Here was my understanding of the issue last time. The yammer metrics use a random sample of requests to estimate the histogram. This allocates a fairly large array of longs (their values are longs rather than floats). A reasonable sample might be 8k entries which would give about 64KB per histogram. There are bounds on accuracy, but they are only probabilistic. I.e. if you try to get 99% < 5 ms of inaccuracy, you will 1% of the time get more than this. This is okay but if you try to alert, in which you realize that being wrong 1% of the time is a lot if you are computing stats every second continuously on many metrics (i.e. 1 in 100 estimates will be outside you bound). This array is copied in full every time you check the metric which is the other cause of the memory pressure.
The better approach to histograms is to calculate buckets boundaries and record arbitrarily many values in those buckets. A simple bucketing approach for latency would be 0, 5ms, 10ms, 15ms, etc, and you just count how many fall in each bucket. Your precision is deterministically bounded by the bucket boundaries, so if you had 5ms buckets you would never have more than 5ms loss of precision. By using non-uniform bucket sizes you can make this work even better (e.g. give ~1ms precision for latencies in the 1ms range, but give only 1 second precision for latencies in the 30 second range). That is what is implemented in that metrics package. I think this bucketing approach is popular now. There is a whole "HDR histogram" library that gives lots of different bucketing methods and implements dynamic resizing so you don't have to specify an upper bound. https://github.com/HdrHistogram/HdrHistogram Whether this matters depends entirely if you want histograms broken down at the client, topic, partition, or broker level or just want overall metrics. If we just want per sever aggregates for histograms then I think the memory usage is not a huge issue. If you want a histogram per topic or client or partition and have 10k of these then that is where you start talking like 1GB of memory with the yammer package, which is what we hit last time. Getting percentiles on the client level is nice, percentiles are definitely better than averages, but I'm not sure it is required. -Jay On Wed, Mar 25, 2015 at 9:43 PM, Neha Narkhede <n...@confluent.io> wrote: > Aditya, > > If we are doing a deep dive, one of the things to investigate would be > memory/GC performance. IIRC, when I was looking into codahale at LinkedIn, > I remember it having quite a few memory management and GC issues while > using histograms. In comparison, histograms in the new metrics package > aren't very well tested. > > Thanks, > Neha > > On Wed, Mar 25, 2015 at 8:25 AM, Aditya Auradkar < > aaurad...@linkedin.com.invalid> wrote: > > > Hey everyone, > > > > Picking up this discussion after yesterdays KIP hangout. For anyone who > > did not join the meeting, we have 2 different metrics packages being used > > by the clients (custom package) and the server (codahale). We are > > discussing whether to migrate the server to the new package. > > > > What information do we need in order to make a decision? > > > > Some pros of the new package: > > - Using the most recent information by combining data from previous and > > current samples. I'm not sure how codahale does this so I'll investigate. > > - We can quota on anything we measure. This is pretty cool IMO. I've > > investigate the feasibility of adding this feature in codahale. > > - Hierarchical metrics. For example: we can define a sensor for overall > > bytes-in/bytes-out and also per-client. Updating the client sensor will > > cause the global byte rate sensor to get modified too. > > > > What are some of the issues with codahale? One previous discussion > > mentions high memory usage but I don't have any experience with it > myself. > > > > Thanks, > > Aditya > > > > > > > > > > > > > -- > Thanks, > Neha >
