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

bad_compressed_dict_page_size.parquet:
Generated by hacking Impala's Parquet writer.
Contains a single string column 'col' with one row ("a"). The compressed_page_size field
in dict page header is modifed to 0 to test if it is correctly handled.

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.

alltypes_agg_bitpacked_def_levels.parquet:
Generated by hacking Impala's Parquet writer to write out bitpacked def levels instead
of the standard RLE-encoded levels. See
https://github.com/timarmstrong/incubator-impala/tree/hack-bit-packed-levels. This
is a single file containing all of the alltypesagg data, which includes a mix of
null and non-null values. This is not actually a valid Parquet file because the
bit-packed levels are written in the reverse order specified in the Parquet spec
for BIT_PACKED. However, this is the order that Impala attempts to read the levels
in - see IMPALA-3006.

signed_integer_logical_types.parquet:
Generated using a utility that uses the java Parquet API.
The file has the following schema:
  schema {
    optional int32 id;
    optional int32 tinyint_col (INT_8);
    optional int32 smallint_col (INT_16);
    optional int32 int_col;
    optional int64 bigint_col;
  }

min_max_is_nan.parquet:
Generated by Impala's Parquet writer before the fix for IMPALA-6527. Git hash: 3a049a53
Created to test the read path for a Parquet file with invalid metadata, namely when
'max_value' and 'min_value' are both NaN. Contains 2 single-column rows:
NaN
42

bad_codec.parquet:
Generated by Impala's Parquet writer, hacked to use the invalid enum value 5000 for the
compression codec. The data in the file is the whole of the "alltypestiny" data set, with
the same columns: id int, bool_col boolean, tinyint_col tinyint, smallint_col smallint,
int_col int, bigint_col bigint, float_col float, double_col double,
date_string_col string, string_col string, timestamp_col timestamp, year int, month int

num_values_def_levels_mismatch.parquet:
A file with a single boolean column with page metadata reporting 2 values but only def
levels for a single literal value. Generated by hacking Impala's parquet writer to
increment page.header.data_page_header.num_values. This caused Impala to hit a DCHECK
(IMPALA-6589).

rle_encoded_bool.parquet:
Parquet v1 file with RLE encoded boolean column "b" and int column "i".
Created for IMPALA-6324, generated with modified parquet-mr. Contains 279 rows,
139 with value false, and 140 with value true. "i" is always 1 if "b" is True
and always 0 if "b" is false.

dict_encoding_with_large_bit_width.parquet:
Parquet file with a single TINYINT column "i" with 33 rows. Created by a modified
Impala to use 9 bit dictionary indices for encoding. Reading this file used to lead
to DCHECK errors (IMPALA-7147).

decimal_stored_as_int32.parquet:
Parquet file generated by Spark 2.3.1 that contains decimals stored as int32.
Impala needs to be able to read such values (IMPALA-5542)

decimal_stored_as_int64.parquet:
Parquet file generated by Spark 2.3.1 that contains decimals stored as int64.
Impala needs to be able to read such values (IMPALA-5542)

primitive_type_widening.parquet:
Parquet file that contains two rows with the following schema:
- int32 tinyint_col1
- int32 tinyint_col2
- int32 tinyint_col3
- int32 tinyint_col4
- int32 smallint_col1
- int32 smallint_col2
- int32 smallint_col3
- int32 int_col1
- int32 int_col2
- float float_col
It is used to test primitive type widening (IMPALA-6373).

corrupt_footer_len_decr.parquet:
Parquet file that contains one row of the following schema:
- bigint c
The footer size is manually modified (using hexedit) to be the original file size minus
1, to cause metadata deserialization in footer parsing to fail, thus trigger the printing
of an error message with incorrect file offset, to verify that it's fixed by IMPALA-6442.

corrupt_footer_len_incr.parquet:
Parquet file that contains one row of the following schema:
- bigint c
The footer size is manually modified (using hexedit) to be larger than the original file
size and cause footer parsing to fail. It's used to test an error message related to
IMPALA-6442.

hive_single_value_timestamp.parq:
Parquet file written by Hive with the followin schema:
i int, timestamp d
Contains a single row. It is used to test IMPALA-7559 which only occurs when all values
in a column chunk are the same timestamp and the file is written with parquet-mr (which
is used by Hive).

out_of_range_time_of_day.parquet:
IMPALA-7595: Parquet file that contains timestamps where the time part is out of the
valid range [0..24H). Before the fix, select * returned these values:
1970-01-01 -00:00:00.000000001  (invalid - negative time of day)
1970-01-01 00:00:00
1970-01-01 23:59:59.999999999
1970-01-01 24:00:00 (invalid - time of day should be less than a whole day)

strings_with_quotes.csv:
Various strings with quotes in them to reproduce bugs like IMPALA-7586.

int64_timestamps_plain.parq:
Parquet file generated with Parquet-mr that contains plain encoded int64 columns with
Timestamp logical types. Has the following columns:
new_logical_milli_utc, new_logical_milli_local,
new_logical_micro_utc, new_logical_micro_local

int64_timestamps_dict.parq:
Parquet file generated with Parquet-mr that contains dictionary encoded int64 columns
with Timestamp logical types. Has the following columns:
id,
new_logical_milli_utc, new_logical_milli_local,
new_logical_micro_utc, new_logical_micro_local

int64_timestamps_at_dst_changes.parquet:
Parquet file generated with Parquet-mr that contains plain encoded int64 columns with
Timestamp logical types. The file contains 3 row groups, and all row groups contain
3 distinct values, so there is a "min", a "max", and a "middle" value. The values were
selected in such a way that the UTC->CET conversion changes the order of the values (this
is possible during Summer->Winter DST change) and "middle" falls outside the "min".."max"
range after conversion. This means that a naive stat filtering implementation could drop
"middle" incorrectly.
Example (all dates are 2017-10-29):
UTC: 00:45:00, 01:00:00, 01:10:00 =>
CET: 02:45:00, 02:00:00, 02:10:00
Columns: rawvalue bigint, rowgroup int, millisutc timsestamp, microsutc timestamp

int64_timestamps_nano.parquet:
Parquet file generated with Parquet-mr that contains int64 columns with nanosecond
precision. Tested separately from the micro/millisecond columns because of the different
valid range.
Columns: rawvalue bigint, nanoutc timestamp, nanononutc timestamp

out_of_range_timestamp_hive_211.parquet:
Hive-generated file with an out-of-range timestamp. Generated with Hive 2.1.1 using
the following query:
create table alltypes_hive stored as parquet as
select * from functional.alltypes
union all
select -1, false, 0, 0, 0, 0, 0, 0, '', '', cast('1399-01-01 00:00:00' as timestamp), 0, 0

out_of_range_timestamp2_hive_211.parquet:
Hive-generated file with out-of-range timestamps every second value, to exercise code
paths in Parquet scanner for non-repeated runs. Generated with Hive 2.1.1 using
the following query:
create table hive_invalid_timestamps stored as parquet as
select id,
  case id % 3
    when 0 then timestamp_col
    when 1 then NULL
    when 2 then cast('1300-01-01 9:9:9' as timestamp)
  end timestamp_col
from functional.alltypes
sort by id

decimal_rtf_tbl.txt:
This was generated using formulas in Google Sheets.  The goal was to create various
decimal values that covers the 3 storage formats with various precision and scale.
This is a reasonably large table that is used for testing min-max filters
with decimal types on Kudu.

decimal_rtf_tiny_tbl.txt:
Small table with specific decimal values picked from decimal_rtf_tbl.txt so that
min-max filter based pruning can be tested with decimal types on Kudu.

date_tbl.orc
Small orc table with one DATE column, created by Hive.

date_tbl.avro
Small avro table with one DATE column, created by Hive.

date_tbl.parquet
Small parquet table with one DATE column, created by Parquet MR.

out_of_range_date.parquet:
Generated with a hacked version of Impala parquet writer.
Contains a single DATE column with 9 values, 4 of which are out of range
and should be read as NULL by Impala:
  -0001-12-31 (invalid - date too small)
   0000-01-01 (invalid - date too small)
   0000-01-02 (invalid - date too small)
   1969-12-31
   1970-01-01
   1970-01-02
   9999-12-30
   9999-12-31
  10000-01-01 (invalid - date too large)

hive2_pre_gregorian.parquet:
Small parquet table with one DATE column, created by Hive 2.1.1.
Used to demonstrate parquet interoperability issues between Hive and Impala for dates
before the introduction of Gregorian calendar in 1582-10-15.

decimals_1_10.parquet:
Contains two decimal columns, one with precision 1, the other with precision 10.
I used Hive 2.1.1 with a modified version of Parquet-MR (6901a20) to create tiny,
misaligned pages in order to test the value-skipping logic in the Parquet column readers.
The modification in Parquet-MR was to set MIN_SLAB_SIZE to 1. You can find the change
here: https://github.com/boroknagyz/parquet-mr/tree/tiny_pages
hive  --hiveconf parquet.page.row.count.limit=5 --hiveconf parquet.page.size=5
 --hiveconf parquet.enable.dictionary=false --hiveconf parquet.page.size.row.check.min=1
create table decimals_1_10 (d_1 DECIMAL(1, 0), d_10 DECIMAL(10, 0)) stored as PARQUET
insert into decimals_1_10 values (1, 1), (2, 2), (3, 3), (4, 4), (5, 5),
                            (NULL, 1), (2, 2), (3, 3), (4, 4), (5, 5),
                            (1, 1), (NULL, 2), (3, 3), (4, 4), (5, 5),
                            (1, 1), (2, 2), (NULL, 3), (4, 4), (5, 5),
                            (1, 1), (2, 2), (3, 3), (NULL, 4), (5, 5),
                            (1, 1), (2, 2), (3, 3), (4, 4), (NULL, 5),
                            (NULL, 1), (NULL, 2), (3, 3), (4, 4), (5, 5),
                            (1, 1), (NULL, 2), (3, 3), (NULL, 4), (5, 5),
                            (1, 1), (2, 2), (3, 3), (NULL, 4), (NULL, 5),
                            (NULL, 1), (2, 2), (NULL, 3), (NULL, 4), (5, 5),
                            (1, 1), (2, 2), (3, 3), (4, 4), (5, NULL);

nested_decimals.parquet:
Contains two columns, one is a decimal column, the other is an array of decimals.
I used Hive 2.1.1 with a modified Parquet-MR, see description at decimals_1_10.parquet.
hive  --hiveconf parquet.page.row.count.limit=5 --hiveconf parquet.page.size=16
 --hiveconf parquet.enable.dictionary=false --hiveconf parquet.page.size.row.check.min=1
create table nested_decimals (d_38 Decimal(38, 0), arr array<Decimal(1, 0)>) stored as parquet;
insert into nested_decimals select 1, array(cast (1 as decimal(1,0)), cast (1 as decimal(1,0)), cast (1 as decimal(1,0)) ) union all
                            select 2, array(cast (2 as decimal(1,0)), cast (2 as decimal(1,0)), cast (2 as decimal(1,0)) ) union all
                            select 3, array(cast (3 as decimal(1,0)), cast (3 as decimal(1,0)), cast (3 as decimal(1,0)) ) union all
                            select 4, array(cast (4 as decimal(1,0)), cast (4 as decimal(1,0)), cast (4 as decimal(1,0)) ) union all
                            select 5, array(cast (5 as decimal(1,0)), cast (5 as decimal(1,0)), cast (5 as decimal(1,0)) ) union all

                            select 1, array(cast (1 as decimal(1,0)) ) union all
                            select 2, array(cast (2 as decimal(1,0)), cast (2 as decimal(1,0)) ) union all
                            select 3, array(cast (3 as decimal(1,0)), cast (3 as decimal(1,0)), cast (3 as decimal(1,0)) ) union all
                            select 4, array(cast (4 as decimal(1,0)), cast (4 as decimal(1,0)), cast (4 as decimal(1,0)), cast (4 as decimal(1,0)) ) union all
                            select 5, array(cast (5 as decimal(1,0)), cast (5 as decimal(1,0)), cast (5 as decimal(1,0)), cast (5 as decimal(1,0)), cast (5 as decimal(1,0)) ) union all

                            select 1, array(cast (NULL as decimal(1, 0)), NULL, NULL) union all
                            select 2, array(cast (2 as decimal(1,0)), NULL, NULL) union all
                            select 3, array(cast (3 as decimal(1,0)), NULL, cast (3 as decimal(1,0))) union all
                            select 4, array(NULL, cast (4 as decimal(1,0)), cast (4 as decimal(1,0)), NULL) union all
                            select 5, array(NULL, cast (5 as decimal(1,0)), NULL, NULL, cast (5 as decimal(1,0)) ) union all

                            select 6, array(cast (6 as decimal(1,0)), NULL, cast (6 as decimal(1,0)) ) union all
                            select 7, array(cast (7 as decimal(1,0)), cast (7 as decimal(1,0)), cast (7 as decimal(1,0)), NULL ) union all
                            select 8, array(NULL, NULL, cast (8 as decimal(1,0)) ) union all
                            select 7, array(cast (7 as decimal(1,0)), cast (7 as decimal(1,0)), cast (7 as decimal(1,0)) ) union all
                            select 6, array(NULL, NULL, NULL, cast (6 as decimal(1,0)) );

double_nested_decimals.parquet:
Contains two columns, one is a decimal column, the other is an array of arrays of
decimals. I used Hive 2.1.1 with a modified Parquet-MR, see description
at decimals_1_10.parquet.
hive  --hiveconf parquet.page.row.count.limit=5 --hiveconf parquet.page.size=16
  --hiveconf parquet.enable.dictionary=false --hiveconf parquet.page.size.row.check.min=1
create table double_nested_decimals (d_38 Decimal(38, 0), arr array<array<Decimal(1, 0)>>) stored as parquet;
insert into double_nested_decimals select 1, array(array(cast (1 as decimal(1,0)), cast (1 as decimal(1,0)) )) union all
                                   select 2, array(array(cast (2 as decimal(1,0)), cast (2 as decimal(1,0)) )) union all
                                   select 3, array(array(cast (3 as decimal(1,0)), cast (3 as decimal(1,0)), cast (3 as decimal(1,0)) )) union all
                                   select 4, array(array(cast (4 as decimal(1,0)), cast (4 as decimal(1,0)), cast (4 as decimal(1,0)) )) union all
                                   select 5, array(array(cast (5 as decimal(1,0)), cast (5 as decimal(1,0)), cast (5 as decimal(1,0)) )) union all

                                   select 1, array(array(cast (1 as decimal(1,0))), array(cast (1 as decimal(1,0))), array(cast (1 as decimal(1,0))) ) union all
                                   select 2, array(array(cast (2 as decimal(1,0))), array(cast (2 as decimal(1,0))) ) union all
                                   select 3, array(array(cast (3 as decimal(1,0))), array(cast (3 as decimal(1,0))), array(cast (3 as decimal(1,0))) ) union all
                                   select 4, array(array(cast (4 as decimal(1,0))), array(cast (4 as decimal(1,0))) ) union all
                                   select 5, array(array(cast (5 as decimal(1,0))), array(cast (5 as decimal(1,0))) ) union all

                                   select 1, array(array(cast (1 as decimal(1,0))) ) union all
                                   select 2, array(array(cast (2 as decimal(1,0))), array(cast (2 as decimal(1,0))) ) union all
                                   select 3, array(array(cast (3 as decimal(1,0))), array(cast (3 as decimal(1,0))), array(cast (3 as decimal(1,0))) ) union all
                                   select 4, array(array(cast (4 as decimal(1,0))), array(cast (4 as decimal(1,0))) ) union all
                                   select 5, array(array(cast (5 as decimal(1,0))) ) union all

                                   select 1, array(array(cast (1 as decimal(1,0))), array(cast (1 as decimal(1,0))), array(cast (1 as decimal(1,0))) ) union all
                                   select 2, array(array(cast (2 as decimal(1,0))), array(cast (2 as decimal(1,0))) ) union all
                                   select 3, array(array(cast (3 as decimal(1,0))) ) union all
                                   select 4, array(array(cast (4 as decimal(1,0))), array(cast (4 as decimal(1,0))) ) union all
                                   select 5, array(array(cast (5 as decimal(1,0))), array(cast (5 as decimal(1,0))), array(cast (5 as decimal(1,0))) ) union all

                                   select 1, array(array(cast (1 as decimal(1,0))), array(cast (1 as decimal(1,0)), cast (1 as decimal(1,0))) ) union all
                                   select 2, array(array(cast (2 as decimal(1,0))) ) union all
                                   select 3, array(array(cast (3 as decimal(1,0)), cast (3 as decimal(1,0))), array(cast (3 as decimal(1,0))) ) union all
                                   select 4, array(array(cast (4 as decimal(1,0))), array(cast (4 as decimal(1,0)), cast (4 as decimal(1,0))), array(cast (4 as decimal(1,0))) ) union all
                                   select 5, array(array(cast (5 as decimal(1,0))), array(cast (5 as decimal(1,0))), array(cast (5 as decimal(1,0))) ) union all

                                   select 1, array(array(cast (NULL as decimal(1,0))), array(cast (NULL as decimal(1,0))), array(cast (1 as decimal(1,0))) ) union all
                                   select 2, array(array(cast (NULL as decimal(1,0))), array(cast (NULL as decimal(1,0))), array(cast (NULL as decimal(1,0))) ) union all
                                   select 3, array(array(cast (NULL as decimal(1,0))), array(cast (3 as decimal(1,0))), NULL ) union all
                                   select 4, array(NULL, NULL, array(cast (NULL as decimal(1,0)), NULL, NULL, NULL, NULL) ) union all
                                   select 5, array(array(NULL, cast (5 as decimal(1,0)), NULL, NULL, NULL) ) union all

                                   select 6, array(array(cast (6 as decimal(1,0)), NULL), array(cast (6 as decimal(1,0))) ) union all
                                   select 7, array(array(cast (7 as decimal(1,0)), cast (7 as decimal(1,0))), NULL ) union all
                                   select 8, array(array(NULL, NULL, cast (8 as decimal(1,0))) ) union all
                                   select 7, array(array(cast (7 as decimal(1,0)), cast (NULL as decimal(1,0))), array(cast (7 as decimal(1,0))) ) union all
                                   select 6, array(array(NULL, NULL, cast (6 as decimal(1,0))), array(NULL, cast (6 as decimal(1,0))) );

alltypes_tiny_pages.parquet:
Created from 'functional.alltypes' with small page sizes.
I used Hive 2.1.1 with a modified Parquet-MR, see description at decimals_1_10.parquet.
I used the following commands to create the file:
hive  --hiveconf parquet.page.row.count.limit=90 --hiveconf parquet.page.size=90 --hiveconf parquet.page.size.row.check.min=7
create table alltypes_tiny_pages stored as parquet as select * from functional_parquet.alltypes

alltypes_tiny_pages_plain.parquet:
Created from 'functional.alltypes' with small page sizes without dictionary encoding.
I used Hive 2.1.1 with a modified Parquet-MR, see description at decimals_1_10.parquet.
I used the following commands to create the file:
hive  --hiveconf parquet.page.row.count.limit=90 --hiveconf parquet.page.size=90 --hiveconf parquet.enable.dictionary=false  --hiveconf parquet.page.size.row.check.min=7
create table alltypes_tiny_pages_plain stored as parquet as select * from functional_parquet.alltypes

parent_table:
Created manually. Contains two columns, an INT and a STRING column. Together they form primary key for the table. This table is used to test primary key and foreign key
relationships along with parent_table_2 and child_table.

parent_table_2:
Created manually. Contains just one int column which is also the table's primary key. This table is used to test primary key and foreign key
relationships along with parent_table and child_table.

child_table:
Created manually. Contains four columns. 'seq' column is the primary key of this table. ('id', 'year') form a foreign key referring to parent_table('id', 'year') and 'a' is a
foreign key referring to parent_table_2's primary column 'a'.

out_of_range_timestamp.orc:
Created with Hive. ORC file with a single timestamp column 'ts'.
Contains one row (1300-01-01 00:00:00) which is outside Impala's valid time range.