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Bagan

Introduction: Automate experimentation and collect data on Gradle projects

Tags:

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Bagan is an experimental framework used to automate the execution, reporting and collection of data for Gradle projects, using Kubernetes.

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How to use Bagan

Once you have downloaded this repository you need to set up the bagan_conf.json. You can define different properties like the type of experiments you want to apply, the target repository or the resources you want to use in the Kubernetes environments.

Bagan will be executed with the ./bagan command following the next format:

./bagan MODE COMMAND

The execution framework and reporting happen in Kubernetes environment. For each experiment, Bagan will create a Helm Release where it will run the target repository applying the experimentation. To report and collect the information of the build, Bagan will inject Talaiot in the Gradle configuration of the project using InfluxDb as time-series database and Grafana as a dashboard visualization tool.

Bagan can deploy a new cluster or will use an existing cluster to execute the experimentation defined in the main conf file.

bagan_conf.json

The main configuration file is bagan_conf.json. The properties to be configured are:

Property	Description
repository	Repository of the project we want to experiment.
gradleCommand	Gradle command to be executed in each Experiment from the repository.
clusterName	Name of the cluster where the experimentation will be executed. If it is not specified the default name is Bagan.
zone	Zone of the machine for Kubernetes Engine in Google Cloud.
machine	Type of Machine used in the experimentation. Check The cost of Bagan.
private	Flag to indicate the target repository is private.
ssh	Pah to the ssh key.
known_hosts	Path to the file that has the name of a server and a public-key that is applicable to the server machine.
iterations	Number of executions of the Gradle command.
experiments	Experimentation properties for the execution.

Example:

{
   "bagan": {
      "repository": "git@github.com:cdsap/TestPrivateRepository.git",
      "gradleCommand": "./gradlew clean assembleDebug",
      "clusterName": "",
      "machine": "n1-standard-4",
      "private": true,
      "ssh": "/user/.ssh/id_rsa",
      "known_hosts": "/user/.ssh/known_hosts",
      "iterations": 20,
      "experiments": {
         "properties": [
            {
               "name": "org.gradle.jvmargs",
               "options": ["-Xmx3g","-Xmx4g"]
            }
         ],
         "branch": [ "develop3","master"]
      }
   }
}

Experiments

An Experiment is an Entity that represents a specific state of the target repository. This state is related to a different configuration of the build system or the control version system. Currently, the types of experiments supported are:

Experiment Type	Description
properties	Specifies different values for Gradle properties.
branch	Specifies different branches for experimentation.
gradleWrapperVersion	Specifies different versions of the wrapper.

Example:

"experiments": {
   "properties": [],
   "branch": [],
   "gradleWrapperVersion": []
}

Bagan will apply a cartesian product for all the experiments, and each combination will be considered as an element to be tested. In case we want to experiment with jvmargs in the project, we will include:

"experiments": {
   "properties": [
      {
         "name": "org.gradle.jvmargs",
         "options": ["-Xmx3g","-Xmx4g"]
      }
   ]
}

And the experiments generated are:

Experiments
org.gradle.jvmargs="-Xmx3g"
org.gradle.jvmargs="-Xmx4g"

We can experiment with different types of experiments, here we are using properties, branches and Gradle Wrapper versions:

"experiments": {
   "properties": [
      {
         "name": "org.gradle.jvmargs",
         "options": ["-Xmx3g","-Xmx4g"]
      },
      {
         "name": "org.gradle.caching",
         "options": ["true","false"]
      },
   ],
   "branch": [ "develop","master"],
   "gradleWrapperVersion" : ["5.5", 5.4"]
}

The experiments generated are:

Experiments
org.gradle.jvmargs="-Xmx3g" org.gradle.caching="true" develop 5.5	org.gradle.jvmargs="-Xmx3g" org.gradle.caching="true" develop 5.4	org.gradle.jvmargs="-Xmx3g" org.gradle.caching="true" master 5.4	org.gradle.jvmargs="-Xmx3g" org.gradle.caching="true" master 5.5
org.gradle.jvmargs="-Xmx4g" org.gradle.caching="true" develop 5.5	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="true" develop 5.4	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="true" master 5.4	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="true" master 5.5
org.gradle.jvmargs="-Xmx3g" org.gradle.caching="false" develop 5.5	org.gradle.jvmargs="-Xmx3g" org.gradle.caching="false" develop 5.4	org.gradle.jvmargs="-Xmx3g" org.gradle.caching="false" master 5.4	org.gradle.jvmargs="-Xmx3g" org.gradle.caching="false" master 5.5
org.gradle.jvmargs="-Xmx4g" org.gradle.caching="false" develop 5.5	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="false" develop 5.4	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="false" master 5.4	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="false" master 5.5

Modes

Bagan uses modes to identify the environment which Kubernetes will use to set up the configuration and execute the experiments. There are supported three modes:

Mode	Description
gcloud	Bagan will be executed in Kubernetes Engine in Gcloud using gcloud sdk.
gcloud_docker	Bagan will be executed in Kubernetes Engine in Gcloud using Docker and avoiding to install additional sdk in your machine. The image is cdsap/bagan-init.
standalone	Bagan will be executed in the environment configured in the host machine.

Commands

Commands are the tasks to be executed in the mode selected. There are two main commands groups.

Meta Commands

Meta Commands offer a complete execution of Bagan. It may contain multiple single commands.

Command	Description
cluster	Create cluster, infrastructure and execute experiments in the environment mode chosen.
infrastructure	Create infrastructure and execute experiments in the environment mode chosen.
experiment	Execute experiments in the environment mode chosen.

Examples:

  ./bagan gcloud cluster
  ./bagan gcloud_docker infrastructure
  ./bagan standalone experiments

Single commands

Meta commands are composed by single commands. You can execute single commands depending on your requirements. For example, if you are using an existing cluster and some of the components required by Bagan are installed, you can execute a single command:

Command	Description
create_cluster	Create a new cluster in the environment mode selected.
infra_pods	Create infrastructure(Grafana + InfluxDb) in the environment mode selected.
credentials	Get the credentials for the current cluster.
secret	Create a secret object in the mode selected, required to experiment with private repositories.
helm	Execute initialization of Helm and the cluster role binding required in Kubernetes.
helm_init	Execute initialization of Helm.
grafana	Install the Chart of Grafana with Helm at the cluster.
influxfb	Install the Chart of InfluxDb with Helm at the cluster.
services	Creates a service port of type Load Balance for Grafana.
remove_experiments	Remove experiments in the cluster.
grafana_dashboard	Retrieve the public IP of the Dashboard created.

Lifecycle Bagan

We can group the execution of Bagan in three main stages:

Verification
Provisioning
Execution Experiments

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Verification

During this phase, Bagan will validate the configuration file bagan_conf.json and the inputs included. It's executed in the host machine and requires jq to perform the validation.

Provisioning

In this phase, Bagan will execute the command included in the mode selected. For gcloud and standalone modes, it will be executed in the host machine. For gcloud_docker will be executed in a docker image(cdsap/bagan-init). Kubectl and Helm will be configured. Depending on the command selected, different actions will be handle. Check commands section.

Execution

The execution phase will be driven by the kscript BaganGenerator.kt. This script has three main functions:

Calculate the combination of experiments
Generate the Grafana Dashboard with the experimentation and Gradle Commands
Create and install the experiment environment.

Before the execution of the experiments, the deployments of Grafana and InfluxDb are created. The release of Grafana includes a provisioned data source for InfluxDb. Later, a Dashboard is generated with the configuration provided by the bagan_conf.json: alt text The dashboard contains five panels:

Graph of the command group by experiment.
Percentile(80) of the experiments on the command configured.
Minimum build times by experiment and command.
Experiment Winner.
Information about the experiments.

To access the dashboard, you have to access:

http://IP:3000/d/IS3q0sSWz

To retrieve the IP, you can use the Bagan command:

./bagan gcloud grafana_dashboard

User and password by default are admin/admin. Additionally, you can create your panels in Grafana. It follows the same scheme used in Talaiot being the primary points build and task:

Internals Bagan

Kubernetes Infrastructure

Independent of the environment selected, the general infrastructure for Bagan in Kubernetes is:

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To create the instances of Grafana and InfluxDb, we are using the Kubernetes package manager Helm. Helm is used to create experiments in Kubernetes too. In case of modes gcloud and gcloud_docker, additional Cluster Role Binding objects are created to be used by tiller/Helm. For gcloud and standalone, we can use kubectl as a command-line interface for running commands against Kubernetes clusters.

We can use the Google cloud console, https://console.cloud.google.com, where we have a user interface to manage the cluster:

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Pod Experiment execution

Every experiment generates a Helm release with this structure:

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values.yaml contains the information related to the experiment and the configuration provided in the Bagan conf.

repository: https://github.com/android/plaid.git
branch: master
configMaps: configmapexperiment1
pod: experiment1
session: sessionId
name: experiment1
image: cdsap/bagan-pod-injector:0.1.6
command: ./gradlew assemble
iterations: 10

configmapexperimentN.yaml contains the data of the permutation calculated for the experiment:

apiVersion: v1
kind: ConfigMap
metadata:
  name: {{ .Values.configMaps }}
  labels:
    app: bagan
    type: experiment
    session: {{ .Values.session }}
data:
  id: {{ .Values.name }}
  properties: |
               org.gradle.jvmargs=-Xmx6g
               org.gradle.workers.max=1

The execution of the build happens inside the pod created by podexperimentN.yaml. The Docker image used by the pod is cdsap/bagan-pod. This pod is responsible for:

Fetch Target repository in a specific volume.
Inject Talaiot in the project
Apply the experimentation for Gradle Properties and Gradle Wrapper versions parsing the data from the configmap.
Execute the build given N iteration

The execution flow is:

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When the Pod is running, it will execute the ExperimentController.kt (using kscript) that starts applying Talaiot in the main Gradle configuration file (groovy/kts):

publishers {
    influxDbPublisher {
        dbName = "tracking"
        url = "http://bagan-influxdb.default:8086"
        taskMetricName = "tasks"
        buildMetricName = "build"
    }
}

Later, the ExperimentController.kt will parse the data of the configmap and will apply the different experiments. Note that in the case of Branch Experimentation, the experiment will be applied in the Pod and not in the configmap.

Requirements by Mode

gcloud

gcloud_docker

jq
Docker

standalone

jq
kubectl configured with existing cluster

The cost of Bagan

In case you are using Google Cloud as Kubernetes environment, you should consider the impact in terms of cost($$) of the experimentation. Android projects are expensive in terms of memory consumption and creating multiples combinations with not enough powerful machine will cause the failure of the experiments:

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Increasing the resources of the machine will help to succeed in the execution of the experiment, but it brings more consumption of resources a.k.a money. Remember that more permutations of the different experiments will generate more Pods. There is no restriction on the machine used in Bagan, for small/medium projects we recommend the n2 series(cost per hour):

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Check more documentation about region, zones and availability in Gcloud here: https://cloud.google.com/compute/docs/regions-zones/#available

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In case you are using Google Cloud only to create the cluster for Bagan, we recommend to remove it after the experimentation and evaluation of the results have finished.

Examples

Example 1

Simple example of a private repository with three modules

bagan_conf
machine	n2-standard-2
zone	asia-southeast1-b
repository	git@github.com:cdsap/TestPrivateRepository.git
private	true
experiments	gradle properties :{ "org.gradle.jvmargs": ["-Xmx3g","-Xmx4g"]}
command	./gradlew clean assembleDebug
iterations	20

Twp experiments of Type Gradle Properties will be generated:

-Xmx3g
-Xmx4g

Result:

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Example 2

Experimentation on Google project Plaid, with kapt properties

bagan_conf
machine	n2-standard-8
zone	asia-southeast1-b
repository	https://github.com/android/plaid.git
private	false
experiments	gradle properties: { "org.gradle.jvmargs": ["-Xmx2g","-Xmx4g"] } { "org.gradle.caching": ["true","false"] } { "kapt.incremental.apt": ["true","false"] } { "kapt.use.worker.api": ["true","false"] }
command	./gradlew clean assembleDebug
iterations	20

"properties": [ { "name": "org.gradle.jvmargs", "options": ["-Xmx2g","-Xmx4g"] }, { "name": "org.gradle.caching", "options": ["true","false"] }, { "name": "kapt.incremental.apt", "options": ["true","false"] }, { "name": "kapt.use.worker.api", "options": ["true","false"] }

16 experiments of Type Gradle Properties will be generated:

Experiments
org.gradle.jvmargs="-Xmx2g" org.gradle.caching="true" kapt.incremental.apt="true" kapt.use.worker.api="true"	org.gradle.jvmargs="-Xmx2g" org.gradle.caching="true" kapt.incremental.apt="true" kapt.use.worker.api="false"	org.gradle.jvmargs="-Xmx2g" org.gradle.caching="true" kapt.incremental.apt="false" kapt.use.worker.api="false"	org.gradle.jvmargs="-Xmx2g" org.gradle.caching="true" kapt.incremental.apt="false" kapt.use.worker.api="true"
org.gradle.jvmargs="-Xmx2g" org.gradle.caching="false" kapt.incremental.apt="true" kapt.use.worker.api="true"	org.gradle.jvmargs="-Xmx2g" org.gradle.caching="false" kapt.incremental.apt="true" kapt.use.worker.api="false"	org.gradle.jvmargs="-Xmx2g" org.gradle.caching="false" kapt.incremental.apt="false" kapt.use.worker.api="true"	org.gradle.jvmargs="-Xmx2g" org.gradle.caching="false" kapt.incremental.apt="false" kapt.use.worker.api="false"
org.gradle.jvmargs="-Xmx4g" org.gradle.caching="true" kapt.incremental.apt="true" kapt.use.worker.api="true"	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="true" kapt.incremental.apt="true" kapt.use.worker.api="false"	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="true" kapt.incremental.apt="false" kapt.use.worker.api="false"	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="true" kapt.incremental.apt="false" kapt.use.worker.api="true"
org.gradle.jvmargs="-Xmx4g" org.gradle.caching="false" kapt.incremental.apt="true" kapt.use.worker.api="true"	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="false" kapt.incremental.apt="true" kapt.use.worker.api="false"	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="false" kapt.incremental.apt="false" kapt.use.worker.api="true"	org.gradle.jvmargs="-Xmx4g" org.gradle.caching="false" kapt.incremental.apt="false" kapt.use.worker.api="false"

Result:

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We don't appreciate on Plaid project significant different using properties like kapt.incremental.apt and kapt.use.worker.api. However, you can notice the benefits of using caching on the Gradle Builds.

Example 3

Experimentation on Android Showcase project, with Gradle properties and different Gradle Wrapper versions

bagan_conf
machine	n2-standard-8
zone	asia-southeast1-b
repository	https://github.com/igorwojda/android-showcase
private	false
experiments	gradle properties: { "org.gradle.jvmargs": ["-Xmx2g","-Xmx4g"] } { "org.gradle.caching": ["true","false"] } { "kapt.incremental.apt": ["true","false"] } { "kapt.use.worker.api": ["true","false"] } Gradle Wrapper versions: ["5.6.1","5.5"]
command	./gradlew clean :app:bundleDebug
iterations	20

Result:

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Best times are using Gradle 5.6.1.

Deploy

Bagan is open source and you can contribute or adapt it under your requirements. The folder structure of the project is:

Folder
baganGenerator
deploy
docker
k8s
scripts

Bagan Generator

Bagan Generator is the Kotlin project with two main applications:

Generation and installation of Experiments and Dashboards.
Experiment controller handling the injection of Talaiot and experimentation on the pod.

We are using kscript to run the Kotlin classes. We need to transform the way we import the dependencies to be used in kscript. The task convertFiles(depending on build task) will convert the imports in a kscript format.

Deploy

Deployment folder used for:

Build the BaganGenerator project.
Copy the binaries required for each image to the folder
Build, tag and push the docker image for bagan-init
Build, tag and push the docker image for bagan-pod

Docker

It contains the Docker images for bagan-init and bagan-pod. It includes the binaries generated by the baganGenerator in the deployment step. In case you want to provide your own images, you can set the values on the deployment file(deploy/deployment.sh):

VERSION="0.1.20"
REGISTRY=""
IMAGE_BAGAN_INIT="cdsap/bagan-init"
IMAGE_BAGAN_POD="cdsap/bagan-pod-injector"

K8s

It contains the Grafana and InfluxDb charts. Grafana contains the provisioned data source configuration for InfluxDb. Also includes the default dashboard that will be updated when experimentation will be executed.

Scripts

It contains the bash scripts related to the verification and provisioning phase. For each mode, there is available one command executor.

Tools/Libraries used in Bagan

The real Bagan

Bagan is an ancient city and a UNESCO World Heritage Site located in the Mandalay Region of Myanmar. If you have the opportunity, visit Bagan: https://en.wikipedia.org/wiki/Bagan

Apps

Android Developer Tools

Android Developer Tools Pro