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https://issues.apache.org/jira/browse/ARROW-10247?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17261996#comment-17261996
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Lance Dacey edited comment on ARROW-10247 at 1/10/21, 3:27 AM:
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What is the best workaround for this issue right now? I was playing around with
making a new partition schema if a dictionary type was found in my partition
columns:
{code:java}
partitioning = None
part_schema = t.select(["project", "date"]).schema
fields = []
for part in part_schema:
if pa.types.is_dictionary(part.type):
fields.append(pa.field(part.name, part.type.value_type))
else:
fields.append(pa.field(part.name, part.type))
new_schema = pa.schema(fields)
partitioning = ds.partitioning(new_schema, flavor="hive")
{code}
This seems to work for me. My only issue is if I have multiple partition
columns with different types.
This would return an error when I read the dataset with ds.dataset():
{code:java}
partitioning = ds.partitioning(pa.schema([('date', pa.date32()), ("project",
pa.dictionary(index_type=pa.int32(), value_type=pa.string()))]),
flavor="hive"){code}
ArrowInvalid: No dictionary provided for dictionary field project:
dictionary<values=string, indices=int32, ordered=0>
And this returns dictionaries for both partitions (instead of date being
pa.date32()) which is not ideal:
{code:java}
partitioning=ds.HivePartitioning.discover(infer_dictionary=True){code}
was (Author: ldacey):
What is the best workaround for this issue right now? If a column in the
partition columns is_dictionary(), then convert it to pa.string() to save the
dataset and then use ds.HivePartitioning.discover(infer_dictionary=True) to
read the dataset later?
> [C++][Dataset] Cannot write dataset with dictionary column as partition field
> -----------------------------------------------------------------------------
>
> Key: ARROW-10247
> URL: https://issues.apache.org/jira/browse/ARROW-10247
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++
> Reporter: Joris Van den Bossche
> Assignee: Ben Kietzman
> Priority: Major
> Labels: dataset, pull-request-available
> Fix For: 3.0.0
>
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> When the column to use for partitioning is dictionary encoded, we get this
> error:
> {code}
> In [9]: import pyarrow.dataset as ds
> In [10]: part = ["xxx"] * 3 + ["yyy"] * 3
> ...: table = pa.table([
> ...: pa.array(range(len(part))),
> ...: pa.array(part).dictionary_encode(),
> ...: ], names=['col', 'part'])
> In [11]: part = ds.partitioning(table.select(["part"]).schema)
> In [12]: ds.write_dataset(table, "test_dataset_dict_part", format="parquet",
> partitioning=part)
> ---------------------------------------------------------------------------
> ArrowTypeError Traceback (most recent call last)
> <ipython-input-12-c7b81c9b0bda> in <module>
> ----> 1 ds.write_dataset(table, "test_dataset_dict_part", format="parquet",
> partitioning=part)
> ~/scipy/repos/arrow/python/pyarrow/dataset.py in write_dataset(data,
> base_dir, basename_template, format, partitioning, schema, filesystem,
> file_options, use_threads)
> 773 _filesystemdataset_write(
> 774 data, base_dir, basename_template, schema,
> --> 775 filesystem, partitioning, file_options, use_threads,
> 776 )
> ~/scipy/repos/arrow/python/pyarrow/_dataset.pyx in
> pyarrow._dataset._filesystemdataset_write()
> ~/scipy/repos/arrow/python/pyarrow/error.pxi in pyarrow.lib.check_status()
> ArrowTypeError: scalar xxx (of type string) is invalid for part:
> dictionary<values=string, indices=int32, ordered=0>
> In ../src/arrow/dataset/filter.cc, line 1082, code:
> VisitConjunctionMembers(*and_.left_operand(), visitor)
> In ../src/arrow/dataset/partition.cc, line 257, code: VisitKeys(expr,
> [&](const std::string& name, const std::shared_ptr<Scalar>& value) { auto&&
> _error_or_value28 = (FieldRef(name).FindOneOrNone(*schema_)); do {
> ::arrow::Status __s =
> ::arrow::internal::GenericToStatus((_error_or_value28).status()); do { if
> ((__builtin_expect(!!(!__s.ok()), 0))) { ::arrow::Status _st = (__s);
> _st.AddContextLine("../src/arrow/dataset/partition.cc", 257,
> "(_error_or_value28).status()"); return _st; } } while (0); } while (false);
> auto match = std::move(_error_or_value28).ValueUnsafe();;; if (match) { const
> auto& field = schema_->field(match[0]); if
> (!value->type->Equals(field->type())) { return Status::TypeError("scalar ",
> value->ToString(), " (of type ", *value->type, ") is invalid for ",
> field->ToString()); } values[match[0]] = value.get(); } return Status::OK();
> })
> In ../src/arrow/dataset/file_base.cc, line 321, code:
> (_error_or_value24).status()
> In ../src/arrow/dataset/file_base.cc, line 354, code: task_group->Finish()
> {code}
> While this seems a quit normal use case, as this column will typically be
> repeated many times (and we also support reading it as such with dictionary
> type, so a roundtrip is currently not possible in that case)
> I tagged it for 2.0.0 for a moment in case it's possible today, but I didn't
> yet look into how easy it would be to fix.
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