In this example, you will go through the optimization of a Spark application running on AWS instances. We’ll be using a PageRank implementation included in Renaissance, an industry-standard Java benchmarking suite, tuning both Java and AWS parameters to improve the performance of our application.
For this example, you’re expected to use two dedicated machines:
an Akamas instance
a Linux-based AWS EC2 instance
The Akamas instance requires provisioning and manipulating instances, therefore it requires to be enabled to do so by setting AWS Policies, integrating with orchestration tools (such as Ansible), and an inventory linked to your AWS EC2 environment.
The Linux-based instance will run the application benchmark, so it requires the latest open-jdk11 release
For this study you’re going to require the following telemetry providers:
CSV Provider to parse the results of the benchmark
Prometheus provider to monitor the instance
AWS Telemetry provider to extract instance price
The renaissance suite provides the benchmark we’re going to optimize.
Since the application consists of a jar file only, the setup is rather straightforward; just download the binary in the ~/renaissance/
folder:
In the same folder upload the template file launch.benchmark.sh.temp
, containing the script that executes the benchmark using the provided parameters and parses the results:
You may find further info about the suite and its benchmarks in the official doc.
In this section, we will guide you through the steps required to set up the optimization on Akamas.
This example requires the installation of the following optimization packs:
Our system could be named renaissance after its application, so you’ll have a system.yaml
file like this:
Then create the new system resource:
The renaissance system will then have three components:
A benchmark component
A Java component
An EC2 component, i.e. the underlying instance
Java component
Create a component-jvm.yaml
file like the following:
Then type:
Benchmark component
Since there is no optimization pack associated with this component, you have to create some extra resources.
A metrics.yaml
file for a new metric tracking execution time:
A component-type benchmark.yaml
:
The component pagerank.yaml
:
Create your new resources, by typing in your terminal the following commands:
EC2 component
Create a component-ec2.yaml
file like the following:
Then create its resource by typing in your terminal:
The workflow in this example is composed of three main steps:
Update the instance type
Run the application benchmark
Stop the instance
To manage the instance we are going to integrate a very simple Ansible in our workflow: the FileConfigurator operator will replace the parameters in the template file in order to generate the code run by the Executor operator, as explained in the Ansible page.
In detail:
Update the instance size
Generate the playbook file from the template
Update the instance using the playbook
Wait for the instance to be available
Run the application benchmark
Configure the benchmark Java launch script
Execute the launch script
Parse PageRank output to make it consumable by the CSV telemetry instance
Stop the instance
Configure the playbook to stop an instance with a specific instance id
Run the playbook to stop the instance
The following is the template of the Ansible playbook:
The following is the workflow configuration file:
If you have not installed the Prometheus telemetry provider or the CSV telemetry provider yet, take a look at the telemetry provider pages Prometheus provider and CSV Provider to proceed with the installation.
Prometheus
Prometheus allows us to gather jvm execution metrics through the jmx exporter: download the java agent required to gather metrics from here, then update the two following files:
The prometheus.yml
file, located in your Prometheus folder:
The config.yml
file you have to create in the ~/renaissance folder:
Now you can create a prometheus-instance.yaml
file:
Then you can install the telemetry instance:
You may find further info on exporting Java metrics to Prometheus here.
CSV - Telemetry instance
Create a telemetry-csv.yaml
file to read the benchmark output:
Then create the resource by typing in your terminal:
Here we provide a reference study for AWS. As we’ve anticipated, the goal of this study is to optimize a sample Java application, the PageRank benchmark you may find in the renaissance benchmark suite by Oracle.
Our goal is rather simple: minimizing the product between the benchmark execution time and the instance price, that is, finding the most cost-effective instance for our application.
Create a study.yaml
file with the following content:
Then create the corresponding Akamas resource and start the study: