hive表信息查询、查看表结构、表操作等

转载:http://blog.csdn.net/lskyne/article/details/38427895

问题导读：
1.如何查看hive表结构？
2.如何查看表结构信息？
3.如何查看分区信息？
4.哪个命令可以模糊搜索表

1.hive模糊搜索表

 show tables like '*name*';

2.查看表结构信息

 desc formatted table_name;
 desc table_name;

3.查看分区信息

 show partitions table_name;

4.根据分区查询数据

 select table_coulm from table_name where partition_name = '2014-02-25';

5.查看hdfs文件信息

 dfs -ls /user/hive/warehouse/table02;

6.从文件加载数据进表(OVERWRITE覆盖,追加不需要OVERWRITE关键字)

 LOAD DATA LOCAL INPATH 'dim_csl_rule_config.txt' OVERWRITE into table dim.dim_csl_rule_config;
  --从查询语句给table插入数据
 INSERT OVERWRITE TABLE test_h02_click_log PARTITION(dt) select *
 from stage.s_h02_click_log where dt='2014-01-22' limit 100;

7.导出数据到文件

hive> insert overwrite local directory '/opt/xuzhiguo' select * from pdca_factory_t where factory_id<4;
Query ID = root_20171228114351_ab465584-9575-4ec2-a6ad-6b7e6ecf227d
Total jobs = 1
Launching Job 1 out of 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=
In order to set a constant number of reducers:
  set mapreduce.job.reduces=
Starting Spark Job = d4aa5361-e1d3-4b0a-802a-62a3c8124a13

Query Hive on Spark job[0] stages:
0

Status: Running (Hive on Spark job[0])
Job Progress Format
CurrentTime StageId_StageAttemptId: SucceededTasksCount(+RunningTasksCount-FailedTasksCount)/TotalTasksCount [StageCost]
2017-12-28 11:44:13,438 Stage-0_0: 0(+1)/1
2017-12-28 11:44:16,547 Stage-0_0: 0(+1)/1
2017-12-28 11:44:19,602 Stage-0_0: 0(+1)/1
2017-12-28 11:44:22,660 Stage-0_0: 0(+1)/1
2017-12-28 11:44:25,692 Stage-0_0: 0(+1)/1
2017-12-28 11:44:28,724 Stage-0_0: 0(+1)/1
2017-12-28 11:44:29,741 Stage-0_0: 1/1 Finished
Status: Finished successfully in 30.46 seconds
Moving data to local directory /opt/xuzhiguo
OK
Time taken: 39.248 seconds
hive> 

 insert overwrite directory '/tmp/csl_rule_cfg' select a.* from dim.dim_csl_rule_config a;
  hive -e "select day_id,pv,uv,ip_count,click_next_count,second_bounce_rate,return_visit,pg_type

 from tmp.tmp_h02_click_log_baitiao_ag_sum where day_id in ('2014-03-06','2014-03-07','2014-03-08','2014-03-09','2014-03-10');">

 /home/jrjt/testan/baitiao.dat;

8.自定义udf函数

  1.继承UDF类
  2.重写evaluate方法
  3.把项目打成jar包
  4.hive中执行命令add jar /home/jrjt/dwetl/PUB/UDF/udf/GetProperty.jar;
  5.创建函数create temporary function get_pro as 'jd.Get_Property'//jd.jd.Get_Property为类路径;

9.查询显示列名 及 行转列显示 

 set hive.cli.print.header=true; // 打印列名
 set hive.cli.print.row.to.vertical=true; // 开启行转列功能, 前提必须开启打印列名功能
 set hive.cli.print.row.to.vertical.num=1; // 设置每行显示的列数

10.查看表文件大小,下载文件到某个目录,显示多少行到某个文件

 dfs -du hdfs://BJYZH3-HD-JRJT-4137.jd.com:54310/user/jrjt/warehouse/stage.db/s_h02_click_log;
 dfs -get /user/jrjt/warehouse/ods.db/o_h02_click_log_i_new/dt=2014-01-21/000212_0 /home/jrjt/testan/;
 head -n 1000 文件名 > 文件名

11.杀死某个任务  不在hive shell中执行

 hadoop job -kill job_201403041453_58315

12.hive-wui路径

    http://172.17.41.38/jobtracker.jsp

13.删除分区

 alter table tmp_h02_click_log_baitiao drop partition(dt='2014-03-01');
 alter table d_h02_click_log_basic_d_fact drop partition(dt='2014-01-17');

14.hive命令行操作15.hive上操作hadoop文件基本命令

    查看文件大小

 dfs -du /user/jrjt/warehouse/tmp.db/tmp_h02_click_log/dt=2014-02-15;

    删除文件

 dfs -rm /user/jrjt/warehouse/tmp.db/tmp_h02_click_log/dt=2014-02-15;

16.插入数据sql、导出数据sql
    1.insert 语法格式为：

    基本的插入语法：

 INSERT OVERWRITE TABLE tablename [PARTITON(partcol1=val1,partclo2=val2)]select_statement FROM from_statement
 insert overwrite table test_insert select * from test_table;

    对多个表进行插入操作：

 FROM fromstatte
 INSERT OVERWRITE TABLE tablename1 [PARTITON(partcol1=val1,partclo2=val2)]select_statement1
 INSERT OVERWRITE TABLE tablename2 [PARTITON(partcol1=val1,partclo2=val2)]select_statement2

 from test_table
 insert overwrite table test_insert1
 select key
 insert overwrite table test_insert2
 select value;

    insert的时候，from子句即可以放在select 子句后面，也可以放在 insert子句前面。

    hive不支持用insert语句一条一条的进行插入操作，也不支持update操作。数据是以load的方式加载到建立好的表中。数据一旦导入就不可以修改。

    2.通过查询将数据保存到filesystem

 INSERT OVERWRITE [LOCAL] DIRECTORY directory SELECT.... FROM .....

    导入数据到本地目录：

 insert overwrite local directory '/home/zhangxin/hive' select * from test_insert1;

    产生的文件会覆盖指定目录中的其他文件，即将目录中已经存在的文件进行删除。

    导出数据到HDFS中：

 insert overwrite directory '/user/zhangxin/export_test' select value from test_table;

    同一个查询结果可以同时插入到多个表或者多个目录中：

 from test_insert1
 insert overwrite local directory '/home/zhangxin/hive' select *
 insert overwrite directory '/user/zhangxin/export_test' select value;

17.mapjoin的使用 应用场景：1.关联操作中有一张表非常小 2.不等值的链接操作

 select /*+ mapjoin(A)*/ f.a,f.b from A t join B f  on ( f.a=t.a and f.ftime=20110802)

18.perl启动任务

 perl /home/jrjt/dwetl/APP/APP/A_H02_CLICK_LOG_CREDIT_USER/bin/a_h02_click_log_credit_user.pl
 APP_A_H02_CLICK_LOG_CREDIT_USER_20140215.dir >& /home/jrjt/dwetl/LOG/APP/20140306/

 <span style="white-space:pre;">                     span> a_h02_click_log_credit_user.pl.4.log

19.查看perl进程

 ps -ef|grep perl

20.hive命令移动表数据到另外一张表目录下并添加分区

 dfs -cp /user/jrjt/warehouse/tmp.db/tmp_h02_click_log/dt=2014-02-18 /user/jrjt/warehouse/ods.db/o_h02_click_log/;
 dfs -cp /user/jrjt/warehouse/tmp.db/tmp_h02_click_log_baitiao/* /user/jrjt/warehouse/dw.db/

 <span style="white-space:pre;">                         span>d_h02_click_log_baitiao_basic_d_fact/;--复制所有分区数据
  alter table d_h02_click_log_baitiao_basic_d_fact add partition(dt='2014-03-11')

 <span style="white-space:pre;"> span>location '/user/jrjt/warehouse/dw.db/d_h02_click_log_baitiao_basic_d_fact/dt=2014-03-11';

21.导出白条数据

 hive -e "select day_id,pv,uv,ip_count,click_next_count,second_bounce_rate,return_visit,pg_type

 from tmp.tmp_h02_click_log_baitiao_ag_sum where day_id like '2014-03%';"> /home/jrjt/testan/baitiao.xlsx;

22.hive修改表名

 ALTER TABLE o_h02_click_log_i RENAME TO o_h02_click_log_i_bk;

23.hive复制表结构

 CREATE TABLE d_h02_click_log_baitiao_ag_sum LIKE tmp.tmp_h02_click_log_baitiao_ag_sum;

24.hive官网网址

    https://cwiki.apache.org/conflue ... ionandConfiguration

   http://www.360doc.com/content/12/0111/11/7362_178698714.shtml

25.hive添加字段

 alter table tmp_h02_click_log_baitiao_ag_sum

 <span style="white-space:pre;"> span>add columns(current_session_timelenth_count bigint comment '页面停留总时长');
  ALTER TABLE tmp_h02_click_log_baitiao CHANGE current_session_timelenth

 <span style="white-space:pre;"> span>current_session_timelenth bigint comment '当前会话停留时间';

26.hive开启简单模式不启用mr

 set hive.fetch.task.conversion=more;

27.以json格式输出执行语句会读取的input table和input partition信息

 Explain dependency query