Serverless Jenkins Pipelines with Fn Project

jenkins-lambdaThe Jenkinsfile-Runner-Fn project is a Fn Project (a container native, cloud agnostic serverless platform) function to run Jenkins pipelines. It will process a GitHub webhook, git clone the repository and execute the Jenkinsfile in that git repository. It allows scalability and pay per use with zero cost if not used.

This function allows Jenkinsfile execution without needing a persistent Jenkins master running in the same way as Jenkins X Serverless, but using the Fn Project platform (and supported providers like Oracle Functions) instead of Kubernetes.

Fn Project vs AWS Lambda

The function is very similar to the one in jenkinsfile-runner-lambda with just a small change in the signature. The main difference between Lambda and Fn is in the packaging, as Lambda layers are limited in size and are expanded in /optwhile Fn allows a custom Dockerfile where you can install whatever you want in a much easier way, just need to include the function code and entrypoint from fnproject/fn-java-fdk.

Oracle Functions

Oracle Functions is a cloud service providing Project Fn function execution (currently in limited availability). jenkinsfile-runner-fn function runs in Oracle Functions, with the caveat that it needs a syslog server running somewhere to get the logs (see below).

Limitations

Current implementation limitations:

Example

See the jenkinsfile-runner-fn-example project for an example that is tested and works.

Extending

You can add your plugins to plugins.txt. You could also add the Configuration as Code plugin for configuration.

Other tools can be added to the Dockerfile.

Installation

Install Fn

Building

Build the function

mvn clean package

Publishing

Create and deploy the function locally

fn create app jenkinsfile-runner
fn --verbose deploy --app jenkinsfile-runner --local

Execution

Invoke the function

cat src/test/resources/github.json | fn invoke jenkinsfile-runner jenkinsfile-runner

Logging

Get the logs for the last execution

fn get logs jenkinsfile-runner jenkinsfile-runner \
$(fn ls calls jenkinsfile-runner jenkinsfile-runner | grep 'ID:' | head -n 1 | sed -e 's/ID: //')

Syslog

Alternatively, start a syslog server to see the logs

docker run -d --rm -it -p 5140:514 --name syslog-ng balabit/syslog-ng:latest
docker exec -ti syslog-ng tail -f /var/log/messages-kv.log

Update the function to send logs to the syslog server

fn update app jenkinsfile-runner --syslog-url tcp://logs-01.loggly.com:514

GitHub events

Add a GitHub json webhook to your git repo pointing to the function url.

More information in the Jenkinsfile-Runner-Fn GitHub page.

Running Jenkins Pipelines in AWS Lambda

jenkins-lambdaThe Jenkinsfile-Runner-Lambda project is a AWS Lambda function to run Jenkins pipelines. It will process a GitHub webhook, git clone the repository and execute the Jenkinsfile in that git repository. It allows huge scalability with 1000+ concurrent builds and pay per use with zero cost if not used.

This function allows Jenkinsfile execution without needing a persistent Jenkins master running in the same way as Jenkins X Serverless, but using AWS Lambda instead of Kubernetes. All the logs are stored in AWS CloudWatch and are easily accessible.

Why???

Why not?

I mean, it could make sense to run Jenkinsfiles in Lambda when you are building AWS related stuff, like creating an artifact and uploading it to S3.

Limitations

Lambda limitations:

  • 15 minutes execution time
  • 3008MB of memory
  • git clone and generated artifacts must fit in the 500MB provided

Current implementation limitations:

Extending

Three lambda layers are created:

  • jenkinsfile-runner: the main library
  • plugins: minimal set of plugins to build a Jenkinsfile
  • tools: git, openjdk, maven

You can add your plugins in a new layer as a zip file inside a plugins dir to be expanded in /opt/plugins. You could also add the Configuration as Code plugin and configure the Artifact Manager S3 to store all your artifacts in S3.

Other tools can be added as new layers, and they will be expanded in /opt. You can find a list of scripts for inspiration in the lambci project (gcc,go,java,php,python,ruby,rust) and bash, git and zip (git is already included in the tools layer here)

The layers are built with Docker, installing jenkinsfile-runner, tools and plugins under /opt which is where Lambda layers are expanded. These files are then zipped for upload to Lambda.

Installation

Create a lambda function jenkinsfile-runner using Java 8 runtime. Use the layers built in target/layer-* and target/jenkinsfile-runner-lambda-*.jar as function. Could use make publish to create them.

Set

  • handler: org.csanchez.jenkins.lambda.Handler::handleRequest
  • memory: 1024MB
  • timeout: 15 minutes
aws lambda create-function \
    --function-name jenkinsfile-runner \
    --handler org.csanchez.jenkins.lambda.Handler::handleRequest \
    --zip-file fileb://target/jenkinsfile-runner-lambda-1.0-SNAPSHOT.jar \
    --runtime java8 \
    --region us-east-1 \
    --timeout 900 \
    --memory-size 1024 \
    --layers output/layers.json

Exposing the Lambda Function

From the lambda function configuration page add a API Gateway trigger. Select Create a new API and choose the security level. Save the function and you will get a http API endpoint.

Note that to achieve asynchronous execution (GitHub webhooks execution will time out if your webhook takes too long) you would need to configure API Gateway to send the payload to SNS and then lambda to listen to SNS events. See an example.

GitHub events

Add a GitHub json webhook to your git repo pointing to the lambda api gateway url.

 

More information in the Jenkinsfile-Runner-Lambda GitHub page.

Google Cloud Next Recap

google-next-logoSeveral interesting announcements from last week Google Next conference.

Knative, a new OSS project built by Google, Red Hat, IBM,… to build, deploy, and manage modern serverless workloads on Kubernetes. Built upon Istio, with 1.0 coming soon and managed Istio on GCP. It includes a build primitive to manage source to kubernetes flows, that can be used independently. Maybe it is the new standard to define sources and builds in Kubernetes. Read more from Mark Chmarny.

GKE on premise, a Google-configured version of Kubernetes with multi-cluster management, running on top of VMware’s vSphere.

Another Kubernetes related mention was the gVisor pod sandbox, with experimental support for Kubernetes, to allow running sandboxed containers in a Kubernetes cluster. Very interesting for multi-tenant clusters and docker image builds.

Cloud Functions are now Generally Available, and more serverless features are launched:

Serverless containers allow you to run container-based workloads in a fully managed environment and still only pay for what you use. Sign up for an early preview of serverless containers on Cloud Functions to run your own containerized functions on GCP with all the benefits of serverless.

A new GKE serverless add-on lets you run serverless workloads on Kubernetes Engine with a one-step deploy. You can go from source to containers instantaneously, auto-scale your stateless container-based workloads, and even scale down to zero.

Cloud Build, a fully-managed CI/CD platform that lets you build and test applications in the cloud. With an interesting approach where all the pipeline steps are containers themselves so it is reasonably easy to extend. It integrates with GitHub for repos with a Dockerfile (let’s see if it lasts long after Microsoft acquisition).

Other interesting announcements include:

  • Edge TPU, a tiny ASIC chip designed to run TensorFlow Lite ML models at the edge.
  • Shielded VMs – untampered virtual machines

  • Titan Security Key, a FIDO security key with firmware developed by Google. Google security was giving away at the conference both NFC and bluetooth keys, a good replacement for the yubikeys specially for mobile devices.

Running a JVM in a Container Without Getting Killed II

A follow up to Running a JVM in a Container Without Getting Killed

In Java 10 there is improved container integration.
No need to add extra flags, the JVM will use 1/4 of the container memory for heap.

$ docker run -m 1GB openjdk:10 java -XshowSettings:vm \
    -version
VM settings:
    Max. Heap Size (Estimated): 247.50M
    Using VM: OpenJDK 64-Bit Server VM

openjdk version "10.0.1" 2018-04-17
OpenJDK Runtime Environment (build 10.0.1+10-Debian-4)
OpenJDK 64-Bit Server VM (build 10.0.1+10-Debian-4, mixed mode)

Java 10 obsoletes the -XX:MaxRAM parameter, as the JVM will correctly detect the value.

You can still use the -XX:MaxRAMFraction=1 option to squeeze all the memory from the container.

$ docker run -m 1GB openjdk:10 java -XshowSettings:vm \
    -XX:MaxRAMFraction=1 -version
OpenJDK 64-Bit Server VM warning: Option MaxRAMFraction was deprecated in version 10.0 and will likely be removed in a future release.
VM settings:
    Max. Heap Size (Estimated): 989.88M
    Using VM: OpenJDK 64-Bit Server VM

openjdk version "10.0.1" 2018-04-17
OpenJDK Runtime Environment (build 10.0.1+10-Debian-4)
OpenJDK 64-Bit Server VM (build 10.0.1+10-Debian-4, mixed mode)

But it can be risky if your container uses off heap memory, as almost all the container memory is allocated to heap. You would have to either set -XX:MaxRAMFraction=2 and use only 50% of the container memory for heap, or resort to Xmx.

Serverless CI/CD with AWS ECS Fargate

Amazon AWS has recently launched ECS Fargate to “run containers without having to manage servers or clusters”.

So this got me interested enough to patch the Jenkins ECS plugin to run Jenkins agents as containers using Fargate model instead of the previous model where you would still need to create and manage VM instances to run the containers.

How does it work?

With the Jenkins ECS plugin you can configure a “Cloud” item that will launch all your agents on ECS Fargate, matching jobs to different container templates using labels. This means you can have unlimited agents with no machines to manage and just pay for what you use.

Some tips on the configuration:

  • Some options need to be configured, like subnet, security group and assign a public ip to the container in order to launch in Fargate.
  • Agents need to adhere to some predefined cpu and memory configurations. For instance for 1 vCPU you can only use 2GB to 8GB in 1GB increments.

Pricing

Price per vCPU is $0.00001406 per second ($0.0506 per hour) and per GB memory is $0.00000353 per second ($0.0127 per hour).

If you compare the price with a m5.large instance (4 vCPU, 16 GB) that costs $0.192 per hour, it would cost you $0,4056 in Fargate, more than twice, ouch! You could build something similar and cheaper with Kubernetes using the cluster autoscaler given you can achieve a high utilization of the machines.

While I was writing this post someone already beat me to submit a PR to the ECS plugin to add the Fargate support.

Speaking Trips on DevOps, Kubernetes, Jenkins

This 2nd half of the year speaking season is starting and you’ll find me speaking about DevOps, Kubernetes, Jenkins,… at

If you organize a conference and would like me to give a talk in 2018 you can find me @csanchez.

Screen Shot 2017-08-24 at 17.07.45.png

Running a JVM in a Container Without Getting Killed

No pun intended

The JDK 8u131 has backported a nice feature in JDK 9, which is the ability of the JVM to detect how much memory is available when running inside a Docker container.

I have talked multiple times about the problems running a JVM inside a container, how it will default in most cases to a max heap of 1/4 of the host memory, not the container.

For example in my machine

$ docker run -m 100MB openjdk:8u121 java -XshowSettings:vm -version
VM settings:
    Max. Heap Size (Estimated): 444.50M
    Ergonomics Machine Class: server
    Using VM: OpenJDK 64-Bit Server VM

Wait, WAT? I set a container memory of 100MB and my JVM sets a max heap of 444M ? It is very likely that it is going to cause the Kernel to kill my JVM at some point.

Let’s try the JDK 8u131 with the experimental option -XX:+UseCGroupMemoryLimitForHeap

$ docker run -m 100MB openjdk:8u131 java \
  -XX:+UnlockExperimentalVMOptions \
  -XX:+UseCGroupMemoryLimitForHeap \
  -XshowSettings:vm -version
VM settings:
    Max. Heap Size (Estimated): 44.50M
    Ergonomics Machine Class: server
    Using VM: OpenJDK 64-Bit Server VM

Ok this makes more sense, the JVM was able to detect the container has only 100MB and set the max heap to 44M.

Let’s try in a bigger container

$ docker run -m 1GB openjdk:8u131 java \
  -XX:+UnlockExperimentalVMOptions \
  -XX:+UseCGroupMemoryLimitForHeap \
  -XshowSettings:vm -version
VM settings:
    Max. Heap Size (Estimated): 228.00M
    Ergonomics Machine Class: server
    Using VM: OpenJDK 64-Bit Server VM

Mmm, now the container has 1GB but JVM is only using 228M as max heap. Can we optimize this even more, given that nothing else other than the JVM is running in the container? Yes we can!

$ docker run -m 1GB openjdk:8u131 java \
  -XX:+UnlockExperimentalVMOptions \
  -XX:+UseCGroupMemoryLimitForHeap \
  -XX:MaxRAMFraction=1 -XshowSettings:vm -version
VM settings:
    Max. Heap Size (Estimated): 910.50M
    Ergonomics Machine Class: server
    Using VM: OpenJDK 64-Bit Server VM

Using -XX:MaxRAMFraction we are telling the JVM to use available memory/MaxRAMFraction as max heap. Using -XX:MaxRAMFraction=1 we are using almost all the available memory as max heap.

UPDATE: follow up for Java 10+ at Running a JVM in a Container Without Getting Killed II