docs(batch): update README (aws#8214)

This should be the final docs update based on the recent changes to:

- Introduce Batch support
- Fix issues with managed/unmanaged compute envs

----

*By submitting this pull request, I confirm that my contribution is made under the terms of the Apache-2.0 license*

packages/@aws-cdk/aws-batch/README.md

@@ -1,4 +1,5 @@
 ## AWS Batch Construct Library
+
 <!--BEGIN STABILITY BANNER-->
 ---
 
@@ -15,37 +16,206 @@
 
 This module is part of the [AWS Cloud Development Kit](https://github.com/aws/aws-cdk) project.
 
-## Launch template support
+AWS Batch is a batch processing tool for efficiently running hundreds of thousands computing jobs in AWS. Batch can dynamically provision different types of compute resources based on the resource requirements of submitted jobs.
+
+AWS Batch simplifies the planning, scheduling, and executions of your batch workloads across a full range of compute services like [Amazon EC2](https://aws.amazon.com/ec2/) and [Spot Resources](https://aws.amazon.com/ec2/spot/).
+
+Batch achieves this by utilizing queue processing of batch job requests. To successfully submit a job for execution, you need the following resources:
+
+1. [Job Definition](#job-definition) - *Group various job properties (container image, resource requirements, env variables...) into a single definition. These definitions are used at job submission time.*
+2. [Compute Environment](#compute-environment) - *the execution runtime of submitted batch jobs*
+3. [Job Queue](#job-queue) - *the queue where batch jobs can be submitted to via AWS SDK/CLI*
+
+For more information on **AWS Batch** visit the [AWS Docs for Batch](https://docs.aws.amazon.com/batch/index.html).
+
+## Compute Environment
+
+At the core of AWS Batch is the compute environment. All batch jobs are processed within a compute environment, which uses resource like OnDemand or Spot EC2 instances.
+
+In **MANAGED** mode, AWS will handle the provisioning of compute resources to accommodate the demand. Otherwise, in **UNMANAGED** mode, you will need to manage the provisioning of those resources.
+
+Below is an example of each available type of compute environment:
+
+```ts
+const defaultVpc = new ec2.Vpc(this, 'VPC');
+
+// default is managed
+const awsManagedEnvironment = new batch.ComputeEnvironment(stack, 'AWS-Managed-Compute-Env', {
+  computeResources: {
+    vpc
+  }
+});
+
+const customerManagedEnvironment = new batch.ComputeEnvironment(stack, 'Customer-Managed-Compute-Env', {
+  managed: false // unmanaged environment
+});
+```
+
+### Spot-Based Compute Environment
+
+It is possible to have AWS Batch submit spotfleet requests for obtaining compute resources. Below is an example of how this can be done:
+
+```ts
+const vpc = new ec2.Vpc(this, 'VPC');
+
+const spotEnvironment = new batch.ComputeEnvironment(stack, 'MySpotEnvironment', {
+  computeResources: {
+    type: batch.ComputeResourceType.SPOT,
+    bidPercentage: 75, // Bids for resources at 75% of the on-demand price
+    vpc,
+  },
+});
+```
+
+### Understanding Progressive Allocation Strategies
+
+AWS Batch uses an [allocation strategy](https://docs.aws.amazon.com/batch/latest/userguide/allocation-strategies.html) to determine what compute resource will efficiently handle incoming job requests. By default, **BEST_FIT** will pick an available compute instance based on vCPU requirements. If none exist, the job will wait until resources become available. However, with this strategy, you may have jobs waiting in the queue unnecessarily despite having more powerful instances available. Below is an example of how that situation might look like:
+
+```
+Compute Environment:
+
+1. m5.xlarge => 4 vCPU
+2. m5.2xlarge => 8 vCPU
+```
+
+```
+Job Queue:
+---------
+| A | B |
+---------
+
+Job Requirements:
+A => 4 vCPU - ALLOCATED TO m5.xlarge
+B => 2 vCPU - WAITING
+```
+
+In this situation, Batch will allocate **Job A** to compute resource #1 because it is the most cost efficient resource that matches the vCPU requirement. However, with this `BEST_FIT` strategy, **Job B** will not be allocated to our other available compute resource even though it is strong enough to handle it. Instead, it will wait until the first job is finished processing or wait a similar `m5.xlarge` resource to be provisioned.
+
+The alternative would be to use the `BEST_FIT_PROGRESSIVE` strategy in order for the remaining job to be handled in larger containers regardless of vCPU requirement and costs.
+
+### Launch template support
 
-### Usage
 Simply define your Launch Template:
 ```typescript
-    const myLaunchTemplate = new ec2.CfnLaunchTemplate(this, 'LaunchTemplate', {
-      launchTemplateName: 'extra-storage-template',
-      launchTemplateData: {
-        blockDeviceMappings: [
-          {
-            deviceName: '/dev/xvdcz',
-            ebs: {
-              encrypted: true,
-              volumeSize: 100,
-              volumeType: 'gp2'
-            }
-          }
-        ]
+const myLaunchTemplate = new ec2.CfnLaunchTemplate(this, 'LaunchTemplate', {
+  launchTemplateName: 'extra-storage-template',
+  launchTemplateData: {
+    blockDeviceMappings: [
+      {
+        deviceName: '/dev/xvdcz',
+        ebs: {
+          encrypted: true,
+          volumeSize: 100,
+          volumeType: 'gp2'
+        }
       }
-    });
+    ]
+  }
+});
 ```
+
 and use it:
 
 ```typescript
-    const myComputeEnv = new batch.ComputeEnvironment(this, 'ComputeEnv', {
-      computeResources: {
-        launchTemplate: {
-          launchTemplateName: myLaunchTemplate.launchTemplateName as string, //or simply use an existing template name
-        },
-        vpc,
-      },
-      computeEnvironmentName: 'MyStorageCapableComputeEnvironment',
-    });
+const myComputeEnv = new batch.ComputeEnvironment(this, 'ComputeEnv', {
+  computeResources: {
+    launchTemplate: {
+      launchTemplateName: myLaunchTemplate.launchTemplateName as string, //or simply use an existing template name
+    },
+    vpc,
+  },
+  computeEnvironmentName: 'MyStorageCapableComputeEnvironment',
+});
+```
+
+### Importing an existing Compute Environment
+
+To import an existing batch compute environment, call `ComputeEnvironment.fromComputeEnvironmentArn()`.
+
+Below is an example:
+
+```ts
+const computeEnv = batch.ComputeEnvironment.fromComputeEnvironmentArn(this, 'imported-compute-env', 'arn:aws:batch:us-east-1:555555555555:compute-environment/My-Compute-Env');
+```
+
+## Job Queue
+
+Jobs are always submitted to a specific queue. This means that you have to create a queue before you can start submitting jobs. Each queue is mapped to at least one (and no more than three) compute environment. When the job is scheduled for execution, AWS Batch will select the compute environment based on ordinal priority and available capacity in each environment.
+
+```ts
+const jobQueue = new batch.JobQueue(stack, 'JobQueue', {
+  computeEnvironments: [
+    {
+      // Defines a collection of compute resources to handle assigned batch jobs
+      computeEnvironment,
+      // Order determines the allocation order for jobs (i.e. Lower means higher preferance for job assignment)
+      order: 1,
+    },
+  ],
+});
+```
+
+### Priorty-Based Queue Example
+
+Sometimes you might have jobs that are more important than others, and when submitted, should take precedence over the existing jobs. To achieve this, you can create a priority based execution strategy, by assigning each queue its own priority:
+
+```ts
+const highPrioQueue = new batch.JobQueue(stack, 'JobQueue', {
+  computeEnvironments: sharedComputeEnvs,
+  priority: 2,
+});
+
+const lowPrioQueue = new batch.JobQueue(stack, 'JobQueue', {
+  computeEnvironments: sharedComputeEnvs,
+  priority: 1,
+});
+```
+
+By making sure to use the same compute environments between both job queues, we will give precedence to the `highPrioQueue` for the assigning of jobs to available compute environments.
+
+### Importing an existing Job Queue
+
+To import an existing batch job queue, call `JobQueue.fromJobQueueArn()`.
+
+Below is an example:
+
+```ts
+const jobQueue = batch.JobQueue.fromJobQueueArn(this, 'imported-job-queue', 'arn:aws:batch:us-east-1:555555555555:job-queue/High-Prio-Queue');
+```
+
+## Job Definition
+
+A Batch Job definition helps AWS Batch understand important details about how to run your application in the scope of a Batch Job. This involves key information like resource requirements, what containers to run, how the compute environment should be prepared, and more. Below is a simple example of how to create a job definition:
+
+```ts
+const repo = ecr.Repository.fromRepositoryName(stack, 'batch-job-repo', 'todo-list');
+
+new batch.JobDefinition(stack, 'batch-job-def-from-ecr', {
+  container: {
+    image: new ecs.EcrImage(repo, 'latest'),
+  },
+});
+```
+
+### Using a local Docker project
+
+Below is an example of how you can create a Batch Job Definition from a local Docker application.
+
+```ts
+new batch.JobDefinition(stack, 'batch-job-def-from-local', {
+  container: {
+    // todo-list is a directory containing a Dockerfile to build the application
+    image: ecs.ContainerImage.fromAsset('../todo-list'),
+  },
+});
+```
+
+### Importing an existing Job Definition
+
+To import an existing batch job definition, call `JobDefinition.fromJobDefinitionArn()`.
+
+Below is an example:
+
+```ts
+const job = batch.JobDefinition.fromJobDefinitionArn(this, 'imported-job-definition', 'arn:aws:batch:us-east-1:555555555555:job-definition/my-job-definition');
 ```