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418 lines
13 KiB
Markdown
418 lines
13 KiB
Markdown
---
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slug: /zh/engines/table-engines/integrations/hive
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sidebar_position: 4
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sidebar_label: Hive
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---
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# Hive {#hive}
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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:
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-文本:只支持简单的标量列类型，除了 `Binary`
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- 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
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- Parquet:支持所有简单标量列类型;只支持 `array` 这样的复杂类型
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## 创建表 {#creating-a-table}
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``` sql
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CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
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(
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name1 [type1] [ALIAS expr1],
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name2 [type2] [ALIAS expr2],
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...
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) ENGINE = Hive('thrift://host:port', 'database', 'table');
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PARTITION BY expr
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```
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查看[CREATE TABLE](../../../sql-reference/statements/create/table.mdx#create-table-query)查询的详细描述。
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表的结构可以与原来的Hive表结构有所不同:
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- 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。
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- 列类型与原Hive表的列类型保持一致。
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- “Partition by expression”应与原Hive表保持一致，“Partition by expression”中的列应在表结构中。
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**引擎参数**
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- `thrift://host:port` — Hive Metastore 地址
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- `database` — 远程数据库名.
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- `table` — 远程数据表名.
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## 使用示例 {#usage-example}
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### 如何使用HDFS文件系统的本地缓存
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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。
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在使用缓存之前，请将其添加到 `config.xml`
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``` xml
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<local_cache_for_remote_fs>
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<enable>true</enable>
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<root_dir>local_cache</root_dir>
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<limit_size>559096952</limit_size>
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<bytes_read_before_flush>1048576</bytes_read_before_flush>
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</local_cache_for_remote_fs>
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```
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- enable: 开启后，ClickHouse将为HDFS (远程文件系统)维护本地缓存。
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- 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。
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- limit_size: 必需的。本地缓存文件的最大大小(单位为字节)。
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- 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。
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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_fs = 0 `, `use_local_cache_for_remote_fs` 默认为 `false`。
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### 查询 ORC 输入格式的Hive 表
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#### 在 Hive 中建表
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``` text
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hive > CREATE TABLE `test`.`test_orc`(
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`f_tinyint` tinyint,
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`f_smallint` smallint,
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`f_int` int,
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`f_integer` int,
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`f_bigint` bigint,
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`f_float` float,
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`f_double` double,
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`f_decimal` decimal(10,0),
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`f_timestamp` timestamp,
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`f_date` date,
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`f_string` string,
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`f_varchar` varchar(100),
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`f_bool` boolean,
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`f_binary` binary,
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`f_array_int` array<int>,
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`f_array_string` array<string>,
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`f_array_float` array<float>,
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`f_array_array_int` array<array<int>>,
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`f_array_array_string` array<array<string>>,
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`f_array_array_float` array<array<float>>)
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PARTITIONED BY (
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`day` string)
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ROW FORMAT SERDE
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'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
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STORED AS INPUTFORMAT
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'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat'
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OUTPUTFORMAT
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'org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat'
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LOCATION
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'hdfs://testcluster/data/hive/test.db/test_orc'
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OK
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Time taken: 0.51 seconds
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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)));
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OK
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Time taken: 36.025 seconds
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hive > select * from test.test_orc;
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OK
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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
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Time taken: 0.295 seconds, Fetched: 1 row(s)
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```
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#### 在 ClickHouse 中建表
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ClickHouse中的表，从上面创建的Hive表中获取数据:
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``` sql
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CREATE TABLE test.test_orc
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(
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`f_tinyint` Int8,
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`f_smallint` Int16,
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`f_int` Int32,
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`f_integer` Int32,
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`f_bigint` Int64,
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`f_float` Float32,
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`f_double` Float64,
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`f_decimal` Float64,
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`f_timestamp` DateTime,
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`f_date` Date,
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`f_string` String,
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`f_varchar` String,
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`f_bool` Bool,
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`f_binary` String,
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`f_array_int` Array(Int32),
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`f_array_string` Array(String),
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`f_array_float` Array(Float32),
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`f_array_array_int` Array(Array(Int32)),
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`f_array_array_string` Array(Array(String)),
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`f_array_array_float` Array(Array(Float32)),
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`day` String
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)
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ENGINE = Hive('thrift://localhost:9083', 'test', 'test_orc')
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PARTITION BY day
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```
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``` sql
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SELECT * FROM test.test_orc settings input_format_orc_allow_missing_columns = 1\G
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```
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``` text
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SELECT *
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FROM test.test_orc
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SETTINGS input_format_orc_allow_missing_columns = 1
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Query id: c3eaffdc-78ab-43cd-96a4-4acc5b480658
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Row 1:
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──────
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f_tinyint: 1
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f_smallint: 2
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f_int: 3
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f_integer: 4
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f_bigint: 5
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f_float: 6.11
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f_double: 7.22
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f_decimal: 8
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f_timestamp: 2021-12-04 04:00:44
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f_date: 2021-12-03
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f_string: hello world
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f_varchar: hello world
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f_bool: true
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f_binary: hello world
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f_array_int: [1,2,3]
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f_array_string: ['hello world','hello world']
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f_array_float: [1.1,1.2]
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f_array_array_int: [[1,2],[3,4]]
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f_array_array_string: [['a','b'],['c','d']]
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f_array_array_float: [[1.11,2.22],[3.33,4.44]]
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day: 2021-09-18
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1 rows in set. Elapsed: 0.078 sec.
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```
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### 查询 Parquest 输入格式的Hive 表
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#### 在 Hive 中建表
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``` text
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hive >
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CREATE TABLE `test`.`test_parquet`(
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`f_tinyint` tinyint,
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`f_smallint` smallint,
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`f_int` int,
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`f_integer` int,
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`f_bigint` bigint,
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`f_float` float,
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`f_double` double,
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`f_decimal` decimal(10,0),
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`f_timestamp` timestamp,
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`f_date` date,
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`f_string` string,
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`f_varchar` varchar(100),
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`f_char` char(100),
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`f_bool` boolean,
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`f_binary` binary,
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`f_array_int` array<int>,
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`f_array_string` array<string>,
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`f_array_float` array<float>,
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`f_array_array_int` array<array<int>>,
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`f_array_array_string` array<array<string>>,
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`f_array_array_float` array<array<float>>)
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PARTITIONED BY (
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`day` string)
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ROW FORMAT SERDE
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'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
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STORED AS INPUTFORMAT
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'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
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OUTPUTFORMAT
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'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
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LOCATION
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'hdfs://testcluster/data/hive/test.db/test_parquet'
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OK
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Time taken: 0.51 seconds
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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)));
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OK
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Time taken: 36.025 seconds
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hive > select * from test.test_parquet;
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OK
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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
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Time taken: 0.766 seconds, Fetched: 1 row(s)
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```
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#### 在 ClickHouse 中建表
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ClickHouse 中的表， 从上面创建的Hive表中获取数据:
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``` sql
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CREATE TABLE test.test_parquet
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(
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`f_tinyint` Int8,
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`f_smallint` Int16,
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`f_int` Int32,
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`f_integer` Int32,
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`f_bigint` Int64,
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`f_float` Float32,
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`f_double` Float64,
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`f_decimal` Float64,
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`f_timestamp` DateTime,
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`f_date` Date,
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`f_string` String,
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`f_varchar` String,
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`f_char` String,
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`f_bool` Bool,
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`f_binary` String,
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`f_array_int` Array(Int32),
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`f_array_string` Array(String),
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`f_array_float` Array(Float32),
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`f_array_array_int` Array(Array(Int32)),
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`f_array_array_string` Array(Array(String)),
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`f_array_array_float` Array(Array(Float32)),
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`day` String
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)
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ENGINE = Hive('thrift://localhost:9083', 'test', 'test_parquet')
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PARTITION BY day
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```
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``` sql
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SELECT * FROM test.test_parquet settings input_format_parquet_allow_missing_columns = 1\G
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```
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``` text
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SELECT *
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FROM test_parquet
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SETTINGS input_format_parquet_allow_missing_columns = 1
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Query id: 4e35cf02-c7b2-430d-9b81-16f438e5fca9
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Row 1:
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──────
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f_tinyint: 1
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f_smallint: 2
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f_int: 3
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f_integer: 4
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f_bigint: 5
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f_float: 6.11
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f_double: 7.22
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f_decimal: 8
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f_timestamp: 2021-12-14 17:54:56
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f_date: 2021-12-14
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f_string: hello world
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f_varchar: hello world
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f_char: hello world
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f_bool: true
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f_binary: hello world
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f_array_int: [1,2,3]
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f_array_string: ['hello world','hello world']
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f_array_float: [1.1,1.2]
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f_array_array_int: [[1,2],[3,4]]
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f_array_array_string: [['a','b'],['c','d']]
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f_array_array_float: [[1.11,2.22],[3.33,4.44]]
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day: 2021-09-18
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1 rows in set. Elapsed: 0.357 sec.
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```
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### 查询文本输入格式的Hive表
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#### 在Hive 中建表
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``` text
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hive >
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CREATE TABLE `test`.`test_text`(
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`f_tinyint` tinyint,
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`f_smallint` smallint,
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`f_int` int,
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`f_integer` int,
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`f_bigint` bigint,
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`f_float` float,
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`f_double` double,
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`f_decimal` decimal(10,0),
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`f_timestamp` timestamp,
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`f_date` date,
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`f_string` string,
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`f_varchar` varchar(100),
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`f_char` char(100),
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`f_bool` boolean,
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`f_binary` binary,
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`f_array_int` array<int>,
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`f_array_string` array<string>,
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`f_array_float` array<float>,
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`f_array_array_int` array<array<int>>,
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`f_array_array_string` array<array<string>>,
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`f_array_array_float` array<array<float>>)
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PARTITIONED BY (
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`day` string)
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ROW FORMAT SERDE
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'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
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STORED AS INPUTFORMAT
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'org.apache.hadoop.mapred.TextInputFormat'
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OUTPUTFORMAT
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'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
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LOCATION
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'hdfs://testcluster/data/hive/test.db/test_text'
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Time taken: 0.1 seconds, Fetched: 34 row(s)
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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)));
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OK
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Time taken: 36.025 seconds
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hive > select * from test.test_text;
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OK
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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
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Time taken: 0.624 seconds, Fetched: 1 row(s)
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```
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#### 在 ClickHouse 中建表
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ClickHouse中的表， 从上面创建的Hive表中获取数据:
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``` sql
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CREATE TABLE test.test_text
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(
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`f_tinyint` Int8,
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`f_smallint` Int16,
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`f_int` Int32,
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`f_integer` Int32,
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`f_bigint` Int64,
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`f_float` Float32,
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`f_double` Float64,
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`f_decimal` Float64,
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`f_timestamp` DateTime,
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`f_date` Date,
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`f_string` String,
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`f_varchar` String,
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`f_char` String,
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`f_bool` Bool,
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`day` String
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)
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ENGINE = Hive('thrift://localhost:9083', 'test', 'test_text')
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PARTITION BY day
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```
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``` sql
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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
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```
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``` text
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SELECT *
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FROM test.test_text
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SETTINGS input_format_skip_unknown_fields = 1, input_format_with_names_use_header = 1, date_time_input_format = 'best_effort'
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Query id: 55b79d35-56de-45b9-8be6-57282fbf1f44
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Row 1:
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──────
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f_tinyint: 1
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f_smallint: 2
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f_int: 3
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f_integer: 4
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f_bigint: 5
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f_float: 6.11
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f_double: 7.22
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f_decimal: 8
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f_timestamp: 2021-12-14 18:11:17
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f_date: 2021-12-14
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f_string: hello world
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f_varchar: hello world
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f_char: hello world
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f_bool: true
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day: 2021-09-18
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```
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