目录
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>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: 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<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) 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 = 1G 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<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) 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 = 1G 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<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) 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
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
