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Fix NaN in read_rows #378

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Fix NaN in read_rows #378

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27 changes: 24 additions & 3 deletions datapipe/meta/sql_meta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -150,12 +150,25 @@ def get_metadata(self, idx: Optional[IndexDF] = None, include_deleted: bool = Fa
with self.dbconn.con.begin() as con:
if idx is None:
sql = self._build_metadata_query(sql, idx, include_deleted)
return cast(MetadataDF, pd.read_sql_query(sql, con=con))
return cast(
MetadataDF,
pd.read_sql_query(
sql,
con=con,
dtype_backend="pyarrow",
),
)

for chunk_idx in self._chunk_idx_df(idx):
chunk_sql = self._build_metadata_query(sql, chunk_idx, include_deleted)

res.append(pd.read_sql_query(chunk_sql, con=con))
res.append(
pd.read_sql_query(
chunk_sql,
con=con,
dtype_backend="pyarrow",
)
)

if len(res) > 0:
return cast(MetadataDF, pd.concat(res))
Expand Down Expand Up @@ -223,6 +236,7 @@ def get_existing_idx(self, idx: Optional[IndexDF] = None) -> IndexDF:
res_df: DataDF = pd.read_sql_query(
sql,
con=con,
dtype_backend="pyarrow",
)

return data_to_index(res_df, self.primary_keys)
Expand Down Expand Up @@ -358,7 +372,14 @@ def get_stale_idx(
with self.dbconn.con.begin() as con:
return cast(
Iterator[IndexDF],
list(pd.read_sql_query(sql, con=con, chunksize=1000)),
list(
pd.read_sql_query(
sql,
con=con,
chunksize=1000,
dtype_backend="pyarrow",
)
),
)

def get_changed_rows_count_after_timestamp(
Expand Down
10 changes: 9 additions & 1 deletion datapipe/step/batch_transform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -177,6 +177,8 @@ def _apply_filters_to_run_config(self, run_config: Optional[RunConfig] = None) -
filters = self.filters
elif isinstance(self.filters, Callable): # type: ignore
filters = self.filters()
else:
filters = {}

if run_config is None:
return RunConfig(filters=filters)
Expand Down Expand Up @@ -260,7 +262,12 @@ def get_full_process_ids(

def alter_res_df():
with ds.meta_dbconn.con.begin() as con:
for df in pd.read_sql_query(u1, con=con, chunksize=chunk_size):
for df in pd.read_sql_query(
u1,
con=con,
chunksize=chunk_size,
dtype_backend="pyarrow",
):
df = df[self.transform_keys]

for k, v in extra_filters.items():
Expand Down Expand Up @@ -299,6 +306,7 @@ def get_change_list_process_ids(
table_changes_df = pd.read_sql_query(
sql,
con=con,
dtype_backend="pyarrow",
)
table_changes_df = table_changes_df[self.transform_keys]

Expand Down
13 changes: 11 additions & 2 deletions datapipe/store/database.py
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Original file line number Diff line number Diff line change
Expand Up @@ -272,15 +272,23 @@ def read_rows(self, idx: Optional[IndexDF] = None) -> pd.DataFrame:
with self.dbconn.con.begin() as con:
for chunk_idx in self._chunk_idx_df(idx):
chunk_sql = sql_apply_idx_filter_to_table(sql, self.data_table, self.primary_keys, chunk_idx)
chunk_df = pd.read_sql_query(chunk_sql, con=con)
chunk_df = pd.read_sql_query(
chunk_sql,
con=con,
dtype_backend="pyarrow",
)

res.append(chunk_df)

return pd.concat(res, ignore_index=True)

else:
with self.dbconn.con.begin() as con:
return pd.read_sql_query(sql, con=con)
return pd.read_sql_query(
sql,
con=con,
dtype_backend="pyarrow",
)

def read_rows_meta_pseudo_df(
self,
Expand All @@ -296,4 +304,5 @@ def read_rows_meta_pseudo_df(
sql,
con=con,
chunksize=chunksize,
dtype_backend="pyarrow",
)
47 changes: 46 additions & 1 deletion datapipe/store/tests/abstract.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
import cloudpickle
import pandas as pd
import pytest
from sqlalchemy import Column, String
from sqlalchemy import Column, Integer, String

from datapipe.run_config import RunConfig
from datapipe.store.table_store import TableStore
Expand Down Expand Up @@ -49,6 +49,51 @@ def test_get_schema(

assert store.get_schema() == schema

def test_multiple_keys_with_none(self, store_maker: TableStoreMaker) -> None:
data_df = pd.DataFrame(
{
"id1": [1, 2, 3],
"id2": ["a", "b", "c"],
"value": [10, 20, 30],
}
)
schema: list[Column] = [
Column("id1", Integer, primary_key=True),
Column("id2", String(100), primary_key=True),
Column("value", Integer),
]

store = store_maker(schema)
store.insert_rows(data_df)

assert_df_equal(
store.read_rows(
data_to_index(
pd.DataFrame.from_dict({"id1": [2, -1], "id2": ["b", None]}).convert_dtypes(
dtype_backend="pyarrow"
),
["id1", "id2"],
)
),
pd.DataFrame.from_dict({"id1": [2], "id2": ["b"], "value": [20]}),
index_cols=["id1", "id2"],
)

assert_df_equal(
store.read_rows(
data_to_index(
pd.DataFrame.from_dict({"id1": [2, None], "id2": ["b", "z"]}).convert_dtypes(
dtype_backend="pyarrow"
),
["id1", "id2"],
)
),
pd.DataFrame.from_dict({"id1": [2], "id2": ["b"], "value": [20]}),
index_cols=["id1", "id2"],
)

assert_ts_contains(store, data_df)

@pytest.mark.parametrize("data_df,schema", DATA_PARAMS)
def test_write_read_rows(
self,
Expand Down
1 change: 1 addition & 0 deletions examples/datatable_batch_transform/app.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,6 +80,7 @@ def count_tbl(
return pd.read_sql_query(
sql,
con=con,
dtype_backend="pyarrow",
)


Expand Down