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impala/testdata/data/README
Fucun Chu 7f60990028 IMPALA-10467: Implement ds_theta_union() function 
This function receives a set of serialized Apache DataSketches Theta
sketches produced by ds_theta_sketch() and merges them into a single
sketch.

An example usage is to create a sketch for each partition of a table,
write these sketches to a separate table and based on which partition
the user is interested of the relevant sketches can be union-ed
together to get an estimate. E.g.:
  SELECT
      ds_theta_estimate(ds_theta_union(sketch_col))
  FROM sketch_tbl
  WHERE partition_col=1 OR partition_col=5;

Testing:
  - Apart from the automated tests I added to this patch I also
    tested ds_theta_union() on a bigger dataset to check that
    serialization, deserialization and merging steps work well. I
    took TPCH25.linelitem, created a number of sketches with grouping
    by l_shipdate and called ds_theta_union() on those sketches

Change-Id: I91baf58c76eb43748acd5245047edac8c66761b2
Reviewed-on: http://gerrit.cloudera.org:8080/17048
Reviewed-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>
2021-02-19 13:32:09 +00:00

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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)
decimal_padded_fixed_len_byte_array.parquet:
Parquet file generated by a hacked Impala where decimals are encoded as
FIXED_LEN_BYTE_ARRAY with an extra byte of padding (IMPALA-2515). The
data is the same as functional.decimal_tbl.
decimal_padded_fixed_len_byte_array2.parquet:
Parquet file generated by a hacked Impala where decimals are encoded as
FIXED_LEN_BYTE_ARRAY with an extra byte of padding (IMPALA-2515).
The Impala was hacked to limit dictionaries to 2000 entries, and
PARQUET_PAGE_ROW_COUNT_LIMIT was set to 1234. The resulted in
a file with two dictionary encoded pages and multiple plain
encoded pages. The values are distributed across the full
range of DECIMAL(10, 0). The file was created as follows:
create table d(d decimal(10, 0)) stored as parquet;
set num_nodes=1;
set PARQUET_PAGE_ROW_COUNT_LIMIT=1234;
insert into d
select distinct cast((1000000000 - (o_orderkey * 110503)) % 1000000000 as decimal(9, 0))
from tpch_parquet.orders where o_orderkey < 50000;
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)
out_of_range_date.orc:
Created using a pre-3.1. Hive version (2.1.1.) to contain an out-of-range date value.
Took advantage of the incompatibility between Hive and Impala when Hive (before 3.1)
writes a date before 1582-10-15 is interpreted incorrectly by Impala. The values I wrote
with Hive:
2019-10-04, 1582-10-15, 0001-01-01, 9999-12-31
This is interpreted by Impala as:
2019-10-04, 1582-10-15, 0000-12-30 (invalid - date too small), 9999-12-31
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.
hive2_pre_gregorian.orc:
Same as the above but in ORC format instead of Parquet.
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.
corrupt_schema.orc:
ORC file from IMPALA-9277, generated by fuzz test. The file contains malformed metadata.
corrupt_root_type.orc:
ORC file for IMPALA-9249, generated by fuzz test. The root type of the schema is not
struct, which used to hit a DCHECK.
hll_sketches_from_hive.parquet:
This file contains a table that has some string columns to store serialized Apache
DataSketches HLL sketches created by Hive. Each column contains a sketch for a
specific data type. Covers the following types: TINYINT, INT, BIGINT, FLOAT, DOUBLE,
STRING, CHAR and VARCHAR. Has an additional column for NULL values.
hll_sketches_from_impala.parquet:
This holds the same sketches as hll_sketches_from_hive.parquet but these sketches were
created by Impala instead of Hive.
theta_sketches_from_hive.parquet:
This file contains a table that has some string columns to store serialized Apache
DataSketches Theta sketches created by Hive. Each column contains a sketch for a
specific data type. Covers the following types: TINYINT, INT, BIGINT, FLOAT, DOUBLE,
STRING, CHAR and VARCHAR. Has an additional column for NULL values.
theta_sketches_from_impala.parquet:
This holds the same sketches as theta_sketches_from_hive.parquet but these sketches were
created by Impala instead of Hive.
kll_sketches_from_hive.parquet:
This file contains a table that has some string columns to store serialized Apache
DataSketches KLL sketches created by Hive. Each column is for a different purpose:
- 'f': Float with distinct values.
- 'repetitions': Float with some repetition in the values.
- 'some_nulls': Float values and some NULLs.
- 'all_nulls': All values are NULLs.
- 'some_nans': Floats with some NaN values.
- 'all_nans': All values are NaNs.
kll_sketches_from_impala.parquet:
This holds the same sketches as kll_sketches_from_hive.parquet but these sketches were
created by Impala instead of Hive.
iceberg:
IMPALA-9741: Support querying Iceberg table by impala
We generated data by spark-shell, version is 2.4.x, and there are two tables' data in
testdata/data/iceberg_test. Iceberg table location contains
two directories: metadata, which contains table metadata managed
by iceberg; data, which contains the data files.
hudi_parquet:
IMPALA-8778: Support read Apache Hudi tables
Hudi parquet is a special format of parquet files managed by Apache Hudi
(hudi.incubator.apache.org) to provide ACID transaction.
In order to provide snapshot isolation between writer and queries,
Hudi will write a newer version of the existing parquet file
if there is any update comes into the file.
Hudi store the indexing information and version information in the file name.
For example:
`ca51fa17-681b-4497-85b7-4f68e7a63ee7-0_1-5-10_20200112194517.parquet`
`ca51fa17-681b-4497-85b7-4f68e7a63ee7-0` is the bloom index hash of this file
`20200112194517` is the timestamp of this version
If there is a record was updated in this file, Hudi will write a new file with
the same indexing hash but a newer version depends on the time of writing.
`ca51fa17-681b-4497-85b7-4f68e7a63ee7-0_1-38-282_20200112194529.parquet`
If the impala table was refreshed after this file was written, impala will
only query on the file with latest version.
streaming.orc:
ORC file generated by Hive Streaming Ingestion. I used a slightly altered version of
TestStreaming.testNoBuckets() from Hive 3.1 to generate this file. It contains
values coming from two transactions. The file has two stripes (one per transaction).
alltypes_non_acid.orc:
Non-acid ORC file generated by Hive 3.1 with the following command:
CREATE TABLE alltypes_clone STORED AS ORC AS SELECT * FROM functional.alltypes.
It's used as an original file in ACID tests.
dateless_timestamps.parq:
Parquet file generated before the removal of dateless timestamp support.
Created as: CREATE TABLE timestamps_pq(t TIMESTAMP) STORED AS parquet;
It contains the folloving lines:
1996-04-22 10:00:00.432100000
1996-04-22 10:00:00.432100000
1996-04-22 10:00:00
1996-04-22 10:00:00
1996-04-22 00:00:00
10:00:00.432100000
10:00:00
full_acid_schema_but_no_acid_version.orc
IMPALA-10115: Impala should check file schema as well to check full ACIDv2 files
Genereted by query-based compaction by Hive 3.1. The file has full ACIDv2 schema, but
doesn't have the file user metadata 'hive.acid.version'.
alltypes_empty_pages.parquet
Parquet file that contians empty data pages. Needed to test IMPALA-9952.
Generated by a modified Impala (git hash e038db44 (between 3.4 and 4.0)). I modified
HdfsParquetTableWriter::ShouldStartNewPage() to randomly start a new page:
int64_t r = random(); if (r % 2 + r % 3 + r % 5 == 0) return true;
Also modified HdfsParquetTableWriter::NewPage() to randomly insert empty pages:
if (r ... ) pages_.push_back(DataPage());
alltypes_invalid_pages.parquet
Parquet file that contains invalid data pages Needed to test IMPALA-9952.
Generated by a modified Impala (git hash e038db44 (between 3.4 and 4.0)). I modified
HdfsParquetTableWriter::ShouldStartNewPage() to randomly start a new page:
int64_t r = random(); if (r % 2 + r % 3 + r % 5 == 0) return true;
Also modified HdfsParquetTableWriter::BaseColumnWriter::Flush to randomly invalidate
the offset index:
if (r ... ) location.offset = -1;
customer_multiblock_page_index.parquet
Parquet file that contains multiple blocks in a single file Needed to test IMPALA-10310.
In order to generate this file, execute the following instruments:
// use 1.11.0 to generate page index
1. export HIVE_AUX_JARS_PATH=/path/parquet-hadoop-1.11.0.jar
// in hive shell
2. SET parquet.block.size=8192; // use little block size
3. SET parquet.page.row.count.limit=10; // little page row count generate multi pages
4. CREATE TABLE customer_multiblock_page_index_6
STORED AS PARQUET
TBLPROPERTIES('parquet.compression'='SNAPPY')
AS SELECT * FROM tpcds.customer
WHERE c_current_cdemo_sk IS NOT NULL ORDER BY c_current_cdemo_sk LIMIT 2000;
generated file will contains multi blocks, multi pages per block.
IMPALA-10361: Use field id to resolve columns for Iceberg tables
We generated data by spark-shell, version is 2.4.x, and table data is in
testdata/data/iceberg_test/hadoop_catalog/iceberg_resolution_test, this table
are generated with HadoopCatalog and Parquet fileformat. We use this table to
test complex types for field id resolving.
alltypes_tiny_rle_dictionary.parquet:
Tiny file using the RLE_DICTIONARY encoding.
Started impala with -write_new_parquet_dictionary_encodings=true
set max_fs_writers=1;
create table att stored as parquet as
select * from functional_parquet.alltypestiny;
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