Showing posts with label Docker. Show all posts
Showing posts with label Docker. Show all posts

Sunday, August 30, 2026

Trivy in CI/CD: How to Add Vulnerability Scanning to Your Pipeline

Security testing shouldn't start after an application reaches production.

If we are already using CI/CD to build, test and deploy our applications, it makes sense to introduce security checks into the same process. This allows us to identify vulnerabilities before they become production problems.

One of the open-source tools we can use for this is Trivy.

Trivy can scan container images, source code repositories and filesystems for vulnerabilities. It can also identify configuration problems, secrets and other security-related issues.

In this article, we will see how we can introduce Trivy into a CI/CD pipeline, starting with a simple local scan and then moving towards using it as a security gate in our pipeline.

What Is Trivy?

Trivy is an open-source security scanner maintained by Aqua Security.

It is commonly used for scanning container images, but it can do much more than that. Depending on how we use it, Trivy can scan:

  • Container images
  • Filesystems
  • Git repositories
  • Infrastructure as Code
  • Kubernetes configurations
  • Dependencies
  • Secrets
  • Licenses

For this article, we will focus mainly on container image vulnerability scanning because it is one of the easiest ways to introduce security scanning into an existing CI/CD process.

Why Add Vulnerability Scanning to CI/CD?

Let's consider a typical application pipeline.

The application is compiled, tests are executed, a Docker image is created and the image is pushed to a container registry. Eventually, that image is deployed to Kubernetes or another production environment.

The problem is that the container image may contain vulnerable operating system packages or application dependencies.

If we only discover those vulnerabilities after deployment, fixing them becomes more complicated.

Instead, we can scan the image before it is pushed or deployed.

A simple pipeline can therefore look like this:

Code
  ↓
Build
  ↓
Unit Tests
  ↓
Build Docker Image
  ↓
Trivy Scan
  ↓
Push Image
  ↓
Deploy

If the vulnerability scan fails, the pipeline stops and the vulnerable image doesn't move further through the deployment process.

This is the basic idea behind adding security into CI/CD.

Step 1: Install Trivy

There are several ways to install Trivy depending on the operating system and environment.

For Ubuntu, we can install it using the official repository.

sudo apt-get install wget gnupg

Add the repository signing key:

wget -qO - https://aquasecurity.github.io/trivy-repo/deb/public.key | \
gpg --dearmor | \
sudo tee /usr/share/keyrings/trivy.gpg > /dev/null

Add the Trivy repository:

echo "deb [signed-by=/usr/share/keyrings/trivy.gpg] \
https://aquasecurity.github.io/trivy-repo/deb \
generic main" | \
sudo tee /etc/apt/sources.list.d/trivy.list

Update the package list:

sudo apt-get update

Install Trivy:

sudo apt-get install trivy

Verify the installation:

trivy --version

We should see the installed Trivy version in the output.

The installation method may change over time, so it is worth checking the current Trivy documentation if we are setting this up on a new machine.

Step 2: Scan a Docker Image

Once Trivy is installed, we can immediately start scanning container images.

For example:

trivy image nginx:latest

Trivy will download the image if it isn't already available locally and scan its packages for known vulnerabilities.

The output will contain information such as:

Library        Vulnerability     Severity
openssl        CVE-XXXX-XXXXX    HIGH
curl           CVE-XXXX-XXXXX    MEDIUM
libxyz         CVE-XXXX-XXXXX    CRITICAL

The exact results will depend on the image version and the vulnerabilities known at the time of the scan.

This is already useful, but we probably don't want every vulnerability to stop our pipeline.

Step 3: Scan Only High and Critical Vulnerabilities

In a CI/CD environment, we usually need to decide which vulnerabilities should block a deployment.

We can filter the results by severity:

trivy image --severity HIGH,CRITICAL nginx:latest

This allows us to focus on vulnerabilities that require immediate attention.

However, filtering the displayed results alone doesn't necessarily make the pipeline fail. We need to explicitly configure the exit code.

Step 4: Make the Pipeline Fail

This is where Trivy becomes useful as a CI/CD security gate.

Consider this command:

trivy image \
  --severity HIGH,CRITICAL \
  --exit-code 1 \
  nginx:latest

The --exit-code 1 option tells Trivy to return a non-zero exit code when vulnerabilities matching the selected criteria are found.

CI/CD systems generally treat a non-zero exit code as a failed step.

So the behaviour becomes:

No HIGH/CRITICAL vulnerabilities
        ↓
Pipeline continues

HIGH/CRITICAL vulnerability found
        ↓
Trivy returns exit code 1
        ↓
Pipeline fails

This is the important difference between simply running a security scan and actually making security part of our deployment process.

Step 5: Scan Our Own Docker Image

Instead of scanning a public image, let's assume our pipeline builds an image called:

myapp:1.0.0

We can scan it using:

trivy image --severity HIGH,CRITICAL myapp:1.0.0

For CI/CD, we can use:

trivy image \
  --severity HIGH,CRITICAL \
  --exit-code 1 \
  myapp:1.0.0

If the scan passes, the pipeline can continue with the next stage.

If the scan finds a HIGH or CRITICAL vulnerability, the pipeline stops.

Step 6: Ignore Vulnerabilities That Don't Have a Fix

This is one area where we need to be careful.

A vulnerability may be known, but there may not yet be a fixed package available.

If we fail the pipeline for every vulnerability regardless of whether a fix exists, developers may quickly start treating the security pipeline as an obstacle rather than a useful control.

Trivy allows us to ignore vulnerabilities for which no fix is currently available.

trivy image \
  --severity HIGH,CRITICAL \
  --ignore-unfixed \
  --exit-code 1 \
  myapp:1.0.0

This means we focus the pipeline gate on vulnerabilities where a fix is available.

That doesn't mean unfixed vulnerabilities should be ignored forever. They should still be tracked and reviewed.

Step 7: Scanning the Source Code

Trivy isn't limited to container images.

We can also scan a project directory:

trivy fs .

We can focus on vulnerabilities:

trivy fs \
  --scanners vuln \
  .

We can also scan for secrets:

trivy fs \
  --scanners secret \
  .

This can help detect accidentally committed credentials, tokens and other sensitive information.

For example, a developer might accidentally commit a configuration file containing an API key.

A source scan gives us another opportunity to detect the problem before the code reaches production.

Step 8: Adding Trivy to a CI/CD Pipeline

Now we can bring everything together.

A simplified pipeline looks like this:

Checkout Code
      ↓
Build Application
      ↓
Run Tests
      ↓
Build Docker Image
      ↓
Trivy Vulnerability Scan
      ↓
Push Image
      ↓
Deploy

The important part is where we place the security scan.

We should scan the exact image that we are planning to deploy.

For example:

docker build -t myapp:$BUILD_ID .

Then:

trivy image \
  --severity HIGH,CRITICAL \
  --ignore-unfixed \
  --exit-code 1 \
  myapp:$BUILD_ID

If the scan passes:

docker push myapp:$BUILD_ID

The deployment can then use that exact image.

This gives us a simple security gate before the artifact moves to the next environment.

Step 9: Generate a Report

Sometimes we don't just want the pipeline to pass or fail. We also want a report that developers and security teams can review.

Trivy supports different output formats.

For example:

trivy image \
  --format json \
  --output trivy-report.json \
  myapp:$BUILD_ID

We can then publish the generated report as a CI/CD pipeline artifact.

This is useful because the pipeline result tells us that something failed, while the report tells us what actually needs to be fixed.

Step 10: Don't Make Every Vulnerability a Pipeline Failure

This is where security implementation requires some judgement.

If we configure the pipeline to fail for every LOW, MEDIUM, HIGH and CRITICAL vulnerability from day one, there is a good chance the pipeline will become difficult to use.

A better approach is to establish a security policy.

For example:

LOW       → Report
MEDIUM    → Report and Track
HIGH      → Review / Block
CRITICAL  → Block

The exact policy will depend on the application and organisation.

For internet-facing applications, we may choose a stricter policy. For internal applications, we may initially use a more gradual approach.

The important thing is to define the policy rather than letting every security finding become an emergency.

Managing Exceptions

There will be situations where a vulnerability needs to be accepted temporarily.

Trivy supports ignore files for this purpose.

For example:

.trivyignore

We can place vulnerability IDs in the file that we have deliberately reviewed and accepted.

However, this should be used carefully.

A common mistake is to keep adding vulnerabilities to .trivyignore simply because they are causing pipeline failures.

That defeats the purpose of having the security scan in the first place.

Every exception should have a reason, an owner and, ideally, an expiry or review date.

Common Mistakes

There are a few mistakes we should avoid when introducing Trivy into CI/CD.

Scanning Only in Production

If we scan only after deployment, we have already allowed the vulnerable artifact into our environment.

Security scanning is more useful when it happens before deployment.

Blocking Everything Immediately

Introducing a security gate without understanding the current vulnerability baseline can cause hundreds of existing issues to break the pipeline.

It is often better to establish a baseline first and then gradually increase the enforcement level.

Ignoring Unfixed Vulnerabilities Forever

Using --ignore-unfixed can make the pipeline more practical, but it shouldn't become an excuse to forget about those vulnerabilities.

The vulnerability may receive a fix later.

Ignoring the Docker Base Image

Many vulnerabilities come from the base image itself.

For example:

FROM ubuntu:latest

The application code may be perfectly secure while the underlying image contains vulnerable packages.

Keeping the base image updated is therefore an important part of container security.

Treating the Scan as the Final Security Check

Trivy is a valuable security tool, but it doesn't replace a complete security program.

Application security also includes:

  • Secure coding
  • Dependency management
  • Secrets management
  • Access control
  • Network security
  • Authentication
  • Authorization
  • Infrastructure security
  • Runtime monitoring

Trivy should be one layer in the overall security process.

Where Should We Run the Scan?

There isn't one universal answer.

A practical approach is to scan at multiple stages.

For example:

Developer Machine
        ↓
Source / Dependency Scan
        ↓
CI Build
        ↓
Container Image Scan
        ↓
Container Registry
        ↓
Deployment
        ↓
Runtime Monitoring

The earlier we identify a problem, the cheaper it usually is to fix.

A vulnerability found during development is much easier to address than one discovered after a production deployment.

Saturday, July 25, 2026

OOMKilled in Kubernetes: Understanding Exit Code 137 and How to Fix It

If you've worked with Kubernetes for some time, you've probably come across a pod that suddenly restarts with the reason OOMKilled and an exit code of 137. It usually happens without much warning. One moment the application is running normally, and the next moment Kubernetes terminates the container and starts a new one.

The first reaction is often to increase the memory limit and redeploy the application. Sometimes that works, but in many cases the same issue returns after a few hours or days because the actual problem was never investigated.

In most production environments, OOMKilled isn't the real problem. It's an indication that the application consumed more memory than it was allowed to use. Our objective shouldn't be to simply allocate more memory. Instead, we should understand why the application exceeded its limit in the first place.

In this article, we will understand what OOMKilled means, why Kubernetes reports Exit Code 137, how to investigate memory-related issues and the best practices to prevent them from happening again.

What Does OOMKilled Mean?

OOM stands for Out Of Memory.

Every container running in Kubernetes has resource limits that define the maximum amount of memory it can consume. If the application crosses that limit, the Linux kernel immediately terminates the process to protect the node from running out of memory.

Kubernetes detects that the container has stopped unexpectedly and restarts it according to the pod's restart policy.

If the application continues exceeding the memory limit after every restart, the pod may eventually enter a CrashLoopBackOff state.

Understanding this behaviour is important because Kubernetes isn't killing the application. The operating system is. Kubernetes simply reports what happened and starts a new container.

Understanding Exit Code 137

When a Linux process exits, it returns an exit code.

For an OOMKilled container, Kubernetes usually reports something similar to the following:

Last State:
  Terminated

Reason:
  OOMKilled

Exit Code:
  137

Exit Code 137 indicates that the process was terminated using the SIGKILL signal.

In Kubernetes, this almost always means one of the following:

  • The application exceeded its configured memory limit.

  • The Linux Out Of Memory Killer terminated the process.

  • Kubernetes restarted the container after it exited.

Whenever we see Exit Code 137, memory usage should be the first thing we investigate.

Confirm That the Pod Was OOMKilled

The easiest way to verify the reason is by describing the pod.

kubectl describe pod <pod-name>

Look for the Last State section.

Last State:
  Terminated

Reason:
  OOMKilled

Exit Code:
  137

If both the reason and exit code match the output above, we've confirmed that memory exhaustion caused the container to terminate.

Before making any configuration changes, we should understand why it happened.

Review the Resource Configuration

The next step is checking the memory requests and limits configured for the container.

Run:

kubectl get pod <pod-name> -o yaml

Locate the resources section.

resources:
  requests:
    memory: "512Mi"
    cpu: "250m"

  limits:
    memory: "1Gi"
    cpu: "500m"

There is often confusion between requests and limits.

A memory request determines the amount of memory Kubernetes reserves for the container during scheduling.

A memory limit defines the maximum memory the application is allowed to consume.

Once the application crosses that limit, the Linux kernel terminates the process immediately.

Setting memory limits too low is one of the most common reasons for OOMKilled pods.

Check Current Memory Usage

Before increasing memory limits, we should understand how much memory the application is actually consuming.

If Metrics Server is installed, run:

kubectl top pod

or

kubectl top pod <pod-name>

Example:

NAME              CPU(cores)   MEMORY(bytes)
payment-api       210m         985Mi

If the container has a memory limit of 1Gi and is already consuming around 985Mi, even a small increase in workload may cause the application to exceed its limit.

Monitoring memory usage gives us a much clearer picture than simply guessing.

Check the Previous Logs

When Kubernetes restarts a container, the current logs may only contain startup information.

The actual problem usually exists in the previous container.

Run:

kubectl logs <pod-name> --previous

Depending on the application, we may find messages related to:

  • Memory allocation failures

  • Large file uploads

  • Garbage collection warnings

  • Cache growth

  • Unexpected spikes in workload

Checking the previous logs has helped us identify the root cause of many production incidents.

Common Reasons for OOMKilled

Although every application behaves differently, most OOMKilled incidents fall into a few common categories.

Memory Leaks

Applications that continuously allocate memory without releasing it eventually consume all available memory.

Initially everything appears normal.

After running for several hours or days, memory usage gradually increases until the container reaches its configured limit.

Monitoring memory growth over time usually helps identify this pattern.

Processing Large Files

Applications processing PDFs, images, videos or large Excel files often require much more memory than expected.

If the entire file is loaded into memory, temporary spikes can exceed the configured limit.

Whenever possible, processing files in smaller chunks significantly reduces memory consumption.

Loading Large Datasets

Another common mistake is loading an entire dataset into memory before processing it.

Instead of loading thousands of records at once, processing them in batches or using streaming techniques keeps memory usage under control.

Traffic Spikes

Applications that perform well under normal traffic may consume considerably more memory during peak usage.

Higher request volumes often result in additional objects being created, more active database connections and larger caches.

Without sufficient memory planning, the container eventually exceeds its limit.

Incorrect Resource Configuration

Sometimes the application itself isn't the problem.

The configured memory limit simply doesn't reflect the application's actual workload.

Development and testing environments often use much smaller datasets than production, making it difficult to estimate realistic memory requirements.

Should We Simply Increase the Memory Limit?

Increasing the memory limit may stop the restarts temporarily, but it shouldn't be the first solution.

Before changing resource limits, it's worth asking a few questions.

  • Did the issue start after a recent deployment?

  • Has application traffic increased?

  • Are larger files being processed?

  • Has a new feature introduced additional memory usage?

  • Is memory usage continuously increasing over time?

Answering these questions often leads us to the actual root cause instead of masking the problem.

Review Recent Deployments

If the application was working correctly yesterday but started failing after a deployment, compare the recent changes.

Run:

kubectl rollout history deployment <deployment-name>

Recent code changes may have introduced:

  • Larger in-memory caches

  • Additional background workers

  • New libraries

  • Bigger response payloads

  • Changes in data processing

If production is impacted, rolling back to the previous deployment can restore service while the investigation continues.

Best Practices to Prevent OOMKilled

Preventing memory issues is much easier than troubleshooting them during an outage.

Some practices that have worked well across production environments include:

  • Configure realistic memory requests and limits.

  • Continuously monitor CPU and memory usage.

  • Process large files in batches.

  • Stream large datasets whenever possible.

  • Optimise application caching.

  • Load test applications before production releases.

  • Review memory consumption after every major deployment.

  • Configure Horizontal Pod Autoscaler where appropriate.

Small improvements in memory management often make a significant difference to application stability.

Common Mistakes

These are some of the mistakes we see most frequently while investigating OOMKilled incidents.

  • Increasing memory limits without identifying the root cause.

  • Ignoring historical memory usage.

  • Deploying applications without load testing.

  • Assuming Kubernetes is responsible for application crashes.

  • Forgetting to review previous container logs.

  • Using the same resource configuration for every environment.

Avoiding these mistakes usually makes troubleshooting much faster.

OOMKilled is one of the most common Kubernetes issues, but it's also one of the easiest to diagnose once we understand what Exit Code 137 represents.

Rather than treating OOMKilled as the actual problem, we should treat it as an indication that the application consumed more memory than its configured limit.

A structured troubleshooting process always produces better results than making configuration changes based on assumptions. Confirm the reason using kubectl describe, review the configured resource limits, analyse memory usage, inspect the previous logs and compare recent deployments before increasing memory.

In many cases, Kubernetes has already provided everything we need to identify the root cause. We simply need to collect the information in the right order and let the evidence guide our investigation.

Why Your Kubernetes Pod Keeps Restarting: A Complete Debugging Guide

If you've been working with Kubernetes for any length of time, you've probably encountered a pod that refuses to stay running. One moment it's starting successfully, and the next moment it's restarting. After a few attempts, Kubernetes reports a CrashLoopBackOff status, leaving many engineers wondering what went wrong.

The first reaction is often to delete the pod and hope Kubernetes creates a healthy replacement. While this may appear to solve the problem temporarily, it rarely fixes the actual issue. More importantly, deleting the pod too quickly can remove valuable information that would have helped identify the root cause.

The good news is that Kubernetes almost always tells us why a pod is restarting. We simply need to know where to look and follow a structured troubleshooting approach instead of making assumptions.

In this article, we will walk through the exact process we can use to diagnose and resolve Kubernetes pods that keep restarting.

Step 1: Check the Pod Status

Whenever a pod starts restarting, our first objective is to understand what Kubernetes already knows about it.

The first command we should run is:

kubectl get pods

Example output:

NAME                    READY   STATUS             RESTARTS   AGE
payment-api-65d8fd      0/1     CrashLoopBackOff   12         18m

Pay close attention to these three columns:

  • STATUS

  • RESTARTS

  • AGE

A restart count of 10 or 20 immediately tells us the application has been crashing repeatedly and Kubernetes has been trying to recover it.

Some common pod statuses include:

StatusMeaning
RunningApplication is healthy
PendingWaiting to be scheduled
CrashLoopBackOffContainer keeps crashing after startup
ImagePullBackOffUnable to pull the container image
ErrImagePullImage download failed
CompletedJob completed successfully
TerminatingPod is shutting down

Once we've confirmed the pod is restarting, the next step is to gather more information.

Step 2: Describe the Pod

One of the most useful commands in Kubernetes troubleshooting is:

kubectl describe pod <pod-name>

Many engineers immediately jump to application logs, but kubectl describe provides far more context.

It includes information such as:

  • Current container state

  • Previous container state

  • Restart count

  • Mounted volumes

  • Environment variables

  • Probe failures

  • Scheduling information

  • Events

The Events section at the bottom deserves special attention because Kubernetes records everything significant that happened to the pod.

For example:

Warning  Unhealthy
Readiness probe failed

Warning  BackOff
Back-off restarting failed container

In many situations, the Events section already points us in the right direction before we even inspect the application logs.

Step 3: Read the Application Logs

Once we've reviewed the pod details, it's time to inspect the application logs.

kubectl logs <pod-name>

If the pod has already restarted, don't forget to check the logs from the previous container instance:

kubectl logs <pod-name> --previous

This is one of the most overlooked commands in Kubernetes.

When a container crashes and restarts, the current logs may only show startup messages. The actual exception often exists only in the previous container's logs. We've solved many production issues simply by checking kubectl logs --previous.

Now that we've gathered the basic information, we can start identifying the actual reason behind the restarts.

Common Causes of Restarting Pods

In most production environments, restarting pods usually fall into one of the following categories.

CrashLoopBackOff

This is probably the most common Kubernetes error.

A CrashLoopBackOff doesn't tell us what failed—it simply tells us Kubernetes keeps restarting the container because it exits shortly after starting.

Common reasons include:

  • Application exceptions

  • Missing environment variables

  • Database connection failures

  • Invalid configuration

  • Missing ConfigMaps

  • Missing Secrets

  • Startup script failures

For example:

Error: Database connection refused

The application exits.

Kubernetes restarts it.

The application crashes again.

Eventually Kubernetes delays each restart attempt and reports CrashLoopBackOff.

It's important to remember that CrashLoopBackOff is a symptom, not the root cause.

The real reason is almost always available in the application logs.

OOMKilled

Another common reason for pod restarts is an out-of-memory condition.

If the application consumes more memory than allowed, the Linux kernel terminates the container.

We can verify this using:

kubectl describe pod <pod-name>

Look for something similar to:

Last State:
Terminated

Reason:
OOMKilled

Typical causes include:

  • Memory leaks

  • Processing large files

  • Image or PDF conversion

  • Loading large datasets into memory

  • Incorrect resource limits

Many teams immediately increase the memory limit and redeploy the application.

While that may stop the restarts temporarily, it's always worth understanding why the application is consuming excessive memory before increasing resources.

Readiness Probe Failures

A readiness probe tells Kubernetes whether the application is ready to receive traffic.

Example:

readinessProbe:
  httpGet:
    path: /health
    port: 8080

If the readiness probe fails:

  • The pod continues running.

  • Kubernetes stops routing traffic to it.

Common reasons include:

  • Wrong endpoint

  • Wrong port

  • Slow application startup

  • Database not yet available

  • Dependent services still starting

Readiness failures usually indicate that the application isn't fully initialised yet.

Liveness Probe Failures

Unlike readiness probes, liveness probes determine whether the application is still healthy.

If a liveness probe keeps failing, Kubernetes assumes the application is unhealthy and restarts it automatically.

Example:

livenessProbe:
  httpGet:
    path: /health
    port: 8080

One common mistake is configuring the liveness probe too aggressively.

For example, if an application requires 60 seconds to initialise but the liveness probe starts after only 15 seconds, Kubernetes may repeatedly kill the container before it has a chance to finish starting.

ImagePullBackOff

Sometimes the container never starts because Kubernetes cannot download the container image.

Possible reasons include:

  • Incorrect image name

  • Wrong image tag

  • Private registry authentication failure

  • Registry connectivity issues

  • Image does not exist

The quickest way to investigate is:

kubectl describe pod <pod-name>

Again, the Events section usually contains the exact reason for the image pull failure.

Configuration Problems

Configuration errors are another common cause of restarting pods.

Examples include:

  • Missing ConfigMaps

  • Missing Secrets

  • Incorrect environment variables

  • Invalid file paths

  • Missing certificates

  • Invalid application configuration

Even a simple typo in an environment variable can prevent an application from starting successfully.

Step 4: Check Resource Usage

Sometimes the problem isn't the application itself.

The Kubernetes node may be under heavy resource pressure.

We can check resource usage using:

kubectl top pod

kubectl top node

These commands help identify:

  • High CPU usage

  • High memory usage

  • Resource spikes

  • Node pressure

If these commands don't work, ensure the Kubernetes Metrics Server is installed.

Step 5: Review Cluster Events

Cluster events often provide additional context that application logs cannot.

Run:

kubectl get events --sort-by=.metadata.creationTimestamp

Look for messages related to:

  • Failed scheduling

  • Failed mounts

  • Failed image pulls

  • Probe failures

  • Resource exhaustion

Events frequently tell the complete story of what happened before the pod entered its restart cycle.

Step 6: Review Recent Deployments

If the application was working yesterday but started restarting after a deployment, don't ignore the possibility that the latest release introduced the issue.

Review the deployment history:

kubectl rollout history deployment payment-api

If required, roll back to the previous version:

kubectl rollout undo deployment payment-api

Rolling back isn't the final solution, but it can restore service quickly while we continue investigating the root cause.

A Simple Troubleshooting Checklist

Whenever we encounter a restarting pod, following a consistent process saves time and avoids unnecessary guesswork.

  1. Check the pod status.

  2. Describe the pod.

  3. Review the current logs.

  4. Review the previous logs.

  5. Check the Events section.

  6. Verify CPU and memory usage.

  7. Inspect readiness and liveness probes.

  8. Check for image pull errors.

  9. Review recent deployments.

Following the same sequence every time helps us diagnose problems much faster.

Common Mistakes

Over the years, we've seen the same mistakes repeated in many Kubernetes environments.

  • Deleting the pod before collecting logs.

  • Ignoring the Events section.

  • Increasing memory without understanding the root cause.

  • Restarting deployments repeatedly instead of investigating.

  • Forgetting to use kubectl logs --previous.

  • Assuming Kubernetes is the problem when the application itself is crashing.

Avoiding these mistakes can significantly reduce troubleshooting time.

Kubernetes doesn't restart containers without a reason. Every restart is a symptom of an underlying issue, whether it's an application crash, resource exhaustion, configuration error, failed health check or infrastructure problem.

The goal isn't to memorise dozens of Kubernetes commands. Instead, we should develop a structured troubleshooting approach that helps us identify the root cause quickly and consistently.

The next time you see a pod stuck in CrashLoopBackOff, resist the temptation to delete it immediately. Start by checking the pod status, describing the pod, reviewing the logs and inspecting the Events section. In most cases, Kubernetes has already provided enough information to lead us to the solution.

Saturday, January 29, 2022

How to Execute Shell Script from Dockerfile

Recently I faced a problem with Docker build. Before we build the image there was a requirement to cleanup something from MongoDB. For that we tried to create shell script and execute it from Dockerfile before we start the service. Here in this blog I will explain how you can execute the shell script from Dockerfile. 

For that first create the shell script in the same context of Dockerfile. That means the file should be in the same folder or subfolder where Dockerfile is there. 

We can not access file outside the Docker build context so file has to be in the same folder.

nano test.sh


You can put your content in this file for the tasks you want to do with this file. 


Now first we will copy the file in docker build context. So that it can be executed. This step is mandatory or else it will not find file. Add following lines of code in your Dockerfile.


FROM alpine3.15

COPY test.sh ./test.sh


Next step we have to change access rights of the script so it can be executed.


RUN chmod +x ./test.sh


Now since we have file in the Docker build context and it has proper access rights we can execute the file. 


RUN sh ./test.sh


That's it and now when to service starts it will execute the file and do all the actions which you have added in shell script.


Hope this helps you. 

Monday, January 3, 2022

Kubernetes ImagePullBackOff error

Recently I faced an issue while working Kubernetes cluster. I was trying to build docker image from local repository to test some changes in production before we merge the code. After building the docker image and recreating the container it was showing ImagePullBackOff error when I check the status with 

kubectl get pods

In this blog I am going to mention the possible cause for this error and will explain the resolution which worked for me.

In a Kubernetes, there’s an agent on for node which is called kubelet. This is responsible for running containers in the nodes. For some reason if image can't be pulled the kublet will throw ImagePullBackOff error. 

There are some possible reasons for this when kubectl is not able to pull image from mentioned registry. 

Either image or tag is not available. 

The name of the image or tag is wrong in deployment yaml file

kubectl does not have access to image registry. 

In my case there was no such issues mentioned above. The image with tag was there. I have specified the correct name in deployment yaml file and because it was a local registry, there was no need of authentication. 

So here is how I solved this issue. I set image pull policy to following value in my deployment yaml file.

imagePullPolicy: IfNotPresent

The reason it worked is, the kubectl was trying to find image in registry but it was not present because it was local registry so then it tried to find image in local repository and caches it. 

This solution worked for me because I was using local registry. If you are facing such issue try setting above value to imagePullPolicy and it may work. Hope this helps you.

Sunday, July 4, 2021

Docker MongoDB terminates when it runs out of memory

When you have multiple services running in docker container it's quite possible that you have an issues with certain services when your docker container runs out of memory. MongoDB is one such service. 

On docker container when you have MongoDB running and when it starts storing huge data it starts consuming lots of memory and that's where you have an issue. MongoDB will crash after sometime where isn't much memory left. 

The reason behind this is the IO model of MongoDB, it tries to keep as much data as possible in cache so read and write operations are much faster. But this creates an issue with docker as we have limited memory and lots of services are sharing that. 

Starting from MongoDB 3.2 on words WiredTiger storage engine is the default one for MongoDB and it's recommended. 

There are various advantages of WiredTiger storage engine. For example,

  • Document Level Concurrency
  • Snapshots and Checkpoints
  • Journal
  • Compression
  • Memory Use
One of most useful feature is Memory use. 

With WiredTiger, MongoDB utilizes both the WiredTiger internal cache and the filesystem cache.

You can control it with --wiredTigerCacheSizeGB configuration.

The --wiredTigerCacheSizeGB limits the size of the WiredTiger internal cache. The operating system will use the available free memory for filesystem cache, which allows the compressed MongoDB data files to stay in memory. In addition, the operating system will use any free RAM to buffer file system blocks and file system cache.

With this setting you can enhance memory usage. MongoDB will not use excessive memory and with heavy data usage on docker container MongoDB will not crash on excessive memory usage.

Hope this helps you.