# Time Series Accuracy - Graphite vs RRDTool

Back in my ISP days, we used data stored in RRDs to bill our customers. I wouldn’t try this with Graphite. In this write up I try to explain why it is so by comparing the method of recording time series used by Graphite, with the one used by RRDTool.

Graphite uses Whisper to store data, which in the FAQ is portrayed as a better alternative to RRDTool, but this is potentially misleading, because the flexibility afforded by the design of Whisper comes at the price of inaccuracy.

A time series is most often described as a sequence of (time, value) tuples [1]. The most naive method of recording a time series is to store timestamps as is. Since the data points might arrive at arbitrary and inexact intervals, to correlate the series with a particular point in time might be tricky. If data points are arriving somewhere in between one minute bounaries (as they always naturally would), to answer the question of what happened during a particular minute would require specifying a range, which is not as clean as being able to specify a precise value. To join two series on a range is even more problematic.

One way to improve upon this is to divide time into equal intervals and assign data points to the intervals. We could then use the beginning of the interval instead of the actual data point timestamp, thereby giving us more uniformity. For example, if our interval size is 10 seconds (I may sometimes refer to it as the step), we could divide the entire timeline starting from the beginning of the epoch and until the end of universe into 10 second slots. Since the first slot begins at 0, any 10-second-step time series will have slots starting at the exact same times. Now correlation across series or other time values becomes much easier.

Calculating the slot is trivially easy: time - time % step (% being the modulo operator). There is, however, a subtle complexity lurking when it comes to storing the datapoint with the adjusted (or aligned) timestamp. Graphite simply changes the timestamp of the data point to the aligned one. If multiple data points arrive in the same step, then the last one “wins”.

On the surface there is little wrong with Graphite’s approach. In fact, under right circumstances, there is absolutely nothing wrong with it. Consider the following example:

Let’s pretend those values are some system metric like the number of files open. The consequence of the 50 being dropped is that we will never know it existed, but towards the end of the 10 second interval it went down to 10, which is still a true fact. If we really wanted to know about the variations within a 10 second interval, we should have chosen a smaller step, e.g. 1 second. By deciding that the step is going to be 10 seconds, we thus declared that variations within a smaller period are of no interest to us, and from this perspective, Graphite is correct.

But what if those numbers are the price of a stock: there may be hundreds of thousand of trades within a 10 second interval, yet we do not want to (or cannot, for technical reasons) record every single one of them? In this scenario having the last value override all previous ones doesn’t exactly seem correct.

Enter RRDTool which uses a different method. RRDTool keeps track of the last timestamp and calculates a weight for every incoming data point based on time since last update or beginning of the step and the step length. Here is what the same sequence of points looks like in RRDTool. The lines marked with a * are not actual data points, but are the last value for the preceding step, it’s used for computing the value for the remainder of the step after a new one has begun.

Note, by the way, that the Whisper FAQ says that “RRD will store your updates in a temporary workspace area and after the minute has passed, aggregate them and store them in the archive”, which to me sounds like there is some sort of a temporary storage area holding all the unsaved updates. In fact, to be able to compute the weighted average, RRD only needs to store the time of the last update and the current sum, i.e. exactly just two variables, regardless of the number of updates in a single step. This is evident from the above figure.

So to compare the results of the two tools:

Before you say “so what, I don’t really understand the difference”, let’s pretend that those numbers were actually the rate of sale of trinkets from our website (per second). Here is a horizontal ascii-art rendition of our timeline, 0 is 1430701270.

At 12 seconds we recorded selling 50 trinkets per second. Assuming we started selling at the beginning of our timeline, i.e. 12 seconds earlier, we can state that during the first step we sold exactly 500 trinkets. Then 2 seconds into the second step we sold another 100 (we’re still selling at 50/s). Then for the next 6 seconds we were selling at 10/s, thus another 60 trinkets, and for the last 2 seconds of the slot we sold another 60 at 30/s. In the third step we were selling steadily at 30/s, thus exactly 300 were sold.

Comparing RRDTool and Graphite side-by-side, the stories are quite different:

Two important observations here:

1. The totals are vastly different.
2. The rate recorded by RRDTool for the second slot (22/s), yields exactly the number of trinkets sold during that period: 220.

Last, but hardly the least, consider what happens when we consolidate data points into larger intervals by averaging the values. Let’s say 20 seconds, twice our step. If we consolidate the second and the third steps, we would get:

Since the Graphite numbers were off to begin with, we have no reason to trust the 400 trinkets number. But using the RRDTool data, the new number happens to still be 100% accurate even after the data points have been consolidated. This is a very useful property of rates in time series. It also explains why RRDTool does not permit updating data prior to the last update: RRD is always accurate.

As an exercise, try seeing it for yourself: pretent the value of 10 in the second step never arrived, which should make the final value of the second slot 34. If the 10 arrived some time later, averaging it in will not give you the correct 22.

Whisper allows past updates, but is quasi-accurate to begin with - I’m not sure I understand which is better - inaccurate data with a data point missing, or the whole inaccurate data. RRD could accomplish the same thing by adding some --inaccurate flag, though it would seem like more of a bug than a feature to me.

If you’re interested in learning more about this, I recommend reading the documentation for rrdtool create, in particular the “It’s always a Rate” section, as well as this post by Alex van den Bogaerdt.

P.S. After this post was written, someone suggested that instead of storing a rate, we coud store a count delta. In other words, instead of recording that we’re selling 10 trinkets per second for the past 6 seconds, we would store the total count of trinkets sold, i.e. 60. At first this seems like the solution to being able to update historical data accurately: if later we found out that we sold another 75 trinkets in the second time slot, we could just add it to the total and all would be well and most importantly accurate.

Here is the problem with this approach: note that in the previous sentence I had to specify that the additional trinkets were sold in the second time slot, a small, but crucial detail. If time series data point is a timestamp and a value, then there isn’t even a way to relay this information in a single data point - we’d need two timestamps. On the other hand if every data point arrived with two timestamps, i.e. as a duration, then which to store, rate or count, becomes a moot point, we can infer one from the other.

So perhaps another way of explaining the historical update problem is that it is possible, but the datapoint must specify a time interval. This is something that neither RRDTool or Graphite currently support, even though it’d be a very useful feature in my opinion.

[1] Perhaps the biggest misconception about time series is that it is a series of data points. What time series represent is continuous rather than descrete, i.e. it’s the line that connects the points that matters, not the specific points themselves, they are just samples at semi-random intervals that help define the line. And as we know, a line cannot be defined by a single point.