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Our SDKs help our customers track click-stream data along with the user attributes. The data is then sent to the MoEngage servers for further processing. However, due to its voluminous nature, the data needs to be optimized, which is achieved by batching the data before it hits our servers. As the traffic(rpm) to our servers isn’t predictable, we first put the data in the AWS SQS to handle any unusual spikes and later process it at the rate which our server can handle.
Moving the data to SQS before it hits the server directly is good practice, though, the following areas needed improvement:
In the picture below, you can see one of our team members posting the improvements we saw after enabling the solution for one of the clients
The obvious solution to this problem is to batch the data before sending to the SQS, but this comes with inherent challenges –
We’ve addressed these issues by defining a few rules, and finally, arrived at a concrete solution (MBatching) in which:
In the picture below, we have explained how MBatching fits into our architecture –
MBatching module has an additional requirement to meet – The solution has to be generic so that we can work with multiple queues on SQS from a single machine.
Anything we build at MoEngage, we try to make it versatile to suit most of our needs across the company.
MBatching internally has four key components:
Internal design overview of MBatching is mentioned in the picture below –
Let’s look at the lifecycle of a message, to understand how all the components work together.
When a message is received at the MBatching module, it first checks if a local queue exists, if not, a queue is created, let’s call this localQ. A counter is maintained to keep track of the no.of messages received in the localQ, and it is incremented accordingly. Next, we check the size of the sum of uncompressed messages in localQ and whether it exceeds a threshold value – 80KB (we have tuned and arrived at this value from our experiments). If it does exceed the threshold, we flush all the messages to another queue, call it mergeQ. Similar to localQ, a counter is maintained to keep track of how many messages are sent to mergeQ and this counter is incremented accordingly. Parallely, the analyzer works on mergeQ in a separate thread to find out the optimum number of messages required to reach the 64KB compressed size. We will discuss the need for two queues after we explain how compression works.
Analyser is responsible to compress the messages in mergeQ and send them to SQS. This is how analyser works –
A message at a given point of time, will reside in localQ or mergeQ, before it eventually gets flushed to SQS.
If we were to do the compression logic in localQ, first we would have to lock the queue. And then all the API requests would be waiting on this queue to become free to put the messages in it until the compression logic is computed. Hence, to optimize the performance, the analyser is initiated in a separate thread, where only 1 API call is taking the load of evaluating compression logic.
Each service can also mention the max time to wait to achieve optimal compression. Monitor thread uses this time to validate data loss periodically, triggered every x mins.
Here is how monitoring thread works –
We were able to reduce our SQS costs by a whopping 90%. Better yet, the response time for MBatching module is 1ms compared to 10ms contacting SQS.
Thanks to our innovative engineering team, we have been using MBatching in production for almost two years now, and it has worked wonders for us.
If you like working on challenging problems at scale and solve them optimally, we would like for you to be part of our team, please check the job openings here.
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