Statistics question on histogram vs. total_num_queries
John Todd
jtodd at loligo.com
Fri Jan 20 21:30:33 UTC 2017
Thank you for the details - this makes more sense. I did not understand
the documentation linkages and missed that interpretation.
The cache miss rate is high because this example server is getting only
cache misses from other recursive resolvers; it’s just a testbed, so
the numbers and balances were not particularly important - we only
needed some ‘real’ data even though it is skewed.
We are not looking for the median; we are attempting to summarize with
better detail than a single statistic, yet not looking at 40 different
buckets. Our intention is to create a summary band set that is less
granular than what is provided by unbound natively. Having 40 lines in a
graph is distracting and difficult to read, so we wanted to condense
these ranges into a set of 5 that were arbitrarily chosen as “Fast”,
“OK”, “Marginal”, “Slow”, and “Very slow”. How we label
these and divide up the 40 existing buckets into those summary bands was
then a matter of subjective iteration as we gained experience with the
monitoring.
I’m pleased to see that the new denominator of num.cachemiss makes the
graphs look as I would expect, and overlaying the resolution speed on
the data we get from other recursive resolver software running in the
same POPs now seems to match very closely. Now that we have good
instrumentation that is consistent across resolvers, we can move forward
with trying changes to increase the speed (reduce latency) of query
responses.
JT
On 20 Jan 2017, at 1:01, W.C.A. Wijngaards via Unbound-users wrote:
> Hi John,
>
> The numbers add up to the num.cachemiss. For your statistics you need
> to add the num.cachehits queries to the 0 bucket, or create another
> bucket with a zero response time for those cachehits queries. Then
> your percentile band can be calculated.
>
> It is also documented, over here:
> http://unbound.net/documentation/unbound-control.html
> (it says that the cachemiss queries are the queries that use recursive
> processing, and the histogram keeps track of queries that needed
> recursive processing).
>
> You have a low cache hit ratio, it looks like. Perhaps increase cache
> memory size in unbound.conf. Or it could be an artifact of artificial
> query inputs.
>
> If the percentile band you are looking for is the median, this is
> already calculated for you in recursion.time.median (that number is
> only for the queries that needed recursion, it does not include the
> cachehits that were answered immediately).
>
> Best regards, Wouter
>
> On 20/01/17 00:13, John Todd via Unbound-users wrote:
>>
>> We import all (“stats_noreset”) our Unbound 1.5.10 data into
>> Prometheus (a TSDB) every 15 seconds. We then generate many
>> different reports out of the collected data. The current test
>> system has very low QPS - 100-300. I admit that the problem I
>> describe below might be an issue with Prometheus or low QPS, but
>> I’d like to ensure my assumptions about counters are correct before
>> I start digging into a system which seems to work quite well for
>> all my other data thus far.
>>
>> I noticed something a bit off with one of the graphs, and I’m
>> trying to understand what I’m doing wrong that is causing the
>> problem. I have added up all of the incremental increasing results
>> for the histograms for a particular interval (i.e.: subtracting
>> last sample from current sample) and then compared that against
>> what I thought should be the same (or extremely close) number which
>> would be contained in the “total.num.queries” counter using the
>> same method for the same time intervals. They are very, very
>> different numbers - an unexpected result. (1)
>>
>> Sample query below for histogram. Hopefully these should be
>> mostly-evident even to a non-Prometheus user - “irate” means
>> “give
>> the per-second rate over time” with “[1m]” giving a one minute
>> maximum time backwards from a sample to get the last value prior.
>> Timespans are not shown here for brevity, but the queries are done
>> with the same time bounds (typically 1 minute buckets. I limit the
>> queries to “prod”uction systems, and only for particular POPs as
>> a
>> templated variable.)
>>
>> sum( irate(unbound_histogram_000000_000000_to_000000_000001
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000001_to_000000_000002
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000002_to_000000_000004
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000004_to_000000_000008
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000008_to_000000_000016
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000016_to_000000_000032
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000032_to_000000_000064
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000064_to_000000_000128
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000128_to_000000_000256
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000256_to_000000_000512
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_000512_to_000000_001024
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_001024_to_000000_002048
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_002048_to_000000_004096
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_004096_to_000000_008192
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_008192_to_000000_016384
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_016384_to_000000_032768
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_032768_to_000000_065536
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_065536_to_000000_131072
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_131072_to_000000_262144
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_262144_to_000000_524288
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000000_524288_to_000001_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000001_000000_to_000002_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000002_000000_to_000004_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000004_000000_to_000008_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000008_000000_to_000016_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000016_000000_to_000032_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000032_000000_to_000064_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000064_000000_to_000128_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000128_000000_to_000256_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000256_000000_to_000512_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_000512_000000_to_001024_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_001024_000000_to_002048_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_002048_000000_to_004096_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_004096_000000_to_008192_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_008192_000000_to_016384_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_016384_000000_to_032768_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_032768_000000_to_065536_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_065536_000000_to_131072_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_131072_000000_to_262144_000000
>> {env="prod",loc=~"$POP"}[1m]) + ignoring(label)
>> irate(unbound_histogram_262144_000000_to_524288_000000
>> {env="prod",loc=~"$POP"}[1m]) )
>>
>> This above query for an example 1 minute period provides a value of
>> 159 total queries per second. I then compare that result to the
>> following query using the exact same time bounds:
>>
>> sum(irate(unbound_total_num_queries {env="prod",loc=~"$POP"}[1m]))
>>
>> The above query results in 241 queries per second for that same 1
>> minute interval. I would expect the values to be within 5% of each
>> other, but they are quite dissimilar. The mismatched ratio I see
>> stays fairly level across QPS variation - around .7:1 though it
>> does spike and dip between .5:1 and .8:1 if I graph the ratio over
>> time.
>>
>> Am I misunderstanding how the histogram.* and total.num.queries
>> are counting queries and replies? My initial assumption was that
>> they would be almost identical for the same timeframe.
>>
>> Do the histogram counters not include NXDOMAIN, nodata, SERVFAIL
>> results? That seems inherently wrong to not measure the reply time
>> for those rcode results (those intervals are still visible to the
>> end user!) though it was worth looking to see if that led to more
>> sensible matching. Even subtracting those rcodes out of the larger
>> number (unbound_total_num_queries) the aggregated histogram values
>> still are 10-20% lower than that value though they are closer; I
>> can’t tell if this is simply by chance, and is possibly the wrong
>> path to a solution.
>>
>> I could “look at the code” but that means farming it out to an
>> actual developer, because I’m merely a pointy-haired
>> boss/graphmonger who wants to build KPI-type statistics and alerts.
>> I’m hoping there is a document or quick answer someone can point me
>> towards which will clear up the discrepancy I am finding.
>>
>> JT
>>
>>
>>
>> (1) Background story: I’m trying to build a graph that is
>> “percentile bands” of response time, and I needed a denominator
>> for
>> my calculations. I could have added up all of the histogram results
>> to get the denominator for my percentage graph, but that made my
>> query too big, so I just used “total.num.queries” but that led to
>> a
>> gap in the stacked graph, meaning down the rabbit hole I went and
>> ended up here.
>>
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