AWS Lambda

We are starting to build one shirt data logic, structure and as an online clothing store we believe good ux and ui is a goal to drive a lot of click through. The problem is, how do we fetch data and how do we abstract the gap between the Front-end devs and backend-devs as we are just two in the technical unit. We decided to go for GraphQL as our application-layer tool and Prisma for our database-layer abstracter.

Reasons :

GraphQL :

1. GraphQL makes fetching of data less painful and organised.

2. GraphQL gives you 100% assurance on data you getting back as opposed to the Rest design .

3. GraphQL comes with a bunch of real-time functionality in form of. subscriptions and finally because we are using React (GraphQL is not React demanding, it's doesn't require a specific framework, language or tool, but it definitely makes react apps fly )

Prisma :

1. Writing revolvers can be fun, but imagine writing revolvers nested deep down, curry braces flying around. This is sure a welcome note to bugs and as a small team we need to focus more on what that matters more. Prisma generates this necessary CRUD resolves, mutations and subscription out of the box.

2. We don't really have much budget at the moment so we are going to run our logic in a scalable cheap and cost effective cloud environment. Oh! It's AWS Lambda and deploying our schema to Lambda is our best bet to minimize cost and same time scale.

We are still at development stage and I believe, working on this start up will increase my dev knowledge. Off for Lunch :)

I'm currently building out a Twitter analysis tool that's using AWS Lambda to stream data into Amazon Kinesis Firehose, which in turns saves the result to Amazon S3. The plan is to have Amazon S3 operate as both a data store and quasi-messaging bus with any post-processing work (e.g., notifications of new tweets going into Slack) fanning out from there. I went with this approach as I can get things up and running quickly and only pay for things on a pay-per-use basis rather than having lots of worker nodes sitting around waiting for work. Amazon Kinesis Firehose also makes it easy to add a different or additional data store in the future.

Working on a project recently, wanted an easy modern frontend to work with, decoupled from our backend. To get things going quickly, decided to go with React, Redux, redux-saga, Bootstrap.

On the backend side, Golang is a personal favourite, and wanted to minimize server overheads so went with a #serverless architecture leveraging AWS Lambda, AWS CloudFormation, Amazon DynamoDB, etc.

For IDE/tooling I tend to stick to the #JetBrains tools: WebStorm / Goland.

Obviously using Git, with GitLab private repo's for managing code/issues/etc.

In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.

While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.

In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.

#ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService

Whenever we need to notify a user of something happening on our platform, whether it’s a personal push notification from one user to another, a new Dub, or a notification going out to millions of users at the same time that new content is available, we rely on AWS Lambda to do this task for us. When we started implementing this feature 2 years ago we were luckily able to get early access to the Lambda Beta and are still happy with the way things are running on there, especially given all the easy to set up integrations with other AWS services.

Lambda enables us to quickly send out million of pushes within a couple of minutes by acting as a multiplexer in front of Amazon SNS. We simply call a first Lambda function with a batch of up to 300 push notifications to be sent, which then calls a subsequent Lambda function with 20 pushes each, which then does the call to SNS to actually send out the push notifications.

This multi-tier process of sending push notifications enables us to quickly adjust our sending volume while keeping costs & maintenance overhead, on our side, to a bare minimum.

#ApplicationHosting