With the latest advances in PostgreSQL (and other db’s), a relational database begins to look like a very viable TS storage platform. In this write up I attempt to show how to store TS in PostgreSQL. (2016-12-17 Update: there is a part 2 of this article.)
A TS is a series of [timestamp, measurement] pairs, where measurement is typically a floating point number. These pairs (aka “data points”) usually arrive at a high and steady rate. As time goes on, detailed data usually becomes less interesting and is often consolidated into larger time intervals until ultimately it is expired.
The obvious approach
The “naive” approach is a three-column table, like so:
(Let’s gloss over some details such as an index on the time column and choice of data type for time and value as it’s not relevant to this discussion.)
One problem with this is the inefficiency of appending data. An insert requires a look up of the new id, locking and (usually) blocks until the data is synced to disk. Given the TS’s “firehose” nature, the database can quite quickly get overwhelmed.
This approach also does not address consolidation and eventual expiration of older data points.
A better alternative is something called a round-robin database. An RRD is a circular structure with a separately stored pointer denoting the last element and its timestamp.
A everyday life example of an RRD is a week. Imagine a structure of 7 slots, one for each day of the week. If you know today’s date and day of the week, you can easily infer the date for each slot. For example if today is Tuesday, April 1, 2008, then the Monday slot refers to March 31st, Sunday to March 30th and (most notably) Wednesday to March 26.
Here’s what a 7-day RRD of average temperature might look as of Tuesday, April 1:
1 2 3 4 5
Come Wednesday, April 2nd, our RRD now loooks like this:
1 2 3 4 5
Note how little has changed, and that the update required no allocation of space: all we did to record 92F on Wednesday is overwrite one value. Even more remarkably, the previous value automatically “expired” when we overwrote it, thus solving the eventual expiration problem without any additional operations.
RRD’s are also very space-efficient. In the above example we specified the date of every slot for clarity. In an actual implementation only the date of the last slot needs to be stored, thus the RRD can be kept as a sequence of 7 numbers plus the position of the last entry and it’s timestamp. In Python syntax it’d look like this:
Round-robin in PostgreSQL
Here is a naive approach to having a round-robin table. Carrying on with our 7 day RRD example, it might look like this:
1 2 3 4 5 6 7 8 9
Somewhere separately we’d also need to record that the last entry is week_day 3 (Tuesday) and it’s 2008-04-01. Come April 2, we could record the temperature using:
This might be okay for a 7-slot RRD, but a more typical TS might have a slot per minute going back 90 days, which would require 129600 rows. For recording data points one at a time it might be fast enough, but to copy the whole RRD would require 129600 UPDATE statements which is not very efficient.
This is where using PostgrSQL arrays become very useful.
Using PostgreSQL arrays
An array would allow us to store the whole series in a single row. Sticking with the 7-day RRD example, our table would be created as follows:
1 2 3
(Nevemind that there is no id column for now)
We could populate the whole RRD in a single statement:
…or record 92F for Wednesday as so:
(In PostgreSQL arrays are 1-based, not 0-based like in most programming languages)
But it could be even more efficient
Under the hood, PostgreSQL data is stored in pages of 8K. It would make sense to keep chunks in which our RRD is written to disk in line with page size, or at least smaller than one page. (PostgreSQL provides configuration parameters for how much of a page is used, etc, but this is way beyond the scope of this article).
Having the series split into chunks also paves the way for some kind of a caching layer, we could have a server which waits for one row worth of data points to accumulate, then flushes then all at once.
For simplicity, let’s take the above example and expand the RRD to 4 weeks, while keeping 1 week per row. In our table definition we need provide a way for keeping the order of every row of the TS with a column named n, and while we’re at it, we might as well introduce a notion of an id, so as to be able to store multiple TS in the same table.
Let’s start with two tables, one called rrd where we would store the last position and date, and another called ts which would store the actual data.
1 2 3 4 5 6 7 8 9
We could then populate the TS with fictitious data like so:
1 2 3 4 5 6
To update the data for April 2, we would:
The last_pos of 25 is n * 7 + 1 (since arrays are 1-based).
This article omits a lot of detail such as having resolution finer than one day, but it does describe the general idea. For an actual implementation of this you might want to check out a project I’ve been working on: tgres