Hatem Helal created ARROW-3564:
----------------------------------
Summary: pyarrow: writing version 2.0 parquet format with
dictionary encoding enabled
Key: ARROW-3564
URL: https://issues.apache.org/jira/browse/ARROW-3564
Project: Apache Arrow
Issue Type: Bug
Components: C++, Python
Affects Versions: 0.11.0
Reporter: Hatem Helal
Attachments: example_v1.0_dict_False.parquet,
example_v1.0_dict_True.parquet, example_v2.0_dict_False.parquet,
example_v2.0_dict_True.parquet, pyarrow_repro.py
Using pyarrow v0.11.0, the following script writes a simple table (lifted from
the [pyarrow doc|https://arrow.apache.org/docs/python/parquet.html]) to both
parquet format versions 1.0 and 2.0, with and without dictionary encoding
enabled.
|{{import}} {{pyarrow.parquet as pq}}
{{import}} {{numpy as np}}
{{import}} {{pandas as pd}}
{{import}} {{pyarrow as pa}}
{{import}} {{itertools}}
{{df }}{{=}} {{pd.DataFrame({}}{{'one'}}{{: [}}{{-}}{{1}}{{, np.nan,
}}{{2.5}}{{],}}
{{ }}{{'two'}}{{: [}}{{'foo'}}{{, }}{{'bar'}}{{, }}{{'baz'}}{{],}}
{{ }}{{'three'}}{{: [}}{{True}}{{, }}{{False}}{{, }}{{True}}{{]},}}
{{ }}{{index}}{{=}}{{list}}{{(}}{{'abc'}}{{))}}
{{table }}{{=}} {{pa.Table.from_pandas(df)}}
{{use_dict }}{{=}} {{[}}{{True}}{{, }}{{False}}{{]}}
{{version }}{{=}} {{[}}{{'1.0'}}{{, }}{{'2.0'}}{{]}}
{{for}} {{tf, v }}{{in}} {{itertools.product(use_dict, version):}}
{{ }}{{filename }}{{=}} {{'example_v'}} {{+}} {{v }}{{+}} {{'_dict_'}}
{{+}} {{str}}{{(tf) }}{{+}} {{'.parquet'}}
{{ }}{{pq.write_table(table, filename, use_dictionary}}{{=}}{{tf,
version}}{{=}}{{v)}}|
Inspecting the written files using
[parquet-tools|https://github.com/apache/parquet-mr/tree/master/parquet-tools]
appears to show that dictionary encoding is not used in either of the version
2.0 files. Both files report that the columns are encoded using {{PLAIN,RLE}}
and that the dictionary page offset is zero. I was expecting that the column
encoding would include {{RLE_DICTIONARY}}. Attached are the script with repro
steps and the files that were generated by it.
Below is the output of using {{parquet-tools meta}} on the version 2.0 files
{panel:title=version='2.0', use_dictionary = True}
{panel}
|{{% parquet-tools meta example_v2.0_dict_True.parquet}}
{{file: file:.../example_v2.0_dict_True.parquet}}
{{creator: parquet-cpp version 1.5.1-SNAPSHOT}}
{{extra: pandas = \{"pandas_version": "0.23.4", "index_columns":
["__index_level_0__"], "columns": [{"metadata": null, "field_name": "one",
"name": "one", "numpy_type": "float64", "pandas_type": "float64"},
\{"metadata": null, "field_name": "three", "name": "three", "numpy_type":
"bool", "pandas_type": "bool"}, \{"metadata": null, "field_name": "two",
"name": "two", "numpy_type": "object", "pandas_type": "bytes"}, \{"metadata":
null, "field_name": "__index_level_0__", "name": null, "numpy_type": "object",
"pandas_type": "bytes"}], "column_indexes": [\{"metadata": null, "field_name":
null, "name": null, "numpy_type": "object", "pandas_type": "bytes"}]}}}
{{file schema: schema}}
{{--------------------------------------------------------------------------------}}
{{one: OPTIONAL DOUBLE R:0 D:1}}
{{three: OPTIONAL BOOLEAN R:0 D:1}}
{{two: OPTIONAL BINARY R:0 D:1}}
{{__index_level_0__: OPTIONAL BINARY R:0 D:1}}
{{row group 1: RC:3 TS:211 OFFSET:4}}
{{--------------------------------------------------------------------------------}}
{{one: DOUBLE SNAPPY DO:0 FPO:4 SZ:65/63/0.97 VC:3 ENC:PLAIN,RLE
ST:[min: -1.0, max: 2.5, num_nulls: 1]}}
{{three: BOOLEAN SNAPPY DO:0 FPO:142 SZ:36/34/0.94 VC:3
ENC:PLAIN,RLE ST:[min: false, max: true, num_nulls: 0]}}
{{two: BINARY SNAPPY DO:0 FPO:225 SZ:60/58/0.97 VC:3
ENC:PLAIN,RLE ST:[min: 0x626172, max: 0x666F6F, num_nulls: 0]}}
{{__index_level_0__: BINARY SNAPPY DO:0 FPO:328 SZ:50/48/0.96 VC:3
ENC:PLAIN,RLE ST:[min: 0x61, max: 0x63, num_nulls: 0]}}|
{panel:title=version='2.0', use_dictionary = False}
{panel}
|{{% parquet-tools meta example_v2.0_dict_False.parquet}}
{{file: file:.../example_v2.0_dict_False.parquet}}
{{creator: parquet-cpp version 1.5.1-SNAPSHOT}}
{{extra: pandas = \{"pandas_version": "0.23.4", "index_columns":
["__index_level_0__"], "columns": [{"metadata": null, "field_name": "one",
"name": "one", "numpy_type": "float64", "pandas_type": "float64"},
\{"metadata": null, "field_name": "three", "name": "three", "numpy_type":
"bool", "pandas_type": "bool"}, \{"metadata": null, "field_name": "two",
"name": "two", "numpy_type": "object", "pandas_type": "bytes"}, \{"metadata":
null, "field_name": "__index_level_0__", "name": null, "numpy_type": "object",
"pandas_type": "bytes"}], "column_indexes": [\{"metadata": null, "field_name":
null, "name": null, "numpy_type": "object", "pandas_type": "bytes"}]}}}
{{file schema: schema}}
{{--------------------------------------------------------------------------------}}
{{one: OPTIONAL DOUBLE R:0 D:1}}
{{three: OPTIONAL BOOLEAN R:0 D:1}}
{{two: OPTIONAL BINARY R:0 D:1}}
{{__index_level_0__: OPTIONAL BINARY R:0 D:1}}
{{row group 1: RC:3 TS:211 OFFSET:4}}
{{--------------------------------------------------------------------------------}}
{{one: DOUBLE SNAPPY DO:0 FPO:4 SZ:65/63/0.97 VC:3 ENC:PLAIN,RLE
ST:[min: -1.0, max: 2.5, num_nulls: 1]}}
{{three: BOOLEAN SNAPPY DO:0 FPO:142 SZ:36/34/0.94 VC:3
ENC:PLAIN,RLE ST:[min: false, max: true, num_nulls: 0]}}
{{two: BINARY SNAPPY DO:0 FPO:225 SZ:60/58/0.97 VC:3
ENC:PLAIN,RLE ST:[min: 0x626172, max: 0x666F6F, num_nulls: 0]}}
{{__index_level_0__: BINARY SNAPPY DO:0 FPO:328 SZ:50/48/0.96 VC:3
ENC:PLAIN,RLE ST:[min: 0x61, max: 0x63, num_nulls: 0]}}|
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