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. >
