Karl Matthias bio photo

Karl Matthias

Principal Systems Engineer at Nitro. Co-Author of "Docker: Up and Running" from O'Reilly Media. Dublin, Ireland.




Cassandra vs MongoDB For Time Series Data

This is how we got a big win by switching from MongoDB to Cassandra for managing our time series data.

Cassandra vs MongoDB


MyDrive has an AWS cloud-hosted data processing platform powered in part by a chain of Resque workers.

Being a telematics company, we process a lot of time series data, and the initial pipeline that was built did a lot of work with MongoDB. This choice was made early on and it was supposed to be a temporary one.

MongoDB behaved reasonably well, but the unpredictability of the load times for different sizes of work was troubling and made pipeline tuning difficult. Some queries would return 30 documents and others 300. With the way Mongo is designed (and most relational datastores also), this resulted in a varying IO load. The query for 30 documents would return much faster than the query for

  1. Neither was particularly quick, either, even with good indexing and relatively decent instances.

Futhermore, we were having to scale our Mongo instances vertically to at least an extent, and we weren’t excited by the long term economy of doing that. In order to get decent performance we were already running multiple extra large instances.


From the beginning we wanted to use Cassandra, which I had previously run at AboutUs starting from version 0.5. It has always proven itself to be intensely reliable, predictable, and resilient. Those features, combined with the horizontal scalability make it a pretty killer data store.

The normal data flow is that we write some data, read it back, modify it, write it, and read it back one more time. These actions happen in different workers. This isn’t the ideal workload for Cassandra, but it’s a reasonably good fit because of how we query it.

Cassandra is really good for time-series data because you can write one column for each period in your series and then query across a range of time using sub-string matching. This is best done using columns for each period rather than rows, as you get huge IO efficiency wins from loading only a single row per query. Cassandra then has to at worst do one seek and then read for all the remaining data as it’s written in sequence to disk.

We designed our schema to use IDs that begin with timestamps so that they can be range queried over arbitrary periods like this, with each row representing one record and each column representing one period in the series. All data is then available to be queried on a row key and start and end times.

With the way our workload behaves, it seems that with MongoDB the number of writes was noticeably impacting read performance. With Cassandra, this does not seem to be the case for our scenario.


The graph below shows the better part of a year of performance, and the blue line is the one most affected by the performance of the data store. It should be obvious from the number of drops in the times that we were both tuning MongoDB and scaling the hardware to keep performance reasonable. But none of these changes were as effective as the move to Cassandra.

We phased MongoDB out by first turning off writes to it, then eventually turning off reads from it as well. These are clearly marked on the graph.

Worth noting is that not only did the performance massively improve (for around the same AWS spend, it should be said), but the predictability is hugely better as well. Notice how much flatter that blue line is on the right end of the graph compared to when Mongo was in heavy use.

One thing that is not at all obvious from the graph is that the system was also under massively heavier strain after the switch to Cassandra because of additional bulk processing going on in the background.

For us this has been an unequivocally big win and I thought it was worth sharing the results.

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