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Hive

The Hive engine allows you to perform SELECT quries on HDFS Hive table. Currently it supports input formats as below:

  • Text: only supports simple scalar column types except binary

  • ORC: support simple scalar columns types except char; only support complex types like array

  • Parquet: support all simple scalar columns types; only support complex types like array

Creating a Table

CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
(
name1 [type1] [ALIAS expr1],
name2 [type2] [ALIAS expr2],
...
) ENGINE = Hive('thrift://host:port', 'database', 'table');
PARTITION BY expr

See a detailed description of the CREATE TABLE query.

The table structure can differ from the original Hive table structure:

  • Column names should be the same as in the original Hive table, but you can use just some of these columns and in any order, also you can use some alias columns calculated from other columns.
  • Column types should be the same from those in the original Hive table.
  • Partition by expression should be consistent with the original Hive table, and columns in partition by expression should be in the table structure.

Engine Parameters

  • thrift://host:port — Hive Metastore address

  • database — Remote database name.

  • table — Remote table name.

Usage Example

How to Use Local Cache for HDFS Filesystem

We strongly advice you to enable local cache for remote filesystems. Benchmark shows that its almost 2x faster with cache.

Before using cache, add it to config.xml

<local_cache_for_remote_fs>
<enable>true</enable>
<root_dir>local_cache</root_dir>
<limit_size>559096952</limit_size>
<bytes_read_before_flush>1048576</bytes_read_before_flush>
</local_cache_for_remote_fs>
  • enable: ClickHouse will maintain local cache for remote filesystem(HDFS) after startup if true.
  • root_dir: Required. The root directory to store local cache files for remote filesystem.
  • limit_size: Required. The maximum size(in bytes) of local cache files.
  • bytes_read_before_flush: Control bytes before flush to local filesystem when downloading file from remote filesystem. The default value is 1MB.

When ClickHouse is started up with local cache for remote filesystem enabled, users can still choose not to use cache with settings use_local_cache_for_remote_fs = 0 in their query. use_local_cache_for_remote_fs is false in default.

Query Hive Table with ORC Input Format

Create Table in Hive

hive > CREATE TABLE `test`.`test_orc`(
`f_tinyint` tinyint,
`f_smallint` smallint,
`f_int` int,
`f_integer` int,
`f_bigint` bigint,
`f_float` float,
`f_double` double,
`f_decimal` decimal(10,0),
`f_timestamp` timestamp,
`f_date` date,
`f_string` string,
`f_varchar` varchar(100),
`f_bool` boolean,
`f_binary` binary,
`f_array_int` array<int>,
`f_array_string` array<string>,
`f_array_float` array<float>,
`f_array_array_int` array<array<int>>,
`f_array_array_string` array<array<string>>,
`f_array_array_float` array<array<float>>)
PARTITIONED BY (
`day` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
STORED AS INPUTFORMAT
'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat'
LOCATION
'hdfs://testcluster/data/hive/test.db/test_orc'

OK
Time taken: 0.51 seconds

hive > insert into test.test_orc partition(day='2021-09-18') select 1, 2, 3, 4, 5, 6.11, 7.22, 8.333, current_timestamp(), current_date(), 'hello world', 'hello world', 'hello world', true, 'hello world', array(1, 2, 3), array('hello world', 'hello world'), array(float(1.1), float(1.2)), array(array(1, 2), array(3, 4)), array(array('a', 'b'), array('c', 'd')), array(array(float(1.11), float(2.22)), array(float(3.33), float(4.44)));
OK
Time taken: 36.025 seconds

hive > select * from test.test_orc;
OK
1 2 3 4 5 6.11 7.22 8 2021-11-05 12:38:16.314 2021-11-05 hello world hello world hello world true hello world [1,2,3] ["hello world","hello world"] [1.1,1.2] [[1,2],[3,4]] [["a","b"],["c","d"]] [[1.11,2.22],[3.33,4.44]] 2021-09-18
Time taken: 0.295 seconds, Fetched: 1 row(s)

Create Table in ClickHouse

Table in ClickHouse, retrieving data from the Hive table created above:

CREATE TABLE test.test_orc
(
`f_tinyint` Int8,
`f_smallint` Int16,
`f_int` Int32,
`f_integer` Int32,
`f_bigint` Int64,
`f_float` Float32,
`f_double` Float64,
`f_decimal` Float64,
`f_timestamp` DateTime,
`f_date` Date,
`f_string` String,
`f_varchar` String,
`f_bool` Bool,
`f_binary` String,
`f_array_int` Array(Int32),
`f_array_string` Array(String),
`f_array_float` Array(Float32),
`f_array_array_int` Array(Array(Int32)),
`f_array_array_string` Array(Array(String)),
`f_array_array_float` Array(Array(Float32)),
`day` String
)
ENGINE = Hive('thrift://202.168.117.26:9083', 'test', 'test_orc')
PARTITION BY day

SELECT * FROM test.test_orc settings input_format_orc_allow_missing_columns = 1\G
SELECT *
FROM test.test_orc
SETTINGS input_format_orc_allow_missing_columns = 1

Query id: c3eaffdc-78ab-43cd-96a4-4acc5b480658

Row 1:
──────
f_tinyint: 1
f_smallint: 2
f_int: 3
f_integer: 4
f_bigint: 5
f_float: 6.11
f_double: 7.22
f_decimal: 8
f_timestamp: 2021-12-04 04:00:44
f_date: 2021-12-03
f_string: hello world
f_varchar: hello world
f_bool: true
f_binary: hello world
f_array_int: [1,2,3]
f_array_string: ['hello world','hello world']
f_array_float: [1.1,1.2]
f_array_array_int: [[1,2],[3,4]]
f_array_array_string: [['a','b'],['c','d']]
f_array_array_float: [[1.11,2.22],[3.33,4.44]]
day: 2021-09-18


1 rows in set. Elapsed: 0.078 sec.

Query Hive Table with Parquet Input Format

Create Table in Hive

hive >
CREATE TABLE `test`.`test_parquet`(
`f_tinyint` tinyint,
`f_smallint` smallint,
`f_int` int,
`f_integer` int,
`f_bigint` bigint,
`f_float` float,
`f_double` double,
`f_decimal` decimal(10,0),
`f_timestamp` timestamp,
`f_date` date,
`f_string` string,
`f_varchar` varchar(100),
`f_char` char(100),
`f_bool` boolean,
`f_binary` binary,
`f_array_int` array<int>,
`f_array_string` array<string>,
`f_array_float` array<float>,
`f_array_array_int` array<array<int>>,
`f_array_array_string` array<array<string>>,
`f_array_array_float` array<array<float>>)
PARTITIONED BY (
`day` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION
'hdfs://testcluster/data/hive/test.db/test_parquet'
OK
Time taken: 0.51 seconds

hive > insert into test.test_parquet partition(day='2021-09-18') select 1, 2, 3, 4, 5, 6.11, 7.22, 8.333, current_timestamp(), current_date(), 'hello world', 'hello world', 'hello world', true, 'hello world', array(1, 2, 3), array('hello world', 'hello world'), array(float(1.1), float(1.2)), array(array(1, 2), array(3, 4)), array(array('a', 'b'), array('c', 'd')), array(array(float(1.11), float(2.22)), array(float(3.33), float(4.44)));
OK
Time taken: 36.025 seconds

hive > select * from test.test_parquet;
OK
1 2 3 4 5 6.11 7.22 8 2021-12-14 17:54:56.743 2021-12-14 hello world hello world hello world true hello world [1,2,3] ["hello world","hello world"] [1.1,1.2] [[1,2],[3,4]] [["a","b"],["c","d"]] [[1.11,2.22],[3.33,4.44]] 2021-09-18
Time taken: 0.766 seconds, Fetched: 1 row(s)

Create Table in ClickHouse

Table in ClickHouse, retrieving data from the Hive table created above:

CREATE TABLE test.test_parquet
(
`f_tinyint` Int8,
`f_smallint` Int16,
`f_int` Int32,
`f_integer` Int32,
`f_bigint` Int64,
`f_float` Float32,
`f_double` Float64,
`f_decimal` Float64,
`f_timestamp` DateTime,
`f_date` Date,
`f_string` String,
`f_varchar` String,
`f_char` String,
`f_bool` Bool,
`f_binary` String,
`f_array_int` Array(Int32),
`f_array_string` Array(String),
`f_array_float` Array(Float32),
`f_array_array_int` Array(Array(Int32)),
`f_array_array_string` Array(Array(String)),
`f_array_array_float` Array(Array(Float32)),
`day` String
)
ENGINE = Hive('thrift://localhost:9083', 'test', 'test_parquet')
PARTITION BY day
SELECT * FROM test.test_parquet settings input_format_parquet_allow_missing_columns = 1\G
SELECT *
FROM test_parquet
SETTINGS input_format_parquet_allow_missing_columns = 1

Query id: 4e35cf02-c7b2-430d-9b81-16f438e5fca9

Row 1:
──────
f_tinyint: 1
f_smallint: 2
f_int: 3
f_integer: 4
f_bigint: 5
f_float: 6.11
f_double: 7.22
f_decimal: 8
f_timestamp: 2021-12-14 17:54:56
f_date: 2021-12-14
f_string: hello world
f_varchar: hello world
f_char: hello world
f_bool: true
f_binary: hello world
f_array_int: [1,2,3]
f_array_string: ['hello world','hello world']
f_array_float: [1.1,1.2]
f_array_array_int: [[1,2],[3,4]]
f_array_array_string: [['a','b'],['c','d']]
f_array_array_float: [[1.11,2.22],[3.33,4.44]]
day: 2021-09-18

1 rows in set. Elapsed: 0.357 sec.

Query Hive Table with Text Input Format

Create Table in Hive

hive >
CREATE TABLE `test`.`test_text`(
`f_tinyint` tinyint,
`f_smallint` smallint,
`f_int` int,
`f_integer` int,
`f_bigint` bigint,
`f_float` float,
`f_double` double,
`f_decimal` decimal(10,0),
`f_timestamp` timestamp,
`f_date` date,
`f_string` string,
`f_varchar` varchar(100),
`f_char` char(100),
`f_bool` boolean,
`f_binary` binary,
`f_array_int` array<int>,
`f_array_string` array<string>,
`f_array_float` array<float>,
`f_array_array_int` array<array<int>>,
`f_array_array_string` array<array<string>>,
`f_array_array_float` array<array<float>>)
PARTITIONED BY (
`day` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
STORED AS INPUTFORMAT
'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
'hdfs://testcluster/data/hive/test.db/test_text'
Time taken: 0.1 seconds, Fetched: 34 row(s)


hive > insert into test.test_text partition(day='2021-09-18') select 1, 2, 3, 4, 5, 6.11, 7.22, 8.333, current_timestamp(), current_date(), 'hello world', 'hello world', 'hello world', true, 'hello world', array(1, 2, 3), array('hello world', 'hello world'), array(float(1.1), float(1.2)), array(array(1, 2), array(3, 4)), array(array('a', 'b'), array('c', 'd')), array(array(float(1.11), float(2.22)), array(float(3.33), float(4.44)));
OK
Time taken: 36.025 seconds

hive > select * from test.test_text;
OK
1 2 3 4 5 6.11 7.22 8 2021-12-14 18:11:17.239 2021-12-14 hello world hello world hello world true hello world [1,2,3] ["hello world","hello world"] [1.1,1.2] [[1,2],[3,4]] [["a","b"],["c","d"]] [[1.11,2.22],[3.33,4.44]] 2021-09-18
Time taken: 0.624 seconds, Fetched: 1 row(s)

Create Table in ClickHouse

Table in ClickHouse, retrieving data from the Hive table created above:

CREATE TABLE test.test_text
(
`f_tinyint` Int8,
`f_smallint` Int16,
`f_int` Int32,
`f_integer` Int32,
`f_bigint` Int64,
`f_float` Float32,
`f_double` Float64,
`f_decimal` Float64,
`f_timestamp` DateTime,
`f_date` Date,
`f_string` String,
`f_varchar` String,
`f_char` String,
`f_bool` Bool,
`day` String
)
ENGINE = Hive('thrift://localhost:9083', 'test', 'test_text')
PARTITION BY day
SELECT * FROM test.test_text settings input_format_skip_unknown_fields = 1, input_format_with_names_use_header = 1, date_time_input_format = 'best_effort'\G
SELECT *
FROM test.test_text
SETTINGS input_format_skip_unknown_fields = 1, input_format_with_names_use_header = 1, date_time_input_format = 'best_effort'

Query id: 55b79d35-56de-45b9-8be6-57282fbf1f44

Row 1:
──────
f_tinyint: 1
f_smallint: 2
f_int: 3
f_integer: 4
f_bigint: 5
f_float: 6.11
f_double: 7.22
f_decimal: 8
f_timestamp: 2021-12-14 18:11:17
f_date: 2021-12-14
f_string: hello world
f_varchar: hello world
f_char: hello world
f_bool: true
day: 2021-09-18

Original article