Making Lambda simpler for data scientists

Presented at AWS Community Day Bengaluru 2019

Transcript

1. Nabarun Pal (@theonlynabarun) Making Lambda Simpler for Data Scientists
2. About Me • Platform Engineer at rorodata • Optimizing development time through simple abstractions/tooling • Venturing into Container Orchestration and Serverless Computing • Contributor to the Kubernetes ecosystem
3. Outline • Genesis • Present Constructs in Python - Threads and Processes • Kubernetes • Serverless • The Abstraction • Requirements • API • Internals • Demo • Performance Metrics • Current Limitations • Future Goals
4. Genesis
5. Multithreading Pros • Lightweight • Shared state between multiple threads • Works flawlessly for I/O-bound applications Cons • Subject to Global Interpreter Lock • Context switching overhead • Code prone to race conditions • Does not work for CPU-bound tasks
6. Multiprocessing Pros • Isolation of memory space • Leverages multiples processors & cores • GIL limitations don’t apply • Synchronization primitives like locks are mandatory unless sharing data • Works well for CPU-bound tasks Cons • Sharing data between processes is a little bit complicated • Bulky memory footprint • Definite scaling
7. Can Kubernetes help? Pros • Abstracts out infrastructure • Simple interface • Can scale based on workload Cons • Layer on top of VM’s - Slow to scale up/down • Autoscaling is not a core functionality • Needs dedicated time to manage
8. What about Serverless? • Zero Infrastructure Management • Near Infinite Scaling • High Availability • No Idle Resources • Suitable for short-lived workloads
9. All problems in computer science can be solved by another level of indirection David Wheeler
10. LambdaPool - “The Indirection”
11. Requirements • Minimum overhead on users • Simple way to create, delete, list and update lambda functions • Coherent ways to invoke the lambda function • Easy to use interface
12. Features • CLI to create, list, update and delete functions • Support for specifying function layers and list the layers used for each functions • LambdaPool interface • LambdaExecutor Interface
13. Installation $ pip install --user https://lambdapool-releases.s3.amazonaws.com/lambdapool-0.9.7.tar.gz
14. CLI
15. LambdaPool • Implements the same interface as ThreadPool and ProcessPool
16. LambdaExecutor • Implements the same interface as ThreadPoolExecutor and ProcessPoolExecutor
17. Demo
18. Internals - Creating/Updating a Function
19. Internals - Invoking a function
20. Benefits • Compute Time • Compute Costs • Developer Time
21. Current Limitations • Serialization of the payload is being a hurdle • Decoupling between function provisioning and invocation • Size of execution environment Inherent to Serverless • Cold start issues • Additional Network Overhead • Not suitable for long running workloads • Troubleshooting is hard • Local testing
22. Future Goals • Distribute lambdapool through PyPI • Permissions management system • System to fetch execution logs • Better layer management • Make the function update process intelligent
23. Conclusions https://github.com/rorodata/lambdapool
24. Thank You! Feedback: https://bit.ly/lambdapoolfeedback Contact Us: opensource@rorodata.com