Sean Mackrory created HADOOP-14972:
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Summary: Histogram metrics types for latency, etc.
Key: HADOOP-14972
URL: https://issues.apache.org/jira/browse/HADOOP-14972
Project: Hadoop Common
Issue Type: Bug
Components: fs/s3
Reporter: Sean Mackrory
Assignee: Sean Mackrory
We'd like metrics to track latencies for various operations, such as latencies
for various request types, etc. This may need to be done different from current
metrics types that are just counters of type long, and it needs to be done
intelligently as these measurements are very numerous, and are primarily
interesting due to the outliers that are unpredictably far from normal.
* An adaptive, sparse histogram type. I envision something configurable with a
maximumum granularity and a maximum number of bins. Initially, datapoints are
tallied in bins with the maximum granularity. As we reach the maximum number of
bins, bins are merged in even / odd pairs. There's some complexity here,
especially to make it perform well and allow safe concurrency, but I like the
ability to configure reasonable limits and retain as much granularity as
possible without knowing the exact shape of the data beforehand.
* LongMetrics named "read_latency_600ms", "read_latency_800ms" to represent
bins. This was suggested to me by [~fabbri]. I initially did not like the idea
of having either so many hard-coded bins for however many op types, but this
could also be done dynamically (we just hard-code which measurements we take,
and with what granularity to group them, e.g. read_latency, 200 ms). The
resulting dataset could be sparse and dynamic to allow for extreme outliers,
but the granularity is still pre-determined.
* We could also simply track a certain number of the highest latencies, and
basic descriptive statistics like a running average, min / max, etc. Inherently
more limited in what it can show us, but much simpler and might still provide
some insight when analyzing performance.
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