ClickHouse(19)ClickHouse集成Hive表引擎详细解析

Hive集成表引擎

Hive引擎允许对HDFS Hive表执行 SELECT 查询。目前它支持如下输入格式:

-文本:只支持简单的标量列类型，除了 Binary

• ORC:支持简单的标量列类型，除了char; 只支持 array 这样的复杂类型

• Parquet:支持所有简单标量列类型;只支持 array 这样的复杂类型

创建表

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

表的结构可以与原来的Hive表结构有所不同:

• 列名应该与原来的Hive表相同，但你可以使用这些列中的一些，并以任何顺序，你也可以使用一些从其他列计算的别名列。
• 列类型与原Hive表的列类型保持一致。
• “Partition by expression”应与原Hive表保持一致，“Partition by expression”中的列应在表结构中。

引擎参数

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

• database — 远程数据库名.

• table — 远程数据表名.

使用示例

如何使用HDFS文件系统的本地缓存

我们强烈建议您为远程文件系统启用本地缓存。基准测试显示，如果使用缓存，它的速度会快两倍。

在使用缓存之前，请将其添加到 config.xml

<local_cache_for_remote_fs>
    <enable>trueenable>
    <root_dir>local_cacheroot_dir>
    <limit_size>559096952limit_size>
    <bytes_read_before_flush>1048576bytes_read_before_flush>
local_cache_for_remote_fs>

• enable: 开启后，ClickHouse将为HDFS (远程文件系统)维护本地缓存。
• root_dir: 必需的。用于存储远程文件系统的本地缓存文件的根目录。
• limit_size: 必需的。本地缓存文件的最大大小(单位为字节)。
• bytes_read_before_flush: 从远程文件系统下载文件时，刷新到本地文件系统前的控制字节数。缺省值为1MB。

当ClickHouse为远程文件系统启用了本地缓存时，用户仍然可以选择不使用缓存，并在查询中设置 use_local_cache_for_remote_storage = 0, use_local_cache_for_remote_storage 默认为 1。

查询 ORC 输入格式的Hive 表

在 Hive 中建表

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, 
  `f_array_string` array, 
  `f_array_float` array, 
  `f_array_array_int` array>, 
  `f_array_array_string` array>, 
  `f_array_array_float` array>)
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)

在 ClickHouse 中建表

ClickHouse中的表，从上面创建的Hive表中获取数据:

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://localhost: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. 

查询 Parquest 输入格式的Hive 表

在 Hive 中建表

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, 
  `f_array_string` array, 
  `f_array_float` array, 
  `f_array_array_int` array>, 
  `f_array_array_string` array>, 
  `f_array_array_float` array>)
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)

在 ClickHouse 中建表

ClickHouse 中的表， 从上面创建的Hive表中获取数据:

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. 

查询文本输入格式的Hive表

在Hive 中建表

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, 
  `f_array_string` array, 
  `f_array_float` array, 
  `f_array_array_int` array>, 
  `f_array_array_string` array>, 
  `f_array_array_float` array>)
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)

在 ClickHouse 中建表

ClickHouse中的表， 从上面创建的Hive表中获取数据:

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

资料分享

ClickHouse经典中文文档分享

参考文章

• ClickHouse(01)什么是ClickHouse,ClickHouse适用于什么场景
• ClickHouse(02)ClickHouse架构设计介绍概述与ClickHouse数据分片设计
• ClickHouse(03)ClickHouse怎么安装和部署
• ClickHouse(04)如何搭建ClickHouse集群
• ClickHouse(05)ClickHouse数据类型详解
• ClickHouse(06)ClickHouse建表语句DDL详细解析
• ClickHouse(07)ClickHouse数据库引擎解析
• ClickHouse(08)ClickHouse表引擎概况
• ClickHouse(09)ClickHouse合并树MergeTree家族表引擎之MergeTree详细解析
• ClickHouse(10)ClickHouse合并树MergeTree家族表引擎之ReplacingMergeTree详细解析
• ClickHouse(11)ClickHouse合并树MergeTree家族表引擎之SummingMergeTree详细解析
• ClickHouse(12)ClickHouse合并树MergeTree家族表引擎之AggregatingMergeTree详细解析
• ClickHouse(13)ClickHouse合并树MergeTree家族表引擎之CollapsingMergeTree详细解析
• ClickHouse(14)ClickHouse合并树MergeTree家族表引擎之VersionedCollapsingMergeTree详细解析
• ClickHouse(15)ClickHouse合并树MergeTree家族表引擎之GraphiteMergeTree详细解析
• ClickHouse(16)ClickHouse日志引擎Log详细解析
• ClickHouse(17)ClickHouse集成JDBC表引擎详细解析
• ClickHouse(18)ClickHouse集成ODBC表引擎详细解析