Over the past 3 years I’ve often received requests from new and existing Kernl customers for some form of analytics on their plugin/theme. I avoided doing this for a long time because I wasn’t sure that I could do so economically at the scale Kernl operates at, but I eventually decided to give Kernl Analytics a whirl and see where things ended up.
Concerns
After deciding to give the analytics offering a try, I had to figure how to build it. When I first set out to build Kernl Analytics I had 3 main concerns:
- Cost – I’ve never created a web service from scratch that needs to INSERT data at 75 rows per second with peaks of up to 500 rows per second. I wanted to be sure that running this service wouldn’t be prohibitively expensive.
- Scale – How much would I need to distribute the load? This is tightly coupled to cost.
- Speed – This project is going to generate a LOT of data by my standards. Can I query it in performant manner?
As development progressed I realized that cost and scale were non-issues. The database that I chose to use (PostgreSQL) can easily withstand this sort of traffic with no tweaking, and I was able to get things started on a $5 Digital Ocean droplet.
Kernl Analytics Architecture & Technology
Kernl Analytics was created to be it’s own micro-service with no public access to the world. All access to it is behind a firewall so that only Kernl’s Node.js servers can send requests to it. For data storage, PostgreSQL was chosen for a few reasons:
- Open Source
- The data is highly relational
- Performance
The application that captures the data, queries it, and runs periodic tasks is a Node.js application written in TypeScript. I chose TypeScript mostly because I’m familiar with it and wanted type safety so I wouldn’t need to write as many tests.
With regards to size of the instance that Kernl Analytics is running on, I currently pay $15/month for a 3 core Digital Ocean droplet. I upgraded to 3 cores so that Postgres could easily handle both writes and multiple read requests at the same time. So far this setup has worked out well!
Pain Points
Overall things went well while implementing Kernl Analytics. In fact they went far better than expected. But that doesn’t mean there weren’t a few pain points along the way.
- Write Volume – Kernl’s scale is just large enough to cause some scaling and performance pains when creating an analytics service. Kernl averages 25 req/s which translates to roughly 75 INSERTs into Postgres. Kernl also has peaks of 150 req/s which scales up to about 450 INSERTs into Postgres. Postgres can easily handle this sort of load, but doing it on a $5 digital ocean droplet was taxing to say the least.
- Hardware Upgrade – I tried to keep costs down as much as possible with Kernl Analytics, but in the end I had to increase the size of the droplet I was using to a $15 / 3-core droplet. I ended up doing that so one or two cores could be dedicated to writes while leaving a single core available for read requests. Postgres determines what actions are executed where, but adding more cores had led to a lot less resource contention.
- Aggregation – Initially the data wasn’t aggregated at all. This caused some pain because even with some indexing, plucking data out of a table with > 2.5 million rows can be sort of slow. It also didn’t help that I was writing data constantly to the table, which further slowed things down. Recently I solved this by doing daily aggregations for Kernl Analytics charts and domain data. This has improved speed significantly.
- Backups & High Availability – To keep costs down the analytics service is not highly available. This is definitely one of those “take out some tech debt” items that will need to be addressed at a later date. Backups also happen only on a daily basis, so its possible to lose a day of data if something serious goes wrong.
Future Plans
Kernl Analytics is a work in progress and there is always room to improve. Future plans for the architecture side of analytics are
- Optimize Indexes – I feel that more speed can be coaxed out of Postgres with some better indexing strategies.
- Writes -vs- Reads – Once I gain a highly available setup for Postgres I plan to split responsibilities for writing and reading. Writes will go to the primary and reads will go to the secondary.
- API – Right now the analytics API is completely private and firewalled off. Eventually I’d like to expose it to customers so that they can use it to do neat things.