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fix: spark-compatible date_trunc — timestamp return type, NULL on invalid unit, and unit from a column - #2237

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fix: spark-compatible date_trunc — timestamp return type, NULL on invalid unit, and unit from a column#2237
davidlghellin wants to merge 11 commits into
lakehq:mainfrom
davidlghellin:fix/date_trunc

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@davidlghellin davidlghellin commented Jul 13, 2026 •
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date_trunc and trunc diverged from Spark in several independent ways. All expected values in the new scenarios were validated against Spark JVM 4.1.1.

1. It must always return timestamp

Spark hardcodes TruncTimestamp.dataType to TimestampType and casts an NTZ input in inputTypes ([v4.1.1 source(https://github.com/apache/spark/blob/v4.1.1/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala)).
Sail returned timestamp_ntz for an NTZ input, which made a downstream comparison against a
timestamp silently drop rows.

This related #1717 (issue #1682) rather than contradicting it: that PR fixed TIMESTAMP → TIMESTAMP; the TIMESTAMP_NTZ → TIMESTAMP leg was still missing.

2. An invalid or NULL unit is NULL, not an error

Spark returns NULL when the unit is NULL or unrecognized; DataFusion's date_trunc errors.

3. The unit may come from a column

The systemic one: our Spark-semantics fixes live in the plan builder and match on Expr::Literal, so when the same value arrives as a column that code never runs and the query falls through to DataFusion's date_trunc, which demands a scalar unit. Spark resolves the unit per row.

A literal argument is constant-folded, so the pre-existing scenarios never exercised the columnar path at all — they were green with the gap fully intact.

How it works now. A literal string unit is resolved while the plan is built and emits the single call it names, so the common case carries no branch. A unit that only exists as a column is resolved by SparkTruncLevel, a small UDF that maps it to its granularity, and the plan dispatches on that
with a CASE.

The unit is resolved in exactly one evaluation, and that is load-bearing rather than an optimization. The dispatch has to tell three outcomes apart — named a granularity / was NULL / matched nothing — and CASE x WHEN NULL never matches, so a NULL selector shares the ELSE with an unmatched one. Every way of splitting them in the plan (coalesce, IS NULL) mentions the unit a second time, and DataFusion will not share a volatile subtree between mentions: a unit like IF(rand() < 0.5, NULL, 'YEAR') would then be drawn once per mention and rows would take a branch neither draw called for.

4. trunc is in scope, and it was corrupting data

trunc shares the unit table, so it gets the same treatment — and fixing it surfaced the worst bug here: trunc(DATE '2300-01-01', 'YEAR') returned 1715-01-01, silently and with no error, on an ordinary date. A DATE reached DataFusion's nanosecond path and the day count wrapped. Truncation now runs entirely in microseconds, as DateTimeUtils.truncTimestamp does.

trunc(1, 'YEAR') returned 1970-01-01 for the same family of reason — Arrow reinterprets an integer as a day count where Spark rejects the query.

Other divergences fixed

  • DST. Turning a wall clock into an instant is the one conversion Arrow's cast gets wrong: it
    resolves the local time with .single(), which has no answer inside a DST gap or the repeated
    hour of a fall-back. DATE and TIMESTAMP_NTZ now localize through SparkTimestamp /
    localize_with_fallback. This bites a plain DATE too, in a zone whose clock moves at midnight.
  • Beyond year 2262. DataFusion converts to nanoseconds and failed with Timestamp ... out of range, including date_trunc('YEAR', DATE '9999-12-31'), which was tagged as a known bug.
  • Unicode. Spark folds the unit with toUpperCase(Locale.ROOT); 'mınute' (dotless i) and 'ſecond' (long s) returned NULL where Spark truncates.
  • String values. Spark's parser takes 2009-2-12 and a bare 2024; Arrow's demands a fixed-position YYYY-MM-DD. Sail's own CAST(… AS TIMESTAMP) already accepted them, so the two surfaces disagreed on the same string.
  • Complex arguments are rejected at analysis with DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE instead of being measured as their string form, and the type gate matches on the LOGICAL type, so a dictionary- or run-end-encoded string is not rejected for its encoding.
  • Error class. Rejections now raise AnalysisException, not IllegalArgumentException.

The value conversion follows Spark's codegen, not its interpreter

Whether an unconvertible value raises under ANSI depends on the unit, and the rule is in
TruncInstant.codeGenHelper, which disagrees with the interpreted evalHelper (codegen is the
default and is what users see):

ANSI on Spark
date_trunc('INVALID', <bad value>) NULL — foldable unit, value never evaluated
date_trunc(IF(id=0,'INVALID','DAY'), <bad value>) CAST_INVALID_INPUT
date_trunc(<NULL unit column>, <bad value>) NULL
trunc(<bad value>, <NULL unit column>) CAST_INVALID_INPUT

The last two differ because nullSafeCodeGen evaluates the left child only:
TruncTimestamp(format, ts) skips the timestamp on a NULL format, while TruncDate(date, format) always evaluates the date. All four are covered.

Tests

From 13 scenarios to 153, plus 10 Python tests for what SQL cannot express (dictionary-encoded columns, and the exception class — Then query error accepts any exception whose message matches). All validated against Spark JVM 4.1.1 except the two plan-snapshot scenarios, which are Sail-only by nature.

Scenarios pin down what a passing NULL would hide: the session time zone is set explicitly (the harness default is UTC, where a hardcoded UTC would pass), DST overlaps assert unix_timestamp because both resolutions render the same wall clock, NULL results assert their type, and error scenarios match Spark's error class rather than .*.

The ClickBench plan shrank 8.4 KB: a literal unit now collapses to one call by construction rather than relying on the simplifier.

Known gaps, deliberately left

Each has a scenario, tagged @sail-bug:

  • Sail raises where Spark raises but reports DataFusion's parser message instead of CAST_INVALID_INPUT — the ANSI string-cast surface, shared by every function that casts a string.
  • A binary unit that is not valid UTF-8 becomes a NULL unit and takes the short circuit, where Spark makes it a string that matches nothing and therefore converts the value.
  • A scalar subquery is treated as foldable. Spark's short circuit depends on its own optimizer folding the subquery, which this stage cannot see.
  • A DATE past Sail's supported range. Spark's DATE reaches year 5881580 and trunc handles it; Sail rejects the value earlier through every construction. That range difference belongs to the CAST/to_date surface, but it is pinned here because it guards something: were the range widened, trunc would wrap silently past year ~294,247 — the same failure this PR fixes at 2262.

The columnar path repeats the value subtree once per branch. Collapsing it would make the value an ordinary argument, always evaluated, which is exactly what the short circuit forbids.

@davidlghellin
fix(date_trunc): always return timestamp; NULL on invalid/null unit
190677a 
Spark's `date_trunc` (TruncTimestamp) always returns TimestampType
regardless of input, but Sail returned `timestamp_ntz` for DATE and
TIMESTAMP_NTZ inputs (it preserved the coerced input's missing timezone).
This made `date_trunc('year', d) = TIMESTAMP '...'` return 0 rows, since
the ntz result never matched a tz-aware literal.

Cast the value argument to `Timestamp(µs, Some(session_tz))` before
truncating, mirroring Spark's coercion to TimestampType. The cast is
gated on date-family types (DATE/TIMESTAMP/TIMESTAMP_NTZ/STRING) so other
types (int, time) still reject like Spark instead of being silently
coerced. For a plain TIMESTAMP column the cast is identity and is elided
by SimplifyExpressions, so there is no runtime overhead in the common path.

Also return a typed-NULL timestamp when the unit is NULL or unrecognized
(Spark returns NULL; DataFusion's date_trunc errors). `is_null_arg` sees
through a `CAST(NULL AS STRING)` wrapper that is not folded at plan time.

Verified against Spark JVM 4.1.1
@davidlghellin
Merge remote-tracking branch 'upstream/main' into fix/date_trunc
ad7f26c
@davidlghellin
fix column
6a6c0ca
@davidlghellin
Merge remote-tracking branch 'upstream/main' into fix/date_trunc
cf7d9e0
Copilot AI review requested due to automatic review settings July 13, 2026 20:16
Copilot AI reviewed Jul 13, 2026
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Pull request overview

This PR aligns Sail’s date_trunc behavior with Spark (validated against Spark JVM 4.1.1) by enforcing a consistent timestamp return type, returning NULL for NULL/invalid units, and supporting per-row truncation when the unit is supplied from a column (while keeping execution vectorized).

Changes:

  • Update the planner to coerce date-family inputs to timestamp (microsecond, session TZ) and ensure date_trunc always returns a timestamp.
  • Add Spark-compatible NULL behavior for NULL/unrecognized units and implement a vectorized per-row unit implementation in SparkDateTrunc when the unit argument is an array.
  • Expand the Spark compatibility feature tests to cover new type, invalid/NULL unit, and column-unit scenarios.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.

File Description
python/pysail/tests/spark/function/features/date_trunc.feature Adds/updates Spark-compat scenarios (return type, invalid/NULL unit behavior, per-row unit from a column).
crates/sail-plan/src/function/scalar/datetime.rs Planner-side Spark semantics: always return timestamp, cast supported inputs to timestamp, and short-circuit invalid/NULL units to typed-NULL.
crates/sail-function/src/scalar/datetime/spark_date_trunc.rs Implements vectorized per-row unit handling by computing truncation once per distinct supported unit and assembling results via interleave.

Comment thread crates/sail-plan/src/function/scalar/datetime.rs Outdated
Comment thread crates/sail-function/src/scalar/datetime/spark_date_trunc.rs Outdated
@davidlghellin
davidlghellin marked this pull request as draft July 13, 2026 20:23
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Spark 3.5.7 Test Report

Commit Information

Commit Revision Branch
After 93ad92e refs/pull/2237/merge
Before b247b02 refs/heads/main

Test Summary

Suite Commit Failed Passed Skipped Warnings Time (s)
doctest-catalog After 10 14 1 6 5.97
Before 10 14 1 6 5.99
doctest-column After 33 2 5.62
Before 33 2 5.53
doctest-dataframe After 14 83 10 3 8.03
Before 14 83 10 3 8.62
doctest-functions After 13 387 9 7 16.10
Before 13 387 9 7 17.16
test-connect After 111 890 170 696 137.33
Before 111 890 170 699 135.17

Test Details

Error Counts 
          148 Total
           85 Total Unique
-------- ---- ----------------------------------------------------------------------------------------------------------
           13 PySparkAssertionError: [DIFFERENT_PANDAS_DATAFRAME] DataFrames are not almost equal:
           10 handle add artifacts
            9 DocTestFailure
            6 UnsupportedOperationException: PlanNode::CacheTable
            5 UnsupportedOperationException: function: input_file_name
            4 AssertionError: False is not true
            3 ValueError: Converting to Python dictionary is not supported when duplicate field names are present
            2 AnalysisException: Could not find config namespace "spark"
            2 AnalysisException: Internal error: Function 'approx_percentile_cont' failed to match any signature, errors: Error during planning: Function 'approx_percentile_cont' expects 2 arguments but received 3,...
            2 AnalysisException: No table format found for: orc
            2 AnalysisException: explode should be rewritten during logical plan analysis
            2 AnalysisException: not supported: function exists
            2 AssertionError
            2 AssertionError: 0 not greater than or equal to 1
            2 IllegalArgumentException: expected value at line 1 column 1
            2 IllegalArgumentException: invalid argument: found FUNCTION at 7:15 expected 'DATABASE', 'SCHEMA', 'NAMESPACE', 'OR', 'TEMP', 'TEMPORARY', 'EXTERNAL', 'TABLE', 'GLOBAL', or 'VIEW'
            2 IllegalArgumentException: invalid argument: found RESET at 0:5 expected something else, ';', statement, or end of input
            2 PySparkNotImplementedError: [NOT_IMPLEMENTED] rdd() is not implemented.
            2 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@bjwplt75b8s8lnn9byry0pm4s(#9) PARTITION BY [#8] ORDER BY [#9 ASC NULLS...
            2 UnsupportedOperationException: approx quantile
            2 UnsupportedOperationException: collect metrics
            2 UnsupportedOperationException: freq items
            2 UnsupportedOperationException: function: session_window
            2 UnsupportedOperationException: handle analyze same semantics
            2 UnsupportedOperationException: user defined data type should only exist in a field
            2 UnsupportedOperationException: with watermark
            2 handle artifact statuses
            1 AnalysisException: Table already exists: tbl1
            1 AnalysisException: Temporary View not found: tab2
(+1)        1 AnalysisException: UNION queries have different number of columns: left has 3 columns whereas right has 2 columns
            1 AnalysisException: not supported: qualified function name
(+1)        1 AssertionError: "Database 'memory:7baee015-aed7-491b-8f17-ad2ea3d297b2' dropped." does not match "No table format found for: jdbc. The JDBC data source is provided by pysail and must be registered bef...
(+1)        1 AssertionError: "Database 'memory:e8d8d53f-ccaa-40d9-8c74-e58ac1b41ae7' dropped." does not match "No table format found for: jdbc. The JDBC data source is provided by pysail and must be registered bef...
            1 AssertionError: 1 != 0
            1 AssertionError: 4 != 10
            1 AssertionError: AnalysisException not raised
            1 AssertionError: AnalysisException not raised by <lambda>
            1 AssertionError: Exception not raised
            1 AssertionError: Exception not raised by <lambda>
            1 AssertionError: Lists differ: [Row([178 chars]on='<<'), Row(function='<='), Row(function='<=[13267 chars]'~')] != [Row([178 chars]on='<='), Row(function='<=>'), Row(function='<[11284 chars]'~')]
            1 AssertionError: Lists differ: [Row(id=90, name='90'), Row(id=91, name='91'), Ro[176 chars]99')] != [Row(id=15, name='15'), Row(id=16, name='16'), Ro[176 chars]24')]
            1 AssertionError: Lists differ: [Row(ln(id)=0.0, ln(id)=0.0, struct(id, name)=Row(id=[1232 chars]0'))] != [Row(ln(id)=4.31748811353631, ln(id)=4.31748811353631[1312 chars]4'))]
            1 AssertionError: Lists differ: [Row(name='Andy', age=30), Row(name='Andy', ag[374 chars]one)] != [Row(age=19, name='Justin'), Row(age=19, name=[374 chars]el')]
            1 AssertionError: Lists differ: [Row(name='Andy', age=30), Row(name='Justin', [34 chars]one)] != [Row(_corrupt_record=' "age":19}\n', name=None[104 chars]el')]
            1 AssertionError: Row(point='[1.0, 2.0]', pypoint='[3.0, 4.0]') != Row(point='(1.0, 2.0)', pypoint='[3.0, 4.0]')
            1 AssertionError: StorageLevel(False, True, True, False, 1) != StorageLevel(False, False, False, False, 1)
            1 AssertionError: Struc[15 chars]eld('a', NullType(), True), StructField('b', L[51 chars]ue)]) != Struc[15 chars]eld('b', LongType(), True), StructField('c', S[15 chars]ue)])
            1 AssertionError: Struc[30 chars]estampType(), True), StructField('val', IntegerType(), True)]) != Struc[30 chars]estampType(), True), StructField('val', IntegerType(), False)])
            1 AssertionError: Struc[32 chars]e(), False), StructField('b', DoubleType(), Fa[158 chars]ue)]) != Struc[32 chars]e(), True), StructField('b', DoubleType(), Tru[154 chars]ue)])
            1 AssertionError: Struc[40 chars]ue), StructField('val', ArrayType(DoubleType(), False), True)]) != Struc[40 chars]ue), StructField('val', PythonOnlyUDT(), True)])
            1 AssertionError: YearMonthIntervalType(0, 1) != YearMonthIntervalType(0, 0)
            1 AssertionError: [1.0, 2.0] != ExamplePoint(1.0,2.0)
            1 AssertionError: dtype('<M8[us]') != 'datetime64[ns]'
            1 IllegalArgumentException: invalid argument: field not found in input schema: col1
            1 IllegalArgumentException: invalid argument: table does not exist: ObjectName([Identifier("test_table")])
            1 PySparkNotImplementedError: [NOT_IMPLEMENTED] toJSON() is not implemented.
            1 PythonException:  AttributeError: 'NoneType' object has no attribute 'partitionId'
            1 PythonException:  AttributeError: 'list' object has no attribute 'x'
            1 PythonException:  AttributeError: 'list' object has no attribute 'y'
            1 SparkRuntimeException: Cast error: Cannot cast string '1997/02/28 10:30:00' to value of Date32 type
            1 SparkRuntimeException: Invalid argument error: 83.140 is too large to store in a Decimal128 of precision 4. Max is 9.999
            1 SparkRuntimeException: Json error: Not valid JSON: EOF while parsing a list at line 1 column 1
            1 SparkRuntimeException: Json error: Not valid JSON: expected value at line 1 column 2
            1 SparkRuntimeException: Parser error: Error while parsing value '0
            1 SparkRuntimeException: This feature is not implemented: Unsupported CAST from Map("entries": non-null Struct("key": non-null Int32, "value": non-null Int32), unsorted) to Null
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@bjwplt75b8s8lnn9byry0pm4s(#9) PARTITION BY [#8] ORDER BY [#9 ASC NULLS...
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@bjwplt75b8s8lnn9byry0pm4s(plus_one@5u7wzy1x1zf4loxm9n1r277lw(#9)) PART...
            1 UnsupportedOperationException: PlanNode::ClearCache
            1 UnsupportedOperationException: PlanNode::IsCached
            1 UnsupportedOperationException: PlanNode::RecoverPartitions
            1 UnsupportedOperationException: Support for 'approx_distinct' for data type Float64 is not implemented
            1 UnsupportedOperationException: apply in pandas with state
            1 UnsupportedOperationException: bucketing for writing listing table format
            1 UnsupportedOperationException: deduplicate within watermark
            1 UnsupportedOperationException: function: input_file_block_length
            1 UnsupportedOperationException: function: input_file_block_start
            1 UnsupportedOperationException: function: map_filter
            1 UnsupportedOperationException: function: map_zip_with
            1 UnsupportedOperationException: function: transform_keys
            1 UnsupportedOperationException: function: transform_values
            1 UnsupportedOperationException: function: zip_with
            1 UnsupportedOperationException: handle analyze semantic hash
            1 UnsupportedOperationException: unknown function: distributed_sequence_id
            1 ValueError: The column label 'id' is not unique.
            1 ValueError: The column label 'struct' is not unique.
(-1)        0 AnalysisException: UNION queries have different number of columns: left has 2 columns whereas right has 3 columns
(-1)        0 AssertionError: "Database 'memory:ca83d0fe-e5da-418f-842a-7b5ccb9da6aa' dropped." does not match "No table format found for: jdbc. The JDBC data source is provided by pysail and must be registered bef...
(-1)        0 AssertionError: "Database 'memory:f695c570-26b2-455e-ad9c-06eb4b1529fa' dropped." does not match "No table format found for: jdbc. The JDBC data source is provided by pysail and must be registered bef...
Passed Tests Diff

(empty)

Failed Tests 
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.cacheTable
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.clearCache
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.createTable
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.functionExists
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.getFunction
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.isCached
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.recoverPartitions
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.refreshByPath
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.refreshTable
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.uncacheTable
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.colRegex
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.dropDuplicatesWithinWatermark
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.explain
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.hint
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.observe
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.randomSplit
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.repartition
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.repartitionByRange
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.sameSemantics
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.sampleBy
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.storageLevel
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.toJSON
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.withWatermark
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrameStatFunctions.sampleBy
pyspark/sql/functions.py::pyspark.sql.functions.approx_percentile
pyspark/sql/functions.py::pyspark.sql.functions.input_file_block_length
pyspark/sql/functions.py::pyspark.sql.functions.input_file_block_start
pyspark/sql/functions.py::pyspark.sql.functions.input_file_name
pyspark/sql/functions.py::pyspark.sql.functions.map_entries
pyspark/sql/functions.py::pyspark.sql.functions.map_filter
pyspark/sql/functions.py::pyspark.sql.functions.map_zip_with
pyspark/sql/functions.py::pyspark.sql.functions.percentile_approx
pyspark/sql/functions.py::pyspark.sql.functions.regexp_instr
pyspark/sql/functions.py::pyspark.sql.functions.session_window
pyspark/sql/functions.py::pyspark.sql.functions.transform_keys
pyspark/sql/functions.py::pyspark.sql.functions.transform_values
pyspark/sql/functions.py::pyspark.sql.functions.zip_with
pyspark/sql/tests/connect/client/test_artifact.py::ArtifactTests::test_add_archive
pyspark/sql/tests/connect/client/test_artifact.py::ArtifactTests::test_add_file
pyspark/sql/tests/connect/client/test_artifact.py::ArtifactTests::test_add_pyfile
pyspark/sql/tests/connect/client/test_artifact.py::ArtifactTests::test_add_zipped_package
pyspark/sql/tests/connect/client/test_artifact.py::ArtifactTests::test_basic_requests
pyspark/sql/tests/connect/client/test_artifact.py::ArtifactTests::test_cache_artifact
pyspark/sql/tests/connect/client/test_artifact.py::ArtifactTests::test_copy_from_local_to_fs
pyspark/sql/tests/connect/client/test_artifact.py::LocalClusterArtifactTests::test_add_archive
pyspark/sql/tests/connect/client/test_artifact.py::LocalClusterArtifactTests::test_add_file
pyspark/sql/tests/connect/client/test_artifact.py::LocalClusterArtifactTests::test_add_pyfile
pyspark/sql/tests/connect/client/test_artifact.py::LocalClusterArtifactTests::test_add_zipped_package
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_collect
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_collect_timestamp
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_column_regexp
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_create_global_temp_view
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_deduplicate_within_watermark_in_batch
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_describe
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_hint
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_join_hint
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_json
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_multi_paths
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_observe
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_orc
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_random_split
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_same_semantics
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_schema
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_semantic_hash
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_simple_udt_from_read
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_sql_with_command
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_stat_approx_quantile
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_stat_freq_items
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_stat_sample_by
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_streaming_local_relation
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_tail
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_to
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_with_local_list
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_with_local_ndarray
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectBasicTests::test_write_operations
pyspark/sql/tests/connect/test_connect_basic.py::SparkConnectSessionTests::test_error_stack_trace
pyspark/sql/tests/connect/test_connect_column.py::SparkConnectColumnTests::test_column_arithmetic_ops
pyspark/sql/tests/connect/test_connect_column.py::SparkConnectColumnTests::test_decimal
pyspark/sql/tests/connect/test_connect_column.py::SparkConnectColumnTests::test_distributed_sequence_id
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_aggregation_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_collection_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_date_ts_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_generator_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_lambda_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_map_collection_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_math_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_normal_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_string_functions_multi_args
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_time_window_functions
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_udf
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_udtf
pyspark/sql/tests/connect/test_connect_function.py::SparkConnectFunctionTests::test_window_functions
pyspark/sql/tests/connect/test_parity_arrow.py::ArrowParityTests::test_createDataFrame_duplicate_field_names
pyspark/sql/tests/connect/test_parity_arrow.py::ArrowParityTests::test_pandas_self_destruct
pyspark/sql/tests/connect/test_parity_arrow.py::ArrowParityTests::test_toPandas_duplicate_field_names
pyspark/sql/tests/connect/test_parity_arrow_python_udf.py::ArrowPythonUDFParityTests::test_udf_with_input_file_name
pyspark/sql/tests/connect/test_parity_arrow_python_udf.py::UDFParityTests::test_udf_with_input_file_name
pyspark/sql/tests/connect/test_parity_catalog.py::CatalogParityTests::test_function_exists
pyspark/sql/tests/connect/test_parity_catalog.py::CatalogParityTests::test_get_function
pyspark/sql/tests/connect/test_parity_catalog.py::CatalogParityTests::test_list_functions
pyspark/sql/tests/connect/test_parity_catalog.py::CatalogParityTests::test_refresh_table
pyspark/sql/tests/connect/test_parity_catalog.py::CatalogParityTests::test_table_cache
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_cache_dataframe
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_cache_table
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_duplicate_field_names
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_extended_hint_types
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_freqItems
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_generic_hints
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_input_files
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_to
pyspark/sql/tests/connect/test_parity_dataframe.py::DataFrameParityTests::test_to_pandas
pyspark/sql/tests/connect/test_parity_datasources.py::DataSourcesParityTests::test_checking_csv_header
pyspark/sql/tests/connect/test_parity_datasources.py::DataSourcesParityTests::test_encoding_json
pyspark/sql/tests/connect/test_parity_datasources.py::DataSourcesParityTests::test_ignore_column_of_all_nulls
pyspark/sql/tests/connect/test_parity_datasources.py::DataSourcesParityTests::test_jdbc
pyspark/sql/tests/connect/test_parity_datasources.py::DataSourcesParityTests::test_jdbc_format
pyspark/sql/tests/connect/test_parity_datasources.py::DataSourcesParityTests::test_linesep_json
pyspark/sql/tests/connect/test_parity_datasources.py::DataSourcesParityTests::test_multiline_json
pyspark/sql/tests/connect/test_parity_datasources.py::DataSourcesParityTests::test_read_multiple_orc_file
pyspark/sql/tests/connect/test_parity_functions.py::FunctionsParityTests::test_approxQuantile
pyspark/sql/tests/connect/test_parity_functions.py::FunctionsParityTests::test_functions_broadcast
pyspark/sql/tests/connect/test_parity_functions.py::FunctionsParityTests::test_input_file_name_udf
pyspark/sql/tests/connect/test_parity_pandas_grouped_map.py::GroupedApplyInPandasTests::test_grouped_over_window
pyspark/sql/tests/connect/test_parity_pandas_grouped_map.py::GroupedApplyInPandasTests::test_grouped_over_window_with_key
pyspark/sql/tests/connect/test_parity_pandas_grouped_map_with_state.py::GroupedApplyInPandasWithStateTests::test_apply_in_pandas_with_state_python_worker_random_failure
pyspark/sql/tests/connect/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_scalar_iter_udf_init
pyspark/sql/tests/connect/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_vectorized_udf_check_config
pyspark/sql/tests/connect/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_vectorized_udf_invalid_length
pyspark/sql/tests/connect/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_bounded_mixed
pyspark/sql/tests/connect/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_bounded_simple
pyspark/sql/tests/connect/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_shrinking_window
pyspark/sql/tests/connect/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_sliding_window
pyspark/sql/tests/connect/test_parity_readwriter.py::ReadwriterParityTests::test_bucketed_write
pyspark/sql/tests/connect/test_parity_readwriter.py::ReadwriterParityTests::test_save_and_load
pyspark/sql/tests/connect/test_parity_readwriter.py::ReadwriterParityTests::test_save_and_load_builder
pyspark/sql/tests/connect/test_parity_readwriter.py::ReadwriterV2ParityTests::test_table_overwrite
pyspark/sql/tests/connect/test_parity_types.py::TypesParityTests::test_cast_to_string_with_udt
pyspark/sql/tests/connect/test_parity_types.py::TypesParityTests::test_cast_to_udt_with_udt
pyspark/sql/tests/connect/test_parity_types.py::TypesParityTests::test_complex_nested_udt_in_df
pyspark/sql/tests/connect/test_parity_types.py::TypesParityTests::test_negative_decimal
pyspark/sql/tests/connect/test_parity_types.py::TypesParityTests::test_parquet_with_udt
pyspark/sql/tests/connect/test_parity_types.py::TypesParityTests::test_udf_with_udt
pyspark/sql/tests/connect/test_parity_types.py::TypesParityTests::test_udt_with_none
pyspark/sql/tests/connect/test_parity_types.py::TypesParityTests::test_yearmonth_interval_type
pyspark/sql/tests/connect/test_parity_udf.py::UDFParityTests::test_udf_with_input_file_name
pyspark/sql/tests/connect/test_parity_udtf.py::ArrowUDTFParityTests::test_udtf_arrow_sql_conf
pyspark/sql/tests/connect/test_utils.py::ConnectUtilsTests::test_assert_approx_equal_decimaltype_custom_rtol_pass
pyspark/sql/tests/connect/test_utils.py::ConnectUtilsTests::test_assert_equal_nested_struct_str_duplicate

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Spark 4.1.1 Test Report

Commit Information

Commit Revision Branch
After 93ad92e refs/pull/2237/merge
Before b247b02 refs/heads/main

Test Summary

Suite Commit Failed Passed Skipped Warnings Time (s)
doctest-catalog After 10 14 1 6.15
Before 10 14 1 7.02
doctest-column After 36 6.33
Before 36 6.79
doctest-dataframe After 27 92 3 2 10.23
Before 27 92 3 2 11.35
doctest-functions After 66 419 10 5 25.38
Before 66 419 10 5 29.61
test-connect After 648 1860 292 409 239.10
Before 647 1861 292 407 270.08

Test Details

Error Counts 
(+1)      751 Total
(+3)      281 Total Unique
-------- ---- ----------------------------------------------------------------------------------------------------------
           60 IllegalArgumentException: missing argument: Python UDF output type
           50 UnsupportedOperationException: unresolved table valued function
           28 DocTestFailure
           22 UnsupportedOperationException: handle add artifacts
           20 AssertionError: 1 != 0 : dict_keys([])
           20 AssertionError: Exception not raised
           14 UnsupportedOperationException: unknown function: kll_sketch_agg_bigint
           12 PySparkAssertionError: [DIFFERENT_ROWS] Results do not match: ( 100.00000 % )
           10 PySparkAssertionError: [DIFFERENT_PANDAS_DATAFRAME] DataFrames are not almost equal:
           10 SparkRuntimeException: Python error: [test::partitions] NotImplementedError: 
           10 UnsupportedOperationException: unsupported subquery type
           10 UnsupportedOperationException: with watermark
            9 AssertionError: 3 != 0 : []
            9 IllegalArgumentException: expected value at line 1 column 1
            9 IllegalArgumentException: invalid argument: found range at 40:45 expected '->', '.', '(', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|'...
            9 UnsupportedOperationException: Physical plan does not support logical expression OuterReferenceColumn(Field { name: "#1", data_type: Int64, nullable: true, metadata: {"SPARK::metadata::json": "{}"} },...
            9 UnsupportedOperationException: collect metrics
            8 AssertionError
            8 UnsupportedOperationException: unknown function: kll_sketch_agg_double
            8 UnsupportedOperationException: unknown function: kll_sketch_agg_float
            7 UnsupportedOperationException: function: input_file_name
            7 UnsupportedOperationException: user defined data type should only exist in a field
            6 AssertionError: 1 != 0
            6 AssertionError: False is not true
            6 IllegalArgumentException: data did not match any variant of untagged enum JsonDataType
            6 PythonException:  ValueError: invalid literal for int() with base 10: 'x'
            6 UnsupportedOperationException: PlanNode::CacheTable
            6 UnsupportedOperationException: direct shuffle partition ID expression
            5 AssertionError: `query_context_type` is required when QueryContext exists. QueryContext: [].
            5 IllegalArgumentException: invalid argument: output mode not supported in group map
            5 PythonException:  TypeError: argument of type 'VariantVal' is not iterable
            4 AnalysisException: Temporary View not found: t2
            4 AssertionError: 3 != 0 : dict_keys([])
            4 AssertionError: unexpectedly None
            4 UnsupportedOperationException: approx quantile
            4 UnsupportedOperationException: freq items
            4 UnsupportedOperationException: transpose
            4 clone session
            3 AnalysisException: Invalid Python user-defined table function return type. Expect a struct type, but got Int32.
            3 AssertionError: 0 not greater than or equal to 1
            3 AssertionError: DayTimeIntervalType(0, 3) != DayTimeIntervalType(1, 3)
            3 AssertionError: Struc[49 chars]valType(0, 3), True), StructField('name', StringType(), True)]) != Struc[49 chars]valType(1, 3), True), StructField('name', StringType(), True)])
            3 IllegalArgumentException: invalid argument: found PARTITION at 281:290 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=...
            3 IllegalArgumentException: invalid argument: found PARTITION at 295:304 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=...
            3 IllegalArgumentException: invalid argument: found PARTITION at 59:68 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=',...
            3 IllegalArgumentException: invalid argument: found WITH at 171:175 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=', '!...
            3 IllegalArgumentException: invalid argument: found WITH at 279:283 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=', '!...
            3 PythonException: 
            3 PythonException:  Exception: self._partition_col was 1 but the row value was 2 PySparkRuntimeError: [UDTF_EXEC_ERROR] User defined table function encountered an error in the 'eval' method: self._parti...
            3 PythonException:  PySparkRuntimeError: [UDTF_CONSTRUCTOR_INVALID_NO_ANALYZE_METHOD] Failed to evaluate the user-defined table function '' because its constructor is invalid: the function does not impl...
            3 PythonException:  TypeError: int() argument must be a string, a bytes-like object or a real number, not 'dict'
            3 UnsupportedOperationException: Physical plan does not support logical expression OuterReferenceColumn(Field { name: "#0", data_type: Int64 }, Column { relation: None, name: "#0" })
            3 UnsupportedOperationException: Physical plan does not support logical expression OuterReferenceColumn(Field { name: "#2", data_type: Int32, nullable: true }, Column { relation: Some(Bare { table: "t" ...
            3 UnsupportedOperationException: cached remote relation
            3 UnsupportedOperationException: table argument options in subquery expression
            3 UnsupportedOperationException: unknown function: distributed_sequence_id
            3 ValueError: Converting to Python dictionary is not supported when duplicate field names are present
            2 AnalysisException: Internal error: Function 'approx_percentile_cont' failed to match any signature, errors: Error during planning: Function 'approx_percentile_cont' expects 2 arguments but received 3,...
            2 AnalysisException: No table format found for: orc
            2 AnalysisException: Spark `variant_get` function: path must be a constant string
(-1)        2 AnalysisException: Write failed for partition 0: External error: Python error: [TestJsonWriter::write] AttributeError: 'NoneType' object has no attribute 'partitionId'
            2 AnalysisException: ambiguous attribute: ObjectName([Identifier("id")])
            2 AnalysisException: element_at expects List or Map type as first argument, got Null
            2 AnalysisException: explode should be rewritten during logical plan analysis
            2 AnalysisException: not supported: function exists
            2 AssertionError: AnalysisException not raised
            2 AssertionError: AnalysisException not raised by <lambda>
            2 AssertionError: PythonException not raised
            2 AssertionError: StructType([StructField('value', BinaryTy[55 chars]se)]) != VariantType()
            2 IllegalArgumentException: invalid argument: found FUNCTION at 7:15 expected 'DATABASE', 'SCHEMA', 'NAMESPACE', 'OR', 'TEMP', 'TEMPORARY', 'EXTERNAL', 'TABLE', 'GLOBAL', or 'VIEW'
            2 IllegalArgumentException: invalid argument: found PARTITION at 97:106 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!='...
            2 IllegalArgumentException: invalid argument: initial input not supported in group map
            2 IllegalArgumentException: invalid argument: missing data source format
            2 PythonException:  AssertionError: assert None is not None
            2 PythonException:  AttributeError: 'NoneType' object has no attribute 'cpus'
            2 PythonException:  PySparkValueError: Exception thrown when converting pandas.Series (int64) with name 'decimal_result' to Arrow Array (decimal128(10, 2)).
            2 PythonException:  PySparkValueError: Exception thrown when converting pandas.Series (int8) with name 'None' to Arrow Array (decimal128(10, 0)).
            2 PythonException:  PySparkValueError: Exception thrown when converting pandas.Series (object) with name 'None' to Arrow Array (int32).
            2 SparkRuntimeException: Error during planning: Correlated scalar subquery must be aggregated to return at most one row
            2 SparkRuntimeException: Python error: [TestDataSource::partitions] NotImplementedError: 
            2 SparkRuntimeException: Python error: [my-json::partitions] AttributeError: 'pyarrow.lib.Schema' object has no attribute 'fieldNames'
            2 TypeError: 'NoneType' object is not iterable
            2 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@42nxldcwlikpshede68hki4hd(#9) PARTITION BY [#8] ORDER BY [#9 ASC NULLS...
            2 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@9iphoaj9v2becke5sqyqokbpc(#9) PARTITION BY [#8] ORDER BY [#9 ASC NULLS...
            2 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: mean_udf@56f23o3q96gbgh8wooin3wlez(#3) PARTITION BY [#2] ORDER BY [#3 ASC ...
            2 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: mean_udf@6blgh7qrs537jqp40qew4vlxb(#3) PARTITION BY [#2] ORDER BY [#3 ASC ...
            2 UnsupportedOperationException: CLUSTER BY for write
            2 UnsupportedOperationException: Physical plan does not support logical expression OuterReferenceColumn(Field { name: "#0", data_type: Int32, nullable: true }, Column { relation: Some(Bare { table: "t1"...
(-1)        2 UnsupportedOperationException: Physical plan does not support logical expression OuterReferenceColumn(Field { name: "#0", data_type: List(Field { data_type: Float64 }), nullable: true, metadata: {"SPA...
            2 UnsupportedOperationException: Physical plan does not support logical expression Wildcard { qualifier: None, options: WildcardOptions { ilike: None, exclude: None, except: None, replace: None, rename:...
            2 UnsupportedOperationException: create resource profile command
            2 UnsupportedOperationException: function: st_setsrid
            2 UnsupportedOperationException: function: st_srid
            2 UnsupportedOperationException: function: try_make_interval
            2 UnsupportedOperationException: handle analyze same semantics
            2 UnsupportedOperationException: named window function arguments
            2 UnsupportedOperationException: unknown function: try_to_date
            2 UnsupportedOperationException: wildcard with plan ID
            2 handle artifact statuses
            1 AnalysisException: Could not find config namespace "mapred"
            1 AnalysisException: Could not find config namespace "spark"
            1 AnalysisException: Database not found: testcat
            1 AnalysisException: Failed to parse date '02-29' with format '%m-%d': input is not enough for unique date and time
            1 AnalysisException: Failed to parse schema '{"fields":[{"metadata":{},"name":"a","nullable":true,"type":"long"}],"type":"struct"}': error in SQL parser: found { at 7:8 expected identifier, or '>'
            1 AnalysisException: No table format found for: xml
            1 AnalysisException: Spark `try_variant_get` function: path must be a constant string
            1 AnalysisException: Table already exists: tbl1
            1 AnalysisException: Temporary View not found: tab2
(+1)        1 AnalysisException: UNION queries have different number of columns: left has 2 columns whereas right has 3 columns
            1 AnalysisException: Write failed for partition 0: External error: Python error: [TestArrowWriter::write] AttributeError: 'NoneType' object has no attribute 'partitionId'
(+1)        1 AnalysisException: Write failed for partition 1: External error: Python error: [TestJsonWriter::write] AttributeError: 'NoneType' object has no attribute 'partitionId'
            1 AnalysisException: ambiguous attribute: ObjectName([Identifier("b")])
            1 AnalysisException: ambiguous attribute: ObjectName([Identifier("i")])
            1 AnalysisException: array_except received incompatible types: List(non-null Int32), List(Int32)
            1 AnalysisException: attribute ObjectName([Identifier("t")]) is missing from the schema: cannot resolve attribute
            1 AnalysisException: attribute ObjectName([Identifier("x")]) is missing from the schema: cannot resolve attribute
            1 AnalysisException: foobar
            1 AnalysisException: join condition should not be empty
            1 AnalysisException: not supported: qualified function name
            1 AnalysisException: one value expected: [Column(Column { relation: None, name: "#0" }), Literal(Int32(123), None)]
(+1)        1 AnalysisException: one value expected: [Column(Column { relation: None, name: "#1" }), Literal(Int64(112556870345828159), None)]
(+1)        1 AnalysisException: one value expected: [Column(Column { relation: None, name: "#1" }), Literal(Int64(2671755996475596953), None)]
            1 AnalysisException: too big
            1 AnalysisException: unsupported extension node for streaming: PythonWriteNode { input: Projection(Projection { expr: [Column(Column { relation: None, name: "_marker" }), Column(Column { relation: None,...
            1 AnalysisException: zero values expected: [Literal(Int32(123), None)]
            1 AssertionError: "'path' is not specified." does not match "missing path in listing table options"
            1 AssertionError: "ARROW_TYPE_MISMATCH.*SQL_MAP_ARROW_ITER_UDF" does not match "Struct field count mismatch: expected 1 fields but found 2 fields"
            1 AssertionError: "DATA_SOURCE_EXTRANEOUS_FILTERS" does not match "Python error: [test::partitions] AssertionError: assert False
            1 AssertionError: "DATA_SOURCE_PUSHDOWN_DISABLED" does not match "Python error: [<reader>::read] AssertionError: assert False
(+1)        1 AssertionError: "Database 'memory:28d51cf8-e9d5-413d-885b-e713686af96b' dropped." does not match "No table format found for: jdbc. The JDBC data source is provided by pysail and must be registered bef...
(+1)        1 AssertionError: "Database 'memory:e270136b-0a9d-4e52-8ba6-6ae485a7bfc9' dropped." does not match "No table format found for: jdbc. The JDBC data source is provided by pysail and must be registered bef...
            1 AssertionError: "Invalid return type" does not match " AttributeError: 'Series' object has no attribute 'columns'
            1 AssertionError: "Python worker process terminated due to idle timeout \(timeout: 1 seconds\)" does not match " PySparkRuntimeError: [UDTF_INVALID_OUTPUT_ROW_TYPE] The type of an individual output row ...
            1 AssertionError: "UNRESOLVED_COLUMN.WITH_SUGGESTION" does not match "attribute ObjectName([Identifier("b")]) is missing from the schema: cannot resolve attribute"
            1 AssertionError: "is null" does not match " ArrowException: Invalid argument error: Column 'a' is declared as non-nullable but contains null values
            1 AssertionError: "requirement failed: Cogroup keys must have same size: 2 != 1" does not match "invalid argument: child plan grouping expressions must have the same length"
            1 AssertionError: '+------------------+\n|from_xml(a, a INT)|\n+--------[75 chars]-+\n' != '+-----------+\n|from_xml(a)|\n+-----------+\n|       [33 chars]-+\n'
            1 AssertionError: '+---[1064 chars]---------------------------------+\nonly showing top 20 rows\n' != '+---[1064 chars]---------------------------------+\nonly showing top 20 rows'
            1 AssertionError: '+---[23 chars]---+-----+\n|  1|    1|\n+---+-----+\nonly showing top 1 row' != '+---[23 chars]---+-----+\n|  1|    1|\n+---+-----+\nonly showing top 1 row\n'
            1 AssertionError: 'INVALID_CLONE_SESSION_REQUEST.TARGET_SESSION_ID_FORMAT' not found in '<_InactiveRpcError of RPC that terminated with:\n\tstatus = StatusCode.UNIMPLEMENTED\n\tdetails = "clone session"...
            1 AssertionError: 'ST_INVALID_SRID_VALUE' != None : Expected error class was 'ST_INVALID_SRID_VALUE', got 'None'.
            1 AssertionError: 'UNSUPPORTED_SUBQUERY_EXPRESSION_CATEGORY.UNSUPPORTED_IN_EXISTS_SUBQUERY' != None : Expected error class was 'UNSUPPORTED_SUBQUERY_EXPRESSION_CATEGORY.UNSUPPORTED_IN_EXISTS_SUBQUERY', ...
            1 AssertionError: 'a NULL, b BOOLEAN, c BINARY' != 'a VOID,b BOOLEAN,c BINARY'
            1 AssertionError: 'bytearray' != 'bytes'
            1 AssertionError: 0 not greater than 0
            1 AssertionError: 0.6363787615254752 != 0.9531453492357947 : Column<'rand(1)'>
            1 AssertionError: 4 != 10
            1 AssertionError: 6 != 0 : []
            1 AssertionError: Exception not raised by <lambda>
            1 AssertionError: Lists differ: [Row([22 chars]e(2018, 12, 31, 16, 0), aware=datetime.datetim[16 chars] 0))] != [Row([22 chars]e(2019, 1, 1, 0, 0), aware=datetime.datetime(2[13 chars] 0))]
            1 AssertionError: Lists differ: [Row([259 chars]681098, ln(id)=1.0986122886681098, struct(id, [975 chars]0'))] != [Row([259 chars]681096, ln(id)=1.0986122886681096, struct(id, [975 chars]0'))]
            1 AssertionError: Lists differ: [Row([47 chars] 3, 6: 3}), Row(map={4: 3, 6: 3}), Row(map={4: 5, 6: 3})] != [Row([47 chars] 3, 6: 3, 5: 5}), Row(map={4: 3, 6: 3}), Row(map={4: 3, 6: 3})]
            1 AssertionError: Lists differ: [Row([719 chars]on='array'), Row(function='array_agg'), Row(fu[12726 chars]'~')] != [Row([719 chars]on='approx_top_k'), Row(function='approx_top_k[13765 chars]'~')]
            1 AssertionError: Lists differ: [Row(id=90, name='90'), Row(id=91, name='91'), Ro[176 chars]99')] != [Row(id=15, name='15'), Row(id=16, name='16'), Ro[176 chars]24')]
            1 AssertionError: Lists differ: [Row(name='Andy', age=30), Row(name='Andy', ag[374 chars]one)] != [Row(age=19, name='Justin'), Row(age=19, name=[374 chars]el')]
            1 AssertionError: Lists differ: [Row(name='Andy', age=30), Row(name='Justin', [34 chars]one)] != [Row(_corrupt_record=' "age":19}\n', name=None[104 chars]el')]
            1 AssertionError: Row(name='Bob', age=27, height=66.0) != Row(name='Alice', age=10, height=10.0)
            1 AssertionError: Row(point='[1.0, 2.0]', pypoint='[3.0, 4.0]') != Row(point='(1.0, 2.0)', pypoint='[3.0, 4.0]')
            1 AssertionError: SparkConnectGrpcException not raised
            1 AssertionError: StorageLevel(False, True, True, False, 1) != StorageLevel(False, False, False, False, 1)
            1 AssertionError: Struc[15 chars]eld('a', NullType(), True), StructField('b', L[51 chars]ue)]) != Struc[15 chars]eld('b', LongType(), True), StructField('c', S[15 chars]ue)])
            1 AssertionError: Struc[23 chars]st', StructType([StructField('value', BinaryTy[194 chars]ue)]) != Struc[23 chars]st', VariantType(), True), StructField('last',[18 chars]ue)])
            1 AssertionError: Struc[30 chars]estampType(), True), StructField('val', IntegerType(), True)]) != Struc[30 chars]estampType(), True), StructField('val', IntegerType(), False)])
            1 AssertionError: Struc[32 chars]e(), False), StructField('b', DoubleType(), Fa[158 chars]ue)]) != Struc[32 chars]e(), True), StructField('b', DoubleType(), Tru[154 chars]ue)])
            1 AssertionError: Struc[40 chars]ue), StructField('val', ArrayType(DoubleType(), False), True)]) != Struc[40 chars]ue), StructField('val', PythonOnlyUDT(), True)])
            1 AssertionError: Struc[99 chars]uct(n, l), x_4)), x_3)))', ArrayType(StructTyp[93 chars]ue)]) != Struc[99 chars]uct(namedlambdavariable() AS n, namedlambdavar[181 chars]se)])
            1 AssertionError: True is not false : Default URL is not secure
            1 AssertionError: VariantType() != StructType([StructField('value', BinaryTy[55 chars]se)])
            1 AssertionError: YearMonthIntervalType(0, 1) != YearMonthIntervalType(0, 0)
            1 AssertionError: [1.0, 2.0] != ExamplePoint(1.0,2.0)
            1 AttributeError: 'NoneType' object has no attribute 'extract_graph'
            1 AttributeError: 'NoneType' object has no attribute 'toText'
            1 DateTimeException: Error parsing timestamp from '082017' using format '%m%Y': input is not enough for unique date and time
            1 DateTimeException: Error parsing timestamp from '2014-31-12' using format '%Y-%d-%pa': input contains invalid characters
(+1)        1 Exception deserializing response!
            1 FileNotFoundError: [Errno 2] No such file or directory: '/home/runner/work/sail/sail/.venvs/test-spark.spark-4.1.1/lib/python3.11/site-packages/pyspark/data/artifact-tests/junitLargeJar.jar'
(+1)        1 FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpcjxe8b22'
            1 IllegalArgumentException: invalid argument: empty data type
            1 IllegalArgumentException: invalid argument: expecting column to drop
            1 IllegalArgumentException: invalid argument: field not found in input schema: col1
            1 IllegalArgumentException: invalid argument: found PARTITION at 100:109 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=...
            1 IllegalArgumentException: invalid argument: found PARTITION at 103:112 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=...
            1 IllegalArgumentException: invalid argument: found PARTITION at 108:117 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=...
            1 IllegalArgumentException: invalid argument: found PARTITION at 111:120 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=...
            1 IllegalArgumentException: invalid argument: found PARTITION at 117:126 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=...
            1 IllegalArgumentException: invalid argument: found PARTITION at 118:127 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=...
            1 IllegalArgumentException: invalid argument: found PARTITION at 53:62 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=',...
            1 IllegalArgumentException: invalid argument: found PARTITION at 69:78 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=',...
            1 IllegalArgumentException: invalid argument: found PARTITION at 90:99 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=',...
            1 IllegalArgumentException: invalid argument: found WITH at 115:119 expected '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|', '!=', '!...
            1 IllegalArgumentException: invalid argument: found abc at 0:3 expected something else, ';', statement, or end of input
            1 IllegalArgumentException: invalid argument: found collate at 13:20 expected string, '.', '[', '::', 'ESCAPE', 'IS', 'NOT', 'IN', '*', '/', '%', 'DIV', '+', '-', '||', '>>>', '>>', '<<', '&', '^', '|',...
            1 IllegalArgumentException: invalid argument: grouping sets with grouping expressions
            1 IllegalArgumentException: invalid argument: invalid user-defined window function type
            1 IllegalArgumentException: invalid argument: table does not exist: ObjectName([Identifier("test_table")])
            1 IndexError: list index out of range
(+1)        1 PySparkAssertionError: Received incorrect server side session identifier for request. Please create a new Spark Session to reconnect. (133ce1a8-fdf6-43ac-bfe7-956b35acb871 != c8190fbb-806b-44b5-a2f6-8...
(+1)        1 PySparkAssertionError: Received incorrect server side session identifier for request. Please create a new Spark Session to reconnect. (289eb5d4-9b4c-49ee-bf24-f2fe9007c36a != f3b9a7a7-7702-4068-a7e2-9...
            1 PySparkNotImplementedError: [NOT_IMPLEMENTED] Invalid return type with grouped aggregate Pandas UDFs: StructType([StructField('value', BinaryType(), False), StructField('metadata', BinaryType(), False...
            1 PySparkNotImplementedError: [NOT_IMPLEMENTED] toJSON() is not implemented.
            1 PythonException:  AssertionError: Undefined error message parameter for error class: UDTF_ARROW_TYPE_CONVERSION_ERROR. Parameters: {'data': "[('x',)]", 'schema': 'struct<a:int>', 'arrow_schema': 'stru...
            1 PythonException:  AssertionError: Undefined error message parameter for error class: UDTF_ARROW_TYPE_CONVERSION_ERROR. Parameters: {'data': '[(ExamplePoint(10.0,20.0),)]', 'schema': 'struct<point:arra...
            1 PythonException:  AssertionError: Undefined error message parameter for error class: UDTF_ARROW_TYPE_CONVERSION_ERROR. Parameters: {'data': '[(ExamplePoint(10.0,20.0),)]', 'schema': 'struct<udt:array<...
            1 PythonException:  AttributeError: 'NoneType' object has no attribute 'partitionId'
            1 PythonException:  AttributeError: 'list' object has no attribute 'x'
            1 PythonException:  AttributeError: 'list' object has no attribute 'y'
            1 PythonException:  KeyError: 'v'
            1 PythonException:  PySparkRuntimeError: [UDTF_ARROW_TYPE_CAST_ERROR] Cannot convert the output value of the column 'point' with type 'object' to the specified return type of the column: 'list<item: dou...
            1 PythonException:  PySparkRuntimeError: [UDTF_ARROW_TYPE_CAST_ERROR] Cannot convert the output value of the column 'udt' with type 'object' to the specified return type of the column: 'list<item: doubl...
            1 PythonException:  PySparkValueError: Exception thrown when converting pandas.Series (int64) with name 'None' to Arrow Array (decimal128(10, 2)).
            1 PythonException:  PySparkValueError: Exception thrown when converting pandas.Series (int8) with name 'int8' to Arrow Array (decimal128(10, 0)).
            1 PythonException:  PySparkValueError: Exception thrown when converting pandas.Series (int8) with name 'value' to Arrow Array (decimal128(10, 0)).
            1 PythonException:  PySparkValueError: Exception thrown when converting pandas.Series (object) with name 'value' to Arrow Array (int32).
            1 PythonException:  TypeError: 'str' object cannot be interpreted as an integer
            1 PythonException:  TypeError: cannot unpack non-iterable NoneType object
            1 PythonException:  ValueError: invalid literal for int() with base 10: 'z'
            1 SparkRuntimeException: Exception: path is not specified
            1 SparkRuntimeException: Execution error: Schema field count mismatch: expected 1 fields, got 2
            1 SparkRuntimeException: Internal error: Cannot run range queries on datatype: Time64(µs).
            1 SparkRuntimeException: Invalid argument error: Column '#3' is declared as non-nullable but contains null values
            1 SparkRuntimeException: Invalid argument error: column types must match schema types, expected List(Struct("value": non-null Binary, "metadata": non-null Binary, metadata: {"variant": "true"}), metadat...
            1 SparkRuntimeException: Json error: Not valid JSON: EOF while parsing a list at line 1 column 1
            1 SparkRuntimeException: Json error: Not valid JSON: expected value at line 1 column 2
            1 SparkRuntimeException: No field named t1."#0". Did you mean '#2'?.
            1 SparkRuntimeException: Parser error: Error while parsing value '0
            1 SparkRuntimeException: Parser error: Invalid timezone "+7:30": failed to parse timezone
            1 SparkRuntimeException: Python error: [TestDataSource::writer] PySparkNotImplementedError: [NOT_IMPLEMENTED] writer is not implemented.
            1 SparkRuntimeException: Python error: [my-json::writer] AttributeError: 'pyarrow.lib.Schema' object has no attribute 'fieldNames'
            1 SparkRuntimeException: Python error: [test::partitions] AssertionError: assert False
            1 SparkRuntimeException: Python error: [testdatasourcepyarrow::partitions] PySparkNotImplementedError: [NOT_IMPLEMENTED] reader is not implemented.
            1 SparkRuntimeException: Schema error: Unsupported type in DDL schema: List { data_type: Int32, nullable: true }. Use PyArrow Schema for complex types.
            1 SparkRuntimeException: Schema error: Unsupported type in DDL schema: Struct { fields: Fields([Field { name: "a", data_type: Int32, nullable: true, metadata: [] }, Field { name: "b", data_type: Int32, ...
            1 SparkRuntimeException: Schema error: Unsupported type in DDL schema: Struct { fields: Fields([Field { name: "y", data_type: Int32, nullable: true, metadata: [] }]) }. Use PyArrow Schema for complex ty...
            1 SparkRuntimeException: Schema error: Unsupported type in DDL schema: Variant. Use PyArrow Schema for complex types.
            1 SparkRuntimeException: This feature is not implemented: Data type Decimal128(38, 18) not supported in row-based write path. Use DataSourceArrowWriter for full type support.
            1 SparkRuntimeException: This feature is not implemented: Unsupported CAST from Map("entries": non-null Struct("key": non-null Int32, "value": non-null Int32), unsorted) to Null
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@42nxldcwlikpshede68hki4hd(#9) PARTITION BY [#8] ORDER BY [#9 ASC NULLS...
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@42nxldcwlikpshede68hki4hd(plus_one@37pn5wx7sqmlbvfr2ebnbd6a0(#9)) PART...
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@9iphoaj9v2becke5sqyqokbpc(#9) PARTITION BY [#8] ORDER BY [#9 ASC NULLS...
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: avg@9iphoaj9v2becke5sqyqokbpc(plus_one@8f9fwaevnfdj031wzemmraerh(#9)) PART...
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: weighted_mean@1h5j8ehtp238nduw020pdafyp(#9, #10) PARTITION BY [#8] ORDER B...
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: weighted_mean@1z16p7g8orl8a73u97f1704fz(#9, #10) PARTITION BY [#8] ORDER B...
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: weighted_mean@5lgtucrfqmvjc9hq9885z0x6d(#9, #10) PARTITION BY [#8] ORDER B...
            1 UnsupportedOperationException: Aggregate can not be used as a sliding accumulator because `retract_batch` is not implemented: weighted_mean@bqv855sdysxo4klnvnyi7f0v3(#9, #10) PARTITION BY [#8] ORDER B...
            1 UnsupportedOperationException: LATERAL table function with criteria
(+1)        1 UnsupportedOperationException: Physical plan does not support logical expression OuterReferenceColumn(Field { name: "#0", data_type: List(Field { data_type: Float64 }), nullable: true, metadata: {"SAI...
            1 UnsupportedOperationException: Physical plan does not support logical expression OuterReferenceColumn(Field { name: "#1", data_type: Int32, nullable: true }, Column { relation: Some(Bare { table: "t1"...
            1 UnsupportedOperationException: Physical plan does not support logical expression ScalarSubquery(<subquery>)
            1 UnsupportedOperationException: Physical plan does not support undecorrelated Subquery
            1 UnsupportedOperationException: PlanNode::ClearCache
            1 UnsupportedOperationException: PlanNode::IsCached
            1 UnsupportedOperationException: PlanNode::RecoverPartitions
            1 UnsupportedOperationException: Support for 'approx_distinct' for data type Float64 is not implemented
            1 UnsupportedOperationException: Support for 'approx_distinct' for data type Struct("name": Utf8, metadata: {"SPARK::metadata::json": "{}"}, "value": Int64, metadata: {"SPARK::metadata::json": "{}"}) is...
            1 UnsupportedOperationException: Unsupported CAST from Struct("c": Int64, metadata: {"SPARK::metadata::json": "{}"}, "d": Float64, metadata: {"SPARK::metadata::json": "{}"}) to Struct("a": Int64, metada...
            1 UnsupportedOperationException: Unsupported CAST from Struct("value": non-null Binary, "metadata": non-null Binary) to Int32
            1 UnsupportedOperationException: apply in pandas with state
            1 UnsupportedOperationException: as of join
            1 UnsupportedOperationException: bucketing for writing listing table format
            1 UnsupportedOperationException: cast Time64(Nanosecond) to Spark data type
            1 UnsupportedOperationException: deduplicate within watermark
            1 UnsupportedOperationException: function: collate
            1 UnsupportedOperationException: function: collation
            1 UnsupportedOperationException: function: input_file_block_length
            1 UnsupportedOperationException: function: input_file_block_start
            1 UnsupportedOperationException: function: java_method
            1 UnsupportedOperationException: function: map_filter
            1 UnsupportedOperationException: function: map_zip_with
            1 UnsupportedOperationException: function: reflect
            1 UnsupportedOperationException: function: schema_of_xml
            1 UnsupportedOperationException: function: session_window
            1 UnsupportedOperationException: function: transform_keys
            1 UnsupportedOperationException: function: transform_values
            1 UnsupportedOperationException: function: try_reflect
            1 UnsupportedOperationException: function: zip_with
            1 UnsupportedOperationException: handle analyze semantic hash
            1 UnsupportedOperationException: unknown function: RANGE
            1 UnsupportedOperationException: unknown function: unwrap_udt
            1 ValueError: The column label 'id' is not unique.
            1 ValueError: The column label 'struct' is not unique.
            1 handle add artifacts
(-1)        0 AnalysisException: UNION queries have different number of columns: left has 3 columns whereas right has 2 columns
(-1)        0 AnalysisException: one value expected: [Column(Column { relation: None, name: "#1" }), Literal(Int64(2202971297470662768), None)]
(-1)        0 AnalysisException: one value expected: [Column(Column { relation: None, name: "#1" }), Literal(Int64(8519083681415775050), None)]
(-1)        0 AssertionError: "Database 'memory:4a98cc38-2a57-46b4-a97c-f872423784e3' dropped." does not match "No table format found for: jdbc. The JDBC data source is provided by pysail and must be registered bef...
(-1)        0 AssertionError: "Database 'memory:a77c834f-3792-4ea8-9cc1-8c9e77a2ea83' dropped." does not match "No table format found for: jdbc. The JDBC data source is provided by pysail and must be registered bef...
(-1)        0 FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmp3bl2rwg3'
(-1)        0 PySparkAssertionError: Received incorrect server side session identifier for request. Please create a new Spark Session to reconnect. (a3cd1aa0-1d34-4c9f-b03e-ec6cd17d007f != 40aeabb2-cc75-4fa8-a912-b...
(-1)        0 PySparkAssertionError: Received incorrect server side session identifier for request. Please create a new Spark Session to reconnect. (d753420f-ccd8-460b-b0f5-083bc8d6d94f != a483e9d1-5ea6-4643-ae66-6...
Passed Tests Diff 
--- before.txt	2026-07-24 05:52:40.218992420 +0000
+++ after.txt	2026-07-24 05:52:40.515992431 +0000
@@ -2232 +2231,0 @@
-pyspark/sql/tests/connect/test_parity_udtf.py::LegacyArrowUDTFParityTests::test_udtf_with_table_argument_query
Failed Tests 
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.cacheTable
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.clearCache
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.createTable
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.functionExists
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.getFunction
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.isCached
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.recoverPartitions
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.refreshByPath
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.refreshTable
pyspark/sql/catalog.py::pyspark.sql.catalog.Catalog.uncacheTable
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame._joinAsOf
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.approxQuantile
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.asTable
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.cache
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.colRegex
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.dropDuplicatesWithinWatermark
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.explain
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.freqItems
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.groupingSets
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.hint
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.lateralJoin
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.localCheckpoint
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.observe
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.pandas_api
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.persist
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.randomSplit
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.repartitionById
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.repartitionByRange
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.sameSemantics
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.sampleBy
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.storageLevel
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.toJSON
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.transpose
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrame.withWatermark
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrameStatFunctions.approxQuantile
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrameStatFunctions.freqItems
pyspark/sql/dataframe.py::pyspark.sql.dataframe.DataFrameStatFunctions.sampleBy
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.approx_count_distinct
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.approx_percentile
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.collation
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.corr
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.cosh
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.degrees
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.exp
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.from_json
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.from_xml
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.get_json_object
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.hash
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.histogram_numeric
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.inline_outer
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.input_file_block_length
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.input_file_block_start
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.input_file_name
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.java_method
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_agg_bigint
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_agg_double
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_agg_float
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_n_bigint
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_n_double
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_n_float
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_quantile_bigint
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_quantile_double
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_quantile_float
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_rank_bigint
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_rank_double
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_get_rank_float
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_merge_bigint
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_merge_double
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_merge_float
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_to_string_bigint
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_to_string_double
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.kll_sketch_to_string_float
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.log2
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.map_filter
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.map_zip_with
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.percentile_approx
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.reflect
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.schema_of_csv
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.schema_of_xml
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.session_window
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.shuffle
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.sin
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.st_setsrid
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.st_srid
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.tan
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.transform_keys
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.transform_values
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_add
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_avg
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_make_interval
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_make_timestamp
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_parse_url
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_reflect
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_subtract
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_to_date
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.try_variant_get
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.udf
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.unwrap_udt
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.uuid
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.variant_get
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.window
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.xxhash64
pyspark/sql/functions/builtin.py::pyspark.sql.functions.builtin.zip_with
pyspark/sql/tests/connect/arrow/test_parity_arrow.py::ArrowParityTests::test_createDataFrame_pandas_duplicate_field_names
pyspark/sql/tests/connect/arrow/test_parity_arrow.py::ArrowParityTests::test_pandas_self_destruct
pyspark/sql/tests/connect/arrow/test_parity_arrow.py::ArrowParityTests::test_toPandas_duplicate_field_names
pyspark/sql/tests/connect/arrow/test_parity_arrow_cogrouped_map.py::CogroupedMapInArrowParityTests::test_cogroup_apply_in_arrow_with_logging
pyspark/sql/tests/connect/arrow/test_parity_arrow_grouped_map.py::ApplyInArrowParityTests::test_apply_in_arrow_iter_with_logging
pyspark/sql/tests/connect/arrow/test_parity_arrow_grouped_map.py::ApplyInArrowParityTests::test_apply_in_arrow_with_logging
pyspark/sql/tests/connect/arrow/test_parity_arrow_map.py::ArrowMapParityTests::test_map_in_arrow_with_barrier_mode
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pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_batch_query_initial_state
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_chaining_ops
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_event_time
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_in_pandas_composite_type
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_large_values
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_non_contiguous_grouping_cols
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_non_contiguous_grouping_cols_with_init_state
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_proc_timer
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_query_restarts
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_with_timers_single_partition
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state.py::TransformWithStateInPySparkParityTests::test_transform_with_state_with_wmark_and_non_event_time
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_list_state_metadata
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_map_state_metadata
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_map_state_metadata_with_init_state
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_basic
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_init_state
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_init_state_with_extra_transformation
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_init_state_with_timers
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_list_state
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_list_state_large_list
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_list_state_large_ttl
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_map_state
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_map_state_large_ttl
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_non_exist_value_state
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_restart_with_multiple_rows_init_state
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_state_with_timers_single_partition
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_transform_with_value_state_metadata
pyspark/sql/tests/connect/pandas/streaming/test_parity_transform_with_state_state_variable.py::TransformWithStateInPySparkStateVariableParityTests::test_value_state_ttl_basic
pyspark/sql/tests/connect/pandas/test_parity_pandas_cogrouped_map.py::CogroupedApplyInPandasTests::test_cogroup_apply_in_pandas_with_logging
pyspark/sql/tests/connect/pandas/test_parity_pandas_cogrouped_map.py::CogroupedApplyInPandasTests::test_cogroup_apply_int_to_decimal_coercion
pyspark/sql/tests/connect/pandas/test_parity_pandas_cogrouped_map.py::CogroupedApplyInPandasTests::test_different_group_key_cardinality
pyspark/sql/tests/connect/pandas/test_parity_pandas_grouped_map.py::ApplyInPandasTests::test_apply_in_pandas_int_to_decimal_coercion
pyspark/sql/tests/connect/pandas/test_parity_pandas_grouped_map.py::ApplyInPandasTests::test_apply_in_pandas_with_logging
pyspark/sql/tests/connect/pandas/test_parity_pandas_grouped_map.py::ApplyInPandasTests::test_arrow_cast_enabled_numeric_to_decimal
pyspark/sql/tests/connect/pandas/test_parity_pandas_grouped_map.py::ApplyInPandasTests::test_arrow_cast_enabled_str_to_numeric
pyspark/sql/tests/connect/pandas/test_parity_pandas_grouped_map.py::ApplyInPandasTests::test_grouped_over_window
pyspark/sql/tests/connect/pandas/test_parity_pandas_grouped_map.py::ApplyInPandasTests::test_grouped_over_window_with_key
pyspark/sql/tests/connect/pandas/test_parity_pandas_map.py::MapInPandasParityTests::test_map_in_pandas_with_barrier_mode
pyspark/sql/tests/connect/pandas/test_parity_pandas_map.py::MapInPandasParityTests::test_map_in_pandas_with_logging
pyspark/sql/tests/connect/pandas/test_parity_pandas_map.py::MapInPandasParityTests::test_violate_not_null
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf.py::PandasUDFParityTests::test_pandas_udf_basic_with_return_type_string
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf.py::PandasUDFParityTests::test_pandas_udf_int_to_decimal_coercion
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf.py::PandasUDFParityTests::test_pandas_udf_return_type_error
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf.py::PandasUDFParityTests::test_udf_wrong_arg
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_grouped_agg.py::PandasUDFGroupedAggParityTests::test_arrow_cast_enabled_numeric_to_decimal
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_grouped_agg.py::PandasUDFGroupedAggParityTests::test_grouped_agg_pandas_udf_with_logging
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_arrow_cast_enabled_numeric_to_decimal
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_arrow_cast_enabled_str_to_numeric
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_scalar_iter_pandas_udf_with_logging
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_scalar_iter_udf_init
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_scalar_pandas_udf_with_logging
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_udafs_with_complex_variant_input
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_udafs_with_complex_variant_output
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_udafs_with_variant_output
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_vectorized_udf_check_config
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_scalar.py::PandasUDFScalarParityTests::test_vectorized_udf_invalid_length
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_arrow_cast_numeric_to_decimal
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_arrow_cast_str_to_numeric
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_bounded_mixed
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_bounded_simple
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_invalid_args
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_kwargs
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_named_arguments
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_named_arguments_negative
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_shrinking_window
pyspark/sql/tests/connect/pandas/test_parity_pandas_udf_window.py::PandasUDFWindowParityTests::test_sliding_window
pyspark/sql/tests/connect/pandas/test_pari

(truncated)

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github-actions Bot commented Jul 13, 2026 •
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Ibis Test Report

Commit Information

Commit Revision Branch
After 93ad92e refs/pull/2237/merge
Before b247b02 refs/heads/main

Test Summary

Suite Commit Failed Passed Skipped Warnings Time (s)
test-ibis After 34 1535 166 4539 225.20
Before 34 1535 166 4524 250.67

Test Details

Error Counts 
           35 Total
           26 Total Unique
-------- ---- ----------------------------------------------------------------------------------------------------------
            5 IllegalArgumentException: invalid argument: found TRUNCATE at 0:8 expected something else, ';', statement, or end of input
            2 AnalysisException: Internal error: Function 'approx_percentile_cont' failed to match any signature, errors: Error during planning: Function 'approx_percentile_cont' requires Float64, but received List...
            2 AssertionError
            2 AssertionError: Series are different
            2 IllegalArgumentException: invalid argument: found PARTITIONS at 5:15 expected 'DATABASES', 'SCHEMAS', 'NAMESPACES', 'CATALOGS', 'TABLES', 'TABLE', 'CREATE', 'COLUMNS', 'VIEWS', 'ALL', 'USER', 'SYSTEM'...
            2 assert ibis.Schema {... float64\n} == ibis.Schema {... float64\n} Full diff: ibis.Schema { carat float64 cut string color string clarity string depth float64 table float64 - price int32 ? ^^ + price i...
            1 AnalysisException: Catalog not found: local
(+1)        1 AnalysisException: Database not found: ibis_database_jwcytqibhnfwtnnlzd6s76wzfu
            1 AssertionError: DataFrame.iloc[:, 0] (column name="id") are different
            1 AssertionError: DataFrame.iloc[:, 0] (column name="playerID") are different
            1 AssertionError: Series NA mask are different
(+1)        1 AssertionError: assert 'ibis_cached_unjclr2klffrjd5iklimuuco3m' not in ['array_types', 'astronauts', 'awards_players', 'basic_table', 'batting', 'complicated', ...]
            1 AssertionError: assert nan == 22
            1 DateTimeException: Error parsing timestamp from '01/01/09' using format '%-%-M/%-d/%y': bad or unsupported format string
            1 Failed: DID NOT RAISE <class 'pyspark.errors.exceptions.base.AnalysisException'>
            1 SparkRuntimeException: Cast error: Casting from Date32 to Float64 not supported
            1 SparkRuntimeException: Error during planning: expr type Struct("StructColumn({'x': xs, 'y': ys})": non-null Struct("x": non-null Int32, "y": non-null Int32)) can't cast to Struct("x": Int64, metadata:...
            1 TypeError: Cannot convert pyarrow.lib.ChunkedArray to pyarrow.lib.Array
            1 UnsupportedOperationException: CommandNode::AnalyzeTable
            1 UnsupportedOperationException: Physical plan does not support logical expression AggregateFunction(AggregateFunction { func: AggregateUDF { inner: ArrayAgg { signature: Signature { type_signature: Any...
            1 UnsupportedOperationException: Physical plan does not support logical expression InSubquery(InSubquery { expr: Column(Column { relation: Some(Bare { table: "t0" }), name: "#0" }), subquery: <subquery>...
            1 UnsupportedOperationException: Physical plan does not support logical expression InSubquery(InSubquery { expr: Column(Column { relation: Some(Bare { table: "t0" }), name: "#1" }), subquery: <subquery>...
            1 UnsupportedOperationException: unsupported ALTER TABLE operation
            1 ValueError: NaTType does not support strftime
            1 assert frozenset({None}) == frozenset({None, 47}) Extra items in the right set: 47 Full diff: frozenset({ None, - 47, })
            1 assert {0.0, 1.0, 2.0, 3.0} == {1, 2, 3} Extra items in the left set: 0.0 Full diff: { + 0.0, - 1, + 1.0, ? ++ - 2, + 2.0, ? ++ - 3, + 3.0, ? ++ }
(-1)        0 AnalysisException: Database not found: ibis_database_2uglxub2nbgf7mvshs5gbp6nzu
(-1)        0 AssertionError: assert 'ibis_cached_s2u5ozc4xnd4nfyx5bbxkuwqoy' not in ['array_types', 'astronauts', 'awards_players', 'basic_table', 'batting', 'complicated', ...]
Passed Tests Diff

(empty)

Failed Tests 
ibis/backends/pyspark/tests/test_basic.py::test_group_by
ibis/backends/pyspark/tests/test_client.py::test_catalog_db_args
ibis/backends/pyspark/tests/test_client.py::test_create_table_with_partition_and_catalog
ibis/backends/pyspark/tests/test_client.py::test_create_table_with_partition_no_catalog
ibis/backends/pyspark/tests/test_ddl.py::test_compute_stats
ibis/backends/pyspark/tests/test_ddl.py::test_drop_non_empty_database
ibis/backends/pyspark/tests/test_ddl.py::test_insert_table
ibis/backends/pyspark/tests/test_ddl.py::test_truncate_table
ibis/backends/tests/test_aggregation.py::test_approx_quantile[pyspark-True-False]
ibis/backends/tests/test_aggregation.py::test_approx_quantile[pyspark-True-True]
ibis/backends/tests/test_aggregation.py::test_date_quantile[pyspark]
ibis/backends/tests/test_aggregation.py::test_group_concat_over_window[pyspark]
ibis/backends/tests/test_client.py::test_insert_overwrite_from_dataframe[pyspark]
ibis/backends/tests/test_client.py::test_insert_overwrite_from_expr[pyspark]
ibis/backends/tests/test_client.py::test_insert_overwrite_from_list[pyspark]
ibis/backends/tests/test_client.py::test_rename_table[pyspark]
ibis/backends/tests/test_export.py::test_table_to_csv[pyspark]
ibis/backends/tests/test_expr_caching.py::test_persist_expression_contextmanager[pyspark]
ibis/backends/tests/test_expr_caching.py::test_persist_expression_release[pyspark]
ibis/backends/tests/test_expr_caching.py::test_persist_expression_repeated_cache[pyspark]
ibis/backends/tests/test_generic.py::test_isin_uncorrelated[pyspark]
ibis/backends/tests/test_generic.py::test_isin_uncorrelated_simple[pyspark]
ibis/backends/tests/test_io.py::test_read_csv[pyspark-default]
ibis/backends/tests/test_io.py::test_read_csv[pyspark-file_name]
ibis/backends/tests/test_join.py::test_join_with_pandas[pyspark]
ibis/backends/tests/test_json.py::test_json_getitem_array[pyspark]
ibis/backends/tests/test_struct.py::test_field_overwrite_always_prefers_unpacked[pyspark]
ibis/backends/tests/test_struct.py::test_isin_struct[pyspark]
ibis/backends/tests/test_struct.py::test_single_field[pyspark-a]
ibis/backends/tests/test_struct.py::test_single_field[pyspark-b]
ibis/backends/tests/test_struct.py::test_single_field[pyspark-c]
ibis/backends/tests/test_temporal.py::test_delta[pyspark-time]
ibis/backends/tests/test_temporal.py::test_string_as_date[pyspark-mysql_format]
ibis/backends/tests/test_temporal.py::test_string_as_timestamp[pyspark-mysql_format]

@codecov

codecov Bot commented Jul 13, 2026 •
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Codecov Report

❌ Patch coverage is 85.71429% with 57 lines in your changes missing coverage. Please review.

Files with missing lines Patch % Lines
...l-function/src/scalar/datetime/spark_date_trunc.rs 82.69% 27 Missing ⚠️
...-function/src/scalar/datetime/spark_trunc_level.rs 67.21% 20 Missing ⚠️
crates/sail-plan/src/function/scalar/datetime.rs 94.77% 8 Missing ⚠️
crates/sail-execution/src/proto/codec.rs 50.00% 1 Missing ⚠️
...il-function/src/scalar/datetime/spark_timestamp.rs 96.29% 1 Missing ⚠️ 
@@            Coverage Diff             @@
##             main    #2237      +/-   ##
==========================================
- Coverage   79.45%   78.04%   -1.41%     
==========================================
  Files         909      911       +2     
  Lines      171731   172405     +674     
==========================================
- Hits       136454   134561    -1893     
- Misses      35277    37844    +2567
Flag Coverage Δ *Carryforward flag
catalog-integration-tests ?
ibis-tests 16.99% <56.89%> (+0.09%) ⬆️
python-unit-tests 62.07% <85.71%> (+0.05%) ⬆️
rust-slow-tests 47.03% <ø> (+0.04%) ⬆️ Carriedforward from becd7cf
rust-unit-tests 41.92% <52.38%> (-1.26%) ⬇️
spark-tests 31.74% <48.37%> (+0.03%) ⬆️

*This pull request uses carry forward flags. Click here to find out more.

Files with missing lines Coverage Δ
crates/sail-execution/src/proto/codec.rs 47.32% <50.00%> (+<0.01%) ⬆️
...il-function/src/scalar/datetime/spark_timestamp.rs 82.82% <96.29%> (+2.59%) ⬆️
crates/sail-plan/src/function/scalar/datetime.rs 87.67% <94.77%> (+0.80%) ⬆️
...-function/src/scalar/datetime/spark_trunc_level.rs 67.21% <67.21%> (ø)
...l-function/src/scalar/datetime/spark_date_trunc.rs 79.67% <82.69%> (+16.04%) ⬆️

... and 77 files with indirect coverage changes

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  • ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
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@davidlghellin
davidlghellin requested a review from Copilot July 13, 2026 21:20
Copilot AI reviewed Jul 13, 2026
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Pull request overview

Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.

Comment thread crates/sail-function/src/scalar/datetime/spark_date_trunc.rs Outdated
@davidlghellin
trunc (TruncDate) and date_trunc (TruncTimestamp) are different Spark
6636ffb 
expressions and do not accept the same units, but Sail's plan builder
feeds both from date_trunc's unit table. Five behaviours diverge:

- trunc(date, 'DAY') and trunc(date, 'HOUR') truncate and return the date
  itself, where Spark returns NULL: a unit finer than a day is not valid
  for trunc.
- an unrecognized unit errors instead of returning NULL.
- a NULL unit errors instead of returning NULL.
- a unit coming from a column errors, because the plan builder only matches
  Expr::Literal, so the query falls through to DataFusion's date_trunc,
  which demands a scalar unit.

They live in date_trunc.feature because a single unit table governs both.
Ten new scenarios, values captured on Spark JVM 4.1.1: five green, five
tagged @sail-bug (strict xfail) and removed by the fix that follows.
@davidlghellin
fix
5cd4991 
The ClickBench plan snapshot changes only in the internal display name of the
expression: the operator that actually runs is still date_trunc(minute, lakehq#4@0), a
single call, because the CASE folds away for a literal unit. No plan regression.
@davidlghellin
merge [spark tests]
f44f271
@davidlghellin
clean bug
3602326
@davidlghellin
davidlghellin marked this pull request as ready for review July 17, 2026 21:45
@shehabgamin shehabgamin added run spark tests Trigger Spark tests on a pull request run ibis tests Trigger Ibis tests on a pull request labels Jul 17, 2026
@shehabgamin shehabgamin self-assigned this Jul 17, 2026
@shehabgamin shehabgamin changed the title fix: Spark-compatible date_trunc — timestamp return type, NULL on invalid unit, and unit from a column fix: spark-compatible date_trunc — timestamp return type, NULL on invalid unit, and unit from a column Jul 21, 2026
shehabgamin
shehabgamin reviewed Jul 22, 2026
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Will re-review after I see the benchmark results!

Comment thread python/pysail/tests/spark/__snapshots__/test_clickbench.plan.yaml Outdated
AggregateExec: mode=FinalPartitioned, gby=[date_trunc(CASE WHEN Utf8("minute") ILIKE Utf8("mon") OR Utf8("minute") ILIKE Utf8("mm") THEN Utf8("month") WHEN Utf8("minute") ILIKE Utf8("yy") OR Utf8("minute") ILIKE Utf8("yyyy") THEN Utf8("year") WHEN Utf8("minute") ILIKE Utf8("dd") THEN Utf8("day") ELSE Utf8("minute") END,hits.#4)@0 as date_trunc(CASE WHEN Utf8("minute") ILIKE Utf8("mon") OR Utf8("minute") ILIKE Utf8("mm") THEN Utf8("month") WHEN Utf8("minute") ILIKE Utf8("yy") OR Utf8("minute") ILIKE Utf8("yyyy") THEN Utf8("year") WHEN Utf8("minute") ILIKE Utf8("dd") THEN Utf8("day") ELSE Utf8("minute") END,hits.#4)], aggr=[count(Int64(1))]
RepartitionExec: partitioning=Hash([date_trunc(CASE WHEN Utf8("minute") ILIKE Utf8("mon") OR Utf8("minute") ILIKE Utf8("mm") THEN Utf8("month") WHEN Utf8("minute") ILIKE Utf8("yy") OR Utf8("minute") ILIKE Utf8("yyyy") THEN Utf8("year") WHEN Utf8("minute") ILIKE Utf8("dd") THEN Utf8("day") ELSE Utf8("minute") END,hits.#4)@0], 4), input_partitions=4
AggregateExec: mode=Partial, gby=[date_trunc(minute, #4@0) as date_trunc(CASE WHEN Utf8("minute") ILIKE Utf8("mon") OR Utf8("minute") ILIKE Utf8("mm") THEN Utf8("month") WHEN Utf8("minute") ILIKE Utf8("yy") OR Utf8("minute") ILIKE Utf8("yyyy") THEN Utf8("year") WHEN Utf8("minute") ILIKE Utf8("dd") THEN Utf8("day") ELSE Utf8("minute") END,hits.#4)], aggr=[count(Int64(1))]
ProjectionExec: expr=[CASE WHEN lower(Utf8("minute")) = Utf8("year") OR lower(Utf8("minute")) = Utf8("yyyy") OR lower(Utf8("minute")) = Utf8("yy") THEN date_trunc(Utf8("year"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("quarter") THEN date_trunc(Utf8("quarter"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("month") OR lower(Utf8("minute")) = Utf8("mon") OR lower(Utf8("minute")) = Utf8("mm") THEN date_trunc(Utf8("month"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("week") THEN date_trunc(Utf8("week"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("day") OR lower(Utf8("minute")) = Utf8("dd") THEN date_trunc(Utf8("day"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("hour") THEN date_trunc(Utf8("hour"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("minute") THEN date_trunc(Utf8("minute"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("second") THEN date_trunc(Utf8("second"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("millisecond") THEN date_trunc(Utf8("millisecond"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("microsecond") THEN date_trunc(Utf8("microsecond"),hits.#4) ELSE TimestampMicrosecond(NULL, Some("UTC")) END@0 as #105, count(Int64(1))@1 as #106, CASE WHEN lower(Utf8("minute")) = Utf8("year") OR lower(Utf8("minute")) = Utf8("yyyy") OR lower(Utf8("minute")) = Utf8("yy") THEN date_trunc(Utf8("year"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("quarter") THEN date_trunc(Utf8("quarter"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("month") OR lower(Utf8("minute")) = Utf8("mon") OR lower(Utf8("minute")) = Utf8("mm") THEN date_trunc(Utf8("month"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("week") THEN date_trunc(Utf8("week"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("day") OR lower(Utf8("minute")) = Utf8("dd") THEN date_trunc(Utf8("day"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("hour") THEN date_trunc(Utf8("hour"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("minute") THEN date_trunc(Utf8("minute"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("second") THEN date_trunc(Utf8("second"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("millisecond") THEN date_trunc(Utf8("millisecond"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("microsecond") THEN date_trunc(Utf8("microsecond"),hits.#4) ELSE TimestampMicrosecond(NULL, Some("UTC")) END@0 as CASE WHEN lower(Utf8("minute")) = Utf8("year") OR lower(Utf8("minute")) = Utf8("yyyy") OR lower(Utf8("minute")) = Utf8("yy") THEN date_trunc(Utf8("year"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("quarter") THEN date_trunc(Utf8("quarter"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("month") OR lower(Utf8("minute")) = Utf8("mon") OR lower(Utf8("minute")) = Utf8("mm") THEN date_trunc(Utf8("month"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("week") THEN date_trunc(Utf8("week"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("day") OR lower(Utf8("minute")) = Utf8("dd") THEN date_trunc(Utf8("day"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("hour") THEN date_trunc(Utf8("hour"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("minute") THEN date_trunc(Utf8("minute"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("second") THEN date_trunc(Utf8("second"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("millisecond") THEN date_trunc(Utf8("millisecond"),hits.#4) WHEN lower(Utf8("minute")) = Utf8("microsecond") THEN date_trunc(Utf8("microsecond"),hits.#4) ELSE TimestampMicrosecond(NULL, Some("UTC")) END]

@shehabgamin shehabgamin Jul 22, 2026

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Given that this seems to significantly modify the ClickBench query plan, can you please do a benchmark?

@davidlghellin davidlghellin Jul 22, 2026

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Sure! I'll benchmark the affected ClickBench query (main vs this branch) and post the before/after here.

@davidlghellin davidlghellin Jul 22, 2026

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Benchmarked the affected ClickBench path (main vs this branch), 1M timestamps, on the projection
whose plan changed.

No regression. The plan diff is cosmetic: ClickBench's unit is the literal 'minute', so the new
11-branch CASE ... date_trunc(...) constant-folds to a single date_trunc('minute', ts) — the
identical physical op as before.

Time (criterion, 1M rows) — within noise, a wash

projection time
old — date_trunc('minute', ts) ~0.94 ms
new — 11-branch CASE (literal unit) ~0.90 ms

Allocations (divan): byte-identical — both 8.131 MB allocated / 8.036 MB peak. Same time, same
memory: after folding it's literally the same plan.

Verifiable directly (cargo run --example explain): the literal unit optimizes to 1 date_trunc
call. Self-contained harness (public datafusion pinned =54.0.0, no Sail checkout):
https://github.com/davidlghellin/sail-benchmarks/tree/master/sail-2237-date-trunc

@shehabgamin

shehabgamin commented Jul 23, 2026

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@davidlghellin Thanks for doing the benchmark!

Here's the initial review from Codex 5.6 ultra

  1. [P1] Evaluate the unit expression only once — crates/sail-plan/src/function/scalar/datetime.rs:100

     The searched CASE clones the normalized unit into every alias predicate. Volatile expressions are therefore reevaluated because DataFusion does not eliminate volatile common subexpressions.

     On the exact PR build, date_trunc(IF(rand(7) < 0.5, 'YEAR', 'MONTH'), ts) produced 2,527 unexpected NULLs in 10,000 rows; Spark produced zero. This also affects trunc. A simple CASE <normalized-unit> WHEN ...
     selector would evaluate it once.

  2. [P1] The CASE suppresses ANSI errors Spark raises — crates/sail-plan/src/function/scalar/datetime.rs:102

     Timestamp/date conversion only occurs inside the selected THEN branch. For a genuinely nonfoldable format:

     SELECT id, date_trunc(
       IF(id = 0, 'INVALID', 'DAY'),
       IF(id = 0, 'not a date', '2024-01-02')
     ) FROM range(2)

     The PR returns NULL for row 0. Spark’s default generated execution raises CAST_INVALID_INPUT, because it evaluates the non-null format and value before parsing format validity. trunc has the same regression at
     crates/sail-plan/src/function/scalar/datetime.rs:112.

     The added VALUES tests miss this because Spark folds those local rows differently.

  3. [P1] Use Spark-compatible localization for DATE/TIMESTAMP_NTZ — crates/sail-plan/src/function/scalar/datetime.rs:131

     The generic Arrow cast/try_cast requires a unique timezone offset, unlike Spark’s fallback localization.

     With session timezone America/Los_Angeles:
      - TIMESTAMP_NTZ '2024-03-10 02:30:00': Spark returns 03:00; the PR errors under ANSI or returns NULL otherwise.
      - TIMESTAMP_NTZ '2024-11-03 01:30:00': Spark returns 01:00; the PR again errors/NULLs.

     Sail already has SparkTimestamp and localize_with_fallback, which are better suited to this conversion. The new tests all retain the harness’s UTC session, so the explicit-zone scenarios do not exercise this
     path.

  4. [P2] Reject complex unit types rather than stringifying them — crates/sail-plan/src/function/scalar/datetime.rs:94

     The unconditional UTF-8 cast accepts arrays:

     date_trunc(array('YEAR'), timestamp_value)
     trunc(date_value, array('YEAR'))

     The PR returns NULL. Spark rejects both during analysis with DATATYPE_MISMATCH; the merge base rejected them too. Spark permits implicit string coercion for applicable atomic types, not arbitrary nested
     values.

  5. [P2] Preserve Spark’s analysis error class — crates/sail-plan/src/function/scalar/datetime.rs:152

     date_trunc('DAY', 1) now produces an IllegalArgumentException via PlanError::invalid. Spark and the merge base produce an AnalysisException with DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE. That difference is
     observable through Spark clients.

@davidlghellin
davidlghellin marked this pull request as draft July 23, 2026 05:30
@davidlghellin

davidlghellin commented Jul 24, 2026 •
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@davidlghellin Thanks for doing the benchmark!

Here's the initial review from Codex 5.6 ultra

Thanks @shehabgamin — all five are addressed. Everything below was measured on Spark JVM 4.1.1 and against the PR build, not reasoned about.

Before the five: trunc(DATE '2300-01-01', 'YEAR') returned 1715-01-01. Silently, no error, on an ordinary date — a DATE value reached DataFusion's nanosecond path and the day count wrapped.
Fixed; trunc now goes through the microsecond kernel like date_trunc.

1. Unit evaluated once — fixed. date_trunc(IF(rand(7) < 0.5, 'YEAR', 'MONTH'), ts) now gives 0 NULLs in 10,000 rows (was 2,527), same for trunc.

A simple-CASE selector turned out not to be enough on its own. The dispatch has to tell three outcomes apart — named a granularity / was NULL / matched nothing — and CASE x WHEN NULL never matches, so a NULL selector shares the ELSE with an unmatched one. Every way of splitting them in the plan (coalesce, IS NULL) mentions the unit a second time, and DataFusion will not share a volatile subtree between mentions. Resolving the unit now happens in one small UDF, which also replaced the per-alias CASE an earlier revision of this PR used.

2. ANSI errors suppressed — fixed, with one correction to the prescription. Spark does not convert the value unconditionally. TruncInstant.codeGenHelper branches on format.foldable: a foldable unit naming no level emits a bare null and never generates the value's code. Always converting would have turned three Spark-NULL cases into errors:

ANSI on Spark
date_trunc('INVALID', <bad value>) NULL
date_trunc(CAST(NULL AS STRING), <bad value>) NULL
trunc(<bad value>, 'DAY') NULL
date_trunc(IF(id=0,'INVALID','DAY'), <bad value>) CAST_INVALID_INPUT

There is a second axis: a NULL unit and an unmatched unit differ, and which one skips the value follows the argument order — nullSafeCodeGen evaluates the left child only, so TruncTimestamp(format, ts) skips the timestamp on a NULL format while TruncDate(date, format) always evaluates the date. All four combinations are covered.

Sail raises where Spark raises, but still carries DataFusion's parser message instead of CAST_INVALID_INPUT. That is the ANSI string-cast surface, shared by every function that casts a string; tagged @sail-bug with an untagged companion that asserts it raises on both engines.

3. Localization — fixed. NTZ and DATE both route through SparkTimestamp / localize_with_fallback. Gap and overlap now match, and so does a DATE whose local midnight does not exist (Africa/Cairo 2024-04-26 → 01:00:00) — the DST scenarios set spark.sql.session.timeZone explicitly, and the overlap ones assert unix_timestamp, since the wall clock renders identically for both resolutions.

4. Complex unit types — fixed. Rejected at analysis with DATATYPE_MISMATCH. The gate matches on the logical type, so dictionary- and run-end-encoded strings are not rejected for their encoding.

5. Error class — fixed. PlanError::AnalysisError → AnalysisException, verified by exception
type: Then query error uses pytest.raises(Exception, match=...) and accepts any class, so there is now a Python test asserting the type directly.

Other divergences found and fixed while here: trunc(1,'YEAR') returned 1970-01-01 (Arrow reinterprets an int as a day count); date_trunc('MM','2009-2-12') failed while CAST on the same string succeeded; 'mınute' / 'ſecond' returned NULL where Spark truncates.

Known gaps left in place, each with a scenario: a binary unit that is not valid UTF-8; a scalar subquery Spark's optimizer does not fold; a DATE past year ~294,247 (no reachable input found — literals, make_date and date_add are all rejected earlier). The columnar path still repeats the value subtree once per branch; collapsing it would make the value an ordinary argument, always evaluated, which is what the short circuit forbids.

Coverage went from 13 scenarios to 153, plus 10 Python tests. All are validated against Spark JVM 4.1.1 except the two plan-snapshot scenarios, which are Sail-only by nature.

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