How we built Analytics

How we built Analytics

We weren’t quite sure how to build these analytics. We slowly established a set of requirements and constraints

  • Zero performance impact
  • Fully decoupled from the current infrastructure
  • Results at most hourly
  • Data is more important than graphs
  • Easily-extensible, in case we want to measure more things

Collecting

There are 2 ways to collect data : on the fly, when things happen, or by just collecting stuff in the database. Since the top requirement was to have 0 performance impact, the most obvious way was to have a “maintenance” script that runs forever and measures what needs to be measured in a loop.

We added a slave server to our main MySQL server(s). We use this server for the analytics. It’s a regular slave because, obviously doing analytics is about reads, not writes. With that, we can guarantee that the performance impact on the current is zero. If the analytics dies, nothing else will be impacted.

The scripts runs at regular intervals so that it takes several set of measures by doing queries on the slave database.

Storage

Since we need to have an extensible schema, we eliminated anything with a “fixed” schema, like MySQL. Additionally, analytics can really be regarded as documents, which naively lead us look at Document stores. We chose MongoDB. It’s probably the most actively maintained of its category and Ruby has a pretty neat library: MongoMapper.

It also has some MapReduce functionalities, which will probably be useful when start having a lot of data and we want to make complex analysis of it. Unfortunately, there is no event-machine driver for it, but it seems that inserts return immediately, which shouldn’t be too bad in terms of performance.

Querying

Right now we don’t have a lot of data, so the regular MongoMapper interface does a pretty good job. Typically, here is how we process results : we get all the data for a given time query in a given time-frame, like a day. We then so some kind of Map-Reduce to get results on an hourly basis : we split the result set by hours (map), compute averages/max/min (reduce) and then return the final result set.

When we’ll start computing more complex stuff, we’ll probably need to use Mongo’s capabilities for MapReduce, but right now we’re “emulating” that behavior from within a Ruby script.

Since the data is not expected to be updated more than hourly, we also cache the results in our Memcache server. Another option that we’re evaluating is storing this as actual documents, so we have the raw data (collected from MySQL), as well as the computed results.

All the results are returned in a Json format, which is pretty convenient for direct export!

Displaying

We did some research on various solutions to display the data. The first choice we had to make was :

  • query some service that would build the graphs for us (like Google Graph)
  • or build the graphs by ourselves.


The first solution initially looked sexier, but then we couldn’t find the flexibility we wanted, and since we export results as Json, we thought* that anyone could actually plug the data we generate for them into the 3rd party* app they wanted.

The second decision was for the “type” of graphs : flash? images? Usually the first one are more interactive, but they require the use of flash :/ Lucklily we found Rapheal.js and its charting library which allows us to generate nice looking interactive graphs by using the browser’s capabilities to deal with SVG and other open formats! We got our winner :) Feel free to look at the source code of the graphs page to see more… Hopefully I can find the time soon to “bundle” that in something nice on github.

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