hi Antoine, All of the Flight traffic is going through a hard-coded single port
https://github.com/apache/arrow/blob/master/cpp/src/arrow/flight/flight-benchmark.cc#L185 What happens if you spin up a different server (and use a different port) for each thread? I'm surprised no one else has mentioned this yet https://issues.apache.org/jira/browse/ARROW-3330 - Wes On Sun, Feb 24, 2019 at 9:20 AM Antoine Pitrou <anto...@python.org> wrote: > > > If that was the case, then we would see 100% CPU usage on all CPU cores, > right? Here my question is why only 2.5 cores are saturated while I'm > pinning the benchmark to 4 physical cores. > > Regards > > Antoine. > > > Le 24/02/2019 à 14:29, Francois Saint-Jacques a écrit : > > A quick glance suggests you're limited by the kernel copying memory around > > (https://gist.github.com/fsaintjacques/1fa00c8e50a09325960d8dc7463c497e). > > I think the next step is to use different physical hosts for client and > > server. This > > way you'll free resources for the server. > > > > François > > > > > > On Thu, Feb 21, 2019 at 12:42 PM Antoine Pitrou <anto...@python.org> wrote: > > > >> > >> We're talking about the BCC tools, which are not based on perf: > >> https://github.com/iovisor/bcc/ > >> > >> Apparently, using Linux perf for the same purpose is some kind of hassle > >> (you need to write perf scripts?). > >> > >> Regards > >> > >> Antoine. > >> > >> > >> Le 21/02/2019 à 18:40, Francois Saint-Jacques a écrit : > >>> You can compile with dwarf (-g/-ggdb) and use `--call-graph=dwarf` to > >> perf, > >>> it'll help the unwinding. Sometimes it's better than the stack pointer > >>> method since it keep track of inlined functions. > >>> > >>> On Thu, Feb 21, 2019 at 12:39 PM Antoine Pitrou <anto...@python.org> > >> wrote: > >>> > >>>> > >>>> Ah, thanks. I'm trying it now. The problem is that it doesn't record > >>>> userspace stack traces properly (it probably needs all dependencies to > >>>> be recompiled with -fno-omit-frame-pointer :-/). So while I know that a > >>>> lot of time is spent waiting for futextes, I don't know if that is for a > >>>> legitimate reason... > >>>> > >>>> Regards > >>>> > >>>> Antoine. > >>>> > >>>> > >>>> Le 21/02/2019 à 17:52, Hatem Helal a écrit : > >>>>> I was thinking of this variant: > >>>>> > >>>>> http://www.brendangregg.com/FlameGraphs/offcpuflamegraphs.html > >>>>> > >>>>> but I must admit that I haven't tried that technique myself. > >>>>> > >>>>> > >>>>> > >>>>> On 2/21/19, 4:41 PM, "Antoine Pitrou" <solip...@pitrou.net> wrote: > >>>>> > >>>>> > >>>>> I don't think that's the answer here. The question is not how > >>>>> to /visualize/ where time is spent waiting, but how to /measure/ > >> it. > >>>>> Normal profiling only tells you where CPU time is spent, not what > >> the > >>>>> process is idly waiting for. > >>>>> > >>>>> Regards > >>>>> > >>>>> Antoine. > >>>>> > >>>>> > >>>>> On Thu, 21 Feb 2019 16:29:15 +0000 > >>>>> Hatem Helal <hhe...@mathworks.com> wrote: > >>>>> > I like flamegraphs for investigating this sort of problem: > >>>>> > > >>>>> > https://github.com/brendangregg/FlameGraph > >>>>> > > >>>>> > There are likely many other techniques for inspecting where time > >>>> is being spent but that can at least help narrow down the search space. > >>>>> > > >>>>> > On 2/21/19, 4:03 PM, "Francois Saint-Jacques" < > >>>> fsaintjacq...@gmail.com> wrote: > >>>>> > > >>>>> > Can you remind us what's the easiest way to get flight > >> working > >>>> with grpc? > >>>>> > clone + make install doesn't really work out of the box. > >>>>> > > >>>>> > François > >>>>> > > >>>>> > On Thu, Feb 21, 2019 at 10:41 AM Antoine Pitrou < > >>>> anto...@python.org> wrote: > >>>>> > > >>>>> > > > >>>>> > > Hello, > >>>>> > > > >>>>> > > I've been trying to saturate several CPU cores using our > >>>> Flight > >>>>> > > benchmark (which spawns a server process and attempts to > >>>> communicate > >>>>> > > with it using multiple clients), but haven't managed to. > >>>>> > > > >>>>> > > The typical command-line I'm executing is the following: > >>>>> > > > >>>>> > > $ time taskset -c 1,3,5,7 > >>>> ./build/release/arrow-flight-benchmark > >>>>> > > -records_per_stream 50000000 -num_streams 16 -num_threads > >> 32 > >>>>> > > -records_per_batch 120000 > >>>>> > > > >>>>> > > Breakdown: > >>>>> > > > >>>>> > > - "time": I want to get CPU user / system / wall-clock > >> times > >>>>> > > > >>>>> > > - "taskset -c ...": I have a 8-core 16-threads machine and > >> I > >>>> want to > >>>>> > > allow scheduling RPC threads on 4 distinct physical cores > >>>>> > > > >>>>> > > - "-records_per_stream": I want each stream to have enough > >>>> records so > >>>>> > > that connection / stream setup costs are negligible > >>>>> > > > >>>>> > > - "-num_streams": this is the number of streams the > >>>> benchmark tries to > >>>>> > > download (DoGet()) from the server to the client > >>>>> > > > >>>>> > > - "-num_threads": this is the number of client threads the > >>>> benchmark > >>>>> > > makes download requests from. Since our client is > >>>> currently > >>>>> > > blocking, it makes sense to have a large number of client > >>>> threads (to > >>>>> > > allow overlap). Note that each thread creates a separate > >>>> gRPC client > >>>>> > > and connection. > >>>>> > > > >>>>> > > - "-records_per_batch": transfer enough records per > >>>> individual RPC > >>>>> > > message, to minimize overhead. This number brings us > >>>> close to the > >>>>> > > default gRPC message limit of 4 MB. > >>>>> > > > >>>>> > > The results I get look like: > >>>>> > > > >>>>> > > Bytes read: 25600000000 > >>>>> > > Nanos: 8433804781 > >>>>> > > Speed: 2894.79 MB/s > >>>>> > > > >>>>> > > real 0m8,569s > >>>>> > > user 0m6,085s > >>>>> > > sys 0m15,667s > >>>>> > > > >>>>> > > > >>>>> > > If we divide (user + sys) by real, we conclude that 2.5 > >>>> cores are > >>>>> > > saturated by this benchmark. Evidently, this means that > >> the > >>>> benchmark > >>>>> > > is waiting a *lot*. The question is: where? > >>>>> > > > >>>>> > > Here is some things I looked at: > >>>>> > > > >>>>> > > - mutex usage inside Arrow. None seems to pop up (printf > >> is > >>>> my friend). > >>>>> > > > >>>>> > > - number of threads used by the gRPC server. gRPC > >>>> implicitly spawns a > >>>>> > > number of threads to handle incoming client requests. > >>>> I've checked > >>>>> > > (using printf...) that several threads are indeed used to > >>>> serve > >>>>> > > incoming connections. > >>>>> > > > >>>>> > > - CPU usage bottlenecks. 80% of the entire benchmark's CPU > >>>> time is > >>>>> > > spent in memcpy() calls in the *client* (precisely, in > >> the > >>>>> > > grpc_byte_buffer_reader_readall() call inside > >>>>> > > arrow::flight::internal::FlightDataDeserialize()). It > >>>> doesn't look > >>>>> > > like the server is the bottleneck. > >>>>> > > > >>>>> > > - the benchmark connects to "localhost". I've changed it > >> to > >>>>> > > "127.0.0.1", it doesn't make a difference. AFAIK, > >>>> localhost TCP > >>>>> > > connections should be well-optimized on Linux. It seems > >>>> highly > >>>>> > > unlikely that they would incur idle waiting times (rather > >>>> than CPU > >>>>> > > time processing packets). > >>>>> > > > >>>>> > > - RAM usage. It's quite reasonable at 220 MB (client) + 75 > >>>> MB > >>>>> > > (server). No swapping occurs. > >>>>> > > > >>>>> > > - Disk I/O. "vmstat" tells me no block I/O happens during > >>>> the > >>>>> > > benchmark. > >>>>> > > > >>>>> > > - As a reference, I can transfer 5 GB/s over a single TCP > >>>> connection > >>>>> > > using plain sockets in a simple Python script. 3 GB/s > >>>> over multiple > >>>>> > > connections doesn't look terrific. > >>>>> > > > >>>>> > > > >>>>> > > So it looks like there's a scalability issue inside our > >>>> current Flight > >>>>> > > code, or perhaps inside gRPC. The benchmark itself, if > >>>> simplistic, > >>>>> > > doesn't look problematic; it should actually be kind of a > >>>> best case, > >>>>> > > especially with the above parameters. > >>>>> > > > >>>>> > > Does anyone have any clues or ideas? In particular, is > >>>> there a simple > >>>>> > > way to diagnose *where* exactly the waiting times happen? > >>>>> > > > >>>>> > > Regards > >>>>> > > > >>>>> > > Antoine. > >>>>> > > > >>>>> > > >>>>> > > >>>>> > >>>>> > >>>>> > >>>>> > >>>>> > >>>> > >>> > >> > >
