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Posted to github@arrow.apache.org by "MassivePizza (via GitHub)" <gi...@apache.org> on 2026/07/07 13:03:02 UTC
[PR] Cache encoded field name in FieldEncoder [arrow-rs]
MassivePizza opened a new pull request, #10296:
URL: https://github.com/apache/arrow-rs/pull/10296
# Which issue does this PR close?
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- Closes #10295.
# Rationale for this change
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See issue.
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See issue.
# Are these changes tested?
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Should be covered by existing tests.
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No.
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Re: [PR] Cache encoded field name in FieldEncoder [arrow-rs]
Posted by "Jefffrey (via GitHub)" <gi...@apache.org>.
Jefffrey commented on PR #10296:
URL: https://github.com/apache/arrow-rs/pull/10296#issuecomment-4999771765
thanks @MassivePizza
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Re: [PR] Cache encoded field name in FieldEncoder [arrow-rs]
Posted by "Jefffrey (via GitHub)" <gi...@apache.org>.
Jefffrey merged PR #10296:
URL: https://github.com/apache/arrow-rs/pull/10296
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Re: [PR] Cache encoded field name in FieldEncoder [arrow-rs]
Posted by "MassivePizza (via GitHub)" <gi...@apache.org>.
MassivePizza commented on PR #10296:
URL: https://github.com/apache/arrow-rs/pull/10296#issuecomment-4971159428
Sidenote: I also looked at using this caching for DictionaryArrays of String, but our product doesn't really generate those.
We'd have to add an optimization pass in Datafusion to actually produce those for e.g. `CASE WHEN` that evaluate to constant strings.
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Re: [PR] Cache encoded field name in FieldEncoder [arrow-rs]
Posted by "MassivePizza (via GitHub)" <gi...@apache.org>.
MassivePizza commented on PR #10296:
URL: https://github.com/apache/arrow-rs/pull/10296#issuecomment-5001638074
Just tried one and got an improvement, so probably noise.
```
bench_nullable_struct time: [8.0262 ms 8.0643 ms 8.1052 ms]
change: [−8.1292% −7.4883% −6.8732%] (p = 0.00 < 0.05)
Performance has improved.
Found 6 outliers among 100 measurements (6.00%)
5 (5.00%) high mild
1 (1.00%) high severe
```
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Re: [PR] Cache encoded field name in FieldEncoder [arrow-rs]
Posted by "MassivePizza (via GitHub)" <gi...@apache.org>.
MassivePizza commented on PR #10296:
URL: https://github.com/apache/arrow-rs/pull/10296#issuecomment-4970318348
Right, I forgot to add those. My org has an internal bench for just encode_string and an integration bench for a struct with standard-length field names. Will sanitize those and commit here.
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Re: [PR] Cache encoded field name in FieldEncoder [arrow-rs]
Posted by "Jefffrey (via GitHub)" <gi...@apache.org>.
Jefffrey commented on PR #10296:
URL: https://github.com/apache/arrow-rs/pull/10296#issuecomment-4976654859
I ran `cargo bench -p arrow --bench json_writer --features "test_utils json"` locally with the bench changes on main then on this branch:
```diff
bench_integer time: [2.4510 ms 2.4599 ms 2.4684 ms]
+ change: [−4.3634% −3.9909% −3.6416%] (p = 0.00 < 0.05)
Performance has improved.
Found 17 outliers among 100 measurements (17.00%)
8 (8.00%) low severe
8 (8.00%) low mild
1 (1.00%) high mild
bench_float time: [2.9815 ms 2.9848 ms 2.9883 ms]
+ change: [−6.1623% −5.8905% −5.6521%] (p = 0.00 < 0.05)
Performance has improved.
Found 7 outliers among 100 measurements (7.00%)
7 (7.00%) high mild
bench_string time: [7.4536 ms 7.4639 ms 7.4741 ms]
+ change: [−2.7981% −2.5786% −2.3719%] (p = 0.00 < 0.05)
Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
1 (1.00%) low mild
bench_mixed time: [10.658 ms 10.680 ms 10.705 ms]
+ change: [−7.4853% −7.1895% −6.9014%] (p = 0.00 < 0.05)
Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
1 (1.00%) high severe
bench_mixed_longname time: [5.1188 ms 5.1363 ms 5.1558 ms]
+ change: [−28.954% −28.551% −28.156%] (p = 0.00 < 0.05)
Performance has improved.
Found 9 outliers among 100 measurements (9.00%)
6 (6.00%) high mild
3 (3.00%) high severe
bench_shortmixed_longname
time: [4.2039 ms 4.2093 ms 4.2148 ms]
+ change: [−29.683% −29.502% −29.324%] (p = 0.00 < 0.05)
Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
1 (1.00%) high mild
bench_dict_array time: [3.6259 ms 3.6349 ms 3.6446 ms]
+ change: [−1.7944% −1.5260% −1.2328%] (p = 0.00 < 0.05)
Performance has improved.
Found 6 outliers among 100 measurements (6.00%)
3 (3.00%) high mild
3 (3.00%) high severe
bench_struct time: [17.071 ms 17.102 ms 17.135 ms]
+ change: [−2.3589% −2.0595% −1.7670%] (p = 0.00 < 0.05)
Performance has improved.
Found 7 outliers among 100 measurements (7.00%)
1 (1.00%) low mild
4 (4.00%) high mild
2 (2.00%) high severe
bench_nullable_struct time: [8.9790 ms 9.0044 ms 9.0332 ms]
- change: [+10.464% +10.847% +11.310%] (p = 0.00 < 0.05)
Performance has regressed.
Found 8 outliers among 100 measurements (8.00%)
2 (2.00%) high mild
6 (6.00%) high severe
bench_list time: [25.390 ms 25.439 ms 25.492 ms]
- change: [+7.6416% +8.2171% +8.7591%] (p = 0.00 < 0.05)
Performance has regressed.
Found 4 outliers among 100 measurements (4.00%)
3 (3.00%) high mild
1 (1.00%) high severe
bench_nullable_list time: [5.4186 ms 5.4606 ms 5.5029 ms]
- change: [+4.2082% +5.2798% +6.3134%] (p = 0.00 < 0.05)
Performance has regressed.
bench_struct_list time: [2.5047 ms 2.5144 ms 2.5257 ms]
+ change: [−4.4805% −3.7484% −3.0407%] (p = 0.00 < 0.05)
Performance has improved.
Found 9 outliers among 100 measurements (9.00%)
7 (7.00%) high mild
2 (2.00%) high severe
f64_to_string time: [2.1164 ms 2.1199 ms 2.1236 ms]
+ change: [−3.0236% −2.7448% −2.4816%] (p = 0.00 < 0.05)
Performance has improved.
Found 7 outliers among 100 measurements (7.00%)
7 (7.00%) high mild
Benchmarking f32_to_string: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 6.0s, enable flat sampling, or reduce sample count to 60.
f32_to_string time: [1.1920 ms 1.1955 ms 1.1995 ms]
change: [+0.5809% +0.8944% +1.2082%] (p = 0.00 < 0.05)
Change within noise threshold.
Benchmarking i64_to_string: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 7.9s, enable flat sampling, or reduce sample count to 50.
i64_to_string time: [1.5718 ms 1.5768 ms 1.5818 ms]
change: [−1.4256% −1.0300% −0.6526%] (p = 0.00 < 0.05)
Change within noise threshold.
Found 2 outliers among 100 measurements (2.00%)
2 (2.00%) high mild
Benchmarking i32_to_string: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 7.4s, enable flat sampling, or reduce sample count to 50.
i32_to_string time: [1.4531 ms 1.4576 ms 1.4621 ms]
+ change: [−2.2713% −1.8183% −1.3782%] (p = 0.00 < 0.05)
Performance has improved.
Found 7 outliers among 100 measurements (7.00%)
6 (6.00%) high mild
1 (1.00%) high severe
mixed_numbers_to_string time: [7.1889 ms 7.2025 ms 7.2169 ms]
- change: [+4.0331% +4.3315% +4.6342%] (p = 0.00 < 0.05)
Performance has regressed.
Found 5 outliers among 100 measurements (5.00%)
4 (4.00%) high mild
1 (1.00%) high severe
```
Can see clear improvements on the longname ones 🚀
I think the other regressions are just noise 🤔
Curious if you see similar results
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