I want to talk with you today about the Write-Ahead Log concept. Why?

Last month, I gave the “PostgreSQL Superpowers” talk again twice at .NET meetups. And I had random thoughts on databases and .NET developers:

• most of them don't do SQL, just EF,

• don't use Explain to analyse query plans,

• MSSQL is still the go-to,

• PostgreSQL is still seen only as a no-cost alternative,

• DB is just a bag for data for them.

• They don't use specific features an exact db gives them (but still choose MSSQL),

• most haven't seen stored procedures and while loop in SQL for some time (if at all),

• rarely use views and even rarer materialised views,

• don’t remember what Write-Ahead Log and Transaction Log are.

Some of those are inherently related to the Microsoft community (preferences for MS tooling), but the rest align with the general state of the art. Of course, those observations are anecdotal evidence, but I noticed that in multiple places and countries, so I think that they’re close to the point.

The observations were a bit disappointing, but I don’t want to complain; I want to be proactive and help you and your colleagues improve. Step by step.

Today, let’s take the first step to close the knowledge of one of the most underrated concepts in distributed systems and databases: Write-Ahead Log.

The Challenge: Reliability

Distributed systems, databases, and message brokers must manage the state reliably. The challenge remains whether you’re dealing with PostgreSQL, Kafka, or MongoDB. You must ensure a system can recover from crashes, maintain durability, and replicate changes efficiently without compromising performance. State management in such systems is filled with complexities—updates can fail midway, nodes can crash unexpectedly, and networks can introduce delays or partitioning. In a nutshell: Fallacies of distributed computing.

To tackle these challenges, most reliable systems rely on Write-Ahead Logs (WALs). The idea is straightforward but essential: you never make a change directly; instead, you first append the change to a durable log. This guarantees that no matter what happens, the system can always recover or replicate its state.

In this article, we’ll explore the core principles behind the Write-Ahead Log. We’ll explain why the WAL is indispensable, how it solves problems like durability, consistency, and replication, and how its implementation differs across PostgreSQL, Kafka, and MongoDB. Yes, all of those different tools use it!

What is a Write-Ahead Log (WAL)?

A write-ahead log follows a basic principle: before applying any changes to the main data store, the system writes the changes to an append-only log. The log serves as a sequential, persistent record of every operation. If the system crashes midway through applying a change, the WAL can be replayed to restore the system to a consistent state.

Imagine a database table where a row is being updated. Without a WAL, the database might overwrite the row in the main storage. If the server crashes halfway through writing the new data during this operation, the old and the new data could be corrupted, leaving the system in an inconsistent state. The update is neither “committed” nor safely recoverable.

The WAL solves this by ensuring the following sequence of events:

1. Log First: The system writes the change (e.g., “update row X with new value”) to the write-ahead log. This log entry is sequentially appended and written for durable storage, such as disks. That’s why the structure is called Write-Ahead Log, as we’re writing new entries ahead of others.

2. Apply Later: The system applies the change to the actual data structures (e.g., tables or indexes) only after the WAL entry has been safely persisted. This step can be asynchronous and may happen with some delay since the log already guarantees the change is not lost.

3. Crash Recovery: If the system crashes after writing the log but before applying the change, the WAL can be replayed on restart. This ensures the operation is eventually applied. The log serves as the source of truth, allowing the system to apply any uncommitted changes and restore a consistent state.

This mechanism guarantees two critical things:

• Durability: Changes are not “lost” once they are logged.

• Consistency: If a crash occurs, the system can always replay the WAL to apply incomplete operations and recover to a consistent state.

The write-ahead log is at the core of modern systems because it satisfies critical properties for ACID transactions, replication, and fault tolerance. It allows systems to trade off complexity in favour of simplicity—sequential appends are far easier, faster, and more reliable than attempting scattered random writes directly to a data store.

For example, imagine this process in practice with a database:

Step 1: A user transaction updates the balance column of a user’s table. The system writes a WAL entry like this:

{
  offset: 34958,
  transaction_id: 123, 
  operation: {
    type: "UPDATE", 
    table: "users", 
    set: "balance=100", 
    where: "id=1"
   }
}

This entry is appended to the WAL file and flushed to disk. Each operation can gather a different set of metadata (update will be different than insert or delete).

Step 2: The system updates the users table in its data files. If this step is interrupted (e.g., due to a power outage), the log ensures that step 1 is already durable.

Step 3: On recovery, the system reads the WAL file, identifies uncommitted changes (like transaction 123), and reapplies them to the table.

Each entry has its monotonic offset. Thanks to that, the WAL Processor can keep the position of the last handled offset and know which entries have not yet been processed.

Why Write-Ahead Logs Are Everywhere

Write-ahead logs solve some of the hardest and most fundamental problems in systems that manage state. Their ubiquity in modern databases, messaging systems, and distributed systems is no coincidence. Let’s unpack the reasons why WALs are essential:

1. Durability

Durability ensures that once a system acknowledges a change, that change will persist—even in the face of crashes or failures. WALs make this possible by first appending changes to a persistent log. Because appends are sequential writes, they are faster and less prone to data corruption than scattered random writes.

For example, in PostgreSQL, the system will not acknowledge a transaction as “committed” until the corresponding WAL entry is safely flushed to disk. This guarantees that no committed transaction will ever be lost, even if the server crashes.

2. Crash Recovery

Systems can fail at any point—hardware faults, software bugs, power outages, you name it. A crash could leave the system in an inconsistent state without a reliable recovery mechanism. By writing changes to the WAL before applying them, systems can recover gracefully:

• Upon restart, the system replays the WAL to “catch up” on any logged but not applied operations.

• Since the WAL is append-only and sequential, replaying it is fast and deterministic.

This approach allows systems to recover within seconds, even after catastrophic failures.

3. Replication

Replication is the process of propagating changes from one system to another, often for high availability, fault tolerance, or read scalability. Logs are the perfect tool for replication because they provide a linear, ordered history of changes. Replicas can replay the WAL to stay in sync with the primary system.

In MongoDB, for instance, the oplog (from operations log - MongoDB’s version of the WAL) propagates changes from the primary node to the secondary nodes. The same happens in PostgreSQL replication.

4. Performance and Efficiency

Appending to a log is far more efficient than making scattered random writes to a database. Storage systems—whether spinning disks or SSDs—are optimized for sequential writes. The WAL takes advantage of this by batching and appending changes, resulting in:

• Faster writes.

• Lower I/O overhead.

• Simplified recovery mechanisms (logs are compact and linear).

Let’s discuss a few examples of using Write-Ahead Log in different systems: PostgreSQL, Kafka and MongoDB...