Test data files for Parquet compatibility and regression testing

TODO: Document what each file is in the table above.

Encrypted Files

Tests files with .parquet.encrypted suffix are encrypted using Parquet Modular Encryption.

A detailed description of the Parquet Modular Encryption specification can be found here:

 https://github.com/apache/parquet-format/blob/encryption/Encryption.md

Following are the keys and key ids (when using key_retriever) used to encrypt the encrypted columns and footer in all the encrypted files:

• Encrypted/Signed Footer:
  • key: {0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5}
  • key_id: “kf”
• Encrypted column named double_field (including column and offset index):
  • key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,0}
  • key_id: “kc1”
• Encrypted column named float_field (including column and offset index):
  • key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,1}
  • key_id: “kc2”

The following files are encrypted with AAD prefix “tester”:

1. encrypt_columns_and_footer_disable_aad_storage.parquet.encrypted
2. encrypt_columns_and_footer_aad.parquet.encrypted

A sample that reads and checks these files can be found at the following tests in Parquet C++:

cpp/src/parquet/encryption/read-configurations-test.cc
cpp/src/parquet/encryption/test-encryption-util.h

The external_key_material_java.parquet.encrypted file was encrypted using parquet-mr with external key material enabled, so the key material is found in the _KEY_MATERIAL_FOR_external_key_material_java.parquet.encrypted.json file. This data was written using the org.apache.parquet.crypto.keytools.mocks.InMemoryKMS KMS client, which is compatible with the TestOnlyInServerWrapKms KMS client used in C++ tests.

Checksum Files

The schema for the datapage_v1-*-checksum.parquet test files is:

message m {
    required int32 a;
    required int32 b;
}

The detailed structure for these files is as follows:

• data/datapage_v1-uncompressed-checksum.parquet:

  [ Column "a" [ Page 0 [correct crc] | Uncompressed Contents ][ Page 1 [correct crc] | Uncompressed Contents ]]
  [ Column "b" [ Page 0 [correct crc] | Uncompressed Contents ][ Page 1 [correct crc] | Uncompressed Contents ]]
  

• data/datapage_v1-snappy-compressed-checksum.parquet:

  [ Column "a" [ Page 0 [correct crc] | Snappy Contents ][ Page 1 [correct crc] | Snappy Contents ]]
  [ Column "b" [ Page 0 [correct crc] | Snappy Contents ][ Page 1 [correct crc] | Snappy Contents ]]
  

• data/datapage_v1-corrupt-checksum.parquet:

  [ Column "a" [ Page 0 [bad crc] | Uncompressed Contents ][ Page 1 [correct crc] | Uncompressed Contents ]]
  [ Column "b" [ Page 0 [correct crc] | Uncompressed Contents ][ Page 1 [bad crc] | Uncompressed Contents ]]
  

The schema for the *-dict-*-checksum.parquet test files is:

• data/rle-dict-snappy-checksum.parquet:

  [ Column "long_field" [ Dict Page [correct crc] | Compressed PLAIN Contents ][ Page 0 [correct crc] | Compressed RLE_DICTIONARY Contents ]]
  [ Column "binary_field" [ Dict Page [correct crc] | Compressed PLAIN Contents ][ Page 0 [correct crc] | Compressed RLE_DICTIONARY Contents ]]
  

• data/plain-dict-uncompressed-checksum.parquet:

  [ Column "long_field" [ Dict Page [correct crc] | Uncompressed PLAIN_DICTIONARY(DICT) Contents ][ Page 0 [correct crc] | Uncompressed PLAIN_DICTIONARY Contents ]]
  [ Column "binary_field" [ Dict Page [correct crc] | Uncompressed PLAIN_DICTIONARY(DICT) Contents ][ Page 0 [correct crc] | Uncompressed PLAIN_DICTIONARY Contents ]]
  

• data/rle-dict-uncompressed-corrupt-checksum.parquet:

  [ Column "long_field" [ Dict Page [bad crc] | Uncompressed PLAIN Contents ][ Page 0 [correct crc] | Uncompressed RLE_DICTIONARY Contents ]]
  [ Column "binary_field" [ Dict Page [bad crc] | Uncompressed PLAIN Contents ][ Page 0 [correct crc] | Uncompressed RLE_DICTIONARY Contents ]]
  

Bloom Filter Files

Bloom filter examples have been generated by parquet-mr. They are not Parquet files but only contain the bloom filter header and payload.

For each of bloom_filter.bin and bloom_filter.xxhash.bin, the bloom filter was generated by inserting the strings “hello”, “parquet”, “bloom”, “filter”.

bloom_filter.bin uses the original Murmur3-based bloom filter format as of https://github.com/apache/parquet-format/commit/54839ad5e04314c944fed8aa4bc6cf15e4a58698.

bloom_filter.xxhash.bin uses the newer xxHash-based bloom filter format as of https://github.com/apache/parquet-format/commit/3fb10e00c2204bf1c6cc91e094c59e84cefcee33.

NaN in stats

Prior to version 1.4.0, the C++ Parquet writer would write NaN values in min and max statistics. (Correction in this issue). It has been updated since to ignore NaN values when calculating statistics, but for backwards compatibility the following rules were established (in PARQUET-1222):

For backwards compatibility when reading files:

• If the min is a NaN, it should be ignored.
• If the max is a NaN, it should be ignored.
• If the min is +0, the row group may contain -0 values as well.
• If the max is -0, the row group may contain +0 values as well.
• When looking for NaN values, min and max should be ignored.

The file nan_in_stats.parquet was generated with:

import pyarrow as pa # version 0.8.0
import pyarrow.parquet as pq
from numpy import NaN

tab = pa.Table.from_arrays(
    [pa.array([1.0, NaN])],
    names="x"
)

pq.write_table(tab, "nan_in_stats.parquet")

metadata = pq.read_metadata("nan_in_stats.parquet")
metadata.row_group(0).column(0)
# <pyarrow._parquet.ColumnChunkMetaData object at 0x7f28539e58f0>
#   file_offset: 88
#   file_path:
#   type: DOUBLE
#   num_values: 2
#   path_in_schema: x
#   is_stats_set: True
#   statistics:
#     <pyarrow._parquet.RowGroupStatistics object at 0x7f28539e5738>
#       has_min_max: True
#       min: 1
#       max: nan
#       null_count: 0
#       distinct_count: 0
#       num_values: 2
#       physical_type: DOUBLE
#   compression: 1
#   encodings: <map object at 0x7f28539eb4e0>
#   has_dictionary_page: True
#   dictionary_page_offset: 4
#   data_page_offset: 36
#   index_page_offset: 0
#   total_compressed_size: 84
#   total_uncompressed_size: 80

Large string map

The file large_string_map.brotli.parquet was generated with:

import pyarrow as pa
import pyarrow.parquet as pq

arr = pa.array([[("a" * 2**30, 1)]], type = pa.map_(pa.string(), pa.int32()))
arr = pa.chunked_array([arr, arr])
tab = pa.table({ "arr": arr })

pq.write_table(tab, "test.parquet", compression='BROTLI')

It is meant to exercise reading of structured data where each value is smaller than 2GB but the combined uncompressed column chunk size is greater than 2GB.

Float16 Files

The files float16_zeros_and_nans.parquet and float16_nonzeros_and_nans.parquet are meant to exercise a variety of test cases regarding Float16 columns (which are represented as 2-byte FixedLenByteArrays), including:

• Basic binary representations of standard values, +/- zeros, and NaN
• Comparisons between finite values
• Exclusion of NaNs from statistics min/max
• Normalizing min/max values when only zeros are present (i.e. min is always -0 and max is always +0)

The aforementioned files were generated with:

import pyarrow as pa
import pyarrow.parquet as pq
import numpy as np

t1 = pa.Table.from_arrays(
    [pa.array([None,
               np.float16(0.0),
               np.float16(np.NaN)], type=pa.float16())],
    names="x")
t2 = pa.Table.from_arrays(
    [pa.array([None,
               np.float16(1.0),
               np.float16(-2.0),
               np.float16(np.NaN),
               np.float16(0.0),
               np.float16(-1.0),
               np.float16(-0.0),
               np.float16(2.0)],
              type=pa.float16())],
    names="x")

pq.write_table(t1, "float16_zeros_and_nans.parquet", compression='none')
pq.write_table(t2, "float16_nonzeros_and_nans.parquet", compression='none')

m1 = pq.read_metadata("float16_zeros_and_nans.parquet")
m2 = pq.read_metadata("float16_nonzeros_and_nans.parquet")

print(m1.row_group(0).column(0))
print(m2.row_group(0).column(0))
# <pyarrow._parquet.ColumnChunkMetaData object at 0x7f79e9a3d850>
#   file_offset: 68
#   file_path:
#   physical_type: FIXED_LEN_BYTE_ARRAY
#   num_values: 3
#   path_in_schema: x
#   is_stats_set: True
#   statistics:
#     <pyarrow._parquet.Statistics object at 0x7f79e9a3d940>
#       has_min_max: True
#       min: b'\x00\x80'
#       max: b'\x00\x00'
#       null_count: 1
#       distinct_count: None
#       num_values: 2
#       physical_type: FIXED_LEN_BYTE_ARRAY
#       logical_type: Float16
#       converted_type (legacy): NONE
#   compression: UNCOMPRESSED
#   encodings: ('PLAIN', 'RLE', 'RLE_DICTIONARY')
#   has_dictionary_page: True
#   dictionary_page_offset: 4
#   data_page_offset: 22
#   total_compressed_size: 64
#   total_uncompressed_size: 64
# <pyarrow._parquet.ColumnChunkMetaData object at 0x7f79ea003c40>
#   file_offset: 80
#   file_path:
#   physical_type: FIXED_LEN_BYTE_ARRAY
#   num_values: 8
#   path_in_schema: x
#   is_stats_set: True
#   statistics:
#     <pyarrow._parquet.Statistics object at 0x7f79e9a3d8a0>
#       has_min_max: True
#       min: b'\x00\xc0'
#       max: b'\x00@'
#       null_count: 1
#       distinct_count: None
#       num_values: 7
#       physical_type: FIXED_LEN_BYTE_ARRAY
#       logical_type: Float16
#       converted_type (legacy): NONE
#   compression: UNCOMPRESSED
#   encodings: ('PLAIN', 'RLE', 'RLE_DICTIONARY')
#   has_dictionary_page: True
#   dictionary_page_offset: 4
#   data_page_offset: 32
#   total_compressed_size: 76
#   total_uncompressed_size: 76

Byte Stream Split

FLOAT and DOUBLE data

byte_stream_split.zstd.parquet is generated by pyarrow 14.0.2 using the following code:

import pyarrow as pa
from pyarrow import parquet as pq
import numpy as np

np.random.seed(0)
table = pa.Table.from_pydict({
  'f32': np.random.normal(size=300).astype(np.float32),
  'f64': np.random.normal(size=300).astype(np.float64),
})

pq.write_table(
  table,
  'byte_stream_split.parquet',
  version='2.6',
  compression='zstd',
  compression_level=22,
  column_encoding='BYTE_STREAM_SPLIT',
  use_dictionary=False,
)

This is a practical case where BYTE_STREAM_SPLIT encoding obtains a smaller file size than PLAIN or dictionary. Since the distributions are random normals centered at 0, each byte has nontrivial behavior.

Additional types

byte_stream_split_extended.gzip.parquet is generated by pyarrow 16.0.0. It contains 7 pairs of columns, each in two variants containing the same values: one PLAIN-encoded and one BYTE_STREAM_SPLIT-encoded:

Version: 2.6
Created By: parquet-cpp-arrow version 16.0.0-SNAPSHOT
Total rows: 200
Number of RowGroups: 1
Number of Real Columns: 14
Number of Columns: 14
Number of Selected Columns: 14
Column 0: float16_plain (FIXED_LEN_BYTE_ARRAY(2) / Float16)
Column 1: float16_byte_stream_split (FIXED_LEN_BYTE_ARRAY(2) / Float16)
Column 2: float_plain (FLOAT)
Column 3: float_byte_stream_split (FLOAT)
Column 4: double_plain (DOUBLE)
Column 5: double_byte_stream_split (DOUBLE)
Column 6: int32_plain (INT32)
Column 7: int32_byte_stream_split (INT32)
Column 8: int64_plain (INT64)
Column 9: int64_byte_stream_split (INT64)
Column 10: flba5_plain (FIXED_LEN_BYTE_ARRAY(5))
Column 11: flba5_byte_stream_split (FIXED_LEN_BYTE_ARRAY(5))
Column 12: decimal_plain (FIXED_LEN_BYTE_ARRAY(4) / Decimal(precision=7, scale=3) / DECIMAL(7,3))
Column 13: decimal_byte_stream_split (FIXED_LEN_BYTE_ARRAY(4) / Decimal(precision=7, scale=3) / DECIMAL(7,3))

To check conformance of a BYTE_STREAM_SPLIT decoder, read each BYTE_STREAM_SPLIT-encoded column and compare the decoded values against the values from the corresponding PLAIN-encoded column. The values should be equal.

Incorrect Map Schema

A number of producers, such as Presto/Trino/Athena, have been creating files with schemas where the Map key fields are marked as optional rather than required. This is not spec-compliant, yet appears in a number of existing data files in the wild.

This issue has been fixed in:

• Trino v386+
• Presto v0.274+

We can recreate these problematic files for testing arrow-rs #5630 with relevant Presto/Trino CLI, or with AWS Athena Console:

CREATE TABLE my_catalog.my_table_name WITH (format = 'Parquet') AS (
    SELECT MAP (
        ARRAY['name', 'parent'],
        ARRAY[
            'report',
            'another'
        ]
    ) my_map
)

The schema in the created file is:

message hive_schema {
  OPTIONAL group my_map (MAP) {
    REPEATED group key_value (MAP_KEY_VALUE) {
      OPTIONAL BYTE_ARRAY key (STRING);
      OPTIONAL BYTE_ARRAY value (STRING);
    }
  }
}