GH-41760: [C++][Parquet] Add file metadata read/write benchmark #41761

pitrou · 2024-05-21T15:27:45Z

Following the discussions on the Parquet ML (see this thread and this thread), and the various complaints about poor Parquet metadata performance on wide schemas, this adds a benchmark to measure the overhead of Parquet file metadata parsing or serialization for different numbers of row groups and columns.

Sample output:

-----------------------------------------------------------------------------------------------------------------------
Benchmark                                                             Time             CPU   Iterations UserCounters...
-----------------------------------------------------------------------------------------------------------------------
WriteFileMetadataAndData/num_columns:1/num_row_groups:1           11743 ns        11741 ns        59930 data_size=54 file_size=290 items_per_second=85.1726k/s
WriteFileMetadataAndData/num_columns:1/num_row_groups:100        843137 ns       842920 ns          832 data_size=5.4k file_size=20.486k items_per_second=1.18635k/s
WriteFileMetadataAndData/num_columns:1/num_row_groups:1000      8232304 ns      8230294 ns           85 data_size=54k file_size=207.687k items_per_second=121.502/s
WriteFileMetadataAndData/num_columns:10/num_row_groups:1         101214 ns       101190 ns         6910 data_size=540 file_size=2.11k items_per_second=9.8824k/s
WriteFileMetadataAndData/num_columns:10/num_row_groups:100      8026185 ns      8024361 ns           87 data_size=54k file_size=193.673k items_per_second=124.621/s
WriteFileMetadataAndData/num_columns:10/num_row_groups:1000    81370293 ns     81343455 ns            8 data_size=540k file_size=1.94392M items_per_second=12.2936/s
WriteFileMetadataAndData/num_columns:100/num_row_groups:1        955862 ns       955528 ns          733 data_size=5.4k file_size=20.694k items_per_second=1.04654k/s
WriteFileMetadataAndData/num_columns:100/num_row_groups:100    80115516 ns     80086117 ns            9 data_size=540k file_size=1.94729M items_per_second=12.4866/s
WriteFileMetadataAndData/num_columns:100/num_row_groups:1000  856428565 ns    856065370 ns            1 data_size=5.4M file_size=19.7673M items_per_second=1.16814/s
WriteFileMetadataAndData/num_columns:1000/num_row_groups:1      9330003 ns      9327439 ns           75 data_size=54k file_size=211.499k items_per_second=107.211/s
WriteFileMetadataAndData/num_columns:1000/num_row_groups:100  834609159 ns    834354590 ns            1 data_size=5.4M file_size=19.9623M items_per_second=1.19853/s

ReadFileMetadata/num_columns:1/num_row_groups:1                    3824 ns         3824 ns       182381 data_size=54 file_size=290 items_per_second=261.518k/s
ReadFileMetadata/num_columns:1/num_row_groups:100                 88519 ns        88504 ns         7879 data_size=5.4k file_size=20.486k items_per_second=11.299k/s
ReadFileMetadata/num_columns:1/num_row_groups:1000               849558 ns       849391 ns          825 data_size=54k file_size=207.687k items_per_second=1.17731k/s
ReadFileMetadata/num_columns:10/num_row_groups:1                  19918 ns        19915 ns        35449 data_size=540 file_size=2.11k items_per_second=50.2138k/s
ReadFileMetadata/num_columns:10/num_row_groups:100               715822 ns       715667 ns          975 data_size=54k file_size=193.673k items_per_second=1.3973k/s
ReadFileMetadata/num_columns:10/num_row_groups:1000             7017008 ns      7015432 ns          100 data_size=540k file_size=1.94392M items_per_second=142.543/s
ReadFileMetadata/num_columns:100/num_row_groups:1                175988 ns       175944 ns         3958 data_size=5.4k file_size=20.694k items_per_second=5.68363k/s
ReadFileMetadata/num_columns:100/num_row_groups:100             6814382 ns      6812781 ns          103 data_size=540k file_size=1.94729M items_per_second=146.783/s
ReadFileMetadata/num_columns:100/num_row_groups:1000           77858645 ns     77822157 ns            9 data_size=5.4M file_size=19.7673M items_per_second=12.8498/s
ReadFileMetadata/num_columns:1000/num_row_groups:1              1670001 ns      1669563 ns          419 data_size=54k file_size=211.499k items_per_second=598.959/s
ReadFileMetadata/num_columns:1000/num_row_groups:100           77339599 ns     77292924 ns            9 data_size=5.4M file_size=19.9623M items_per_second=12.9378/s

GitHub Issue: [C++][Parquet] Add file metadata read/write benchmark #41760

pitrou · 2024-05-21T15:27:59Z

@mapleFU @emkornfield FYI

pitrou · 2024-05-21T15:29:25Z

Benchmark results here. We see that performance is O(num_columns * num_row_groups).

-------------------------------------------------------------------------------------------------------------
Benchmark                                                   Time             CPU   Iterations UserCounters...
-------------------------------------------------------------------------------------------------------------
WriteMetadata/num_columns:1/num_row_groups:1            11493 ns        11491 ns        61331 file_size=459 items_per_second=87.0256k/s
WriteMetadata/num_columns:1/num_row_groups:100         820026 ns       819815 ns          854 file_size=37.383k items_per_second=1.21979k/s
WriteMetadata/num_columns:1/num_row_groups:1000       8024208 ns      8022519 ns           87 file_size=374.885k items_per_second=124.649/s
WriteMetadata/num_columns:10/num_row_groups:1           98586 ns        98558 ns         7083 file_size=3.762k items_per_second=10.1463k/s
WriteMetadata/num_columns:10/num_row_groups:100       7816090 ns      7814200 ns           89 file_size=358.835k items_per_second=127.972/s
WriteMetadata/num_columns:10/num_row_groups:1000     79490918 ns     79462535 ns            8 file_size=3.614M items_per_second=12.5845/s
WriteMetadata/num_columns:100/num_row_groups:1         932833 ns       932560 ns          759 file_size=37.352k items_per_second=1.07232k/s
WriteMetadata/num_columns:100/num_row_groups:100     78799934 ns     78771226 ns            9 file_size=3.61693M items_per_second=12.695/s
WriteMetadata/num_columns:100/num_row_groups:1000   857600506 ns    857330657 ns            1 file_size=36.2887M items_per_second=1.16641/s
WriteMetadata/num_columns:1000/num_row_groups:1       9051274 ns      9049407 ns           77 file_size=376.655k items_per_second=110.504/s
WriteMetadata/num_columns:1000/num_row_groups:100   827747343 ns    827468643 ns            1 file_size=36.4815M items_per_second=1.20851/s
WriteMetadata/num_columns:10000/num_row_groups:1     95165920 ns     95125167 ns            7 file_size=3.82213M items_per_second=10.5125/s
WriteMetadata/num_columns:10000/num_row_groups:100 8698273757 ns   8693696946 ns            1 file_size=369.089M items_per_second=0.115026/s

ReadMetadata/num_columns:1/num_row_groups:1              3767 ns         3766 ns       185550 file_size=459 items_per_second=265.553k/s
ReadMetadata/num_columns:1/num_row_groups:100           87250 ns        87235 ns         8004 file_size=37.383k items_per_second=11.4633k/s
ReadMetadata/num_columns:1/num_row_groups:1000         831546 ns       831380 ns          842 file_size=374.885k items_per_second=1.20282k/s
ReadMetadata/num_columns:10/num_row_groups:1            19477 ns        19474 ns        35220 file_size=3.762k items_per_second=51.3513k/s
ReadMetadata/num_columns:10/num_row_groups:100         698405 ns       698268 ns          994 file_size=358.835k items_per_second=1.43211k/s
ReadMetadata/num_columns:10/num_row_groups:1000       6841245 ns      6839685 ns          102 file_size=3.614M items_per_second=146.206/s
ReadMetadata/num_columns:100/num_row_groups:1          174932 ns       174898 ns         3979 file_size=37.352k items_per_second=5.71763k/s
ReadMetadata/num_columns:100/num_row_groups:100       6640500 ns      6638581 ns          105 file_size=3.61693M items_per_second=150.635/s
ReadMetadata/num_columns:100/num_row_groups:1000     75471970 ns     75433100 ns            9 file_size=36.2887M items_per_second=13.2568/s
ReadMetadata/num_columns:1000/num_row_groups:1        1671059 ns      1670522 ns          421 file_size=376.655k items_per_second=598.615/s
ReadMetadata/num_columns:1000/num_row_groups:100     74756894 ns     74713295 ns            9 file_size=36.4815M items_per_second=13.3845/s
ReadMetadata/num_columns:10000/num_row_groups:1      17139819 ns     17132091 ns           41 file_size=3.82213M items_per_second=58.37/s
ReadMetadata/num_columns:10000/num_row_groups:100   774363229 ns    773118912 ns            1 file_size=369.089M items_per_second=1.29346/s

mapleFU · 2024-05-21T16:11:28Z

cpp/src/parquet/metadata_benchmark.cc

+    writer_properties_ =
+        prop_builder.version(ParquetVersion::PARQUET_2_6)->disable_dictionary()->build();


Should we later add benchmark for drop statistics? ( this is useful in some case )

(besides, should we later add benchmark for byte-array statistics decoding ?)

What do you mean with "drop statistics"?

(decrypt can also be added later)

(besides, should we later add benchmark for byte-array statistics decoding ?)

We can add benchmarks for whatever may be a performance bottleneck :-)

What do you mean with "drop statistics"?

By default, parquet enable "statistics" for column, this will add a Statistics in column-chunk. We may add benchmark for droping the "statistics". And for ByteArray, this might be a bit long.

And we can add it in further patches, this could be a good start

The stats are already encoded so I think it is pretty trivial. But may be different if it is BYTE_ARRAY type with large strings.

That'd be valuable, but here we want to benchmark metadata cost in isolation of other factors?

It's a good idea to also try this with the statistics dropped since these do have a big impact and have been heavily discussed. That is to say, run the numbers with builder.disable_statistics(). Based on previous profiling I have done in #39676 this will cut the metadata read time by about 1/3. Still the main conclusion that the scaling is O(num_columns * num_row_groups) should still hold. @pitrou

Also, thanks a bunch for going to the effort to create these benchmarks! I think they are very helpful for the discussion!

mapleFU · 2024-05-21T16:15:53Z

cpp/src/parquet/metadata_benchmark.cc

+    benchmark.ReadFile(contents);
+  }
+  state.SetItemsProcessed(state.iterations());
+  state.counters["file_size"] = static_cast<double>(contents->size());


add metadata()->SerializeToString().size() as metadata size here?

What additional information would it give?

File has been write with different batches with multiple columns. The file_size might also changed if encoding changed. Instead of batch-size, shouldn't we focus more on file metadata thrift size / runtrip, since no "data" is being read, and here we only benchmark reading metadata?

File has been write with different batches with multiple columns.

Varying the number of columns is important here.

The file_size might also changed if encoding changed.

Did you check the data? It's tiny (one INT32 value per column chunk). The entire file will consist of metadata overhead.

Did you check the data? It's tiny (one INT32 value per column chunk). The entire file will consist of metadata overhead.

I'll download one. I think we'd better get "concrete" size of footer, but we can choose current impl if you insist

( Besides, footer read by default would be 64KiB, if the footer is greater than 64KiB, a more around of read and parse might be run here)

"Metadata" is not only the footer, it's all the Thrift overhead + other signaling : page headers, statistics, column chunk metadata...

You're right

auto reader = ParquetFileReader::Open(source, props); auto metadata = reader->metadata();

Any way, this only reads from footer?

I think the concern from @mapleFU is that the write and read paths in this benchmark are not identical. On the read path, only file footer is loaded. However, there are more work done on the write path (e.g. encoding and writing page headers).

Ah, I see, that's a good point. I can try to rewrite the write benchmark so as to only measure the metadata write part of writing a file, though that's dependent on where exactly it happens in the Parquet API calls.

Ok, I'm not sure it makes sense, as metadata and data are quite intertwined in practice (why would we not measure page header serialization?).

The reason the benchmarks are not symmetrical is that reading and writing is not symmetrical. You have to write an entire Parquet file at once (there's no practical way to avoid that), while you can read a Parquet file piecewise or partially.

I can rename the write benchmark to something else if that's less misleading...

pitrou · 2024-05-22T12:07:47Z

Besides changing the benchmark names, I've fixed a bug where the benchmark was writing too many rows. Updated benchmark numbers:

------------------------------------------------------------------------------------------------------------------------
Benchmark                                                              Time             CPU   Iterations UserCounters...
------------------------------------------------------------------------------------------------------------------------
WriteFileMetadataAndData/num_columns:1/num_row_groups:1            11753 ns        11751 ns        60000 data_size=54 file_size=290 items_per_second=85.0979k/s
WriteFileMetadataAndData/num_columns:1/num_row_groups:100         826599 ns       826369 ns          845 data_size=5.4k file_size=20.486k items_per_second=1.21011k/s
WriteFileMetadataAndData/num_columns:1/num_row_groups:1000       8125279 ns      8123486 ns           86 data_size=54k file_size=207.687k items_per_second=123.1/s
WriteFileMetadataAndData/num_columns:10/num_row_groups:1          100357 ns       100329 ns         6982 data_size=540 file_size=2.11k items_per_second=9.96719k/s
WriteFileMetadataAndData/num_columns:10/num_row_groups:100       7913763 ns      7911870 ns           89 data_size=54k file_size=193.673k items_per_second=126.392/s
WriteFileMetadataAndData/num_columns:10/num_row_groups:1000     80283750 ns     80259596 ns            8 data_size=540k file_size=1.94392M items_per_second=12.4596/s
WriteFileMetadataAndData/num_columns:100/num_row_groups:1         937374 ns       937047 ns          745 data_size=5.4k file_size=20.694k items_per_second=1.06718k/s
WriteFileMetadataAndData/num_columns:100/num_row_groups:100     78617577 ns     78590896 ns            9 data_size=540k file_size=1.94729M items_per_second=12.7241/s
WriteFileMetadataAndData/num_columns:100/num_row_groups:1000   847927859 ns    847245720 ns            1 data_size=5.4M file_size=19.7673M items_per_second=1.1803/s
WriteFileMetadataAndData/num_columns:1000/num_row_groups:1       9169263 ns      9167280 ns           76 data_size=54k file_size=211.499k items_per_second=109.084/s
WriteFileMetadataAndData/num_columns:1000/num_row_groups:100   826783518 ns    826541970 ns            1 data_size=5.4M file_size=19.9623M items_per_second=1.20986/s
WriteFileMetadataAndData/num_columns:10000/num_row_groups:1     97516452 ns     97475073 ns            7 data_size=540k file_size=2.15317M items_per_second=10.259/s
WriteFileMetadataAndData/num_columns:10000/num_row_groups:100 8294650136 ns   8292024551 ns            1 data_size=54M file_size=201.916M items_per_second=0.120598/s

ReadFileMetadata/num_columns:1/num_row_groups:1                     3669 ns         3668 ns       190349 data_size=54 file_size=290 items_per_second=272.625k/s
ReadFileMetadata/num_columns:1/num_row_groups:100                  84829 ns        84814 ns         8322 data_size=5.4k file_size=20.486k items_per_second=11.7905k/s
ReadFileMetadata/num_columns:1/num_row_groups:1000                814708 ns       814559 ns          858 data_size=54k file_size=207.687k items_per_second=1.22766k/s
ReadFileMetadata/num_columns:10/num_row_groups:1                   19247 ns        19243 ns        36787 data_size=540 file_size=2.11k items_per_second=51.9679k/s
ReadFileMetadata/num_columns:10/num_row_groups:100                690304 ns       690181 ns         1006 data_size=54k file_size=193.673k items_per_second=1.4489k/s
ReadFileMetadata/num_columns:10/num_row_groups:1000              6723370 ns      6721934 ns          105 data_size=540k file_size=1.94392M items_per_second=148.767/s
ReadFileMetadata/num_columns:100/num_row_groups:1                 171196 ns       171161 ns         4077 data_size=5.4k file_size=20.694k items_per_second=5.84244k/s
ReadFileMetadata/num_columns:100/num_row_groups:100              6585073 ns      6583501 ns          106 data_size=540k file_size=1.94729M items_per_second=151.895/s
ReadFileMetadata/num_columns:100/num_row_groups:1000            75581479 ns     75544415 ns            9 data_size=5.4M file_size=19.7673M items_per_second=13.2372/s
ReadFileMetadata/num_columns:1000/num_row_groups:1               1631872 ns      1631517 ns          435 data_size=54k file_size=211.499k items_per_second=612.926/s
ReadFileMetadata/num_columns:1000/num_row_groups:100            75144953 ns     75104316 ns            9 data_size=5.4M file_size=19.9623M items_per_second=13.3148/s
ReadFileMetadata/num_columns:10000/num_row_groups:1             16866177 ns     16857406 ns           41 data_size=540k file_size=2.15317M items_per_second=59.3211/s
ReadFileMetadata/num_columns:10000/num_row_groups:100          780744719 ns    779370723 ns            1 data_size=54M file_size=201.916M items_per_second=1.28309/s

m4rs-mt · 2024-05-22T12:11:23Z

@pitrou, thank you very much for your work on these benchmarks. They actually confirm the observation we made months ago. Great job! 👍

… data size

pitrou · 2024-05-22T12:51:50Z

I'll merge once CI is green.

conbench-apache-arrow · 2024-05-22T16:36:04Z

After merging your PR, Conbench analyzed the 7 benchmarking runs that have been run so far on merge-commit f3d4639.

There were no benchmark performance regressions. 🎉

The full Conbench report has more details. It also includes information about 12 possible false positives for unstable benchmarks that are known to sometimes produce them.

…apache#41761) Following the discussions on the Parquet ML (see [this thread](https://lists.apache.org/thread/5jyhzkwyrjk9z52g0b49g31ygnz73gxo) and [this thread](https://lists.apache.org/thread/vs3w2z5bk6s3c975rrkqdttr1dpsdn7h)), and the various complaints about poor Parquet metadata performance on wide schemas, this adds a benchmark to measure the overhead of Parquet file metadata parsing or serialization for different numbers of row groups and columns. Sample output: ``` ----------------------------------------------------------------------------------------------------------------------- Benchmark Time CPU Iterations UserCounters... ----------------------------------------------------------------------------------------------------------------------- WriteFileMetadataAndData/num_columns:1/num_row_groups:1 11743 ns 11741 ns 59930 data_size=54 file_size=290 items_per_second=85.1726k/s WriteFileMetadataAndData/num_columns:1/num_row_groups:100 843137 ns 842920 ns 832 data_size=5.4k file_size=20.486k items_per_second=1.18635k/s WriteFileMetadataAndData/num_columns:1/num_row_groups:1000 8232304 ns 8230294 ns 85 data_size=54k file_size=207.687k items_per_second=121.502/s WriteFileMetadataAndData/num_columns:10/num_row_groups:1 101214 ns 101190 ns 6910 data_size=540 file_size=2.11k items_per_second=9.8824k/s WriteFileMetadataAndData/num_columns:10/num_row_groups:100 8026185 ns 8024361 ns 87 data_size=54k file_size=193.673k items_per_second=124.621/s WriteFileMetadataAndData/num_columns:10/num_row_groups:1000 81370293 ns 81343455 ns 8 data_size=540k file_size=1.94392M items_per_second=12.2936/s WriteFileMetadataAndData/num_columns:100/num_row_groups:1 955862 ns 955528 ns 733 data_size=5.4k file_size=20.694k items_per_second=1.04654k/s WriteFileMetadataAndData/num_columns:100/num_row_groups:100 80115516 ns 80086117 ns 9 data_size=540k file_size=1.94729M items_per_second=12.4866/s WriteFileMetadataAndData/num_columns:100/num_row_groups:1000 856428565 ns 856065370 ns 1 data_size=5.4M file_size=19.7673M items_per_second=1.16814/s WriteFileMetadataAndData/num_columns:1000/num_row_groups:1 9330003 ns 9327439 ns 75 data_size=54k file_size=211.499k items_per_second=107.211/s WriteFileMetadataAndData/num_columns:1000/num_row_groups:100 834609159 ns 834354590 ns 1 data_size=5.4M file_size=19.9623M items_per_second=1.19853/s ReadFileMetadata/num_columns:1/num_row_groups:1 3824 ns 3824 ns 182381 data_size=54 file_size=290 items_per_second=261.518k/s ReadFileMetadata/num_columns:1/num_row_groups:100 88519 ns 88504 ns 7879 data_size=5.4k file_size=20.486k items_per_second=11.299k/s ReadFileMetadata/num_columns:1/num_row_groups:1000 849558 ns 849391 ns 825 data_size=54k file_size=207.687k items_per_second=1.17731k/s ReadFileMetadata/num_columns:10/num_row_groups:1 19918 ns 19915 ns 35449 data_size=540 file_size=2.11k items_per_second=50.2138k/s ReadFileMetadata/num_columns:10/num_row_groups:100 715822 ns 715667 ns 975 data_size=54k file_size=193.673k items_per_second=1.3973k/s ReadFileMetadata/num_columns:10/num_row_groups:1000 7017008 ns 7015432 ns 100 data_size=540k file_size=1.94392M items_per_second=142.543/s ReadFileMetadata/num_columns:100/num_row_groups:1 175988 ns 175944 ns 3958 data_size=5.4k file_size=20.694k items_per_second=5.68363k/s ReadFileMetadata/num_columns:100/num_row_groups:100 6814382 ns 6812781 ns 103 data_size=540k file_size=1.94729M items_per_second=146.783/s ReadFileMetadata/num_columns:100/num_row_groups:1000 77858645 ns 77822157 ns 9 data_size=5.4M file_size=19.7673M items_per_second=12.8498/s ReadFileMetadata/num_columns:1000/num_row_groups:1 1670001 ns 1669563 ns 419 data_size=54k file_size=211.499k items_per_second=598.959/s ReadFileMetadata/num_columns:1000/num_row_groups:100 77339599 ns 77292924 ns 9 data_size=5.4M file_size=19.9623M items_per_second=12.9378/s ``` * GitHub Issue: apache#41760 Authored-by: Antoine Pitrou <antoine@python.org> Signed-off-by: Antoine Pitrou <antoine@python.org>

pitrou requested a review from wgtmac as a code owner May 21, 2024 15:27

pitrou requested a review from mapleFU May 21, 2024 15:27

github-actions bot added Component: Parquet Component: C++ awaiting review Awaiting review labels May 21, 2024

pitrou mentioned this pull request May 21, 2024

GH-41702: [C++][Parquet] Thrift: generate template method to accelerate reading thrift #41703

Merged

pitrou added the Component: Benchmarking label May 21, 2024

mapleFU reviewed May 21, 2024

View reviewed changes

pitrou mentioned this pull request May 21, 2024

[C++][Parquet] Fast Random Rowgroup Reads #39676

Open

github-actions bot added awaiting committer review Awaiting committer review and removed awaiting review Awaiting review labels May 21, 2024

mapleFU reviewed May 21, 2024

View reviewed changes

github-actions bot added awaiting changes Awaiting changes and removed awaiting committer review Awaiting committer review labels May 21, 2024

mapleFU approved these changes May 21, 2024

View reviewed changes

rok approved these changes May 21, 2024

View reviewed changes

github-actions bot added awaiting merge Awaiting merge and removed awaiting changes Awaiting changes labels May 21, 2024

emkornfield approved these changes May 21, 2024

View reviewed changes

wgtmac approved these changes May 22, 2024

View reviewed changes

pitrou added 2 commits May 22, 2024 09:38

apacheGH-41760: [C++][Parquet] Add file metadata read/write benchmark

0801c64

Fix conversion warnings/errors

e4e326d

rok mentioned this pull request May 22, 2024

perf: Chunk read performance on wide tables lancedb/lance#2367

Open

Fix data size, change benchmark names, and add statistics about total…

c54125d

… data size

pitrou force-pushed the gh41760-pq-md-benchmark branch from 0db476c to c54125d Compare May 22, 2024 12:22

pitrou merged commit f3d4639 into apache:main May 22, 2024
32 of 33 checks passed

pitrou removed the awaiting merge Awaiting merge label May 22, 2024

pitrou mentioned this pull request May 22, 2024

[C++][Parquet] Add file metadata read/write benchmark #41760

Closed

pitrou deleted the gh41760-pq-md-benchmark branch May 22, 2024 13:09

mapleFU mentioned this pull request May 25, 2024

[C++][Parquet][Benchmark] Adding benchmarking for reading Statistics #41826

Open

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

GH-41760: [C++][Parquet] Add file metadata read/write benchmark #41761

GH-41760: [C++][Parquet] Add file metadata read/write benchmark #41761

pitrou commented May 21, 2024 •

edited

Loading

pitrou commented May 21, 2024

pitrou commented May 21, 2024

mapleFU May 21, 2024

mapleFU May 21, 2024

pitrou May 21, 2024

mapleFU May 21, 2024

pitrou May 21, 2024

mapleFU May 21, 2024

mapleFU May 21, 2024

rok May 21, 2024

corwinjoy May 22, 2024

corwinjoy May 22, 2024

mapleFU May 21, 2024 •

edited

Loading

pitrou May 21, 2024

mapleFU May 21, 2024 •

edited

Loading

pitrou May 21, 2024

mapleFU May 21, 2024 •

edited

Loading

pitrou May 21, 2024 •

edited

Loading

mapleFU May 21, 2024

wgtmac May 22, 2024

pitrou May 22, 2024

pitrou May 22, 2024

pitrou commented May 22, 2024

m4rs-mt commented May 22, 2024

pitrou commented May 22, 2024

conbench-apache-arrow bot commented May 22, 2024

		writer_properties_ =
		prop_builder.version(ParquetVersion::PARQUET_2_6)->disable_dictionary()->build();

GH-41760: [C++][Parquet] Add file metadata read/write benchmark #41761

GH-41760: [C++][Parquet] Add file metadata read/write benchmark #41761

Conversation

pitrou commented May 21, 2024 • edited Loading

pitrou commented May 21, 2024

pitrou commented May 21, 2024

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

mapleFU May 21, 2024 • edited Loading

Choose a reason for hiding this comment

Choose a reason for hiding this comment

mapleFU May 21, 2024 • edited Loading

Choose a reason for hiding this comment

Choose a reason for hiding this comment

mapleFU May 21, 2024 • edited Loading

Choose a reason for hiding this comment

pitrou May 21, 2024 • edited Loading

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

pitrou commented May 22, 2024

m4rs-mt commented May 22, 2024

pitrou commented May 22, 2024

conbench-apache-arrow bot commented May 22, 2024

pitrou commented May 21, 2024 •

edited

Loading

mapleFU May 21, 2024 •

edited

Loading

mapleFU May 21, 2024 •

edited

Loading

mapleFU May 21, 2024 •

edited

Loading

pitrou May 21, 2024 •

edited

Loading