After looking more at Pivot Tracing tool described in my earlier post, I asked myself about the limitations of such monitoring approach. Pivot tracing is not a universal tool, so it appears that there are few problems it does not address well enough.
The basic idea of the Pivot Tracing is to collect the information about the request as the request propagates through the system. The image below shows a partial illustration of request propagation along with information collection at pivot points.
As the request passes through a pivot point in system A, we can collect some parameters, xA and yA, and use the baggage mechanism to send these parameters further along the request. Once the request reaches next pivot point, say in system B, we can also collect some information zB on that system. Note that system B does not have access to xA and yA directly without the Pivot Tracing tool, but thanks to the baggage mechanism, we have these parameters available at pivot point in system B.
Why is it important? It is fairly boring when we look at only one request trace, however when we look at all the requests happening in the system over some time interval things start to get a lot more exciting.
We can aggregate the data over all the requests to have a system-wide statistics reported to the system user, and have parameters from one system propagate to another system with the baggage allowing much more complex requests aggregations. Having xA and yA at the system B enables us to aggregate on parameters in B, such as zB over different values of xA or yA. For example, I can now compute the sum of zB for all requests going through system B for different values of xA. This would not have been possible without having information propagate with the baggage from one pivot point to another. Another scenario would be aggregating variable z across both systems B and C for different values of parameters coming from A.
Aggregation of requests is extremely important, as it enables the system-wide monitoring of the distributed application, instead of looking at individual request traces. However, is this aggregation correct? Does it have errors? And what can cause the errors? Looking back at Figure 2, we see many requests executing in parallel, these requests are causally independent, so how does the system know these requests indeed happened between T0 and T2? Time skew between servers can impact the accuracy of reporting, especially if some requests run on disjointed set of servers (they do not share any common servers). Is this a big problem for Pivot Tracing? I think in most cases it is ok, as long as time skew is kept within the reasonable bounds. If the monitoring is run continuously over some period of time, missing some requests in one window will only make them counted in the other time-window.
Pivot Tracing is not capable to answer all kinds of queries. With the example above, we were aggregating the requests over some time period, but what if we want to know something about the system at exact instance? What if user desires to learn something about the system at time T1 (Figure 2). This is not possible with Pivot Tracing tool. For one, we cannot even be sure that T1 is the same time at all the requests due to the time skew. Second, even if we can guarantee exact time synchronization, there is no guarantee that all requests will be at the correct Pivot Point to collect such information at T1. In other words, Pivot Tracing cannot provide a user with consistent global information about the system at any exact point of time.
Instantaneous information may be useful for debugging and monitoring systems. For example, recently I had a need to find out how many nodes perform BDB JE log compaction in my Voldemort cluster at the same time. The compaction is not triggered directly by the requests, instead a separate local thread periodically checks if compaction is needed. As such, with a Pivot Tracing style tool, it would have been impossible for me to even instrument the Pivot Points as no request actually goes and triggers the compaction. In addition, the time skew would not have allowed me to know for sure whether the compaction was running at all nodes concurrently or it simply appears so from the misaligned time. What I need is a consistent global snapshot of parameters in my Voldemort cluster…