Ooso lets you run MapReduce jobs in a serverless way. It is based on managed cloud services, Amazon S3 and AWS Lambda and is mainly an alternative to standard ad-hoc querying and batch processing tools such as Hadoop and Spark.

Table of contents

• I. Architecture and workflow
• II. How to use the library
  • 1. Project structure
  • 2. Library dependency
  • 3. Classes to implement
  • 4. Configuration file
  • 5. Project packaging
• III. AWS Infrastructure
  • 1. S3 Buckets
  • 2. IAM Roles and policies
  • 3. Lambda functions
  • 4. Deployment
• IV. Running the job

---

I. Architecture and workflow

The library workflow is as follows:

1. The workflow begins by invoking the Mappers Driver lambda function
2. The Mappers Driver does two things:
   1. It computes batches of data splits and assigns each batch to a Mapper
   2. It invokes a Mappers Listener lambda function which is responsible of detecting the end of the map phase
3. Once the Mappers Listener detects the end of the map phase, it invokes a first instance of the Reducers Driver function
4. The Reducers Driver is somewhat similar to the Mappers Driver:
   1. It computes batches from either the Map Output Bucket if we are in the first step of the reduce phase, or from previous reducers outputs located in the Reduce Output Bucket. It then assigns each batch to a Reducer
   2. It also invokes a Reducers Listener for each step of the reduce phase.
5. Once the Reducers Listener detects the end of a reduce step, it decides whether to invoke the next Reducers Driver if the previous reduce step produced more than one file. Otherwise, there is no need to invoke a Reducers Driver, because the previous step would have produced one single file which is the result of the job

---

II. How to use the library

1. Project Structure

The easiest way is to clone the repository and use the provided example project directory which has the following structure:

.
├── package.sh
├── provide_job_info.py
├── pom.xml
└── src
    └── main
        ├── java
        │   ├── mapper
        │   │   └── Mapper.java
        │   └── reducer
        │       └── Reducer.java
        └── resources
            └── jobInfo.json

2. Library dependency

Declare the library dependency in the pom.xml file

    <dependencies>
    ...
        <dependency>
            <groupId>fr.d2-si</groupId>
            <artifactId>ooso</artifactId>
            <version>0.0.4</version>
        </dependency>
    ...
    </dependencies>

3. Classes to implement

Implement your Mapper, Reducer and Launcher:

• The class Mapper is the implementation of your mappers. It must extend the fr.d2si.ooso.mapper.MapperAbstract class which looks like the following:

  public abstract class MapperAbstract {
      public abstract String map(BufferedReader objectBufferedReader);
  }

  The map method receives a BufferedReader as a parameter which is a reader of the batch part that the mapper lambda processes. The Reader closing is done internally for you.

• The class Reducer is the implementation of your reducers. It must extend the fr.d2si.ooso.reducer.ReducerAbstract class which looks like the following:

  public abstract class ReducerAbstract {
      public abstract String reduce(List<ObjectInfoSimple> batch);
  }

  The reduce method receives a list of ObjectInfoSimple instances, which encapsulate information about the objects to be reduced. In order to get a reader from an ObjectInfoSimple instance, you can do something like this:

  public String reduce(List<ObjectInfoSimple> batch) {
  
      for (ObjectInfoSimple objectInfo : batch) {
  
          BufferedReader objectBufferedReader = Commons.getReaderFromObjectInfo(objectInfo);
  
          //do something with the reader then close it
          objectBufferedReader.close();
      }
  }

  For the reducer, you are responsible of closing the opened readers.

• The Launcher is responsible of starting your job. Under the hood, it serializes your Mapper and Reducer and sends them to your Mappers Driver which then propagates them to the rest of the lambdas.

  All you need to do is to create a class with a main method and instantiate a Launcher that points to your Mapper and Reducer. Your class should look like this:

  public class JobLauncher {
      public static void main(String[] args) {
          //setup your launcher
          Launcher myLauncher = new Launcher()
                                          .withMapper(new Mapper())
                                          .withReducer(new Reducer());
          //launch your job
          myLauncher.launchJob();
      }
  }

  In order to make our jar package executable, you need to set the main class that serves as the application entry point.

  If you are using the maven shade plugin, you can do so as follows:

  <build>
      ...
          <plugins>
              <plugin>
                  ...
                  <configuration>
                      <transformers>
                          <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                              <mainClass>job.JobLauncher</mainClass>
                          </transformer>
                      </transformers>
                  </configuration>
                  ...
              </plugin>
          </plugins>
      ...
      </build>

  Please take a look at one of our example pom.xml files for further details on how to configure your maven project.

4. Configuration file

Edit the jobInfo.json file located at src/main/resources to reflect your infrastructure details:

{
  "jobId": "your-job-id",
  "jobInputBucket": "input",
  "mapperOutputBucket": "mapper-output",
  "reducerOutputBucket": "reducer-output",
  "mapperFunctionName": "mapper",
  "mappersDriverFunctionName": "mappers_driver",
  "mappersListenerFunctionName": "mappers_listener",
  "reducerFunctionName": "reducer",
  "reducersDriverFunctionName": "reducers_driver",
  "reducersListenerFunctionName": "reducers_listener",
  "mapperForceBatchSize": "-1",
  "reducerForceBatchSize": "-1",
  "mapperMemory": "1536",
  "reducerMemory": "1536",
  "disableReducer": "false"
}

Below is the description of some attributes (the rest is self explanatory).

Attribute	Description
jobId	Used to identify a job and separate outputs in order to avoid overwriting data between jobs
jobInputBucket	Contains the dataset splits that each Mapper will process
mapperOutputBucket	The bucket where the mappers will put their results
reducerOutputBucket	The bucket where the reducers will put their results
reducerMemory and mapperMemory	The amount of memory(and therefore other resources) allocated to the lambda functions. They are used internally by the library to compute the batch size that each mapper/reducer will process.
mapperForceBatchSize and reducerForceBatchSize	Used to force the library to use the specified batch size instead of automatically computing it. reducerForceBatchSize must be greater or equal than 2
disableReducer	If set to "true", disables the reducer

5. Project packaging

In order to generate the jar file used during the deployment of the lambda, you need to install maven.

Then, run package.sh script to create the project jar:

./package.sh

---

III. AWS Infrastructure

Before diving into the infrastructure details, please have a look at the deployment section.

1. S3 Buckets

Our lambda functions use S3 Buckets to fetch needed files and put the result of their processing.

You need three buckets:

• An input bucket containing your data splits
• Two intermediary buckets used by the mappers and reducers

You must use the same bucket names used in the configuration step above.

You may create the buckets using the console, the command line or our Terraform template.

2. IAM Roles and policies

a. Create an IAM role with the following trust policy

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

You may create the IAM role using the console, the command line or our Terraform template.

b. Attach the following policies to your role

• arn:aws:iam::aws:policy/AWSLambdaFullAccess
• arn:aws:iam::aws:policy/AmazonS3FullAccess

Note that these policies are too broad. You may use more fine-grained policies/roles for each lambda.

You may attach the policies using the console, the command line or our Terraform template.

3. Lambda functions

Create the required lambdas with the following details:

Lambda Name	Handler	Memory	Function package	Runtime
mappers_driver	fr.d2si.ooso.mappers_driver.MappersDriver	1536	example-project/target/job.jar	java8
mappers_listener	fr.d2si.ooso.mappers_listener.MappersListener	1536	example-project/target/job.jar	java8
mapper	fr.d2si.ooso.mapper_wrapper.MapperWrapper	same value as in the configuration file	example-project/target/job.jar	java8
reducers_driver	fr.d2si.ooso.reducers_driver.ReducersDriver	1536	example-project/target/job.jar	java8
reducers_listener	fr.d2si.ooso.reducers_listener.ReducersListener	1536	example-project/target/job.jar	java8
reducer	fr.d2si.ooso.reducer_wrapper.ReducerWrapper	same value as in the configuration file	example-project/target/job.jar	java8

We assume that the project jar is located at example-project/target/job.jar.

You may create the lambda functions using the console, the command line or our Terraform template.

Note that you'll only need to deploy the lambdas once. You will be able to run all your jobs even if your business code changes without redeploying the infrastructure.

4. Deployment

a. The easy way

We provide a fully functional Terraform template that creates everything for you, except the input bucket. This template uses the job configuration file. Here is how to use it:

• install Terraform
• make sure your job configuration file is correct
• run the following commands

   cd terraform
   terraform plan
   terraform apply
  

For more info about Terraform, check Terraform documentation.

b. The less easy way

You may use any deployment method you are familiar with. The AWS console, the AWS cli, python scripts, ... However we recommend using an Infrastructure-As-Code (IAC) tool such as Terraform or CloudFormation.

---

IV. Running the job

In order to run your job, you may execute the main method of the same jar that you used during the deployment. You may either execute it from your IDE or using the command line as follows:

    java -jar job.jar

---

The logo is made by Freepik from www.flaticon.com and is licensed by CC 3.0 BY