Dirty Read	Reading uncommitted data from another transaction.	You see your friend’s new balance before they commit.	May show rolled-back data. PostgreSQL prevents this.
Non-Repeatable Read	Same query returns different results within one transaction.	You read 1000 BDT → another updates to 2000 BDT → you re-read → 2000.	Value changed mid-transaction.
Phantom Read	Re-running a query returns a different number of rows.	You count 5 orders → another adds 1 → you count again → 6.	New rows appeared during your transaction.

Higher isolation reduces anomalies — at the cost of speed.

Transaction Isolation Levels (PostgreSQL)

Level	Prevents	Allows	Best For
Read Uncommitted	Nothing	Dirty reads	Not supported (too unsafe).
Read Committed (Default)	Dirty reads	Non-repeatable & phantom reads	General workloads — balanced choice.
Repeatable Read	Dirty + Non-repeatable reads	Phantom reads	Analytical queries, consistent snapshots.
Serializable	All anomalies	None	Banking, critical transactions.

Banking Scenario Example

Two users withdraw from the same account (1000 BDT):

BEGIN TRANSACTION ISOLATION LEVEL SERIALIZABLE;
UPDATE accounts SET balance = balance - 1000 WHERE id = 1;
COMMIT;

Read Committed: both might withdraw — race condition
Serializable: only one withdrawal succeeds

Quick Recap

Concept	Safe?	Speed	Ideal Use Case
Immediate Consistency	High	Slower	Banking, inventory
Eventual Consistency	Eventually	Faster	Social media, caching
Serializable	Very Safe	Slowest	Finance, critical data
Read Committed	Balanced	Fast	Default workloads

Visual Summary — From Transactions to Reliable Systems

🧠 START → Understanding Reliable Databases
    ↓
📦 Transaction
    • Group of operations acting as one (e.g., money transfer)
    ↓
⚖ ACID Properties — Ensure Reliability
    ├── 🔸 Atomicity → All or none succeed
    ├── 🔸 Consistency → Database remains valid
    ├── 🔸 Isolation → Independent transactions
    └── 🔸 Durability → Data stays permanent
    ↓
🔍 Concurrency & Anomalies
    ├── Dirty Read → Read uncommitted data
    ├── Non-Repeatable Read → Changing results
    └── Phantom Read → Changing row counts
    ↓
🧱 Isolation Levels (PostgreSQL)
    ├── Read Committed → Default, safe & fast
    ├── Repeatable Read → Consistent snapshots
    └── Serializable → Full protection
    ↓
🏦 Consistency Models (Distributed)
    ├── Immediate Consistency → Banking
    └── Eventual Consistency → Social apps
    ↓
🧩 Choose Wisely
    ├── Accuracy → Immediate + Serializable
    └── Scale → Eventual + Read Committed
    ↓
🎯 END → Reliable, Scalable & Consistent Data Systems