Sorry, I hate opening so many issues in the same week. I've been testing a big PR on my side and these things are cropping up, don't want to lose site of them even if I code around some for now.
Description
I think col_vals_in_set might not work properly when using ibis.pyspark. It looks like the issue might actually be with narwhals, but I'm having a tough time debugging.
The duckdb validation works, but he pyspark validation generates this SQL which fails (scrubbed for readability):
SELECT
campaign_id,
start_date,
end_date,
campaign_name,
channel_id,
target_id,
campaign_code,
outbound_file_name,
execution_cost,
campaign_manager,
target_cell_names,
channel_id IN (303, 304, 441, 461, 621) AS `pb_is_good_`
FROM comms_media_dev.dart_core.test_campaign WHERE channel_id IN (303, 304, 441, 461, 621) IS NULL
Reproducible example
I created a simple df, wrote to parquet in the working directory, and read it into duckdb & pyspark via ibis:
import ibis
import os
import pandas as pd
import pointblank as pb
import numpy as np
from pyspark.sql import SparkSession
from ibis import _
pdf = pd.DataFrame({
'id': range(1, 101),
'value': np.random.rand(100),
'category': np.random.choice(['A', 'B', 'C'], 100)
})
parquet_path = os.path.join(os.getcwd(), "temp.parquet")
pdf.to_parquet(parquet_path, index=False)
iduck = ibis.duckdb.connect()
df_duck = iduck.read_parquet(parquet_path, table_name = "test")
spark = SparkSession.builder.getOrCreate()
ispark = ibis.pyspark.connect(spark)
df_spark = ispark.read_parquet(parquet_path, table_name = "test")I setup a validation and ran with duckdb, works as expected:
validate_duck = (
pb.Validate(data=df_duck)
.col_vals_in_set(columns = ["category"], set = ["A", "B"])
.interrogate()
)
validate_duckI setup the same validation and ran with pyspark, errors on the erroneous SQL:
validate_pyspark = (
pb.Validate(data=df_spark)
.col_vals_in_set(columns = ["category"], set = ["A", "B"])
.interrogate()
)
validate_pysparkExpected result
The pyspark backend should've produced the same result as the duckdb backend.
Development environment
macOS 14.5
pointblank 0.14.0
Additional context
Here's the error:
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ in <module>:4 │
│ │
│ 1 validate_pyspark = ( │
│ 2 │ pb.Validate(data=df_spark) │
│ 3 │ .col_vals_in_set(columns = ["category"], set = ["A", "B"]) │
│ ❱ 4 │ .interrogate() │
│ 5 ) │
│ 6 validate_pyspark │
│ 7 │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/pointblank/validate.py:13244 in │
│ interrogate │
│ │
│ 13241 │ │ │ │ # Solely for the col_vals_in_set assertion type, any Null values in the │
│ 13242 │ │ │ │ # `pb_is_good_` column are counted as failing test units │
│ 13243 │ │ │ │ if assertion_type == "col_vals_in_set": │
│ ❱ 13244 │ │ │ │ │ null_count = _count_null_values_in_column(tbl=results_tbl, column="p │
│ 13245 │ │ │ │ │ validation.n_failed += null_count │
│ 13246 │ │ │ │ │
│ 13247 │ │ │ │ # For column-value validations, the number of test units is the number o │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/pointblank/_utils.py:375 in │
│ _count_null_values_in_column │
│ │
│ 372 │ │
│ 373 │ # Always collect table if it is a LazyFrame; this is required to get the row count │
│ 374 │ if _is_lazy_frame(tbl_filtered): │
│ ❱ 375 │ │ tbl_filtered = tbl_filtered.collect() │
│ 376 │ │
│ 377 │ return len(tbl_filtered) │
│ 378 │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/narwhals/dataframe.py:2472 in │
│ collect │
│ │
│ 2469 │ │ """ │
│ 2470 │ │ collect = self._compliant_frame.collect │
│ 2471 │ │ if backend is None: │
│ ❱ 2472 │ │ │ return self._dataframe(collect(None, **kwargs), level="full") │
│ 2473 │ │ eager_backend = Implementation.from_backend(backend) │
│ 2474 │ │ if can_lazyframe_collect(eager_backend): │
│ 2475 │ │ │ return self._dataframe(collect(eager_backend, **kwargs), level="full") │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/narwhals/_ibis/dataframe.py:114 │
│ in collect │
│ │
│ 111 │ │ │ from narwhals._arrow.dataframe import ArrowDataFrame │
│ 112 │ │ │ │
│ 113 │ │ │ return ArrowDataFrame( │
│ ❱ 114 │ │ │ │ to_pyarrow_table(self.native.to_pyarrow()), │
│ 115 │ │ │ │ validate_backend_version=True, │
│ 116 │ │ │ │ version=self._version, │
│ 117 │ │ │ │ validate_column_names=True, │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/ibis/expr/types/relations.py:625 │
│ in to_pyarrow │
│ │
│ 622 │ │ limit: int | str | None = None, │
│ 623 │ │ **kwargs: Any, │
│ 624 │ ) -> pa.Table: │
│ ❱ 625 │ │ return super().to_pyarrow(params=params, limit=limit, **kwargs) │
│ 626 │ │
│ 627 │ def _fast_bind(self, *args, **kwargs): │
│ 628 │ │ # allow the first argument to be either a dictionary or a list of values │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/ibis/expr/types/core.py:605 in │
│ to_pyarrow │
│ │
│ 602 │ │ │ If the passed expression is a Column, a pyarrow array is returned. │
│ 603 │ │ │ If the passed expression is a Scalar, a pyarrow scalar is returned. │
│ 604 │ │ """ │
│ ❱ 605 │ │ return self._find_backend(use_default=True).to_pyarrow( │
│ 606 │ │ │ self, params=params, limit=limit, **kwargs │
│ 607 │ │ ) │
│ 608 │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/ibis/backends/pyspark/__init__.p │
│ y:1050 in to_pyarrow │
│ │
│ 1047 │ │ │
│ 1048 │ │ table_expr = expr.as_table() │
│ 1049 │ │ output = pa.Table.from_pandas( │
│ ❱ 1050 │ │ │ self.execute(table_expr, params=params, limit=limit, **kwargs), │
│ 1051 │ │ │ preserve_index=False, │
│ 1052 │ │ ) │
│ 1053 │ │ table = PyArrowData.convert_table(output, table_expr.schema()) │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/ibis/backends/pyspark/__init__.p │
│ y:506 in execute │
│ │
│ 503 │ │ │
│ 504 │ │ schema = table.schema() │
│ 505 │ │ │
│ ❱ 506 │ │ with self._safe_raw_sql(sql) as query: │
│ 507 │ │ │ df = query.toPandas() # blocks until finished │
│ 508 │ │ │ result = PySparkPandasData.convert_table(df, schema) │
│ 509 │ │ return expr.__pandas_result__(result) │
│ │
│ /opt/homebrew/Cellar/python@3.11/3.11.10/Frameworks/Python.framework/Versions/3.11/lib/python3.1 │
│ 1/contextlib.py:137 in __enter__ │
│ │
│ 134 │ │ # they are only needed for recreation, which is not possible anymore │
│ 135 │ │ del self.args, self.kwds, self.func │
│ 136 │ │ try: │
│ ❱ 137 │ │ │ return next(self.gen) │
│ 138 │ │ except StopIteration: │
│ 139 │ │ │ raise RuntimeError("generator didn't yield") from None │
│ 140 │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/ibis/backends/pyspark/__init__.p │
│ y:482 in _safe_raw_sql │
│ │
│ 479 │ │
│ 480 │ @contextlib.contextmanager │
│ 481 │ def _safe_raw_sql(self, query: str) -> Any: │
│ ❱ 482 │ │ yield self.raw_sql(query) │
│ 483 │ │
│ 484 │ def raw_sql(self, query: str | sg.Expression, **kwargs: Any) -> Any: │
│ 485 │ │ with contextlib.suppress(AttributeError): │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/ibis/backends/pyspark/__init__.p │
│ y:487 in raw_sql │
│ │
│ 484 │ def raw_sql(self, query: str | sg.Expression, **kwargs: Any) -> Any: │
│ 485 │ │ with contextlib.suppress(AttributeError): │
│ 486 │ │ │ query = query.sql(dialect=self.dialect) │
│ ❱ 487 │ │ return self._session.sql(query, **kwargs) │
│ 488 │ │
│ 489 │ def execute( │
│ 490 │ │ self, │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/pyspark/sql/session.py:1631 in │
│ sql │
│ │
│ 1628 │ │ │ │ litArgs = self._jvm.PythonUtils.toArray( │
│ 1629 │ │ │ │ │ [_to_java_column(lit(v)) for v in (args or [])] │
│ 1630 │ │ │ │ ) │
│ ❱ 1631 │ │ │ return DataFrame(self._jsparkSession.sql(sqlQuery, litArgs), self) │
│ 1632 │ │ finally: │
│ 1633 │ │ │ if len(kwargs) > 0: │
│ 1634 │ │ │ │ formatter.clear() │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/py4j/java_gateway.py:1322 in │
│ __call__ │
│ │
│ 1319 │ │ │ proto.END_COMMAND_PART │
│ 1320 │ │ │
│ 1321 │ │ answer = self.gateway_client.send_command(command) │
│ ❱ 1322 │ │ return_value = get_return_value( │
│ 1323 │ │ │ answer, self.gateway_client, self.target_id, self.name) │
│ 1324 │ │ │
│ 1325 │ │ for temp_arg in temp_args: │
│ │
│ /Users/m109993/Github/camper/.venv/lib/python3.11/site-packages/pyspark/errors/exceptions/captur │
│ ed.py:185 in deco │
│ │
│ 182 │ │ │ if not isinstance(converted, UnknownException): │
│ 183 │ │ │ │ # Hide where the exception came from that shows a non-Pythonic │
│ 184 │ │ │ │ # JVM exception message. │
│ ❱ 185 │ │ │ │ raise converted from None │
│ 186 │ │ │ else: │
│ 187 │ │ │ │ raise │
│ 188 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
ParseException:
[PARSE_SYNTAX_ERROR] Syntax error at or near 'IS'.(line 1, pos 152)
== SQL ==
SELECT `t0`.`id`, `t0`.`value`, `t0`.`category`, `t0`.`category` IN ('A', 'B') AS `pb_is_good_` FROM `test` AS `t0`
WHERE `t0`.`category` IN ('A', 'B') IS NULL
-------------------------------------------------------------------------------------------------------------------
-------------------------------------^^^
Sorry, I hate opening so many issues in the same week. I've been testing a big PR on my side and these things are cropping up, don't want to lose site of them even if I code around some for now.
Description
I think
col_vals_in_setmight not work properly when using ibis.pyspark. It looks like the issue might actually be with narwhals, but I'm having a tough time debugging.The duckdb validation works, but he pyspark validation generates this SQL which fails (scrubbed for readability):
Reproducible example
I created a simple df, wrote to parquet in the working directory, and read it into duckdb & pyspark via ibis:
I setup a validation and ran with duckdb, works as expected:
I setup the same validation and ran with pyspark, errors on the erroneous SQL:
Expected result
The pyspark backend should've produced the same result as the duckdb backend.
Development environment
macOS 14.5
pointblank 0.14.0
Additional context
Here's the error: