I was referring to this article by Shipilev (there are few small issues forgotten in that url you pasted):


And his lovely recommendation on it: "System.nanoTime is as bad as String.intern now: you can use it, but use it wisely. ". And Cassandra uses it quite a lot in the write path at least. There isn't necessarily a better option in Java for it, but for that reason we shouldn't push them everywhere in the code "for fun".

  - Micke

On 02/22/2018 06:08 PM, Jeremiah D Jordan wrote:
re: nanoTime vs currentTimeMillis there is a good blog post here about the 
timing of both and how your choice of Linux clock source can drastically effect 
the speed of the calls, and also showing that in general on linux there is no 
perf improvement for one over the other.

On Feb 22, 2018, at 11:01 AM, Blake Eggleston <beggles...@apple.com> wrote:

Hi Micke,

This is really cool, thanks for taking the time to investigate this. I believe 
the metrics around memtable insert time come in handy in identifying high 
partition contention in the memtable. I know I've been involved in a situation 
over the past year where we got actionable info from this metric. Reducing 
resolution to milliseconds is probably a no go since most things in this path 
should complete in less than a millisecond.

Revisiting the use of the codahale metrics in the hot path like this definitely 
seems like a good idea though. I don't think it's been something we've talked 
about a lot, and it definitely looks like we could benefit from using something 
more specialized here. I think it's worth doing, especially since there won't 
be any major changes to how we do threading in 4.0. It's probably also worth 
opening a JIRA and investigating the calls to nano time. We at least need 
microsecond resolution here, and there could be something we haven't thought 
of? It's worth a look at least.



On 2/22/18, 6:10 AM, "Michael Burman" <mibur...@redhat.com> wrote:


    I wanted to get some input from the mailing list before making a JIRA
    and potential fixes. I'll touch the performance more on latter part, but
    there's one important question regarding the write latency metric
    recording place. Currently we measure the writeLatency (and metric write
    sampler..) in ColumnFamilyStore.apply() and this is also the metric we
    then replicate to Keyspace metrics etc.

    This is an odd place for writeLatency. Not to mention it is in a
    hot-path of Memtable-modifications, but it also does not measure the
    real write latency, since it completely ignores the CommitLog latency in
    that same process. Is the intention really to measure
    Memtable-modification latency only or the actual write latencies?

    Then the real issue.. this single metric is a cause of huge overhead in
    Memtable processing. There are several metrics / events in the CFS apply
    method, including metric sampler, storageHook reportWrite,
    colUpdateTimeDeltaHistogram and metric.writeLatency. These are not free
    at all when it comes to the processing. I made a small JMH benchmark
    here: https://gist.github.com/burmanm/b5b284bc9f1d410b1d635f6d3dac3ade
    that I'll be referring to.

    The most offending of all these metrics is the writeLatency metric. What
    it does is update the latency in codahale's timer, doing a histogram
    update and then going through all the parent metrics also which update
    the keyspace writeLatency and globalWriteLatency. When measuring the
    performance of Memtable.put with parameter of 1 partition (to reduce the
    ConcurrentSkipListMap search speed impact - that's separate issue and
    takes a little bit longer to solve although I've started to prototype
    something..) on my machine I see 1.3M/s performance with the metric and
    when it is disabled the performance climbs to 4M/s. So the overhead for
    this single metric is ~2/3 of total performance. That's insane. My perf
    stats indicate that the CPU is starved as it can't get enough data in.

    Removing the replication from TableMetrics to the Keyspace & global
    latencies in the write time (and doing this when metrics are requested
    instead) improves the performance to 2.1M/s on my machine. It's an
    improvement, but it's still huge amount. Even when we pressure the
    ConcurrentSkipListMap with 100 000 partitions in one active Memtable,
    the performance drops by about ~40% due to this metric, so it's never free.

    i did not find any discussion replacing the metric processing with
    something faster, so has this been considered before? At least for these
    performance sensitive ones. The other issue is obviously the use of
    System.nanotime() which by itself is very slow (two System.nanotime()
    calls eat another ~1M/s from the performance)

    My personal quick fix would be to move writeLatency to Keyspace.apply,
    change write time aggregates to read time processing (metrics are read
    less often than we write data) and maybe even reduce the nanotime ->
    currentTimeMillis (even given it's relative lack of precision). That is
    - if these metrics make any sense at all at CFS level? Maybe these
    should be measured from the network processing time (including all the
    deserializations and such) ? Especially if at some point the smarter
    threading / eventlooping changes go forward (in which case they might
    sleep at some "queue" for a while).

       - Micke

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