Skip to main content

Debugging with Git Bisect - Find Bugs Fast

Tracking down a bug in a large codebase can be frustrating. **Git bisect** helps by using a binary search to quickly find the exact commit that introduced the issue.


## Step 1: Start Git Bisect

Begin by starting bisect mode:

```sh

git bisect start

```


## Step 2: Mark a Good and Bad Commit

Specify a known working commit:

```sh

git bisect good a1b2c3d

```

Mark the current commit (where the bug exists) as bad:

```sh

git bisect bad

```


## Step 3: Test and Mark Commits

Git checks out a middle commit. Run your app and test it:

```sh

./gradlew bootRun

```

If the bug exists:

```sh

git bisect bad

```

If the bug is not present:

```sh

git bisect good

```


## Step 4: Find the Bad Commit

Once Git finds the problematic commit, it displays:

```sh

1234567 is the first bad commit

```


## Step 5: Reset Git Bisect

Once the issue is identified, reset bisect mode:

```sh

git bisect reset

```


## Conclusion

Using **git bisect** can save you hours when debugging! Instead of checking each commit manually, bisect efficiently pinpoints the issue in just a few steps.


Comments

Popular posts from this blog

Mastering Java Logging: A Guide to Debug, Info, Warn, and Error Levels

Comprehensive Guide to Java Logging Levels: Trace, Debug, Info, Warn, Error, and Fatal Comprehensive Guide to Java Logging Levels: Trace, Debug, Info, Warn, Error, and Fatal Logging is an essential aspect of application development and maintenance. It helps developers track application behavior and troubleshoot issues effectively. Java provides various logging levels to categorize messages based on their severity and purpose. This article covers all major logging levels: Trace , Debug , Info , Warn , Error , and Fatal , along with how these levels impact log printing. 1. Trace The Trace level is the most detailed logging level. It is typically used for granular debugging, such as tracking every method call or step in a complex computation. Use this level sparingly, as it ...

Advanced Kafka Resilience: Dead-Letter Queues, Circuit Breakers, and Exactly-Once Delivery

Introduction In distributed systems, failures are inevitable—network partitions, broker crashes, or consumer lag can disrupt data flow. While retries help recover from transient issues, you need stronger guarantees for mission-critical systems. This guide covers three advanced Kafka resilience patterns: Dead-Letter Queues (DLQs) – Handle poison pills and unprocessable messages. Circuit Breakers – Prevent cascading failures when Kafka is unhealthy. Exactly-Once Delivery – Avoid duplicates in financial/transactional systems. Let's dive in! 1. Dead-Letter Queues (DLQs) in Kafka What is a DLQ? A dedicated Kafka topic where "failed" messages are sent after max retries (e.g., malformed payloads, unrecoverable errors). ...