| File | Description |
|---|---|
| delta_byte_array.parquet | string columns with DELTA_BYTE_ARRAY encoding. See delta_byte_array.md for details. |
| delta_length_byte_array.parquet | string columns with DELTA_LENGTH_BYTE_ARRAY encoding. |
| delta_binary_packed.parquet | INT32 and INT64 columns with DELTA_BINARY_PACKED encoding. See delta_binary_packed.md for details. |
| delta_encoding_required_column.parquet | required INT32 and STRING columns with delta encoding. See delta_encoding_required_column.md for details. |
| delta_encoding_optional_column.parquet | optional INT64 and STRING columns with delta encoding. See delta_encoding_optional_column.md for details. |
| nested_structs.rust.parquet | Used to test that the Rust Arrow reader can lookup the correct field from a nested struct. See ARROW-11452 |
| data_index_bloom_encoding_stats.parquet | optional STRING column. Contains optional metadata: bloom filters, column index, offset index and encoding stats. |
| data_index_bloom_encoding_with_length.parquet | Same as data_index_bloom_encoding_stats.parquet but has bloom_filter_length populated in the ColumnMetaData |
| null_list.parquet | an empty list. Generated from this json {"emptylist":[]} and for the purposes of testing correct read/write behaviour of this base case. |
| alltypes_tiny_pages.parquet | small page sizes with dictionary encoding with page index from impala. |
| alltypes_tiny_pages_plain.parquet | small page sizes with plain encoding with page index impala. |
| rle_boolean_encoding.parquet | option boolean columns with RLE encoding |
| fixed_length_byte_array.parquet | optional FIXED_LENGTH_BYTE_ARRAY column with page index. See fixed_length_byte_array.md for details. |
| int32_with_null_pages.parquet | optional INT32 column with random null pages. See int32_with_null_pages.md for details. |
| datapage_v1-uncompressed-checksum.parquet | uncompressed INT32 columns in v1 data pages with a matching CRC |
| datapage_v1-snappy-compressed-checksum.parquet | compressed INT32 columns in v1 data pages with a matching CRC |
| datapage_v1-corrupt-checksum.parquet | uncompressed INT32 columns in v1 data pages with a mismatching CRC |
| overflow_i16_page_cnt.parquet | row group with more than INT16_MAX pages |
| bloom_filter.bin | deprecated bloom filter binary with binary header and murmur3 hashing |
| bloom_filter.xxhash.bin | bloom filter binary with thrift header and xxhash hashing |
| nan_in_stats.parquet | statistics contains NaN in max, from PyArrow 0.8.0. See note below on "NaN in stats". |
| rle-dict-snappy-checksum.parquet | compressed and dictionary-encoded INT32 and STRING columns in format v2 with a matching CRC |
| plain-dict-uncompressed-checksum.parquet | uncompressed and dictionary-encoded INT32 and STRING columns in format v1 with a matching CRC |
| rle-dict-uncompressed-corrupt-checksum.parquet | uncompressed and dictionary-encoded INT32 and STRING columns in format v2 with a mismatching CRC |
| large_string_map.brotli.parquet | MAP(STRING, INT32) with a string column chunk of more than 2GB. See note below |
| float16_nonzeros_and_nans.parquet | Float16 (logical type) column with NaNs and nonzero finite min/max values |
| float16_zeros_and_nans.parquet | Float16 (logical type) column with NaNs and zeros as min/max values. . See note below |
| concatenated_gzip_members.parquet | 513 UINT64 numbers compressed using 2 concatenated gzip members in a single data page |
| byte_stream_split.zstd.parquet | Standard normals with BYTE_STREAM_SPLIT encoding. See note below |
| incorrect_map_schema.parquet | Contains a Map schema without explicitly required keys, produced by Presto. See note |
| column_chunk_key_value_metadata.parquet | two INT32 columns, one with column chunk key-value metadata {"foo": "bar", "thisiskeywithoutvalue": null} note that the second key "thisiskeywithoutvalue", does not have a value, but the value can be mapped to an empty string "" when read depending on the client |
| sort_columns.parquet | INT64 and BYTE_ARRAY columns with first column with nulls first and descending, second column with nulls last and ascending. This file contains two row groups with same data and sorting columns. |
| old_list_structure.parquet | Single LIST<LIST> column with legacy two-level list structure. See old_list_structure.md |
| repeated_primitive_no_list.parquet | REPEATED INT32 and BYTE_ARRAY fields without LIST annotation. See note |
| map_no_value.parquet | MAP with null values, MAP with INT32 keys and no values, and LIST column with same values as the MAP keys. See map_no_value.md |
| page_v2_empty_compressed.parquet | An INT32 column with DataPageV2, all values are null, the zero-sized data is compressed using ZSTD. This is a valid non-zero bytes ZSTD stream that uncompresses into 0 bytes. |
| datapage_v2_empty_datapage.snappy.parquet | A compressed FLOAT column with DataPageV2, a single row, value is null, the file uses Snappy compression, but there is no data for uncompression (see related issue). The zero bytes must not be attempted to be uncompressed, as this is an invalid Snappy stream. |
| unknown-logical-type.parquet | A file containing a column annotated with a LogicalType whose identifier has been set to an abitrary high value to check the behaviour of an old reader reading a file written by a new writer containing an unsupported type (see related issue). |
| int96_from_spark.parquet | Single column of (deprecated) int96 values that originated as Apache Spark microsecond-resolution timestamps. Some values are outside the range typically representable by 64-bit nanosecond-resolution timestamps. See int96_from_spark.md for details. |
| binary_truncated_min_max.parquet | A file containing six columns with exact, fully-truncated and partially-truncated max and min statistics and with the expected is_{min/max}_value_exact. (see note). |
TODO: Document what each file is in the table above.
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":
- encrypt_columns_and_footer_disable_aad_storage.parquet.encrypted
- 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.
The encrypt_columns_and_footer_bloom_filter.parquet.encrypted file enables Bloom filters
on double_field and float_field.
The files in data/aes256 were encrypted with the following keys and key ids (when using key_retriever) using parquet-mr:
- Encrypted/Signed Footer:
- key: {0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1}
- key_id: "kf"
- Encrypted column named double_field (including column and offset index):
- key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2}
- key_id: "kc1"
- Encrypted column named float_field (including column and offset index):
- key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,3}
- key_id: "kc2"
- Encrypted column named boolean_field (including column and offset index):
- key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,4}
- key_id: "kc3"
- Encrypted column named int32_field (including column and offset index):
- key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,5}
- key_id: "kc4"
- Encrypted column named ba_field (including column and offset index):
- key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,6}
- key_id: "kc5"
- Encrypted column named flba_field (including column and offset index):
- key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,7}
- key_id: "kc6"
- Encrypted column named int64_field (including column and offset index):
- key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,8}
- key_id: "kc7"
- Encrypted column named int96_field (including column and offset index):
- key: {1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,9}
- key_id: "kc8"
The corresponding schema in Java is:
// byte order is LITTLE_ENDIAN and PageWrite checksum is disabled.
public static final String BOOLEAN_FIELD_NAME = "boolean_field";
public static final String INT32_FIELD_NAME = "int32_field";
public static final String INT64_FIELD_NAME = "int64_field";
public static final String INT96_FIELD_NAME = "int96_field";
public static final String FLOAT_FIELD_NAME = "float_field";
public static final String DOUBLE_FIELD_NAME = "double_field";
public static final String BINARY_FIELD_NAME = "ba_field";
public static final String FIXED_LENGTH_BINARY_FIELD_NAME = "flba_field";
private static final MessageType SCHEMA = new MessageType(
"schema",
new PrimitiveType(REQUIRED, BOOLEAN, BOOLEAN_FIELD_NAME),
Types.required(INT32).as(LogicalTypeAnnotation.timeType(true, MILLIS)).named(INT32_FIELD_NAME),
new PrimitiveType(REPEATED, INT64, INT64_FIELD_NAME),
Types.required(INT96).named(INT96_FIELD_NAME),
new PrimitiveType(REQUIRED, FLOAT, FLOAT_FIELD_NAME),
new PrimitiveType(REQUIRED, DOUBLE, DOUBLE_FIELD_NAME),
new PrimitiveType(OPTIONAL, BINARY, BINARY_FIELD_NAME),
Types.required(FIXED_LEN_BYTE_ARRAY).length(FIXED_LENGTH).named(FIXED_LENGTH_BINARY_FIELD_NAME));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 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.
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: 80The 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.
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.
minis always -0 andmaxis 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: 76byte_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.
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.
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:
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);
}
}
}
Metadata for file: repeated_primitive_no_list.parquet
version: 1
num of rows: 4
created by: parquet-rs version 53.2.0
message schema {
REPEATED INT32 Int32_list;
REPEATED BYTE_ARRAY String_list (UTF8);
REQUIRED group group_of_lists {
REPEATED INT32 Int32_list_in_group;
REPEATED BYTE_ARRAY String_list_in_group (UTF8);
}
}
For the file: binary_truncated_min_max.parquet
The file contains six columns written with parquet-rs 55.1.0 with statistics_truncate_length=2.
The contents are the following:
| column_name | min | is_min_value_exact | max | is_max_value_exact |
|---|---|---|---|---|
| utf8_full_truncation | "Al" | false | "Kf" | false |
| binary_full_truncation | "0x416C" | false | "0x4B66" | false |
| utf8_partial_truncation | "Al" | false | "🚀Kevin Bacon" | true |
| binary_partial_truncation | "0x416C" | false | "0xFFFF0102" | true |
| utf8_no_truncation | "Al" | true | "Ke" | true |
| binary_no_truncation | "0x416C" | true | "0x4B65" | true |
Columns utf8_full_truncation and binary_full_truncation are truncating the min/max values and is_{min/max}_value_exact are false.
Columns utf8_partial_truncation and binary_partial_truncation are truncating min value but can't truncate the maximum value. is_min_value_exact is false but is_max_value_exact is true.
Columns utf8_no_truncation and binary_no_truncation contain min and max value that fit on min/max. Both is_{min/max}_value_exact are true.
Some info:
$ java -jar parquet-cli/target/parquet-cli-1.16.0-SNAPSHOT-runtime.jar meta /home/raulcd/code/parquet_truncate_file_generator/binary_truncated_min_max.parquet
File path: /home/raulcd/code/parquet_truncate_file_generator/binary_truncated_min_max.parquet
Created by: parquet-rs version 55.1.0
Properties:
ARROW:schema: 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
Schema:
message arrow_schema {
required binary utf8_full_truncation (STRING);
required binary binary_full_truncation;
required binary utf8_partial_truncation (STRING);
required binary binary_partial_truncation;
required binary utf8_no_truncation (STRING);
required binary binary_no_truncation;
}
Row group 0: count: 12 117.83 B records start: 4 total(compressed): 1.381 kB total(uncompressed):1.381 kB
--------------------------------------------------------------------------------
type encodings count avg size nulls min / max
utf8_full_truncation BINARY _ BB_ 12 20.83 B 0 "Al" / "Kf"
binary_full_truncation BINARY _ BB_ 12 20.83 B 0 "0x416C" / "0x4B66"
utf8_partial_truncation BINARY _ BB_ 12 21.50 B 0 "Al" / "🚀Kevin Bacon"
binary_partial_truncation BINARY _ BB_ 12 19.67 B 0 "0x416C" / "0xFFFF0102"
utf8_no_truncation BINARY _ BB_ 12 17.50 B 0 "Al" / "Ke"
binary_no_truncation BINARY _ BB_ 12 17.50 B 0 "0x416C" / "0x4B65"
and
java -jar parquet-cli/target/parquet-cli-1.16.0-SNAPSHOT-runtime.jar cat /home/raulcd/code/parquet_truncate_file_generator/binary_truncated_min_max.parquet
{"utf8_full_truncation": "Blart Versenwald III", "binary_full_truncation": "Blart Versenwald III", "utf8_partial_truncation": "Blart Versenwald III", "binary_partial_truncation": "Blart Versenwald III", "utf8_no_truncation": "Blart Versenwald III", "binary_no_truncation": "Blart Versenwald III"}
{"utf8_full_truncation": "Alice Johnson", "binary_full_truncation": "Alice Johnson", "utf8_partial_truncation": "Alice Johnson", "binary_partial_truncation": "Alice Johnson", "utf8_no_truncation": "Al", "binary_no_truncation": "Al"}
{"utf8_full_truncation": "Bob Smith", "binary_full_truncation": "Bob Smith", "utf8_partial_truncation": "Bob Smith", "binary_partial_truncation": "Bob Smith", "utf8_no_truncation": "Bob Smith", "binary_no_truncation": "Bob Smith"}
{"utf8_full_truncation": "Charlie Brown", "binary_full_truncation": "Charlie Brown", "utf8_partial_truncation": "Charlie Brown", "binary_partial_truncation": "Charlie Brown", "utf8_no_truncation": "Charlie Brown", "binary_no_truncation": "Charlie Brown"}
{"utf8_full_truncation": "Diana Prince", "binary_full_truncation": "Diana Prince", "utf8_partial_truncation": "Diana Prince", "binary_partial_truncation": "Diana Prince", "utf8_no_truncation": "Diana Prince", "binary_no_truncation": "Diana Prince"}
{"utf8_full_truncation": "Edward Norton", "binary_full_truncation": "Edward Norton", "utf8_partial_truncation": "Edward Norton", "binary_partial_truncation": "Edward Norton", "utf8_no_truncation": "Edward Norton", "binary_no_truncation": "Edward Norton"}
{"utf8_full_truncation": "Fiona Apple", "binary_full_truncation": "Fiona Apple", "utf8_partial_truncation": "Fiona Apple", "binary_partial_truncation": "Fiona Apple", "utf8_no_truncation": "Fiona Apple", "binary_no_truncation": "Fiona Apple"}
{"utf8_full_truncation": "George Lucas", "binary_full_truncation": "George Lucas", "utf8_partial_truncation": "George Lucas", "binary_partial_truncation": "George Lucas", "utf8_no_truncation": "George Lucas", "binary_no_truncation": "George Lucas"}
{"utf8_full_truncation": "Helen Keller", "binary_full_truncation": "Helen Keller", "utf8_partial_truncation": "Helen Keller", "binary_partial_truncation": "Helen Keller", "utf8_no_truncation": "Helen Keller", "binary_no_truncation": "Helen Keller"}
{"utf8_full_truncation": "Ivan Drago", "binary_full_truncation": "Ivan Drago", "utf8_partial_truncation": "Ivan Drago", "binary_partial_truncation": "Ivan Drago", "utf8_no_truncation": "Ivan Drago", "binary_no_truncation": "Ivan Drago"}
{"utf8_full_truncation": "Julia Roberts", "binary_full_truncation": "Julia Roberts", "utf8_partial_truncation": "Julia Roberts", "binary_partial_truncation": "Julia Roberts", "utf8_no_truncation": "Julia Roberts", "binary_no_truncation": "Julia Roberts"}
{"utf8_full_truncation": "Kevin Bacon", "binary_full_truncation": "Kevin Bacon", "utf8_partial_truncation": "🚀Kevin Bacon", "binary_partial_truncation": "ÿÿ\u0001\u0002", "utf8_no_truncation": "Ke", "binary_no_truncation": "Ke"}