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fix: unordered mode too many labels issue. #1148

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Merged
merged 4 commits into from
Nov 13, 2024
Merged

fix: unordered mode too many labels issue. #1148

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e712c65
fix: unordered mode too many labels issue.
Genesis929 Nov 12, 2024
882f705
update test
Genesis929 Nov 12, 2024
6f39d68
update test
Genesis929 Nov 12, 2024
ebc6d05
Update test
Genesis929 Nov 13, 2024
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5 changes: 3 additions & 2 deletions 5 bigframes/session/executor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -379,11 +379,12 @@ def _run_execute_query(
job_config.maximum_bytes_billed = (
bigframes.options.compute.maximum_bytes_billed
)
# Note: add_labels is global scope which may have unexpected effects
bq_io.add_labels(job_config, api_name=api_name)

if not self.strictly_ordered:
job_config.labels["bigframes-mode"] = "unordered"

# Note: add_labels is global scope which may have unexpected effects
bq_io.add_labels(job_config, api_name=api_name)
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Highly recommend adding a test using the customer's use case

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Added

try:
query_job = self.bqclient.query(sql, job_config=job_config)
return (
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31 changes: 18 additions & 13 deletions 31 tests/system/small/test_series.py
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Expand Up @@ -2768,20 +2768,25 @@ def test_series_case_when(scalars_dfs_maybe_ordered):

# TODO(tswast): pandas case_when appears to assume True when a value is
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@shobsi shobsi Nov 12, 2024 •
edited
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I would keep this comment, or rather add a dedicated test that clearly conveys the intent - test_series_case_when_with_large_number_of_operations, or even test_series_large_number_of_operations, with comment that describes the labels limit

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Updated to have all cases in the original test. Keep only one test can be overall faster I think.

# null. I suspect this should be considered a bug in pandas.
bf_result = bf_series.case_when(
[
((bf_series > 100).fillna(True), bf_series - 1),
((bf_series > 0).fillna(True), pd.NA),
((bf_series < -100).fillna(True), -1000),
]
).to_pandas()
pd_result = pd_series.case_when(
[
(pd_series > 100, pd_series - 1),
(pd_series > 0, pd.NA),
(pd_series < -100, -1000),
]

# Generate 150 conditions to test case_when with a large number of conditions
bf_conditions = (
[((bf_series > 645).fillna(True), bf_series - 1)]
+ [((bf_series > (-100 + i * 5)).fillna(True), i) for i in range(148, 0, -1)]
+ [((bf_series <= -100).fillna(True), pd.NA)]
)

pd_conditions = (
[((pd_series > 645), pd_series - 1)]
+ [((pd_series > (-100 + i * 5)), i) for i in range(148, 0, -1)]
+ [(pd_series <= -100, pd.NA)]
)

assert len(bf_conditions) == 150

bf_result = bf_series.case_when(bf_conditions).to_pandas()
pd_result = pd_series.case_when(pd_conditions)

pd.testing.assert_series_equal(
bf_result,
pd_result.astype(pd.Int64Dtype()),
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