impala/testdata/data

bad_parquet_data.parquet:
Generated with parquet-mr 1.2.5
Contains 3 single-column rows:
"parquet"
"is"
"fun"

bad_rle_literal_count.parquet:
Generated by hacking Impala's Parquet writer.
Contains a single bigint column 'c' with the values 1, 3, 7 stored
in a single data chunk as dictionary plain. The RLE encoded dictionary
indexes are all literals (and not repeated), but the literal count
is incorrectly 0 in the file to test that such data corruption is
proprly handled.

bad_rle_repeat_count.parquet:
Generated by hacking Impala's Parquet writer.
Contains a single bigint column 'c' with the value 7 repeated 7 times
stored in a single data chunk as dictionary plain. The RLE encoded dictionary
indexes are a single repeated run (and not literals), but the repeat count
is incorrectly 0 in the file to test that such data corruption is proprly
handled.

zero_rows_zero_row_groups.parquet:
Generated by hacking Impala's Parquet writer.
The file metadata indicates zero rows and no row groups.

zero_rows_one_row_group.parquet:
Generated by hacking Impala's Parquet writer.
The file metadata indicates zero rows but one row group.

huge_num_rows.parquet
Generated by hacking Impala's Parquet writer.
The file metadata indicates 2 * MAX_INT32 rows.
The single row group also has the same number of rows in the metadata.

repeated_values.parquet:
Generated with parquet-mr 1.2.5
Contains 3 single-column rows:
"parquet"
"parquet"
"parquet"

multiple_rowgroups.parquet:
Generated with parquet-mr 1.2.5
Populated with:
hive> set parquet.block.size=500;
hive> INSERT INTO TABLE tbl
      SELECT l_comment FROM tpch.lineitem LIMIT 1000;

alltypesagg_hive_13_1.parquet:
Generated with parquet-mr version 1.5.0-cdh5.4.0-SNAPSHOT
hive> create table alltypesagg_hive_13_1 stored as parquet as select * from alltypesagg;

bad_column_metadata.parquet:
Generated with hacked version of parquet-mr 1.8.2-SNAPSHOT
Schema:
 {"type": "record",
  "namespace": "org.apache.impala",
  "name": "bad_column_metadata",
  "fields": [
      {"name": "id", "type": ["null", "long"]},
      {"name": "int_array", "type": ["null", {"type": "array", "items": ["null", "int"]}]}
  ]
 }
Contains 3 row groups, each with ten rows and each array containing ten elements. The
first rowgroup column metadata for 'int_array' incorrectly states there are 50 values
(instead of 100), and the second rowgroup column metadata for 'id' incorrectly states
there are 11 values (instead of 10). The third rowgroup has the correct metadata.

data-bzip2.bz2:
Generated with bzip2, contains single bzip2 stream
Contains 1 column, uncompressed data size < 8M

large_bzip2.bz2:
Generated with bzip2, contains single bzip2 stream
Contains 1 column, uncompressed data size > 8M

data-pbzip2.bz2:
Generated with pbzip2, contains multiple bzip2 streams
Contains 1 column, uncompressed data size < 8M

large_pbzip2.bz2:
Generated with pbzip2, contains multiple bzip2 stream
Contains 1 column, uncompressed data size > 8M

out_of_range_timestamp.parquet:
Generated with a hacked version of Impala parquet writer.
Contains a single timestamp column with 4 values, 2 of which are out of range
and should be read as NULL by Impala:
   1399-12-31 00:00:00 (invalid - date too small)
   1400-01-01 00:00:00
   9999-12-31 00:00:00
  10000-01-01 00:00:00 (invalid - date too large)

table_with_header.csv:
Created with a text editor, contains a header line before the data rows.

table_with_header_2.csv:
Created with a text editor, contains two header lines before the data rows.

table_with_header.gz, table_with_header_2.gz:
Generated by gzip'ing table_with_header.csv and table_with_header_2.csv.

deprecated_statistics.parquet:
Generated with with hive shell, which uses parquet-mr version 1.5.0-cdh5.12.0-SNAPSHOT
Contains a copy of the data in functional.alltypessmall with statistics that use the old
'min'/'max' fields.

repeated_root_schema.parquet:
Generated by hacking Impala's Parquet writer.
Created to reproduce IMPALA-4826. Contains a table of 300 rows where the
repetition level of the root schema is set to REPEATED.
Reproduction steps:
1: Extend HdfsParquetTableWriter::CreateSchema with the following line:
   file_metadata_.schema[0].__set_repetition_type(FieldRepetitionType::REQUIRED);
2: Run test_compute_stats and grab the created Parquet file for
   alltypes_parquet table.

binary_decimal_dictionary.parquet,
binary_decimal_no_dictionary.parquet:
Generated using parquet-mr and contents verified using parquet-tools-1.9.1.
Contains decimals stored as variable sized BYTE_ARRAY with both dictionary
and non-dictionary encoding respectively.