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Re: [Go] High memory usage on CSV read into table


I'm curious how the file is only 100MB if it's producing ~6GB of
strings in memory. Is it compressed?
On Mon, Nov 19, 2018 at 4:48 PM Daniel Harper <djharperuk@xxxxxxxxx> wrote:
>
> Thanks,
>
> I've tried the new code and that seems to have shaved about 1GB of memory
> off, so the heap is about 8.84GB now, here is the updated pprof output
> https://i.imgur.com/itOHqBf.png
>
> It looks like the majority of allocations are in the memory.GoAllocator
>
> (pprof) top
> Showing nodes accounting for 8.84GB, 100% of 8.84GB total
> Showing top 10 nodes out of 41
>       flat  flat%   sum%        cum   cum%
>     4.24GB 47.91% 47.91%     4.24GB 47.91%
> github.com/apache/arrow/go/arrow/memory.(*GoAllocator).Allocate
>     2.12GB 23.97% 71.88%     2.12GB 23.97%
> github.com/apache/arrow/go/arrow/memory.NewResizableBuffer (inline)
>     1.07GB 12.07% 83.95%     1.07GB 12.07%
> github.com/apache/arrow/go/arrow/array.NewData
>     0.83GB  9.38% 93.33%     0.83GB  9.38%
> github.com/apache/arrow/go/arrow/array.NewStringData
>     0.33GB  3.69% 97.02%     1.31GB 14.79%
> github.com/apache/arrow/go/arrow/array.(*BinaryBuilder).newData
>     0.18GB  2.04% 99.06%     0.18GB  2.04%
> github.com/apache/arrow/go/arrow/array.NewChunked
>     0.07GB  0.78% 99.85%     0.07GB  0.78%
> github.com/apache/arrow/go/arrow/array.NewInt64Data
>     0.01GB  0.15%   100%     0.21GB  2.37%
> github.com/apache/arrow/go/arrow/array.(*Int64Builder).newData
>          0     0%   100%        6GB 67.91%
> github.com/apache/arrow/go/arrow/array.(*BinaryBuilder).Append
>          0     0%   100%     4.03GB 45.54%
> github.com/apache/arrow/go/arrow/array.(*BinaryBuilder).Reserve
>
>
> I'm a bit busy at the moment but I'll probably repeat the same test on the
> other Arrow implementations (e.g. Java) to see if they allocate a similar
> amount.
>
>
> Daniel Harper
> http://djhworld.github.io
>
>
> On Mon, 19 Nov 2018 at 10:17, Sebastien Binet <binet@xxxxxxx> wrote:
>
> > hi Daniel,
> > On Sun, Nov 18, 2018 at 10:17 PM Daniel Harper <djharperuk@xxxxxxxxx>
> > wrote:
> >
> > > Sorry just realised SVG doesn't work.
> > >
> > > PNG of the pprof can be found here: https://i.imgur.com/BVXv1Jm.png
> > >
> > >
> > > Daniel Harper
> > > http://djhworld.github.io
> > >
> > >
> > > On Sun, 18 Nov 2018 at 21:07, Daniel Harper <djharperuk@xxxxxxxxx>
> > wrote:
> > >
> > > > Wasn't sure where the best place to discuss this, but I've noticed that
> > > > when running the following piece of code
> > > >
> > > > https://play.golang.org/p/SKkqPWoHPPS
> > > >
> > > > On a CSV files that contains roughly 1 million records (about 100mb of
> > > > data), the memory usage of the process leaps to about 9.1GB
> > > >
> > > > The records look something like this
> > > >
> > > >
> > > >
> > >
> > "2018-08-27T20:00:00Z","cdnA","dash","audio","http","programme-1","3577","2018","08","27","2018-08-27","live"
> > > >
> > > >
> > >
> > "2018-08-27T20:00:01Z","cdnB","hls","video","https","programme-2","14","2018","08","27","2018-08-27","ondemand"
> > > >
> > > > I've attached a pprof output of the process.
> > > >
> > > > From the looks of it the heavy use of _strings_ might be where most of
> > > the
> > > > memory is going.
> > > >
> > > > Is this expected? I'm new to the code, happy to help where possible!
> > >
> >
> > it's somewhat expected.
> >
> > you use `io.ReadFile` to get your data.
> > this will read the whole file in memory and stick it there: so there's
> > that.
> > for much bigger files, I would recommend using `os.Open`.
> >
> > also, you don't release the individual records once passed to the table, so
> > you have a memory leak.
> > here is my current attempt:
> > - https://play.golang.org/p/ns3GJW6Wx3T
> >
> > finally, as I was alluding to on the #data-science slack channel, right now
> > Go arrow/csv will create a new Record for each row in the incoming CSV
> > file.
> > so you get a bunch of overhead for every row/record.
> >
> > a much more efficient way would be to chunk `n` rows into a single Record.
> > an even more efficient way would be to create a dedicated csv.table type
> > that implements array.Table (as it seems you're interested in using that
> > interface) but only reads the incoming CSV file piecewise (ie: implementing
> > the chunking I was alluding to above but w/o having to load the whole
> > []Record slice.)
> >
> > as a first step to improve this issue, implementing chunking would already
> > shave off a bunch of overhead.
> >
> > -s
> >