{"payload":{"feedbackUrl":"https://github.com/orgs/community/discussions/53140","repo":{"id":485987020,"defaultBranch":"main","name":"arrow","ownerLogin":"drin","currentUserCanPush":false,"isFork":true,"isEmpty":false,"createdAt":"2022-04-27T00:06:41.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/222710?v=4","public":true,"private":false,"isOrgOwned":false},"refInfo":{"name":"","listCacheKey":"v0:1705099821.0","currentOid":""},"activityList":{"items":[{"before":"255d0beb91a71e85b0e524dd4f4e2cd7accf619b","after":"23558bf7f7d9c1fa5097ccdbdc47166073d3a87f","ref":"refs/heads/ARROW-8991-newfn-scalar-hash-fresh","pushedAt":"2024-06-28T00:09:18.000Z","pushType":"force_push","commitsCount":0,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-17211: refresh history of scalar hash benchmark\n\nThis commit includes additions to the general hashing benchmarks that\ncover the use of hashing functions in key_hash.h without carrying the\nburden of a long dev history.\n\nSome existing benchmark names were changed to distinguish between the\nuse of Int32 and Int64 types, new benchmarks were added that use the\nfunctions declared in key_hash.h. The reason the new benchmarks are\nadded is because it is claimed they prioritize speed over cryptography\nas they're primarily used for join algorithms and other processing\ntasks, which the hashing benchmark can now provide observability for.\n\nIssue: GH-17211\nIssue: ARROW-8991","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1527742274\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/17211\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/17211/hovercard\" href=\"https://github.com/apache/arrow/issues/17211\">apacheGH-17211</a>: refresh history of scalar hash benchmark"}},{"before":"68db6621c07624babec05caccbf47c079a5ed20e","after":"62ee67616d793d1a1df5e8ca7807e29381adaba9","ref":"refs/heads/main","pushedAt":"2024-06-27T23:55:27.000Z","pushType":"push","commitsCount":4,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-42116: [C++] Support list-view typed arrays in array_take and array_filter (#42117)\n\n### Rationale for this change\n\nCompleting the type coverage in array_take and array_filter.\n\n### What changes are included in this PR?\n\nAdd support for `ListView` and `LargeListView` in `\"array_take\"`, `\"array_filter\"` and all the functions that indirectly rely on these to do their thing.\n\n### Are these changes tested?\n\nNew test cases were added.\n* GitHub Issue: #42116\n\nAuthored-by: Felipe Oliveira Carvalho <felipekde@gmail.com>\nSigned-off-by: Felipe Oliveira Carvalho <felipekde@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2347540103\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/42116\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/42116/hovercard\" href=\"https://github.com/apache/arrow/issues/42116\">apacheGH-42116</a>: [C++] Support list-view typed arrays in array_take an…"}},{"before":"89d6354068c11a66fcec2f34d0414daca327e2e0","after":"68db6621c07624babec05caccbf47c079a5ed20e","ref":"refs/heads/main","pushedAt":"2024-06-26T16:57:42.000Z","pushType":"push","commitsCount":45,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-43045: [CI][Python] Pin openjdk=17 in python substrait integration (#43051)\n\n### Rationale for this change\n\nSubstrait builds and releases using JDK 17.\n\nIn the Substrait repo, Gradle was updated from 7.4.2 -> 8.8. https://github.com/substrait-io/substrait-java/commit/77e79ad6fc7f100d38bbe12d9e83ad03adff313b\n\nGradle 8.8 can not be used with openjdk 22 yet, which is the latest version downloaded from condaforge. My testing showed that openjdk 21 also fails with the same error.\n\n### What changes are included in this PR?\n\n* Pin openjdk=17\n\n### Are these changes tested?\n\nTesting via crossbow\n\n### Are there any user-facing changes?\n\nNo\n* GitHub Issue: #43045\n\nAuthored-by: Dane Pitkin <dpitkin@apache.org>\nSigned-off-by: Dane Pitkin <dpitkin@apache.org>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2372925521\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/43045\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/43045/hovercard\" href=\"https://github.com/apache/arrow/issues/43045\">apacheGH-43045</a>: [CI][Python] Pin openjdk=17 in python substrait integ…"}},{"before":"f6127a6d18af12ce18a0b8b1eac02346721cc399","after":"89d6354068c11a66fcec2f34d0414daca327e2e0","ref":"refs/heads/main","pushedAt":"2024-06-19T20:55:15.000Z","pushType":"push","commitsCount":267,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-40384: [Python] Expand the C Device Interface bindings to support import on CUDA device (#40385)\n\n### Rationale for this change\n\nFollow-up on https://github.com/apache/arrow/issues/39979 which added `_export_to_c_device`/`_import_from_c_device` methods, but for now only for CPU devices.\n\n### What changes are included in this PR?\n\n* Ensure `pyarrow.cuda` is imported before importing data through the C Interface, to ensure the CUDA device is registered\n* Add tests for exporting/importing with the device interface on CUDA\n\n### Are these changes tested?\n\nYes, added tests for CUDA.\n\n* GitHub Issue: #40384\n\nAuthored-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>\nSigned-off-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2171729272\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/40384\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/40384/hovercard\" href=\"https://github.com/apache/arrow/issues/40384\">apacheGH-40384</a>: [Python] Expand the C Device Interface bindings to su…"}},{"before":"2d977e408519d524a259246487a4c6b0f355ae21","after":"f6127a6d18af12ce18a0b8b1eac02346721cc399","ref":"refs/heads/main","pushedAt":"2024-05-09T19:12:35.000Z","pushType":"push","commitsCount":105,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-41356: [Release][Docs] Update post release documentation task to remove the warnings banner for stable version (#41377)\n\n### Rationale for this change\n\nWith every release dev documentation is moved to `docs/` and becomes stable version of the documentation but the  version warnings banner is still present.\n\n### What changes are included in this PR?\n\nThis PR removes the banner before the dev docs are copied to the `docs/` folder.\n\n### Are these changes tested?\n\nNot yet.\n\n### Are there any user-facing changes?\n\nNo.\n* GitHub Issue: #41356\n\nLead-authored-by: AlenkaF <frim.alenka@gmail.com>\nCo-authored-by: Alenka Frim <AlenkaF@users.noreply.github.com>\nCo-authored-by: Raúl Cumplido <raulcumplido@gmail.com>\nSigned-off-by: Sutou Kouhei <kou@clear-code.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2259322293\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/41356\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/41356/hovercard\" href=\"https://github.com/apache/arrow/issues/41356\">apacheGH-41356</a>: [Release][Docs] Update post release documentation tas…"}},{"before":"515c61dd617e65c01a6e40e570487ad4ae9f151c","after":"2d977e408519d524a259246487a4c6b0f355ae21","ref":"refs/heads/main","pushedAt":"2024-04-19T07:15:28.000Z","pushType":"push","commitsCount":163,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-41231: [C#] Slice values array when writing a sliced list view array to IPC format (#41255)\n\n### Rationale for this change\n\nReduces IPC file sizes when writing sliced list view arrays.\n\n### What changes are included in this PR?\n\nUpdates `ArrowSreamWriter` so it only writes the required range of values for a list view array, and adjusts the offset values accordingly.\n\n### Are these changes tested?\n\nYes, this is covered by existing tests and I've also added a new test to verify the behaviour with list view arrays that have unordered offsets.\n\n### Are there any user-facing changes?\n\nYes, this might reduce IPC file sizes for users writing sliced data.\n* GitHub Issue: #41231\n\nLead-authored-by: Adam Reeve <adreeve@gmail.com>\nCo-authored-by: Curt Hagenlocher <curt@hagenlocher.org>\nSigned-off-by: Curt Hagenlocher <curt@hagenlocher.org>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2245546544\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/41231\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/41231/hovercard\" href=\"https://github.com/apache/arrow/issues/41231\">apacheGH-41231</a>: [C#] Slice values array when writing a sliced list vi…"}},{"before":"00a48217e93bea1e84f10dbfdf3c0c93dfe1ea3d","after":"515c61dd617e65c01a6e40e570487ad4ae9f151c","ref":"refs/heads/main","pushedAt":"2024-03-27T18:59:37.000Z","pushType":"push","commitsCount":83,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-40773: [Java] add `DENSEUNION` case to StructWriters, resolves #40773 (#40809)\n\n### What changes are included in this PR?\n\nAdding a `DENSEUNION` case to the `StructWriters` template so that one can create StructVectors with a DenseUnionVector child.\n\n### Are these changes tested?\n\nYes\n\n### Are there any user-facing changes?\n\nNo\n* GitHub Issue: #40773\n\nAuthored-by: James Henderson <james@jarohen.dev>\nSigned-off-by: David Li <li.davidm96@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2205553837\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/40773\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/40773/hovercard\" href=\"https://github.com/apache/arrow/issues/40773\">apacheGH-40773</a>: [Java] add <code>DENSEUNION</code> case to StructWriters, <span class=\"issue-keyword tooltipped tooltipped-se\" aria-label=\"This commit closes issue #40773.\">resolves</span> "}},{"before":"3d467ac7bfae03cf2db09807054c5672e1959aec","after":"00a48217e93bea1e84f10dbfdf3c0c93dfe1ea3d","ref":"refs/heads/main","pushedAt":"2024-03-15T17:21:17.000Z","pushType":"push","commitsCount":79,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-38768: [Python] Slicing an array backwards beyond the start now includes first item. (#39240)\n\n### What changes are included in this PR?\n\nMinor changes in `_normalize_slice` so `start` and `stop` are both computed in a single if/else block instead of having them modified later in case of a negative `step`.\n\n### Are these changes tested?\n\nYes.\n\n### Are there any user-facing changes?\n\nFixing wrong data returned in an edge case.\n* Closes: #38768\n\nAuthored-by: LucasG0 <guillermou.lucas@gmail.com>\nSigned-off-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1999667473\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/38768\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/38768/hovercard\" href=\"https://github.com/apache/arrow/issues/38768\">apacheGH-38768</a>: [Python] Slicing an array backwards beyond the start …"}},{"before":"30e6d72242e376baa598b2e8f1d9b80d800a974c","after":"3d467ac7bfae03cf2db09807054c5672e1959aec","ref":"refs/heads/main","pushedAt":"2024-03-05T19:55:17.000Z","pushType":"push","commitsCount":17,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-20127: [Python][CI] Remove legacy hdfs tests from hdfs and hypothesis setup (#40363)\n\n### Rationale for this change\n\nSmall follow-up on https://github.com/apache/arrow/pull/39825, which removed the `test_hdfs.py` file itself, but didn't remove it from the hypothesis script\n\n* GitHub Issue: #20127\n\nAuthored-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>\nSigned-off-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1527864001\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/20127\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/20127/hovercard\" href=\"https://github.com/apache/arrow/issues/20127\">apacheGH-20127</a>: [Python][CI] Remove legacy hdfs tests from hdfs and h…"}},{"before":"a03d957b5b8d0425f9d5b6c98b6ee1efa56a1248","after":"30e6d72242e376baa598b2e8f1d9b80d800a974c","ref":"refs/heads/main","pushedAt":"2024-03-01T19:01:40.000Z","pushType":"push","commitsCount":86,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-40268: [Archery] Bump the version of pygit2, adapt to API changes (#40269)\n\n### Rationale for this change\n\n`archery crossbow submit ...` fails with newer versions of pygit2\n\n### What changes are included in this PR?\n\nAdapt away from deprecated [sic] APIs in pygit2 to ones that work with current versions, bump the pin\n\n### Are these changes tested?\n\nManually, yes, I can use `archery crossbow submit ...` again. CI will run using archery in a bunch of places on this PR too.\n\n### Are there any user-facing changes?\n\nNo\n* GitHub Issue: #40268\n\nAuthored-by: Jonathan Keane <jkeane@gmail.com>\nSigned-off-by: Jonathan Keane <jkeane@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2157771672\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/40268\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/40268/hovercard\" href=\"https://github.com/apache/arrow/issues/40268\">apacheGH-40268</a>: [Archery] Bump the version of pygit2, adapt to API ch…"}},{"before":"abf4fbf924391149ba2717aa9b57090094271a5d","after":"a03d957b5b8d0425f9d5b6c98b6ee1efa56a1248","ref":"refs/heads/main","pushedAt":"2024-02-17T03:17:54.000Z","pushType":"push","commitsCount":22,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-40055: [Java][Docs] Simplify use of Filter and Expression into Dataset Substrait (#40056)\n\n### Rationale for this change\n\nSimplify creation of SQL Expression Filter and Projections into Arrow Java Dataset module using new [Substrait Feature for SQL Expressions](https://github.com/substrait-io/substrait-java/releases/tag/v0.26.0).\n\n### What changes are included in this PR?\n\nUpdate Apache Arrow Java Dataset Substrait documentation\n\n### Are these changes tested?\n\nYes\n\n### Are there any user-facing changes?\n\nNo\n* Closes: #40055\n\nAuthored-by: david dali susanibar arce <davi.sarces@gmail.com>\nSigned-off-by: David Li <li.davidm96@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2131013127\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/40055\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/40055/hovercard\" href=\"https://github.com/apache/arrow/issues/40055\">apacheGH-40055</a>: [Java][Docs] Simplify use of Filter and Expression in…"}},{"before":"a0dec7f39394e619c8bdfe0eacb6ecde73a9ec12","after":"abf4fbf924391149ba2717aa9b57090094271a5d","ref":"refs/heads/main","pushedAt":"2024-02-09T18:44:20.000Z","pushType":"push","commitsCount":2,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-39942: [Python] Make capsule name check more lenient (#39977)\n\n### Rationale for this change\n\nWhile #39969 fixed the immediate issue caused by the update of the capsule name used by reticulate whilst converting an R \"external pointer\", it will still result in an error if somebody is using an older version of the Arrow R package.\n\n### What changes are included in this PR?\n\nThe pyarrow Cython code was modified to accept capsules with the name NULL or \"r_extptr\".\n\n### Are these changes tested?\n\nNot sure where the best place for this is, but:\n\nCRAN arrow + released pyarrow + new reticulate (errors):\n\n``` r\nlibrary(arrow, warn.conflicts = FALSE)\nreticulate::use_virtualenv(\"~/Desktop/rscratch/arrow/.venv\")\npackageVersion(\"arrow\")\n#> [1] '14.0.0.2'\npackageVersion(\"reticulate\")\n#> [1] '1.35.0'\npa <- reticulate::import(\"pyarrow\")\npa[[\"__version__\"]]\n#> [1] \"15.0.0\"\n\nreticulate::r_to_py(arrow::int32())\n#> PyCapsule_GetPointer called with incorrect name\n```\n\nCRAN arrow + pyarrow from this PR + old reticulate:\n\n``` r\nlibrary(arrow, warn.conflicts = FALSE)\nreticulate::use_virtualenv(\"~/Desktop/rscratch/arrow/.venv\")\npackageVersion(\"arrow\")\n#> [1] '14.0.0.2'\npackageVersion(\"reticulate\")\n#> [1] '1.34.0'\npa <- reticulate::import(\"pyarrow\")\npa[[\"__version__\"]]\n#> [1] \"16.0.0.dev92+geafcff7a5\"\n\nreticulate::r_to_py(arrow::int32())\n#> DataType(int32)\n```\n\nCRAN arrow + pyarrow from this PR + new reticulate:\n\n``` r\nlibrary(arrow, warn.conflicts = FALSE)\nreticulate::use_virtualenv(\"~/Desktop/rscratch/arrow/.venv\")\npackageVersion(\"arrow\")\n#> [1] '14.0.0.2'\npackageVersion(\"reticulate\")\n#> [1] '1.35.0'\npa <- reticulate::import(\"pyarrow\")\npa[[\"__version__\"]]\n#> [1] \"16.0.0.dev92+geafcff7a5\"\n\nreticulate::r_to_py(arrow::int32())\n#> DataType(int32)\n```\n\n### Are there any user-facing changes?\n\nNo\n* Closes: #39942\n\nLead-authored-by: Dewey Dunnington <dewey@fishandwhistle.net>\nCo-authored-by: Dewey Dunnington <dewey@voltrondata.com>\nCo-authored-by: Antoine Pitrou <pitrou@free.fr>\nCo-authored-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>\nSigned-off-by: Dewey Dunnington <dewey@voltrondata.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2117864047\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39942\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/39942/hovercard\" href=\"https://github.com/apache/arrow/issues/39942\">apacheGH-39942</a>: [Python] Make capsule name check more lenient (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2123385040\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39977\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/apache/arrow/pull/39977/hovercard\" href=\"https://github.com/apache/arrow/pull/39977\">apache…</a>"}},{"before":"87dd4c4ceaef316033f3709e496805710555764e","after":"a0dec7f39394e619c8bdfe0eacb6ecde73a9ec12","ref":"refs/heads/main","pushedAt":"2024-02-09T05:21:19.000Z","pushType":"push","commitsCount":74,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-39352: [FS][Azure] Enable azure in builds (#39971)\n\n\n\n### Rationale for this change\n\n### What changes are included in this PR?\nEnable Azure in linux and mac os wheel builds. Tried to copy GCS\n\nDon't enable Azure for windows builds because windows builds where all failing. Failures were a combination of cmake version or `Could not find a package configuration file provided by \"wil\"`. I think it makes sense to come back to windows builds in another PR.  \n\n### Are these changes tested?\nThere is no new functionality to test. \n\n### Are there any user-facing changes?\nNo\n\n* Closes: #39352\n\nAuthored-by: Thomas Newton <thomas.w.newton@gmail.com>\nSigned-off-by: Sutou Kouhei <kou@clear-code.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2053893566\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39352\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/39352/hovercard\" href=\"https://github.com/apache/arrow/issues/39352\">apacheGH-39352</a>: [FS][Azure] Enable azure in builds (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2122495836\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39971\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/apache/arrow/pull/39971/hovercard\" href=\"https://github.com/apache/arrow/pull/39971\">apache#39971</a>)"}},{"before":"d7924a788b9f8c4497bb842069a0adda68a81f74","after":"255d0beb91a71e85b0e524dd4f4e2cd7accf619b","ref":"refs/heads/ARROW-8991-newfn-scalar-hash-fresh","pushedAt":"2024-01-29T17:07:58.000Z","pushType":"force_push","commitsCount":0,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-17211: refresh history of scalar hash benchmark\n\nThis commit includes additions to the general hashing benchmarks that\ncover the use of hashing functions in key_hash.h without carrying the\nburden of a long dev history.\n\nSome existing benchmark names were changed to distinguish between the\nuse of Int32 and Int64 types, new benchmarks were added that use the\nfunctions declared in key_hash.h. The reason the new benchmarks are\nadded is because it is claimed they prioritize speed over cryptography\nas they're primarily used for join algorithms and other processing\ntasks, which the hashing benchmark can now provide observability for.\n\nIssue: GH-17211\nIssue: ARROW-8991","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1527742274\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/17211\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/17211/hovercard\" href=\"https://github.com/apache/arrow/issues/17211\">apacheGH-17211</a>: refresh history of scalar hash benchmark"}},{"before":"800254fb16f23af57916768124fb90e0050a8335","after":"87dd4c4ceaef316033f3709e496805710555764e","ref":"refs/heads/main","pushedAt":"2024-01-29T17:05:05.000Z","pushType":"push","commitsCount":1,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"MINOR: [Python][CI] Add upper bound on pytest version (#39827)\n\n### Rationale for this change\n\nThe PyArrow test suite relies on the pytest-lazy-fixture plugin, which breaks on pytest 8.0.0: https://github.com/TvoroG/pytest-lazy-fixture/issues/65\n\n### What changes are included in this PR?\n\nAvoid installing pytest 8 on CI builds, by putting an upper bound on the pytest version.\n\n### Are these changes tested?\n\nYes, by construction.\n\n### Are there any user-facing changes?\n\nNo.\n\nAuthored-by: Antoine Pitrou <antoine@python.org>\nSigned-off-by: Antoine Pitrou <antoine@python.org>","shortMessageHtmlLink":"MINOR: [Python][CI] Add upper bound on pytest version (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2105793403\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39827\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/apache/arrow/pull/39827/hovercard\" href=\"https://github.com/apache/arrow/pull/39827\">apache#39827</a>)"}},{"before":"92da7cf7f0e40102609a27aca21e5b1586469e27","after":"d7924a788b9f8c4497bb842069a0adda68a81f74","ref":"refs/heads/ARROW-8991-newfn-scalar-hash","pushedAt":"2024-01-29T16:49:53.000Z","pushType":"push","commitsCount":1183,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"Merge remote-tracking branch 'drin/ARROW-8991-newfn-scalar-hash' into ARROW-8991-newfn-scalar-hash-fresh\n\nusing a merge commit to overwrite commit history while maintaining\ncurrent state of code","shortMessageHtmlLink":"Merge remote-tracking branch 'drin/ARROW-8991-newfn-scalar-hash' into…"}},{"before":"7269dba0a440a0a4e2f3138e4fb1d439ccc5b6b8","after":"d7924a788b9f8c4497bb842069a0adda68a81f74","ref":"refs/heads/ARROW-8991-newfn-scalar-hash-fresh","pushedAt":"2024-01-29T16:44:47.000Z","pushType":"push","commitsCount":50,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"Merge remote-tracking branch 'drin/ARROW-8991-newfn-scalar-hash' into ARROW-8991-newfn-scalar-hash-fresh\n\nusing a merge commit to overwrite commit history while maintaining\ncurrent state of code","shortMessageHtmlLink":"Merge remote-tracking branch 'drin/ARROW-8991-newfn-scalar-hash' into…"}},{"before":"94127019ac15b94cfd1e1fc8e5ff14f3291d1917","after":"7269dba0a440a0a4e2f3138e4fb1d439ccc5b6b8","ref":"refs/heads/ARROW-8991-newfn-scalar-hash-fresh","pushedAt":"2024-01-29T16:35:32.000Z","pushType":"force_push","commitsCount":0,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-17211: refresh history of scalar hash benchmark\n\nThis commit includes additions to the general hashing benchmarks that\ncover the use of hashing functions in key_hash.h without carrying the\nburden of a long dev history.\n\nSome existing benchmark names were changed to distinguish between the\nuse of Int32 and Int64 types, new benchmarks were added that use the\nfunctions declared in key_hash.h. The reason the new benchmarks are\nadded is because it is claimed they prioritize speed over cryptography\nas they're primarily used for join algorithms and other processing\ntasks, which the hashing benchmark can now provide observability for.\n\nIssue: GH-17211\nIssue: ARROW-8991","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1527742274\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/17211\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/17211/hovercard\" href=\"https://github.com/apache/arrow/issues/17211\">apacheGH-17211</a>: refresh history of scalar hash benchmark"}},{"before":"87ed8ac432dc7e77fa0275a24e3edda9b0bc2a2e","after":"800254fb16f23af57916768124fb90e0050a8335","ref":"refs/heads/main","pushedAt":"2024-01-29T16:31:28.000Z","pushType":"push","commitsCount":70,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-39740: [C++] Fix filter and take kernel for month_day_nano intervals (#39795)\n\n### Rationale for this change\n\nThe filter and take functions were not correctly supported on month_day_nano intervals.\n\n### What changes are included in this PR?\n\n* Expand the primitive filter implementation to handle all possible fixed-width primitive types (including fixed-size binary)\n* Expand the take filter implementation to handle all well-known fixed-width primitive types (including month_day_nano, decimal128 and decimal256)\n* Add benchmarks for taking and filtering fixed-size binary\n\nThese changes allow for very significant performance improvements filtering and taking fixed-size binary data:\n```\n----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\nNon-regressions: (90)\n----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n                                              benchmark           baseline          contender  change %                                                                                                                                                                                                                                                                   counters\n          FilterFixedSizeBinaryFilterNoNulls/524288/0/8      1.716 GiB/sec     33.814 GiB/sec  1870.862      {'family_index': 0, 'per_family_instance_index': 0, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/0/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 2462, 'byte_width': 8.0, 'data null%': 0.0, 'mask null%': 0.0, 'select%': 99.9}\n   TakeFixedSizeBinaryRandomIndicesWithNulls/524288/1/8 380.056M items/sec   7.098G items/sec  1767.491                                {'family_index': 3, 'per_family_instance_index': 6, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/1/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 505, 'byte_width': 8.0, 'null_percent': 100.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/0/9      1.916 GiB/sec     33.721 GiB/sec  1659.766      {'family_index': 0, 'per_family_instance_index': 1, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/0/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 2750, 'byte_width': 9.0, 'data null%': 0.0, 'mask null%': 0.0, 'select%': 99.9}\n          FilterFixedSizeBinaryFilterNoNulls/524288/9/8    917.713 MiB/sec      9.193 GiB/sec   925.719    {'family_index': 0, 'per_family_instance_index': 18, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/9/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1271, 'byte_width': 8.0, 'data null%': 10.0, 'mask null%': 0.0, 'select%': 99.9}\n         FilterFixedSizeBinaryFilterNoNulls/524288/12/8      1.004 GiB/sec      9.374 GiB/sec   833.673   {'family_index': 0, 'per_family_instance_index': 24, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/12/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1440, 'byte_width': 8.0, 'data null%': 90.0, 'mask null%': 0.0, 'select%': 99.9}\n          FilterFixedSizeBinaryFilterNoNulls/524288/3/8      1.625 GiB/sec     15.009 GiB/sec   823.442      {'family_index': 0, 'per_family_instance_index': 6, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/3/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 2328, 'byte_width': 8.0, 'data null%': 0.1, 'mask null%': 0.0, 'select%': 99.9}\n          FilterFixedSizeBinaryFilterNoNulls/524288/9/9   1021.638 MiB/sec      9.126 GiB/sec   814.670    {'family_index': 0, 'per_family_instance_index': 19, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/9/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1428, 'byte_width': 9.0, 'data null%': 10.0, 'mask null%': 0.0, 'select%': 99.9}\n          FilterFixedSizeBinaryFilterNoNulls/524288/6/8      1.235 GiB/sec     10.814 GiB/sec   775.869     {'family_index': 0, 'per_family_instance_index': 12, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/6/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1762, 'byte_width': 8.0, 'data null%': 1.0, 'mask null%': 0.0, 'select%': 99.9}\n         FilterFixedSizeBinaryFilterNoNulls/524288/12/9      1.123 GiB/sec      9.120 GiB/sec   712.196   {'family_index': 0, 'per_family_instance_index': 25, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/12/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1598, 'byte_width': 9.0, 'data null%': 90.0, 'mask null%': 0.0, 'select%': 99.9}\n          FilterFixedSizeBinaryFilterNoNulls/524288/6/9      1.370 GiB/sec     10.499 GiB/sec   666.348     {'family_index': 0, 'per_family_instance_index': 13, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/6/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1958, 'byte_width': 9.0, 'data null%': 1.0, 'mask null%': 0.0, 'select%': 99.9}\n          FilterFixedSizeBinaryFilterNoNulls/524288/3/9      1.814 GiB/sec     13.394 GiB/sec   638.343      {'family_index': 0, 'per_family_instance_index': 7, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/3/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 2600, 'byte_width': 9.0, 'data null%': 0.1, 'mask null%': 0.0, 'select%': 99.9}\n          FilterFixedSizeBinaryFilterNoNulls/524288/2/8     12.155 GiB/sec     77.799 GiB/sec   540.051      {'family_index': 0, 'per_family_instance_index': 4, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/2/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 17222, 'byte_width': 8.0, 'data null%': 0.0, 'mask null%': 0.0, 'select%': 1.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/2/9     13.507 GiB/sec     84.361 GiB/sec   524.592      {'family_index': 0, 'per_family_instance_index': 5, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/2/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 19469, 'byte_width': 9.0, 'data null%': 0.0, 'mask null%': 0.0, 'select%': 1.0}\n         TakeFixedSizeBinaryMonotonicIndices/524288/1/8 194.493M items/sec 732.378M items/sec   276.557                                      {'family_index': 4, 'per_family_instance_index': 6, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/1/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 259, 'byte_width': 8.0, 'null_percent': 100.0}\n     TakeFixedSizeBinaryRandomIndicesNoNulls/524288/1/8 200.981M items/sec 747.628M items/sec   271.989                                  {'family_index': 2, 'per_family_instance_index': 6, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/1/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 268, 'byte_width': 8.0, 'null_percent': 100.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/0/8    947.631 MiB/sec      3.318 GiB/sec   258.565    {'family_index': 1, 'per_family_instance_index': 0, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/0/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1329, 'byte_width': 8.0, 'data null%': 0.0, 'mask null%': 5.0, 'select%': 99.9}\n        FilterFixedSizeBinaryFilterWithNulls/524288/3/8    911.406 MiB/sec      3.121 GiB/sec   250.677    {'family_index': 1, 'per_family_instance_index': 6, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/3/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1275, 'byte_width': 8.0, 'data null%': 0.1, 'mask null%': 5.0, 'select%': 99.9}\n          FilterFixedSizeBinaryFilterNoNulls/524288/1/8      1.045 GiB/sec      3.535 GiB/sec   238.406      {'family_index': 0, 'per_family_instance_index': 2, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/1/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1496, 'byte_width': 8.0, 'data null%': 0.0, 'mask null%': 0.0, 'select%': 50.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/6/8    899.161 MiB/sec      2.915 GiB/sec   232.029   {'family_index': 1, 'per_family_instance_index': 12, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/6/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1260, 'byte_width': 8.0, 'data null%': 1.0, 'mask null%': 5.0, 'select%': 99.9}\n        FilterFixedSizeBinaryFilterWithNulls/524288/9/8    829.852 MiB/sec      2.617 GiB/sec   222.914  {'family_index': 1, 'per_family_instance_index': 18, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/9/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1157, 'byte_width': 8.0, 'data null%': 10.0, 'mask null%': 5.0, 'select%': 99.9}\n     TakeFixedSizeBinaryRandomIndicesNoNulls/524288/0/8 234.268M items/sec 752.809M items/sec   221.345                                    {'family_index': 2, 'per_family_instance_index': 8, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/0/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 312, 'byte_width': 8.0, 'null_percent': 0.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/1/9      1.171 GiB/sec      3.711 GiB/sec   216.957      {'family_index': 0, 'per_family_instance_index': 3, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/1/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1674, 'byte_width': 9.0, 'data null%': 0.0, 'mask null%': 0.0, 'select%': 50.0}\n         TakeFixedSizeBinaryMonotonicIndices/524288/0/8 249.393M items/sec 787.274M items/sec   215.676                                        {'family_index': 4, 'per_family_instance_index': 8, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/0/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 333, 'byte_width': 8.0, 'null_percent': 0.0}\n   TakeFixedSizeBinaryRandomIndicesWithNulls/524288/0/8 234.268M items/sec 736.727M items/sec   214.481                                  {'family_index': 3, 'per_family_instance_index': 8, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/0/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 313, 'byte_width': 8.0, 'null_percent': 0.0}\n      TakeFixedSizeBinaryMonotonicIndices/524288/1000/8 134.852M items/sec 423.748M items/sec   214.231                                     {'family_index': 4, 'per_family_instance_index': 0, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/1000/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 202, 'byte_width': 8.0, 'null_percent': 0.1}\n       FilterFixedSizeBinaryFilterWithNulls/524288/12/8    913.734 MiB/sec      2.599 GiB/sec   191.245 {'family_index': 1, 'per_family_instance_index': 24, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/12/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1292, 'byte_width': 8.0, 'data null%': 90.0, 'mask null%': 5.0, 'select%': 99.9}\n  TakeFixedSizeBinaryRandomIndicesNoNulls/524288/1000/8 138.218M items/sec 309.307M items/sec   123.783                                 {'family_index': 2, 'per_family_instance_index': 0, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/1000/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 184, 'byte_width': 8.0, 'null_percent': 0.1}\nTakeFixedSizeBinaryRandomIndicesWithNulls/524288/1000/8 132.755M items/sec 293.027M items/sec   120.727                               {'family_index': 3, 'per_family_instance_index': 0, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/1000/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 179, 'byte_width': 8.0, 'null_percent': 0.1}\n    TakeFixedSizeBinaryRandomIndicesNoNulls/524288/10/8 125.492M items/sec 272.996M items/sec   117.540                                  {'family_index': 2, 'per_family_instance_index': 2, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/10/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 174, 'byte_width': 8.0, 'null_percent': 10.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/9/9    926.938 MiB/sec      1.904 GiB/sec   110.379  {'family_index': 1, 'per_family_instance_index': 19, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/9/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1295, 'byte_width': 9.0, 'data null%': 10.0, 'mask null%': 5.0, 'select%': 99.9}\n        TakeFixedSizeBinaryMonotonicIndices/524288/10/8 158.754M items/sec 331.106M items/sec   108.565                                      {'family_index': 4, 'per_family_instance_index': 2, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/10/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 167, 'byte_width': 8.0, 'null_percent': 10.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/0/9      1.031 GiB/sec      2.129 GiB/sec   106.621    {'family_index': 1, 'per_family_instance_index': 1, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/0/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1477, 'byte_width': 9.0, 'data null%': 0.0, 'mask null%': 5.0, 'select%': 99.9}\n        FilterFixedSizeBinaryFilterWithNulls/524288/3/9   1020.776 MiB/sec      2.056 GiB/sec   106.293    {'family_index': 1, 'per_family_instance_index': 7, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/3/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1430, 'byte_width': 9.0, 'data null%': 0.1, 'mask null%': 5.0, 'select%': 99.9}\n        FilterFixedSizeBinaryFilterWithNulls/524288/4/8    890.785 MiB/sec      1.768 GiB/sec   103.293    {'family_index': 1, 'per_family_instance_index': 8, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/4/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1242, 'byte_width': 8.0, 'data null%': 0.1, 'mask null%': 5.0, 'select%': 50.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/6/9   1005.839 MiB/sec      1.984 GiB/sec   102.023   {'family_index': 1, 'per_family_instance_index': 13, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/6/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1407, 'byte_width': 9.0, 'data null%': 1.0, 'mask null%': 5.0, 'select%': 99.9}\n        FilterFixedSizeBinaryFilterWithNulls/524288/1/8    916.810 MiB/sec      1.762 GiB/sec    96.757    {'family_index': 1, 'per_family_instance_index': 2, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/1/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1270, 'byte_width': 8.0, 'data null%': 0.0, 'mask null%': 5.0, 'select%': 50.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/7/8    890.211 MiB/sec      1.694 GiB/sec    94.853   {'family_index': 1, 'per_family_instance_index': 14, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/7/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1235, 'byte_width': 8.0, 'data null%': 1.0, 'mask null%': 5.0, 'select%': 50.0}\n     TakeFixedSizeBinaryRandomIndicesNoNulls/524288/2/8  95.788M items/sec 184.004M items/sec    92.095                                   {'family_index': 2, 'per_family_instance_index': 4, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/2/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 124, 'byte_width': 8.0, 'null_percent': 50.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/10/8    862.497 MiB/sec      1.616 GiB/sec    91.823 {'family_index': 1, 'per_family_instance_index': 20, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/10/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1200, 'byte_width': 8.0, 'data null%': 10.0, 'mask null%': 5.0, 'select%': 50.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/12/9      1.005 GiB/sec      1.904 GiB/sec    89.431 {'family_index': 1, 'per_family_instance_index': 25, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/12/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1442, 'byte_width': 9.0, 'data null%': 90.0, 'mask null%': 5.0, 'select%': 99.9}\n         TakeFixedSizeBinaryMonotonicIndices/524288/2/8 123.065M items/sec 228.755M items/sec    85.881                                       {'family_index': 4, 'per_family_instance_index': 4, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/2/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 164, 'byte_width': 8.0, 'null_percent': 50.0}\n         FilterFixedSizeBinaryFilterNoNulls/524288/10/8    930.637 MiB/sec      1.669 GiB/sec    83.659   {'family_index': 0, 'per_family_instance_index': 20, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/10/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1293, 'byte_width': 8.0, 'data null%': 10.0, 'mask null%': 0.0, 'select%': 50.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/4/8      1.034 GiB/sec      1.871 GiB/sec    81.019      {'family_index': 0, 'per_family_instance_index': 8, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/4/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1482, 'byte_width': 8.0, 'data null%': 0.1, 'mask null%': 0.0, 'select%': 50.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/7/8   1004.789 MiB/sec      1.772 GiB/sec    80.538     {'family_index': 0, 'per_family_instance_index': 14, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/7/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1404, 'byte_width': 8.0, 'data null%': 1.0, 'mask null%': 0.0, 'select%': 50.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/13/8    920.819 MiB/sec      1.616 GiB/sec    79.686 {'family_index': 1, 'per_family_instance_index': 26, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/13/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1285, 'byte_width': 8.0, 'data null%': 90.0, 'mask null%': 5.0, 'select%': 50.0}\n         FilterFixedSizeBinaryFilterNoNulls/524288/13/8    974.713 MiB/sec      1.669 GiB/sec    75.388   {'family_index': 0, 'per_family_instance_index': 26, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/13/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1363, 'byte_width': 8.0, 'data null%': 90.0, 'mask null%': 0.0, 'select%': 50.0}\n  TakeFixedSizeBinaryRandomIndicesWithNulls/524288/10/8 107.165M items/sec 187.372M items/sec    74.845                                {'family_index': 3, 'per_family_instance_index': 2, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/10/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 143, 'byte_width': 8.0, 'null_percent': 10.0}\n   TakeFixedSizeBinaryRandomIndicesWithNulls/524288/2/8  72.662M items/sec 114.781M items/sec    57.965                                  {'family_index': 3, 'per_family_instance_index': 4, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/2/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 96, 'byte_width': 8.0, 'null_percent': 50.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/10/9    976.180 MiB/sec      1.480 GiB/sec    55.260 {'family_index': 1, 'per_family_instance_index': 21, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/10/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1358, 'byte_width': 9.0, 'data null%': 10.0, 'mask null%': 5.0, 'select%': 50.0}\n         FilterFixedSizeBinaryFilterNoNulls/524288/10/9      1.023 GiB/sec      1.581 GiB/sec    54.502   {'family_index': 0, 'per_family_instance_index': 21, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/10/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1466, 'byte_width': 9.0, 'data null%': 10.0, 'mask null%': 0.0, 'select%': 50.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/4/9    992.477 MiB/sec      1.453 GiB/sec    49.957    {'family_index': 1, 'per_family_instance_index': 9, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/4/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1400, 'byte_width': 9.0, 'data null%': 0.1, 'mask null%': 5.0, 'select%': 50.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/7/9    997.679 MiB/sec      1.450 GiB/sec    48.846   {'family_index': 1, 'per_family_instance_index': 15, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/7/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1389, 'byte_width': 9.0, 'data null%': 1.0, 'mask null%': 5.0, 'select%': 50.0}\n         FilterFixedSizeBinaryFilterNoNulls/524288/13/9      1.071 GiB/sec      1.581 GiB/sec    47.526   {'family_index': 0, 'per_family_instance_index': 27, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/13/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1538, 'byte_width': 9.0, 'data null%': 90.0, 'mask null%': 0.0, 'select%': 50.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/13/9      1.008 GiB/sec      1.485 GiB/sec    47.328 {'family_index': 1, 'per_family_instance_index': 27, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/13/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1446, 'byte_width': 9.0, 'data null%': 90.0, 'mask null%': 5.0, 'select%': 50.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/1/9      1.003 GiB/sec      1.452 GiB/sec    44.708    {'family_index': 1, 'per_family_instance_index': 3, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/1/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1437, 'byte_width': 9.0, 'data null%': 0.0, 'mask null%': 5.0, 'select%': 50.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/7/9      1.105 GiB/sec      1.568 GiB/sec    41.954     {'family_index': 0, 'per_family_instance_index': 15, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/7/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1587, 'byte_width': 9.0, 'data null%': 1.0, 'mask null%': 0.0, 'select%': 50.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/4/9      1.163 GiB/sec      1.613 GiB/sec    38.639      {'family_index': 0, 'per_family_instance_index': 9, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/4/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 1662, 'byte_width': 9.0, 'data null%': 0.1, 'mask null%': 0.0, 'select%': 50.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/14/9      8.884 GiB/sec     12.117 GiB/sec    36.381 {'family_index': 1, 'per_family_instance_index': 29, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/14/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 12508, 'byte_width': 9.0, 'data null%': 90.0, 'mask null%': 5.0, 'select%': 1.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/11/9      8.886 GiB/sec     12.075 GiB/sec    35.892 {'family_index': 1, 'per_family_instance_index': 23, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/11/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 12716, 'byte_width': 9.0, 'data null%': 10.0, 'mask null%': 5.0, 'select%': 1.0}\n      TakeFixedSizeBinaryMonotonicIndices/524288/1000/9 134.765M items/sec 182.868M items/sec    35.694                                     {'family_index': 4, 'per_family_instance_index': 1, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/1000/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 206, 'byte_width': 9.0, 'null_percent': 0.1}\n          FilterFixedSizeBinaryFilterNoNulls/524288/5/8     11.393 GiB/sec     15.091 GiB/sec    32.453     {'family_index': 0, 'per_family_instance_index': 10, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/5/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 16510, 'byte_width': 8.0, 'data null%': 0.1, 'mask null%': 0.0, 'select%': 1.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/8/8     11.573 GiB/sec     15.102 GiB/sec    30.496     {'family_index': 0, 'per_family_instance_index': 16, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/8/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 16684, 'byte_width': 8.0, 'data null%': 1.0, 'mask null%': 0.0, 'select%': 1.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/11/8      7.740 GiB/sec     10.059 GiB/sec    29.956 {'family_index': 1, 'per_family_instance_index': 22, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/11/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 10972, 'byte_width': 8.0, 'data null%': 10.0, 'mask null%': 5.0, 'select%': 1.0}\n       FilterFixedSizeBinaryFilterWithNulls/524288/14/8      7.733 GiB/sec      9.915 GiB/sec    28.213 {'family_index': 1, 'per_family_instance_index': 28, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/14/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 10991, 'byte_width': 8.0, 'data null%': 90.0, 'mask null%': 5.0, 'select%': 1.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/5/8      7.682 GiB/sec      9.765 GiB/sec    27.109   {'family_index': 1, 'per_family_instance_index': 10, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/5/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 10991, 'byte_width': 8.0, 'data null%': 0.1, 'mask null%': 5.0, 'select%': 1.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/8/9      8.856 GiB/sec     11.180 GiB/sec    26.241   {'family_index': 1, 'per_family_instance_index': 17, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/8/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 12571, 'byte_width': 9.0, 'data null%': 1.0, 'mask null%': 5.0, 'select%': 1.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/8/8      7.735 GiB/sec      9.710 GiB/sec    25.530   {'family_index': 1, 'per_family_instance_index': 16, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/8/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 11069, 'byte_width': 8.0, 'data null%': 1.0, 'mask null%': 5.0, 'select%': 1.0}\n        TakeFixedSizeBinaryMonotonicIndices/524288/10/9 128.606M items/sec 160.249M items/sec    24.604                                      {'family_index': 4, 'per_family_instance_index': 3, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/10/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 209, 'byte_width': 9.0, 'null_percent': 10.0}\n         FilterFixedSizeBinaryFilterNoNulls/524288/11/8     12.033 GiB/sec     14.737 GiB/sec    22.478   {'family_index': 0, 'per_family_instance_index': 22, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/11/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 17220, 'byte_width': 8.0, 'data null%': 10.0, 'mask null%': 0.0, 'select%': 1.0}\n         FilterFixedSizeBinaryFilterNoNulls/524288/14/8     12.141 GiB/sec     14.761 GiB/sec    21.579   {'family_index': 0, 'per_family_instance_index': 28, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/14/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 17343, 'byte_width': 8.0, 'data null%': 90.0, 'mask null%': 0.0, 'select%': 1.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/5/9      8.825 GiB/sec     10.633 GiB/sec    20.489   {'family_index': 1, 'per_family_instance_index': 11, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/5/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 12543, 'byte_width': 9.0, 'data null%': 0.1, 'mask null%': 5.0, 'select%': 1.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/2/8      8.300 GiB/sec      9.969 GiB/sec    20.117    {'family_index': 1, 'per_family_instance_index': 4, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/2/8', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 11819, 'byte_width': 8.0, 'data null%': 0.0, 'mask null%': 5.0, 'select%': 1.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/5/9     12.954 GiB/sec     15.192 GiB/sec    17.273     {'family_index': 0, 'per_family_instance_index': 11, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/5/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 18572, 'byte_width': 9.0, 'data null%': 0.1, 'mask null%': 0.0, 'select%': 1.0}\n          FilterFixedSizeBinaryFilterNoNulls/524288/8/9     13.181 GiB/sec     15.222 GiB/sec    15.490     {'family_index': 0, 'per_family_instance_index': 17, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/8/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 18904, 'byte_width': 9.0, 'data null%': 1.0, 'mask null%': 0.0, 'select%': 1.0}\n        FilterFixedSizeBinaryFilterWithNulls/524288/2/9      9.344 GiB/sec     10.632 GiB/sec    13.784    {'family_index': 1, 'per_family_instance_index': 5, 'run_name': 'FilterFixedSizeBinaryFilterWithNulls/524288/2/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 13291, 'byte_width': 9.0, 'data null%': 0.0, 'mask null%': 5.0, 'select%': 1.0}\n         FilterFixedSizeBinaryFilterNoNulls/524288/11/9     13.566 GiB/sec     14.894 GiB/sec     9.789   {'family_index': 0, 'per_family_instance_index': 23, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/11/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 19349, 'byte_width': 9.0, 'data null%': 10.0, 'mask null%': 0.0, 'select%': 1.0}\n         FilterFixedSizeBinaryFilterNoNulls/524288/14/9     13.603 GiB/sec     14.863 GiB/sec     9.265   {'family_index': 0, 'per_family_instance_index': 29, 'run_name': 'FilterFixedSizeBinaryFilterNoNulls/524288/14/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 19490, 'byte_width': 9.0, 'data null%': 90.0, 'mask null%': 0.0, 'select%': 1.0}\n    TakeFixedSizeBinaryRandomIndicesNoNulls/524288/10/9 124.390M items/sec 133.566M items/sec     7.377                                  {'family_index': 2, 'per_family_instance_index': 3, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/10/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 164, 'byte_width': 9.0, 'null_percent': 10.0}\n         TakeFixedSizeBinaryMonotonicIndices/524288/2/9 116.792M items/sec 124.182M items/sec     6.328                                       {'family_index': 4, 'per_family_instance_index': 5, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/2/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 161, 'byte_width': 9.0, 'null_percent': 50.0}\n  TakeFixedSizeBinaryRandomIndicesNoNulls/524288/1000/9 135.860M items/sec 142.524M items/sec     4.905                                 {'family_index': 2, 'per_family_instance_index': 1, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/1000/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 180, 'byte_width': 9.0, 'null_percent': 0.1}\nTakeFixedSizeBinaryRandomIndicesWithNulls/524288/1000/9 131.123M items/sec 137.400M items/sec     4.788                               {'family_index': 3, 'per_family_instance_index': 1, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/1000/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 176, 'byte_width': 9.0, 'null_percent': 0.1}\n     TakeFixedSizeBinaryRandomIndicesNoNulls/524288/0/9 220.634M items/sec 230.872M items/sec     4.640                                    {'family_index': 2, 'per_family_instance_index': 9, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/0/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 295, 'byte_width': 9.0, 'null_percent': 0.0}\n     TakeFixedSizeBinaryRandomIndicesNoNulls/524288/2/9  97.425M items/sec 101.477M items/sec     4.159                                   {'family_index': 2, 'per_family_instance_index': 5, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/2/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 130, 'byte_width': 9.0, 'null_percent': 50.0}\n  TakeFixedSizeBinaryRandomIndicesWithNulls/524288/10/9 104.830M items/sec 108.346M items/sec     3.354                                {'family_index': 3, 'per_family_instance_index': 3, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/10/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 100, 'byte_width': 9.0, 'null_percent': 10.0}\n   TakeFixedSizeBinaryRandomIndicesWithNulls/524288/1/9 378.858M items/sec 387.322M items/sec     2.234                                {'family_index': 3, 'per_family_instance_index': 7, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/1/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 506, 'byte_width': 9.0, 'null_percent': 100.0}\n   TakeFixedSizeBinaryRandomIndicesWithNulls/524288/0/9 221.900M items/sec 226.450M items/sec     2.050                                  {'family_index': 3, 'per_family_instance_index': 9, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/0/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 295, 'byte_width': 9.0, 'null_percent': 0.0}\n         TakeFixedSizeBinaryMonotonicIndices/524288/0/9 248.664M items/sec 253.037M items/sec     1.758                                        {'family_index': 4, 'per_family_instance_index': 9, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/0/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 332, 'byte_width': 9.0, 'null_percent': 0.0}\n     TakeFixedSizeBinaryRandomIndicesNoNulls/524288/1/9 197.730M items/sec 201.173M items/sec     1.741                                  {'family_index': 2, 'per_family_instance_index': 7, 'run_name': 'TakeFixedSizeBinaryRandomIndicesNoNulls/524288/1/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 264, 'byte_width': 9.0, 'null_percent': 100.0}\n   TakeFixedSizeBinaryRandomIndicesWithNulls/524288/2/9  73.196M items/sec  74.167M items/sec     1.327                                  {'family_index': 3, 'per_family_instance_index': 5, 'run_name': 'TakeFixedSizeBinaryRandomIndicesWithNulls/524288/2/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 96, 'byte_width': 9.0, 'null_percent': 50.0}\n         TakeFixedSizeBinaryMonotonicIndices/524288/1/9 192.545M items/sec 188.138M items/sec    -2.289                                      {'family_index': 4, 'per_family_instance_index': 7, 'run_name': 'TakeFixedSizeBinaryMonotonicIndices/524288/1/9', 'repetitions': 1, 'repetition_index': 0, 'threads': 1, 'iterations': 257, 'byte_width': 9.0, 'null_percent': 100.0}\n\n```\n\n### Are these changes tested?\n\nYes.\n\n### Are there any user-facing changes?\n\nNo.\n\n* Closes: #39740\n\nAuthored-by: Antoine Pitrou <antoine@python.org>\nSigned-off-by: Antoine Pitrou <antoine@python.org>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2094196530\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39740\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/39740/hovercard\" href=\"https://github.com/apache/arrow/issues/39740\">apacheGH-39740</a>: [C++] Fix filter and take kernel for month_day_nano i…"}},{"before":"5a635b2b73b86b8ccadea3ac799db77071fd2657","after":"94127019ac15b94cfd1e1fc8e5ff14f3291d1917","ref":"refs/heads/ARROW-8991-newfn-scalar-hash-fresh","pushedAt":"2024-01-12T22:52:49.000Z","pushType":"force_push","commitsCount":0,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-17211: refresh history of scalar hash benchmark\n\nThis commit includes additions to the general hashing benchmarks that\ncover the use of hashing functions in key_hash.h without carrying the\nburden of a long dev history.\n\nSome existing benchmark names were changed to distinguish between the\nuse of Int32 and Int64 types, new benchmarks were added that use the\nfunctions declared in key_hash.h. The reason the new benchmarks are\nadded is because it is claimed they prioritize speed over cryptography\nas they're primarily used for join algorithms and other processing\ntasks, which the hashing benchmark can now provide observability for.\n\nIssue: GH-17211\nIssue: ARROW-8991","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1527742274\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/17211\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/17211/hovercard\" href=\"https://github.com/apache/arrow/issues/17211\">apacheGH-17211</a>: refresh history of scalar hash benchmark"}},{"before":"3cc04f1e8389deea18b88eedc5b4e3458467d9c6","after":"87ed8ac432dc7e77fa0275a24e3edda9b0bc2a2e","ref":"refs/heads/main","pushedAt":"2024-01-12T22:52:05.000Z","pushType":"push","commitsCount":2,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-39564: [CI][Java] Set correct version on Java BOM (#39580)\n\n### Rationale for this change\n\nThe version set currently on the maintenance branch is incorrect for Java BOM.\n\n### What changes are included in this PR?\n\nSuggested changes to set specifically version for BOM and maven.\n\n### Are these changes tested?\n\nI will trigger java-jars via archery but I think this is currently only reproducible on the maintenance branch. So we will have to merge and validate there.\n\n### Are there any user-facing changes?\nNo\n* Closes: #39564\n\nAuthored-by: Raúl Cumplido <raulcumplido@gmail.com>\nSigned-off-by: Raúl Cumplido <raulcumplido@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2076869530\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39564\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/39564/hovercard\" href=\"https://github.com/apache/arrow/issues/39564\">apacheGH-39564</a>: [CI][Java] Set correct version on Java BOM (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2078500175\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39580\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/apache/arrow/pull/39580/hovercard\" href=\"https://github.com/apache/arrow/pull/39580\">apache#39580</a>"}},{"before":null,"after":"5a635b2b73b86b8ccadea3ac799db77071fd2657","ref":"refs/heads/ARROW-8991-newfn-scalar-hash-fresh","pushedAt":"2024-01-12T22:50:21.000Z","pushType":"branch_creation","commitsCount":0,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-17211: refresh history of scalar hash benchmark\n\nThis commit includes additions to the general hashing benchmarks that\ncover the use of hashing functions in key_hash.h without carrying the\nburden of a long dev history.\n\nSome existing benchmark names were changed to distinguish between the\nuse of Int32 and Int64 types, new benchmarks were added that use the\nfunctions declared in key_hash.h. The reason the new benchmarks are\nadded is because it is claimed they prioritize speed over cryptography\nas they're primarily used for join algorithms and other processing\ntasks, which the hashing benchmark can now provide observability for.\n\nIssue: GH-17211\nIssue: ARROW-8991","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1527742274\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/17211\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/17211/hovercard\" href=\"https://github.com/apache/arrow/issues/17211\">apacheGH-17211</a>: refresh history of scalar hash benchmark"}},{"before":null,"after":"92da7cf7f0e40102609a27aca21e5b1586469e27","ref":"refs/heads/ARROW-8991-newfn-scalar-hash","pushedAt":"2024-01-12T19:13:02.000Z","pushType":"branch_creation","commitsCount":0,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"[ARROW-8991] new functionality for light array","shortMessageHtmlLink":"[ARROW-8991] new functionality for light array"}},{"before":"92da7cf7f0e40102609a27aca21e5b1586469e27","after":null,"ref":"refs/heads/ARROW-8991-newfn-scalar-hash","pushedAt":"2024-01-12T19:12:58.000Z","pushType":"branch_deletion","commitsCount":0,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"}},{"before":"07a46555e74501f96973dc43ef54a4669d261876","after":"3cc04f1e8389deea18b88eedc5b4e3458467d9c6","ref":"refs/heads/main","pushedAt":"2024-01-12T16:34:46.000Z","pushType":"push","commitsCount":14,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-39523: [R] Don't override explicitly set NOT_CRAN=false when on dev version (#39524)\n\n### Rationale for this change\n\nThe default linux build used in the lto job should not build with aws/gcs. A change in the build system changed this.\n\n### What changes are included in this PR?\n\nRevert to old behavior by not overriding explicitly set `NOT_CRAN=false`.\n\n### Are these changes tested?\n\nCI\n\n### Are there any user-facing changes?\n\nNo\n* Closes: #39523\n\nLead-authored-by: Jacob Wujciak-Jens <jacob@wujciak.de>\nCo-authored-by: Sutou Kouhei <kou@cozmixng.org>\nSigned-off-by: Jacob Wujciak-Jens <jacob@wujciak.de>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2071470757\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39523\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/39523/hovercard\" href=\"https://github.com/apache/arrow/issues/39523\">apacheGH-39523</a>: [R] Don't override explicitly set NOT_CRAN=false when…"}},{"before":"1c48d69844cb00918be9255f60d7eb0f59792a8b","after":"07a46555e74501f96973dc43ef54a4669d261876","ref":"refs/heads/main","pushedAt":"2024-01-10T19:30:36.000Z","pushType":"push","commitsCount":111,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-39515: [Python] Pass in type to `MapType.from_arrays` (#39516)\n\n\n\n### Rationale for this change\n\nFor Iceberg we want to add metadata type the type (the field-id), therefore we need to pass in the type analog to what we do for `ListArray.from_arrays(self, offsets, values, DataType type=None, MemoryPool pool=None, mask=None)`.\n\n### What changes are included in this PR?\n\nUpdated a keyword argument for the `type`, and make sure that the the static method to create the MapType is exposed from the cpp side.\n\n### Are these changes tested?\n\nI've added a simple test.\n\n### Are there any user-facing changes?\n\n* Closes: #39515\n\nAuthored-by: Fokko Driesprong <fokko@tabular.io>\nSigned-off-by: AlenkaF <frim.alenka@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2071171925\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39515\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/39515/hovercard\" href=\"https://github.com/apache/arrow/issues/39515\">apacheGH-39515</a>: [Python] Pass in type to <code>MapType.from_arrays</code> (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2071189253\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39516\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/apache/arrow/pull/39516/hovercard\" href=\"https://github.com/apache/arrow/pull/39516\">apach…</a>"}},{"before":"1e7175db8d78313935cd1161728e9ae9dae57c9c","after":"1c48d69844cb00918be9255f60d7eb0f59792a8b","ref":"refs/heads/main","pushedAt":"2023-12-20T03:44:49.000Z","pushType":"push","commitsCount":215,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"MINOR: [R] Update NEWS.md for 14.0.2 (#39286)\n\nUpdate NEWS.md with recent changes\n\nAuthored-by: Jacob Wujciak-Jens <jacob@wujciak.de>\nSigned-off-by: Jacob Wujciak-Jens <jacob@wujciak.de>","shortMessageHtmlLink":"MINOR: [R] Update NEWS.md for 14.0.2 (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"2047294486\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/39286\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/apache/arrow/pull/39286/hovercard\" href=\"https://github.com/apache/arrow/pull/39286\">apache#39286</a>)"}},{"before":"c49e24273160ac1ce195f02dbd14acd7d0f6945e","after":"1e7175db8d78313935cd1161728e9ae9dae57c9c","ref":"refs/heads/main","pushedAt":"2023-11-15T19:45:11.000Z","pushType":"push","commitsCount":85,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-38503: [Go][Parquet] Make the arrow column writer internal (#38727)\n\nThis makes it so the Arrow column writer is not exported from the `pqarrow` package.  This follows up on comments from #38581.\n* Closes: #38503\n\nAuthored-by: Tim Schaub <tim@planet.com>\nSigned-off-by: Matt Topol <zotthewizard@gmail.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1966744234\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/38503\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/38503/hovercard\" href=\"https://github.com/apache/arrow/issues/38503\">apacheGH-38503</a>: [Go][Parquet] Make the arrow column writer internal (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1994013759\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/38727\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/apache/arrow/pull/38727/hovercard\" href=\"https://github.com/apache/arrow/pull/38727\">a…</a>"}},{"before":"23c1bf9ff74b749c11385b38911d6aa8d85e180c","after":"c49e24273160ac1ce195f02dbd14acd7d0f6945e","ref":"refs/heads/main","pushedAt":"2023-10-31T21:46:02.000Z","pushType":"push","commitsCount":240,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"GH-38420: [MATLAB] Implement a `DatetimeValidator` class that validates a MATLAB `cell` array contains only values of zoned or unzoned `datetime`s (#38533)\n\n\n\n### Rationale for this change\n\nThis is a followup to #38419.\n\nAdding this `DatetimeTypeValidator` class is a step towards implementing the `arrow.array.ListArray.fromMATLAB()` method for creating `ListArray`s whose `ValueType`s is a timestamp array from a MATLAB `cell` array.\n\nThis validator will ensure the cell array contain only `datetime`s or unzoned `datetime`s. This is a requirement when creating a `List` of `Timestamp`s because two MATLAB `datetime`s can only be concatenated together if they are either both zoned or both unzoned:\n\n```matlab\n>> d1 = datetime(2023, 10, 31, TimeZone=\"America/New_York\");\n>> d2 =datetime(2023, 11, 1);\n>> [d1; d2]\nError using datetime/vertcat\nUnable to concatenate a datetime array that has a time zone with one that does not have a time\nzone.\n```\n\n### What changes are included in this PR?\n\nAdded a new MATLAB class called `arrow.array.internal.list.DatetimeValidator`, which inherits from `arrow.array.internal.list.ClassTypeValidator`.\n\n This new class defines one property called `HasTimeZone`, which is a scalar `logical` indicating if the validator expects all `datetime`s to be zoned or not. \n\nAdditionally, `DatetimeValidator` overrides the `validateElement` method. It first call's `ClassTypeValidator`'s implementation of `validateElement` to verify the input element is a `datetime`. If so, it then confirms that the input `datetime`'s TimeZone property is empty or nonempty, based on the validator's `HasTimeZone`  property value.\n\n### Are these changes tested?\n\nYes, I added a new test class called `tDatetimeValidator.m`.\n\n### Are there any user-facing changes?\n\nNo.\n\n### Future Directions\n\n1. #38417 \n2. #38354 \n* Closes: #38420\n\nAuthored-by: Sarah Gilmore <sgilmore@mathworks.com>\nSigned-off-by: Kevin Gurney <kgurney@mathworks.com>","shortMessageHtmlLink":"<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1957823916\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/38420\" data-hovercard-type=\"issue\" data-hovercard-url=\"/apache/arrow/issues/38420/hovercard\" href=\"https://github.com/apache/arrow/issues/38420\">apacheGH-38420</a>: [MATLAB] Implement a <code>DatetimeValidator</code> class that v…"}},{"before":"7b30ba48e7f3605507d1daecbd041c16b667178a","after":"23c1bf9ff74b749c11385b38911d6aa8d85e180c","ref":"refs/heads/main","pushedAt":"2023-09-26T20:39:47.000Z","pushType":"push","commitsCount":20,"pusher":{"login":"drin","name":"octalene","path":"/drin","primaryAvatarUrl":"https://avatars.githubusercontent.com/u/222710?s=80&v=4"},"commit":{"message":"MINOR: [JS] update link to issues (#37882)\n\nUpdate link to use GitHub for issues.","shortMessageHtmlLink":"MINOR: [JS] update link to issues (<a class=\"issue-link js-issue-link\" data-error-text=\"Failed to load title\" data-id=\"1913896596\" data-permission-text=\"Title is private\" data-url=\"https://github.com/apache/arrow/issues/37882\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/apache/arrow/pull/37882/hovercard\" href=\"https://github.com/apache/arrow/pull/37882\">apache#37882</a>)"}}],"hasNextPage":true,"hasPreviousPage":false,"activityType":"all","actor":null,"timePeriod":"all","sort":"DESC","perPage":30,"cursor":"djE6ks8AAAAEcVt62wA","startCursor":null,"endCursor":null}},"title":"Activity · drin/arrow"}