几种保存Hive查询结果的方法

很多时候，我们需要将Hive的查询（select）结果保存起来，方便进一步处理或查看。
在Hive里面提供了不同的方式来保存查询结果，在这里做下总结：

一、保存结果到本地

方法1：调用hive标准输出，将查询结果写到指定的文件中

这个方法最为常见，笔者也经常使用。sql的查询结果将直接保存到/tmp/out.txt中

$ hive -e "select user, login_timestamp from user_login" > /tmp/out.txt

当然我们也可以查询保存到某个文件file.sql中，按下面的方式执行查询，并保存结果

$ hive -f file.sql > /tmp/out.txt

 下面是file.sql的内容：

$ cat file.sql

select user, login_timestamp from user_login

hive客户的的详细使用方法可以参考hive的官方文档《Hive Batch Mode Commands》

hive -e '<query-string>' executes the query string.hive -f <filepath> executes one or more SQL queries from a file.

方法2：使用INSERT OVERWRITE LOCAL DIRECTORY结果到本地

hive> insert overwrite local directory "/tmp/out/"                                            > select user, login_time from user_login;

上面的命令会将select user, login_time from user_login的查询结果保存到/tmp/out/本地目录下。

我们查看一下/tmp/out/目录下的文件，发现命令执行后，多了两个文件：

$ find /tmp/out/ -type f/tmp/out/.000000_0.crc/tmp/out/000000_0

这两个文件存放的内容不一样，其中000000_0存放查询的结果，带有crc后缀的存放那个文件的crc32校验。

用vim打开查看下000000_0的内容：

vim /tmp/out/000000_0 1 user_1^A20140701 2 user_2^A20140701 3 user_2^A20140701

可以看到，导出的查询结果字段之间是用^A（Ctrl+A）作为分割符，行与行之间用\n作为分割。

默认的字段分割符有时候可能不太方便，幸好Hive提供了修改分割符号的方法，我们只要在导出时指定就可以了：

hive> insert overwrite local directory "/tmp/out/"    > row format delimited fields terminated by "\t"     > select user, login_time from user_login;

$ vim /tmp/out/000000_01 user_1 201407012 user_2 201407013 user_2 20140701

可以看到字段分割符已经变成了tab（人眼看起来更舒服^-^）。

同样，我们也可以指定复杂类型(collection、map)的输出格式

更多关于INSERT OVERWRITE LOCAL DIRECTORY的语法，可以参考HIVE的官方文档《Writing data into the filesystem from queries》。

Standard syntax:INSERT OVERWRITE <span style="color:#ff0000;">[LOCAL] </span>DIRECTORY directory1  [ROW FORMAT row_format] [STORED AS file_format] (Note: Only available starting with Hive 0.11.0)  SELECT ... FROM ... Hive extension (multiple inserts):FROM from_statementINSERT OVERWRITE [LOCAL] DIRECTORY directory1 select_statement1[INSERT OVERWRITE [LOCAL] DIRECTORY directory2 select_statement2] ...  row_format  : DELIMITED [FIELDS TERMINATED BY char [ESCAPED BY char]] [COLLECTION ITEMS TERMINATED BY char]        [MAP KEYS TERMINATED BY char] [LINES TERMINATED BY char]        [NULL DEFINED AS char] (Note: Only available starting with Hive 0.13)

二、保存结果到hdfs

保存查询结果到hdfs很简单，使用INSERT OVERWRITE DIRECTORY就可以完成操作：

hive> insert overwrite directory "/tmp/out/"    > row format delimited fields terminated by "\t"     > select user, login_time from user_login;

需要注意的是，跟保存到本地文件系统的差别是，保存到hdfs时命令不需要指定LOCAL项

更多关于INSERT OVERWRITE DIRECTORY的语法，可以参考HIVE的官方文档《Writing data into the filesystem from queries》。

三、保存结果到HIVE表

方法1、已经建好结果表，使用INSERT OVERWRITE TABLE以覆盖方式写入结果表

如果结果表已经建好，可以使用INSERT OVERWRITE TABLE将结果写入结果表：

hive> desc query_result;OKuser                string,

login_time          biginthive> insert overwrite table query_result         > select user, login_time from user_login;

hive> select * from query_result;             OKuser_120140701user_220140701user_320140701

当然，HIVE也提供了追加方式INSERT TABLE，可以在原有数据后面加上新的查询结果。在上面这个例子基础上，我们再追加一个查询结果：

hive> insert into table query_result    > select * from query_result;

hive> select * from query_result;OKuser_120140701user_220140701user_320140701<span style="color:#ff0000;">user_120140701user_220140701user_320140701</span>

注意标红的部分，使用INSERT TABLE后，query_result增加了三行数据

更多关于INSERT OVERWRITE TABLE的语法，可以参考HIVE官方文档《Inserting data into Hive Tables from queries》

Standard syntax:INSERT OVERWRITE TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...) [IF NOT EXISTS]] select_statement1 FROM from_statement;INSERT INTO TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)] select_statement1 FROM from_statement; Hive extension (multiple inserts):FROM from_statementINSERT OVERWRITE TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...) [IF NOT EXISTS]] select_statement1[INSERT OVERWRITE TABLE tablename2 [PARTITION ... [IF NOT EXISTS]] select_statement2][INSERT INTO TABLE tablename2 [PARTITION ...] select_statement2] ...;FROM from_statementINSERT INTO TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)] select_statement1[INSERT INTO TABLE tablename2 [PARTITION ...] select_statement2][INSERT OVERWRITE TABLE tablename2 [PARTITION ... [IF NOT EXISTS]] select_statement2] ...; Hive extension (dynamic partition inserts):INSERT OVERWRITE TABLE tablename PARTITION (partcol1[=val1], partcol2[=val2] ...) select_statement FROM from_statement;INSERT INTO TABLE tablename PARTITION (partcol1[=val1], partcol2[=val2] ...) select_statement FROM from_statement;

方法2、如果需要新建一个表，用于存放查询结果，可以使用CREATE TABLE AS SELECT语法

hive> create table query_result     > as    > select user, login_time from user_login;

hive> select * from query_result;            OKuser_120140701user_220140701user_320140701

更多关于CREATE TABLE AS SELECT的语法，可以参考HIVE官方文档《Create Table As Select (CTAS)》

CREATE [TEMPORARY] [EXTERNAL] TABLE [IF NOT EXISTS] [db_name.]table_name   (Note: TEMPORARY available starting with Hive 0.14.0)  [(col_name data_type [COMMENT col_comment], ...)]  [COMMENT table_comment]  [PARTITIONED BY (col_name data_type [COMMENT col_comment], ...)]  [CLUSTERED BY (col_name, col_name, ...) [SORTED BY (col_name [ASC|DESC], ...)] INTO num_buckets BUCKETS]  [SKEWED BY (col_name, col_name, ...) ON ([(col_value, col_value, ...), ...|col_value, col_value, ...])                                      [STORED AS DIRECTORIES] (Note: Only available starting with Hive 0.10.0)]  [   [ROW FORMAT row_format] [STORED AS file_format]   | STORED BY 'storage.handler.class.name' [WITH SERDEPROPERTIES (...)]  (Note: Only available starting with Hive 0.6.0)  ]  [LOCATION hdfs_path]  [TBLPROPERTIES (property_name=property_value, ...)]  (Note: Only available starting with Hive 0.6.0)  [AS select_statement]  (Note: Only available starting with Hive 0.5.0, and not supported when creating external tables.) CREATE [TEMPORARY] [EXTERNAL] TABLE [IF NOT EXISTS] [db_name.]table_name  LIKE existing_table_or_view_name  [LOCATION hdfs_path] data_type  : primitive_type  | array_type  | map_type  | struct_type  | union_type (Note: Only available starting with Hive 0.7.0) primitive_type  : TINYINT  | SMALLINT  | INT  | BIGINT  | BOOLEAN  | FLOAT  | DOUBLE  | STRING  | BINARY (Note: Only available starting with Hive 0.8.0)  | TIMESTAMP (Note: Only available starting with Hive 0.8.0)  | DECIMAL (Note: Only available starting with Hive 0.11.0)  | DECIMAL(precision, scale) (Note: Only available starting with Hive 0.13.0)  | VARCHAR (Note: Only available starting with Hive 0.12.0)  | CHAR (Note: Only available starting with Hive 0.13.0) array_type  : ARRAY < data_type > map_type  : MAP < primitive_type, data_type > struct_type  : STRUCT < col_name : data_type [COMMENT col_comment], ...> union_type   : UNIONTYPE < data_type, data_type, ... > (Note: Only available starting with Hive 0.7.0) row_format  : DELIMITED [FIELDS TERMINATED BY char [ESCAPED BY char]] [COLLECTION ITEMS TERMINATED BY char]        [MAP KEYS TERMINATED BY char] [LINES TERMINATED BY char]        [NULL DEFINED AS char] (Note: Only available starting with Hive 0.13)  | SERDE serde_name [WITH SERDEPROPERTIES (property_name=property_value, property_name=property_value, ...)] file_format:  : SEQUENCEFILE  | TEXTFILE  | RCFILE     (Note: Only available starting with Hive 0.6.0)  | ORC        (Note: Only available starting with Hive 0.11.0)  | AVRO       (Note: Only available starting with Hive 0.14.0)  | INPUTFORMAT input_format_classname OUTPUTFORMAT output_format_classname

四、使用hdfs直接导出表

Hive是构建在hdfs上的，因此，我们可以使用hdfs的命令hadoop dfs -get直接导出表。

首先、我们先找到要导出的表存放到哪个目录下：

hive> show create table user_login;OKCREATE  TABLE `user_login`(  `user` string,   `login_time` bigint)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'<span style="color:#ff0000;">LOCATION  'file:/user/hive/warehouse/test.db/user_login'</span>TBLPROPERTIES (  'totalSize'='160',   'numRows'='10',   'rawDataSize'='150',   'COLUMN_STATS_ACCURATE'='true',   'numFiles'='1',   'transient_lastDdlTime'='1411544983')Time taken: 0.174 seconds, Fetched: 18 row(s)

可以看到，user_login表存放到在file:/user/hive/warehouse/test.db/user_login。

接下来，直接利用hadoop dfs -get导出到本地：

hadoop dfs -get file:/user/hive/warehouse/test.db/user_login  /tmp/out/

更多关于hadoop  dfs -get命令，可以参考hadoop dfs命令界面文档《File System Shell》