feat(operator): read the whole-number parts out of a timestamp column

[kz930]

What changes were proposed in this PR?

An operator that reads the year, quarter, month, day, weekday, day of year, ISO week, hour, minute or second out of a timestamp column, each added as a whole-number column of its own.

Nothing here is parsed. The column is already a moment when it arrives, and which text became which moment was settled upstream, the way KNIME, Alteryx and Spark all separate parsing from extraction. The parts are read as ISO-8601 states them, because the two runtimes disagree by default: pandas counts Monday as 0, java.time counts it as 1.

Any related issues, documentation, discussions?

Not part of #8325: that set makes the operators Texera already had exportable. This adds a new one, which implements the trait from the start rather than gaining it. The trait is in #8327, which has merged.

Closes #8478, the task this change is the whole of.

Aggregate groups by a column's values, and a timestamp has a different value in almost every row, so grouping by one puts each row in its own group. Sales by month could not be asked for, though the platform carries a TIMESTAMP type, a Gantt chart and a time-series plot.

How was this PR tested?

Twelve unit tests in the operator's own spec, over the schema it declares, the values the executor reads and the Python it emits. The parity harness runs it on 13 configurations, one per field plus a hostile column name and an empty cell, comparing the engine's answer against the exported script's.

The operator in the editor, reading event_time and asking for the year, the month and the weekday:

The three whole-number columns beside the timestamp they came from. 2024-01-15 is a Monday, 2024-07-04 a Thursday and 2025-02-09 a Sunday, reading 1, 4 and 7 as ISO-8601 states them rather than the 0, 3 and 6 pandas would give:

Was this PR authored or co-authored using generative AI tooling?

Generated-by: Claude Code (Opus 5)

[github-actions]

Automated Reviewer Suggestions

Based on the git blame history of the changed files, we recommend the following reviewers:

• Contributors with relevant context: @aglinxinyuan
  You can notify them by mentioning @aglinxinyuan in a comment.

[github-actions]

⚠️ Benchmark changes need a look

🟢 0 better · 🔴 7 worse · ⚪ 8 noise (<±5%) · 0 without baseline

Compared against main 75c85aa benchmarked on this same runner, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.

Dashboard · Run

	config	throughput	MB/s	latency	max Δ latest / 7d
🔴	bs=10 sw=10 sl=64	352	0.215	25,336/74,051/74,051 us	🔴 +117.6% / 🔴 +359.0%
🔴	bs=100 sw=10 sl=64	783	0.478	124,131/159,809/159,809 us	🔴 +16.2% / 🔴 +47.0%
⚪	bs=1000 sw=10 sl=64	907	0.553	1,098,777/1,152,424/1,152,424 us	⚪ within ±5% / 🔴 +11.1%

Baseline details

Latest main 75c85aa from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	352 tuples/sec	408 tuples/sec	755.28 tuples/sec	-13.7%	-53.4%
bs=10 sw=10 sl=64	MB/s	0.215 MB/s	0.249 MB/s	0.461 MB/s	-13.7%	-53.4%
bs=10 sw=10 sl=64	p50	25,336 us	23,679 us	12,957 us	+7.0%	+95.5%
bs=10 sw=10 sl=64	p95	74,051 us	34,032 us	16,134 us	+117.6%	+359.0%
bs=10 sw=10 sl=64	p99	74,051 us	34,032 us	20,333 us	+117.6%	+264.2%
bs=100 sw=10 sl=64	throughput	783 tuples/sec	824 tuples/sec	980.1 tuples/sec	-5.0%	-20.1%
bs=100 sw=10 sl=64	MB/s	0.478 MB/s	0.503 MB/s	0.598 MB/s	-5.0%	-20.1%
bs=100 sw=10 sl=64	p50	124,131 us	119,840 us	101,894 us	+3.6%	+21.8%
bs=100 sw=10 sl=64	p95	159,809 us	137,502 us	108,718 us	+16.2%	+47.0%
bs=100 sw=10 sl=64	p99	159,809 us	137,502 us	122,482 us	+16.2%	+30.5%
bs=1000 sw=10 sl=64	throughput	907 tuples/sec	915 tuples/sec	1,011 tuples/sec	-0.9%	-10.3%
bs=1000 sw=10 sl=64	MB/s	0.553 MB/s	0.559 MB/s	0.617 MB/s	-1.1%	-10.4%
bs=1000 sw=10 sl=64	p50	1,098,777 us	1,093,457 us	996,422 us	+0.5%	+10.3%
bs=1000 sw=10 sl=64	p95	1,152,424 us	1,126,982 us	1,037,670 us	+2.3%	+11.1%
bs=1000 sw=10 sl=64	p99	1,152,424 us	1,126,982 us	1,072,152 us	+2.3%	+7.5%

Raw CSV

config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,568.47,200,128000,352,0.215,25336.28,74051.10,74051.10
1,100,10,64,20,2555.13,2000,1280000,783,0.478,124130.63,159809.45,159809.45
2,1000,10,64,20,22061.29,20000,12800000,907,0.553,1098776.74,1152424.07,1152424.07

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 93.89%. Comparing base (75c85aa) to head (95aadac).

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #8480      +/-   ##
============================================
+ Coverage     93.62%   93.89%   +0.26%     
+ Complexity     4857     4840      -17     
============================================
  Files          1212     1210       -2     
  Lines         50037    49261     -776     
  Branches       6132     5991     -141     
============================================
- Hits          46847    46253     -594     
+ Misses         1676     1516     -160     
+ Partials       1514     1492      -22     

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access-control-service	80.18% <ø> (ø)
agent-service	99.32% <ø> (ø)		Carriedforward from dcb25ef
amber	89.91% <ø> (+0.05%)	⬆️	Carriedforward from dcb25ef
computing-unit-managing-service	77.14% <ø> (ø)
config-service	87.12% <ø> (ø)
file-service	83.65% <ø> (ø)
frontend	96.80% <ø> (+0.64%)	⬆️	Carriedforward from dcb25ef
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pyamber	98.47% <ø> (ø)		Carriedforward from dcb25ef
workflow-compiling-service	74.09% <ø> (ø)

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[kz930]

@carloea2 this adds an operator that reads the year, month, weekday and the rest out of a timestamp column, each as a whole-number column of its own. Would you take a look when you have a moment?