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.
>
