Unum is a system for building and running large FaaS applications that consist of many FaaS functions.

A key differentiator of Unum is the ability to run complex FaaS applications without relying on orchestrator services (e.g., AWS Step Functions), while offering the same conveniences and guarantees of state-of-the-art orchestrators. Unum applications only require a FaaS scheduler (e.g., Lambda, Google Cloud Functions) and a strongly consistent data store (e.g., DynamoDB, Firestore). Both components are core serverless services that are universally available across cloud platforms.

Application developers can express applications as AWS Step Functions state machines. Unum compiles Step Functions state machines into an intermediate representation (IR) and distributes it to the Lambda functions. Alternatively, developers can write hand-optimized Unum IR, which allows developers to build patterns that are not supported by or more efficient than AWS Step Funtions. During execution, a Unum runtime library wraps each function and runs in-site with user-defined FaaS functions to provide orchestration, error handling and exactly-once execution guarantees in a decentralized manner, all without a separate orchestrator service.

Not relying on orchestrator services has several advantages. From an application's perspective, Unum affords applications more flexibility to implement application-specific patterns and optimizations. Applications are not restricted by the patterns and their implementations of the orchestrator. Instead, they can define and implement their own patterns as long as the pattern can be modeled as a directed graph. Additionally, Unum applications are easily portable across platforms. One can write an application using the Step Functions language, and with a couple of commands, compile it into the Unum IR and run it on both AWS and Google Cloud. Last but not least, our experiments show that Unum is much cheaper--up to 9x--to use than standalone orchestrator services. Unum directly incurs costs from the underlying FaaS and data stores which have enjoyed years of research and engineering efforts to make them efficient, and their APIs are usually an-order-of-magnitude cheaper than orchestrator services.

From a cloud provider's perspective, Unum is better as it obviates the need to host an additional complex service (i.e., serverless orchestrators) and frees up both engineering and hardware resources such that providers can focus on fewer, core services (e.g., FaaS schedulers and data stores) in their serverless infrastructure. Moreover, application-level orchestration built on top of existing storage and FaaS services in the serverless infrastructure can benefit automatically from improvements to cost and performance to these services.

To see examples of Unum applications, visit the Unum application repo.

App developers can write serverless applications with Unum similarly to AWS SAM: Component functions each lives inside its own directory and a Unum template file lists all resources in the application and specifies a set of global configurations. Application logic is written as an AWS Step Functions state machine using the Amazon State Language.

Practically, a typical Unum application with three FaaS functions written in Python would look something like the following:

myapp/
 |- unum-template.yaml
 |- unum-step-functions.json
 |- function1/
   |- app.py
   |- requirements.txt
 |- function2/
   |- app.py
   |- requirements.txt
 |- function3/
   |- app.py
   |- requirements.txt

unum-template.yaml is the Unum template for the application, unum-step-functions.json is the application definition written as AWS Step Functions, and each component FaaS function has its own directory where the app logic lives in app.py and any Python dependencies are listed in requirements.txt. If you are familiar with writing serverless applications on AWS, you might notice that this programming interface with Unum resembles that of regular AWS Lambda and Step Functions if you are using developement tools such as AWS SAM or CloudFormation.

To bulid and deploy your serverless application, use the unum-cli. unum-cli does the following

1. Based on the Step Functions state machine, derives an intermediate representation that decentralizes the orchestration logic in the state machine to component functions (function1, function2, and function3 in the example).
2. Load platform-specific Unum runtime into each component function to create the executables for the target platform. During execution, the Unum runtime interposes on application logic (i.e., app.py) to provide orchestration, error handling and exactly-once execution guarantee.
3. Generate a platform-specific template (e.g., AWS SAM template) from the Unum template
4. Deploy the application to target platform
5. Rebuild and redeploy the application after making code changes

Current implementation supports Python Lambda functions on AWS and applications are deployed as CloudFormation stacks.

To build an unum application for AWS, run the following command the in an unum application directory:

unum-cli build -t -w unum-step-functions.json -p aws

The -t option would generate an AWS CloudFormation template (named template.yaml) based on unum-template.yaml on the fly. You can also generate a template.yaml without building the application by running

unum-cli template -p aws

With the template.yaml in the directory, you can simply run

unum-cli build

to build the application for AWS. unum-cli build internally calls AWS SAM to build the application and you will see the build artifacts under the .aws-sam/ directory.

To deploy your application to AWS, run

unum-cli deploy

If you want to build before deploying, use the following command to combine the two actions

unum-cli deploy -b

Without the -b option, unum will try to deploy the existing build artifacts and you might see No changes to deploy because your code changes haven't been built yet.

unum-cli deploy internally calls AWS SAM to deploy the application as an AWS CloudFormation stack. Before calling unum-cli deploy for AWS, make sure that you have the environment set up to work with AWS and SAM.