JSPM

Punchcard

Punchcard is a TypeScript project aimed at imagining what the future of software development might look like for the Cloud. It builds on top of the AWS Cloud Development Kit (CDK), AWS's infrastructure-as-code product, adding a variety of high-level and type-safe features.

The name punchcard comes from the idea that, as infrastructure-as-code projects mature, the technology we are used to using today will feel as ancient to us as punchcards do today. You know, those times when we were restricted to writing and sharing software on cards with holes in them ;)

Let's walk through some punchcard features to demonstrate:

Runtime Code and Dependencies

Punchcard unifies "infrastructure code" with "runtime code" - i.e. you can declare and implement an AWS Lambda Function with the same code:

new Function(stack, 'MyFunction', {
  handle: async (event) => {
    console.log('hello world');
  }
});

To contact other services in your Function, data structures such as SNS Topics, SQS Queues, DynamoDB Tables, etc. are declared as a Dependency.

This will create the required IAM policies for your Function's IAM Role, add any environment variables for details such as the Topic's ARN, and automatically create a client for accessing the Construct. The result is that your handle function is now passed a topic instance which you can interact with:

new Function(stack, 'MyFunction', {
  depends: topic,
  handle: async (event, topic) => {
    await topic.publish({
      key: 'some key',
      count: 1,
      timestamp: new Date()
    });
  }
});

Furthermore, its interface is higher-level than what would normally be expected when using the aws-sdk, and it's also type-safe: the argument to the publish method is not an opaque string or Buffer, it is an object with keys and rich types such as Date. This is because data structures in punchcard, such as Topic, Queue, Stream, etc. are generic with statically declared types (like an Array<T>):

const topic = new Topic(stack, 'Topic', {
  /**
   * Message is a JSON Object with properties: `key`, `count` and `timestamp`.
   */
  type: struct({
    key: string(),
    count: integer(),
    timestamp
  })
});

This Topic is now of type:

Topic<{
  key: string;
  count: number;
  timestamp: Date;
}>

This feature in punchcard becomes even more evident when using DynamoDB. To demonstrate, let's create a DynamoDB HashTable and use it in a Function:

(by HashTable, we mean a DynamoDB Table with only a partitionKey and no sortKey)

const table = new HashTable(stack, 'my-table', {
  partitionKey: 'id',
  shape: {
    id: string(),
    count: integer({
      minimum: 0
    })
  },
  billingMode: BillingMode.PAY_PER_REQUEST
});

Now, when getting an item from DynamoDB, there is no need to use AttributeValues such as { S: 'my string' }, like you would when using the low-level aws-sdk. You simply use ordinary javascript types:

const item = await table.get({
  id: 'state'
});

The interface is statically typed and derived from the definition of the HashTable - we specified the partitionKey as the id field which has type string, and so the object passed to the get method must correspond.

PutItem and UpdateItem have similarly high-level and statically checked interfaces. More interestingly, condition and update expressions are built with helpers derived (again) from the table definition:

// put an item if it doesn't already exist
await table.put({
  item: {
    id: 'state',
    count: 1
  },
  if: item => attribute_not_exists(item.id)
});

// increment the count property by 1
await table.update({
  key: {
    id: 'state'
  },
  actions: item => [
    item.count.increment(1)
  ]
});

If your table is a SortedTable, which is a DynamoDB Table with both a partitionKey and a sortKey, then you can also build typesafe query expressions:

await table.query({
  key: {
    id: 'id',
    count: greaterThan(1)
  },
})

Enumerable Data Structures

Punchcard also has the concept of Enumerable data structures, which should feel similar to in-memory arrays/lists because of its chainable API, including operations such as map, flatMap, filter, collect etc.

Data structures that implement Enumerable are: Topic, Queue, Stream, Bucket and (Glue) Table.

Let's look at some examples of how powerful this flow can be.

Given a SNS Topic:

const topic = new Topic(stack, 'Topic', {
  type: struct({
    key: string(),
    count: integer(),
    timestamp
  })
});

You can attach a new Lambda Function to process each notification:

topic.enumerable().forEach(stack, 'ForEachNotification', {
  handle: async (notification) => {
    console.log(`notification delayed by ${new Date().getTime() - notification.timestamp.getTime()}ms`);
  }
})

Or, create a new SQS Queue and subscribe notifications to it:

(Messages in the Queue are of the same type as the notifications in the Topic.)

const queue = topic.toQueue(stack, 'MyNewQueue');

We can then, perhaps, map over each message in the Queue and collect the results into a new AWS Kinesis Stream:

const stream = queue.enumerable()
  .map({
    handle: async(message, e) => {
      return {
        ...message,
        tags: ['some', 'tags'],
      };
    }
  })
  .toStream(stack, 'Stream', {
    // partition values across shards by the 'key' field
    partitionBy: value => value.key,

    // type of the data in the stream
    type: struct({
      key: string(),
      count: integer(),
      tags: array(string()),
      timestamp
    })
  });

With data in a Stream, we might want to write out all records to a new S3 Bucket by attaching a new Firehose DeliveryStream to it:

const s3DeliveryStream = stream.toS3(stack, 'ToS3');

With data now flowing to S3, let's partition and catalog it in a Glue Table (backed by a new S3 Bucket) so we can easily query it with AWS Athena, AWS EMR and AWS Glue:

import glue = require('@aws-cdk/aws-glue');

const database = new glue.Database(stack, 'Database', {
  databaseName: 'my_database'
});
s3DeliveryStream.toGlueTable(stack, 'ToGlue', {
  database,
  tableName: 'my_table',
  columns: stream.type.shape,
  partition: {
    // Glue Table partition keys: minutely using the timestamp field
    keys: {
      year: integer(),
      month: integer(),
      day: integer(),
      hour: integer(),
      minute: integer()
    },
    get: record => ({
      // define the mapping of a record to its Glue Table partition keys
      year: record.timestamp.getUTCFullYear(),
      month: record.timestamp.getUTCMonth(),
      day: record.timestamp.getUTCDate(),
      hour: record.timestamp.getUTCHours(),
      minute: record.timestamp.getUTCMinutes(),
    })
  }
});

Conclusion

This style of programming should feel familiar - the idea is to map ordinary in-memory data structures and algorithms to the AWS Cloud. So, instead of using in-memory hash maps, you use a DynamoDB Table, and instead of an in-memory queue, it's an AWS SQS Queue, etc.

Examples

More detailed examples can be found in the source:

• Stream Processing - respond to SNS notifications with a Lambda Function; subscribe notifications to a SQS Queue and process them with a Lambda Function; process and forward data from a SQS Queue to a Kinesis Stream; sink records from the Stream to S3 and catalog it in a Glue Table.
• Invoke a Function from another Function - call a Function from another Function
• Real-Time Data Lake - collects data with Kinesis and persists to S3, exposed as a Glue Table in a Glue Database.
• Scheduled Lambda Function - runs a Lambda Function every minute and stores data in a DynamoDB Table.
• Pet Store API Gateway - implementation of the Pet Store API Gateway canonical example.

Getting Started

This library is built with (and relies on) the AWS CDK, so make sure you read their documentation first.

A punchcard application is not too different than a CDK application - the only difference is that you must export the @aws-cdk/core.App as default from your application's entry-point. Instructions below should be all that is required to get started:

Install punchcard and the aws-cdk:

npm install --save-dev aws-cdk
npm install --save @aws-cdk/core
npm install --save punchcard

Create an index.ts file to contain your application's entrypoint:

import cdk = require('@aws-cdk/cdk');
import punchcard = require('punchcard');

const app = new cdk.App();
const stack = new cdk.Stack(app, 'CronStack');

// NOTE: make sure you export the app as default, or else your code won't run at runtime
export default app;

// create and use punchcard or CDK constructs
const table = new punchcard.HashTable(stack, 'MyTable', {
  // ...
});

Compile your code and deploy the app with the cdk:

npm run build
./node_modules/aws-cdk/bin/cdk deploy -a ./index.js

License

This library is licensed under the Apache 2.0 License.