Check out my new course Learn you some Lambda best practice for great good! and learn the best practices for performance, cost, security, resilience, observability and scalability.
If you enjoy reading these exercises then please buy Crista’s book to support her work.
Style 30 – Map Reduce
- Input data is divided in chunks, similar to what an inverse multiplexer does to input signals.
- A map function applies a given worker function to each chunk of data, potentially in parallel.
- A reduce function takes the results of the many worker functions and recombines them into a coherent output.
Map reduce should be a familiar paradigm to most developers nowadays. In today’s style we’ll use a very simple map-reduce flow to solver the term frequency problem.
As you can see from the above diagram, we’ll take an input text and split it into X no. of chunks which can be processed in parallel. To do this, we’ll first add a partition function below.
Each chunk of the original input text can then be split and filtered (using the list of stop words) independently.
Two things to note about the splitWords function below:
a) it returns an array of (word, count) tuples
b) it doesn’t perform any local aggregation, so if the same word appear twice in its chunk then it’ll appear as (word, 1) twice in the returned array
Once all the chunks has been processed, we can reduce over them with the countWords function below.
Here, I have taken an imperative approach for efficiency sake, and because that’s what Crista had done in her Python solution. Alternatively, you could have written something along the lines of the following.
which is much more concise and functional in style, but about 25% slower (~100ms) than the imperative approach.
Next, we’ll add a sort function that’ll sort the array of (word, count) values in descending order by the count (i.e. the snd element in the tuple).
And finally, we wire everything together using pipes.
or is it?
So far, I have followed Crista’s approach in her Python solution, and the solution meets the constraints of this style. But, we haven’t taken advantage of the potential for parallelism here.
Version 2 – Parallel Processing
To make the processing of the chunks happen in parallel, let’s modify the splitWords function to make it async.
Notice that not much has changed from our non-async version! Except for the minor optimization we added to aggregate the words in the chunk instead of returning duplicated (word, 1) tuples.
The next thing we need to do, is to wrap the original pipeline in an async workflow.
Here, once the partitions have been created, we’ll process them in parallel and wait for all the parallel processing to complete using Async.Parallel before proceeding with the rest of the pipeline as before.
I specialise in rapidly transitioning teams to serverless and building production-ready services on AWS.
Are you struggling with serverless or need guidance on best practices? Do you want someone to review your architecture and help you avoid costly mistakes down the line? Whatever the case, I’m here to help.
Check out my new course, Learn you some Lambda best practice for great good! In this course, you will learn best practices for working with AWS Lambda in terms of performance, cost, security, scalability, resilience and observability. Enrol now and enjoy a special preorder price of £9.99 (~$13).
Are you working with Serverless and looking for expert training to level-up your skills? Or are you looking for a solid foundation to start from? Look no further, register for my Production-Ready Serverless workshop to learn how to build production-grade Serverless applications!
Here is a complete list of all my posts on serverless and AWS Lambda. In the meantime, here are a few of my most popular blog posts.
- Lambda optimization tip – enable HTTP keep-alive
- You are thinking about serverless costs all wrong
- Many faced threats to Serverless security
- We can do better than percentile latencies
- I’m afraid you’re thinking about AWS Lambda cold starts all wrong
- Yubl’s road to Serverless
- AWS Lambda – should you have few monolithic functions or many single-purposed functions?
- AWS Lambda – compare coldstart time with different languages, memory and code sizes
- Guys, we’re doing pagination wrong