The Hark Programming Language

Formerly, Teal.

Change your remotes: git remote set-url origin git@github.com:condense9/hark-lang.git

Hark hides the complexity of AWS Lambda + SQS, so you can build serverless data workflows without managing infrastructure.

Describe your workflows in a real programming language with first-class functions, concurrency, and native Python inter-op. Test end-to-end locally, then deploy to serverless AWS infrastructure in under 60s and start workflows from anything that can invoke Lambda.

Like AWS Step Functions but cheaper and much nicer to use (overheads: a little Lambda runtime, and a DynamoDB for Hark state).

Like Serverless Framework, but handles runtime glue logic in addition to deployment.

Status: Hark works well for small workflows: 5-10 Lambda invocations. Larger workflows may cause problems, and there is a known issue caused by DynamoDB restrictions (#12).

Get started in 2 minutes.

Read the documentation.

PyCon Africa 2020 Demos!.

Contributing

Hark is growing rapidly, and contributions are warmly welcomed.

Is Hark for me?

Hark is for you if:

• You use Python for processing data, or writing business process workflows.
• You want an alternative to AWS Step Functions.
• You don't want to to deploy and manage a task platform (Airflow, Celery, etc).

Data in: You can invoke Hark like any Lambda function (AWS cli, S3 trigger, API gateway, etc).

Data out: Use the Python libraries you already have for database access. Hark just connects them together.

Development: Hark runs locally, so you can thoroughly test Hark programs before deployment (using minio and localstack for any additional infrastructure that your code uses.

Operating: Hark enables contextual cross-thread logging and stacktraces out of the box, since the entire application is described in one place.

Hark is like...	But...
AWS Step Functions	Hark programs aren't bound to AWS and don't use Step Functions under the hood (just plain Lambda + DynamoDB).
Orchestrators (Apache Airflow, etc)	You don't have to manage infrastructure, or think in terms of DAGs, and you can test everything locally.
Task runners (Celery, etc)	You don't have to manage infrastructure.
Azure Durable Functions	While powerful, Durable Functions (subjectively) feel complex - their behaviour isn't always obvious.

Read more...

Up and running in 2 minutes

All you need:

• An AWS account, and AWS CLI configured.
• A Python 3.8 virtual environment

Hark is built with Python, and distributed as a Python package. To install it, run:

$ pip install hark-lang

This gives you the hark executable. Try hark -h.

Copy the following snippet into a file called service.hk:

// service.hk

fn main() {
  print("Hello World!");
}

Run it (-f main is optional, and main is the default):

~/new_project $> hark service.hk -f main

Initialise the project (required for deployment):

~/new_project $> hark init

And deploy the service to your AWS account (requires AWS credentials and AWS_DEFAULT_REGION to be defined):

~/new_project $> hark deploy

Finally, invoke it in AWS (-f main is optional, as before):

~/new_project $> hark invoke -f main

That's it! You now have a Hark instance configured in your AWS account, built on the AWS serverless platform (S3 + Lambda + DynamoDB). More info...

Explore a more complex example: Fractals.

Create an issue if none of this makes sense, or you'd like help getting started.

Read more...

• about the language
• about the development process
• about configuring Hark

Why should I learn a new language?

It's a big ask! There's so much that's missing from a brand new language. For now, think about it like learning a new library or API -- you can do most of the hard work in regular Python, using existing packages and code, while Hark lets you express things you can't easily do in Python.

They key concept is this: when running in AWS, Hark threads run in separate lambda invocations, and the language comes with primitives to manage these threads.

Concurrency & Synchronisation

This is useful when a set computations are related, and must be kept together.

/**
 * Return f(x) + g(x), computing f(x) and g(x) in parallel in two separate
 * threads (Lambda invocations in AWS).
 */
fn compute(x) {
  a = async f(x);     // Start computing f(x) in a new thread
  b = async g(x);     // Likewise with g(x)
  await a + await b;  // Stop this thread, and resume when {a, b} are ready
}

Traditional approach: Manually store intermediate results in an external database, and build the synchronisation logic into the cloud functions f and g, or use an orchestrator service.

Read more...

Trivial Pipelines

Use this approach when each individual function may take several minutes (and hence, together would break the 5 minute AWS Lambda limit).

/**
 * Compute f(g(h(x))), using a separate lambda invocation for each
 * function call.
 */
fn pipeline(x) {
  a = async h(x);
  b = async g(await a);
  f(await b);
}

Traditional approach: This is functionally similar to a "chain" of AWS Lambda functions and SQS queues.

Mapping / reducing

Hark functions are first-class, and can be passed around (closures and anonymous functions are planned, giving Hark object-oriented capabilities).

/**
 * Compute [f(element) for element in x], using a separate lambda invocation for
 * each application of f.
 */
fn map(f, x, accumulator) {
  if nullp(x) {
    accumulator
  }
  else {
    // The Hark compiler has tail-recursion optimisation
    map(func, rest(x), append(accumulator, async f(first(x))))
  }
}

This could be used like:

fn add2(x) {
  x + 2
}

fn main() {
  futures = map(add2, [1, 2, 3, 4], []);
  // ...
}

Read more...

Notes about syntax

The syntax should look familiar, but there are a couple of things to point out.

No 'return' statement

Every expression must return a value, so there is no return statement. The last expression in a 'block' (expressions between { and }) is returned implicitly.

fn foo() {
  "something"
}

fn main() {
  print(foo())  // -> prints "something"
}

Semi-colons are required...

... when there is more than one expression in a block.

This is ok:

fn main() {
  print("done")
}

So is this:

fn main() {
  print("one");
  print("two")
}

And this:

fn main() {
  print("one");
  print("two");
}

But this is not ok:

fn main() {
  print("one")  // <- missing semicolon!
  print("two")
}

'print' returns the value printed

In this snippet, "Hello Worlds!" is actually printed twice. First in bar, then in main.

fn bar() {
  print("Hello Worlds!")
}

fn main() {
  print(bar())
}

$> hark -q service.hk
Hello Worlds!
Hello Worlds!

'if' is an expression, and returns a value

Think about it like this: An if expression represents a choice between values.

v = if something { true_value } else { false_value };

// if 'something' is not true, v is set to null
v = if something { value };

FAQ

Why is this not a library/DSL in Python?

When Hark threads wait on a Future, they stop completely. The Lambda function saves the machine state and then terminates. When the Future resolves, the resolving thread restarts any waiting threads by invoking new Lambdas to pick up execution.

To achieve the same thing in Python, the framework would need to dump the entire Python VM state to disk, and then reload it at a later point -- this may be possible, but would certainly be non-trivial. An alternative approach would be to build a langauge on top of Python that looked similar to Python, but hark wrong because it was really faking things under the hood.

How is Hark like Go?

Goroutines are very lightweight, while Hark async functions are pretty heavy -- they involve creating a new Lambda (or process, when running locally).

Hark's concurrency model is similar to Go's, but channels are not fully implemented so data can only be sent to/from a thread at call/return points.

Is this an infrastructure-as-code tool?

No, Hark does not do general-purpose infrastructure management. There are already great tools to do that (Terraform, Pulumi, Serverless Framework, etc).

Instead, Hark reduces the amount of infrastructure you need. Instead of a distinct Lambda function for every piece of application logic, you only need the core Hark interpreter (purely serverless) infrastructure.

Hark will happily manage that infrastructure for you (through hark deploy and hark destroy), or you can set it up with your in-house custom system.

Current Limitations and Roadmap

Hark is beta quality, which means that it's not thoroughly tested or feature complete. This is a non-exhaustive list.

Libraries

Only one Hark program file is supported, but a module/package system is planned.

Error Handling

There's no error handling - if your function fails, you'll have to restart the whole process manually. An exception handling system is planned.

Typing

Function inputs and outputs aren't typed. This is a limitation, and will be fixed soon, probably using ProtoBufs as the interface definition language.

Calling Arbitrary Services

Currently you can only call Hark or Python functions -- arbitrary microservices can't be called. Before Hark v1.0 is released, this will be possible. You will be able to call a long-running third party service (e.g. an AWS ML service) as a normal Hark function and await on the result.

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About

Hark is maintained by Condense9 Ltd.. Get in touch with ric@condense9.com for help getting running, or if you need enterprise deployment.

Hark started because we couldn't find any data engineering tools that were productive and hark like software engineering. As an industry, we've spent decades growing a wealth of computer science knowledge, but building data pipelines in $IaC, or manually crafting workflow DAGs with $AutomationTool, just isn't software.

License

Apache License (Version 2.0). See LICENSE for details.

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