Serverless Computation Environment Ric da Silva @rmhsilva condense9.com Building a with Python, naturally 

Hark Beyond infrastructure (as-code) Compilers 2 

Serverless Computation Functions as a Service (FaaS) 3 

4 So many tools & services: deployment, logging, monitoring, authentication, … A “Typical” Serverless Architecture 

Project: Spec Python functions: f, g, h. Input: some parameter, x. Output: store value y in a database. y = f(g(h(x))) Constraints: - You must use AWS Lambda. - f, g, h take several minutes each to run. 5 Disclaimer: don’t use these function names at home! 

Version 1 6 f(x) g(x) h(x) {“x”: “foo”} Store y = f(g(h(x))) ✔ Asynchronous invoke 

Version 1: Testing 1. Unit-test f, h, g 2. Build an integration test 3. localstack 4. Staging env, CI 7 f(x) g(x) h(x) {“x”: “foo”} Store https://localstack.cloud/ 

Version 2 8 f(x) g(x) h(x) {“x”: “foo”} Store Inputs AWS Simple Queue Service Failures (“Dead Letter” Queue) 

Hark Checklist ☐ Use Python ☐ Local testing ☐ Contextual debugging ☐ Operational support ☐ Easy cross-cloud portability 9 

Let’s try Hark. 

Series Computation 11 

Parallel Computation Conditionals 12 

Lists, mapping 13 

API Endpoint 14 (But slowww for now) 

More Stacktraces Upload triggers (AWS S3) Custom permissions (IAM statements) Custom build scripts (eg ./build.sh) Lambda configuration (memory, extra layers, etc) 15 

16 a = get_future(“a”) if a.resolved: data_stack.push(a.value) else: this_thread.wait_for(a) wait_for: sort of like an implicit callback. This thread physically stops. When a resolves, this thread is continued. 

f(x) data stack call stack trigger(fn, args) IP=0 f(x) Hark “CPU” Threads Waiting? Yes No IP=1 stdout Instructions async g(…) Finish New Thread (fn, args) New Thread Continue Thread (thread_id) Runtime Executable, Futures, CPU State, Stacks Memory (DynamoDB) … 

until finished 18 Trigger API Deploy app code List sessions Get logs/results/… S3 Uploads Thread state Futures Hark & Python code New Session SNS status notifications Application data sources/sinks Logs Events StdOut Results Other AWS Services Hark CLI tool CI/CD Instance Data Control API API Gateway Manual Trigger Event data Runtime Hark Key-Value Store + Bucket ✨ https://github.com/boto/boto3 https://github.com/pynamodb/PynamoDB https://github.com/prompt-toolkit/python-prompt-toolkit https://github.com/pavdmyt/yaspin https://github.com/CITGuru/PyInquirer/ 

19 .hk import(f, src.main) fn main(x) { if x > 5 { print(“nope”) } else { Lex+Parse Optimise Compile Hark Executale Better AST Tail-call recursion, in particular https://commons.wikimedia.org/wiki/File:Abstract_syntax_tree_for_Euclidean_algorithm.svg - Import statements - Numbers, strings, … - Operations (+, *, …) - Function definitions - Assignments (x = 5) - Function Calls - … Convert the AST into byte-code that the Hark VM can execute Abstract Syntax Tree (AST) 01101 01011 Pack dataclasses, attrs functools typing parsy, sly https://github.com/python-attrs/attrs https://github.com/python-parsy/parsy https://github.com/dabeaz/sly ✨ Executable = Byte-Code + Python function references + Symbol table + Debugging info Byte-code 

Checklist ✔ Use Python ✔ Local testing ✔ Contextual debugging ☐ Operational support ☐ True cross-cloud portability 20 

Limitations Bandwidth (to/from session memory) Lambda startup time Account concurrency limits Others? 21 

Hark Beyond infrastructure?! condense9.com, github/condense9 @rmhsilva 22