lllangWV opened a new issue, #45113:
URL: https://github.com/apache/arrow/issues/45113
### Describe the usage question you have. Please include as many useful
details as possible.
**Title:** Improving Deserialization Speed for PyArrow to Python Objects
Hello,
I am working with materials data stored in Parquet files, where a column
`structure` contains serialized dictionaries representing structures from the
`Structure` class in the `pymatgen` package. This class stores site and lattice
information and provides a `.to_dict()` method for serialization.
I have a dataset of ~80,000 structures. To deserialize these into
`Structure` objects, I use the following process:
```python
ds = ds.dataset(dataset_dir, format="parquet")
table = ds.to_table(columns=['structure'])
df = table.to_pandas() # ~8.20 seconds
df['structure_py'] = df['structure'].map(Structure.from_dict) # ~116 seconds
```
The majority of the time is spent mapping the dictionaries to `Structure`
objects via `Structure.from_dict`. I attempted using `pa.ExtensionArray` and
`pa.ExtensionType` to optimize this process but achieved similar performance,
as the bottleneck appears to be in the `Structure.from_dict` calls.
Here's an example of my `ExtensionType` implementation:
```python
class StructureType(pa.ExtensionType):
def __init__(self, data_type: pa.DataType):
if not pa.types.is_struct(data_type):
raise TypeError(f"data_type must be a struct type, not
{data_type}")
super().__init__(data_type, "matgraphdb.structure")
def __arrow_ext_serialize__(self) -> bytes:
return b""
@classmethod
def __arrow_ext_deserialize__(cls, storage_type, serialized):
assert pa.types.is_struct(storage_type)
return StructureType(storage_type)
def __arrow_ext_class__(self):
return StructureArray
class StructureArray(pa.ExtensionArray):
def to_structure(self):
return self.storage.to_pandas().map(Structure.from_dict)
```
Despite these efforts, the deserialization time remains substantial. Below
is the type of the `structure` column:
```python
struct<@class: string, @module: string, charge: double, lattice: struct<a:
double, alpha: double, b: double, beta: double, c: double, gamma: double, ...>,
sites: list<element: struct<...>>>
```
Is there a recommended approach within PyArrow to speed up deserialization
of such complex structured data into Python objects?
Best regards,
Logan Lang
### Component(s)
Parquet, Python
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