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Making Lambda simpler for data scientists

Nabarun Pal
July 27, 2019
21

Making Lambda simpler for data scientists

Presented at AWS Community Day Bengaluru 2019

Nabarun Pal

July 27, 2019
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Transcript

  1. 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
  2. Outline • Genesis • Present Constructs in Python - Threads

    and Processes • Kubernetes • Serverless • The Abstraction • Requirements • API • Internals • Demo • Performance Metrics • Current Limitations • Future Goals
  3. 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
  4. 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
  5. 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
  6. What about Serverless? • Zero Infrastructure Management • Near Infinite

    Scaling • High Availability • No Idle Resources • Suitable for short-lived workloads
  7. 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
  8. 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
  9. CLI

  10. 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
  11. Future Goals • Distribute lambdapool through PyPI • Permissions management

    system • System to fetch execution logs • Better layer management • Make the function update process intelligent