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The Teal Programming Language

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Tests PyPI Code style: black Python 3.8

The Teal Programming Language

Teal 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 Teal state).

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

Status: Teal 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!.


Teal is growing rapidly, and contributions are warmly welcomed.

Is Teal for me?

Teal 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 Teal like any Lambda function (AWS cli, S3 trigger, API gateway, etc).

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

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

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

Teal Cloud (coming soon) will make this even easier, with a graphical console, one-click rollbacks of entire serverless applications, and more. Sign up to the mailing list to hear about this first:

Teal is like... But...
AWS Step Functions Teal 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.


Why should I learn a new language?

Because it'll give you a new way to think about cloud software.

Teal is not a Python replacement. It has one specific goal: eliminate the need to implement infrastructure.

Writing your own infrastructure is like writing assembly code - only do it if you really have extreme or unusual requirements. Otherwise, use a high-level language (Teal!) that hides the complexity and compiles to "soft infrastructure".

Up and running in 2 minutes

All you need:

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

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

$ pip install teal-lang

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

Copy the following snippet into a file called


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

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

~/new_project $> teal -f main

Initialise the project (required for deployment):

~/new_project $> teal init

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

~/new_project $> teal deploy

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

~/new_project $> teal invoke -f main

That's it! You now have a Teal 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.


Things Teal can do

When running in AWS, Teal threads run in separate lambda invocations.

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.


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

Teal functions are first-class, and can be passed around (closures and anonymous functions are planned, giving Teal 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) {
  else {
    // The Teal 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], []);
  // ...


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() {

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() {

So is this:

fn main() {

And this:

fn main() {

But this is not ok:

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

'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() {
$> teal -q
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 };


Why is this not a library/DSL in Python?

When Teal 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 felt wrong because it was really faking things under the hood.

How is Teal like Go?

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

Teal'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, Teal does not do general-purpose infrastructure management. There are already great tools to do that (Terraform, Pulumi, Serverless Framework, etc).

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

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

Current Limitations and Roadmap

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


Only one Teal 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.


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 Teal or Python functions -- arbitrary microservices can't be called. Before Teal 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 Teal function and await on the result.


Teal is maintained by Condense9 Ltd.. Get in touch with for help getting running, or if you need enterprise deployment.

Teal started because we couldn't find any data engineering tools that were productive and felt 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.


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

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