liamphmurphy opened a new issue, #43893:
URL: https://github.com/apache/arrow/issues/43893
### Describe the bug, including details regarding any error messages,
version, and platform.
Following a schema merge operation involving nested columns, PyArrow seems
to struggle with loading data with the following error:
`pyarrow.lib.ArrowTypeError: struct fields don't match or are in the wrong
order: Input fields: struct<c: int64> output fields: struct<c: int64, d: int64>`
I have confirmed this does not happen with a schema merge that DOES NOT
involve any nested columns.
I believe this is a PyArrow specific problem as Spark does not have this
problem.
Below is an example of how this can be reproduced:
```
import pyarrow as pa
import polars as pl
from deltalake import write_deltalake
# Create a pyarrow table, include a nested column 'd'
df = pa.table({
"a": [1, 2, 3],
"b": [{"c": 1}, {"c": 2}, {"c": 3}]
})
# Create a PyArrow schema, include a nested column 'd'
schema = pa.schema([
pa.field("a", pa.int64()),
pa.field("b", pa.struct([
pa.field("c", pa.int64())
]))
])
local_path = "./tables/merge_delta_table"
# Write the table to delta lake
write_deltalake(local_path, data=df, engine="rust", schema=schema,
mode="append")
# Create a new table with a different schema, adding
df2 = pa.table({
"a": [4, 5, 6],
"b": [{"d": 2, "c": 1}, {"c": 2}, {"c": 3}]
})
schema2 = pa.schema([
pa.field("a", pa.int64()),
pa.field("b", pa.struct([
pa.field("d", pa.int64()),
pa.field("c", pa.int64())
]))
])
# Write the new table to the same delta lake
write_deltalake(local_path, data=df2, schema=schema2, engine="rust",
mode="append", schema_mode="merge")
# Now read the delta lake using polars
df = pl.read_delta(local_path)
print(df)
```
### Component(s)
Parquet, Python
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