[jira] [Updated] (ARROW-12519) [C++] Create/document better characterization of jemalloc/mimalloc

["Jonathan Keane (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-12519?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Jonathan Keane updated ARROW-12519:
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    Attachment: csv-uncompressed-8core.png

> [C++] Create/document better characterization of jemalloc/mimalloc
> ------------------------------------------------------------------
>
>                 Key: ARROW-12519
>                 URL: https://issues.apache.org/jira/browse/ARROW-12519
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: C++
>            Reporter: Weston Pace
>            Priority: Major
>         Attachments: csv-uncompressed-8core.png
>
>
> The following script reads in a large dataset 10 times in a loop.  The dataset being referred to is from Ursa benchmarks here ([https://github.com/ursacomputing/benchmarks).]  However, any sufficiently large db should be sufficient.  The dataset is ~5-6 GB when deserialized into an Arrow table.  The conversion to a dataframe is not zero-copy and so the loop requires about 8.6GB.
> Running this code 10 times with mimalloc consumes 27GB of RAM.  It is pretty deterministic.  Even putting a 1 second sleep in between each run yields the same result.  On the other hand if I put the read into its own method (second version below) then it uses only 14 GB.
> Our current rule of thumb seems to be "as long as the allocators stabilize to some number at some point then it is not a bug" so technically both 27GB and 14GB are valid.
> If we can't put any kind of bound whatsoever on the RAM that Arrow needs then it will eventually become a problem for adoption.  I think we need to develop some sort of characterization around how much mimalloc/jemalloc should be allowed to over-allocate before we consider it a bug and require changing the code to avoid the situation (or documenting that certain operations are not valid).
>  
> ----CODE----
>  
> // First version (uses ~27GB)
> {code:java}
> import time
> import pyarrow as pa
> import pyarrow.parquet as pq
> import psutil
> import os
> pa.set_memory_pool(pa.mimalloc_memory_pool())
> print(pa.default_memory_pool().backend_name)
> for _ in range(10):
>     table = pq.read_table('/home/pace/dev/benchmarks/benchmarks/data/temp/fanniemae_2016Q4.uncompressed.parquet')
>     df = table.to_pandas()
>     print(pa.total_allocated_bytes())
>     proc = psutil.Process(os.getpid())
>     print(proc.memory_info())
> {code}
> // Second version (uses ~14GB)
> {code:java}
> import time
> import pyarrow as pa
> import pyarrow.parquet as pq
> import psutil
> import os
> pa.set_memory_pool(pa.mimalloc_memory_pool())
> print(pa.default_memory_pool().backend_name)
> def bm():
>     table = pq.read_table('/home/pace/dev/benchmarks/benchmarks/data/temp/fanniemae_2016Q4.uncompressed.parquet')
>     df = table.to_pandas()
>     print(pa.total_allocated_bytes())
>     proc = psutil.Process(os.getpid())
>     print(proc.memory_info())
> for _ in range(10):
>     bm()
> {code}



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