1.HQL是否区分大小写 不区分
hive> select AGE from default.studeNT; --不区分大小写，即使是表中字段
2.查看创建表过程 show create table
hive> show create table default.student; --查看创建表过程的命令
OK
createtab_stmt
CREATE TABLE `default.student`(
`age` int,
`name` string)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ' ' --行内分隔符
STORED AS INPUTFORMAT
'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
LOCATION
'hdfs://hadoop01:9000/user/hive/warehouse/student'
TBLPROPERTIES (
'numFiles'='1',
'last_modified_by'='root',
'last_modified_time'='1439795658',
'COLUMN_STATS_ACCURATE'='true',
'transient_lastDdlTime'='1439795895',
'numRows'='0',
'totalSize'='74',
'rawDataSize'='0')
Time taken: 0.063 seconds, Fetched: 20 row(s)
3.hive是否支持跨数据库查询？ 支持
hive> select * from default.logs; --和普通sql一样，数据库名.数据表名
4.hive是否可以打印列名? set hive.cli.print.header=true;
hive> select * from default.student limit 5;
OK
1 zhangsan --显示结果不包含列名
2 lisi
3 wangwu
4 zhaoba
5 liliuy
Time taken: 0.047 seconds, Fetched: 5 row(s)
hive> set hive.cli.print.header=true; --设置true，即显示列名
hive> select * from default.student limit 5;
OK
student.age student.name --显示结果包含列名
1 zhangsan
2 lisi
3 wangwu
4 zhaoba
5 liliuy
Time taken: 0.05 seconds, Fetched: 5 row(s)
5.hive是否可以设置reduce的个数 set mapred.reduce.tasks=5;
hive> set mapred.reduce.tasks;
mapred.reduce.tasks=-1 --默认reduce个数为-1
hive> insert overwrite local directory '/root/hive/a' --只能产生一个结果文件
> row format delimited fields terminated by ' '
> select * from test.t081901
> distribute by length(country)
> sort by te;
hive> set mapred.reduce.tasks=4; --设置4个reduce
hive> insert overwrite local directory '/root/hive/a' --产生4个结果文件
> row format delimited fields terminated by ' '
> select * from test.t081901
> distribute by length(country) --和普通的MR一样，当【reduce个数>partition个数】，只有两个(等于分区数)结果文件中有数据。
> sort by te;
6.hive是否支持 exist 或 in 查询吗? 支持
hive> select * from default.student where student.age in(3,5,29);
OK
student.age student.name
3 wangwu
5 liliuy
Time taken: 10.217 seconds, Fetched: 2 row(s)
7.hive是否可以查询hive表的集群文件位置？ desc formatted T_NAME
INPUT__FILE__NAME字段可以查询出Table在Hdfs上的location
hive> desc formatted default.student; --desc formatted <T_Name>
OK
col_name data_type comment
# col_name data_type comment
age int
name string
# Detailed Table Information
Database: default --所属数据库
Owner: root --Owner
CreateTime: Mon Aug 17 14:24:29 SGT 2015
LastAccessTime: UNKNOWN
Protect Mode: None
Retention: 0
Location: hdfs://hadoop01:9000/user/hive/warehouse/student --HDFS位置
Table Type: MANAGED_TABLE --内部表
Table Parameters:
COLUMN_STATS_ACCURATE true
last_modified_by root
last_modified_time 1439795658
numFiles 1
numRows 0
rawDataSize 0
totalSize 74
transient_lastDdlTime 1439795895
# Storage Information
SerDe Library: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat: org.apache.hadoop.mapred.TextInputFormat
OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed: No
Num Buckets: -1
Bucket Columns: []
Sort Columns: []
Storage Desc Params:
field.delim
serialization.format
Time taken: 0.078 seconds, Fetched: 35 row(s)
hive> select name,age,INPUT__FILE__NAME --其中INPUT__FILE__NAME之间是两个下划线
> from default.student;
OK
name age input__file__name
zhangsan 1 hdfs://hadoop01:9000/user/hive/warehouse/student/student
lisi 2 hdfs://hadoop01:9000/user/hive/warehouse/student/student
wangwu 3 hdfs://hadoop01:9000/user/hive/warehouse/student/student
zhaoba 4 hdfs://hadoop01:9000/user/hive/warehouse/student/student
liliuy 5 hdfs://hadoop01:9000/user/hive/warehouse/student/student
jiujiu 9 hdfs://hadoop01:9000/user/hive/warehouse/student/student
shiba 18 hdfs://hadoop01:9000/user/hive/warehouse/student/student
xiyita 28 hdfs://hadoop01:9000/user/hive/warehouse/student/student
NULL NULL hdfs://hadoop01:9000/user/hive/warehouse/student/student
Time taken: 9.902 seconds, Fetched: 9 row(s)
8.hive是否支持本地执行模式
- 支持，当数据量小的时候，本地执行比提交到hadoop集群执行效率提升很大（大批量小作业，尽量采用本地执行模式）
- set hive.exec.mode.local.auto=true;(默认false)
- 满足如下条件才能真正使用本地模式：
- 1、job的数据数据大小必须小于参数hive.exec.mode.local.auto.inputbytes.max(默认值128M)
- 2、job的map处理的文件数小于参数hive.exec.mode.local.auto.input.files.max(默认值4)
- 3、job的reduce数必须小于等于1
- 用参数hive.mapred.local.mem来设置local mode下mapper和reducer task jvm heap size（在本地模式的最大内存量，以字节为单位，0为不限制）
hive> set hive.exec.mode.local.auto=true; --启用本地模式
hive> select name,age,INPUT__FILE__NAME
> from default.student;
Automatically selecting local only mode for query
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there is no reduce operator
Execution log at: /tmp/root/root_20150819122121_4a47f38a-3214-4990-a164-ce5b8a89b88e.log
Job running in-process (local Hadoop) --本地模式的Hadoop
Hadoop job information for null: number of mappers: 0; number of reducers: 0 --0个mapper和0个reducer
2015-08-19 12:21:51,828 null map = 100%, reduce = 0%
Ended Job = job_local531808121_0001 --从jobId也可以看出是Local_Job
Execution completed successfully
MapredLocal task succeeded --本地MR任务完成
OK
name age input__file__name
zhangsan 1 hdfs://hadoop01:9000/user/hive/warehouse/student/student
lisi 2 hdfs://hadoop01:9000/user/hive/warehouse/student/student
wangwu 3 hdfs://hadoop01:9000/user/hive/warehouse/student/student
zhaoba 4 hdfs://hadoop01:9000/user/hive/warehouse/student/student
liliuy 5 hdfs://hadoop01:9000/user/hive/warehouse/student/student
jiujiu 9 hdfs://hadoop01:9000/user/hive/warehouse/student/student
shiba 18 hdfs://hadoop01:9000/user/hive/warehouse/student/student
xiyita 28 hdfs://hadoop01:9000/user/hive/warehouse/student/student
NULL NULL hdfs://hadoop01:9000/user/hive/warehouse/student/student
Time taken: 5.216 seconds, Fetched: 9 row(s)
hive> select name,age,INPUT__FILE__NAME --采用分布式执行MR任务
> from default.student;
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there is no reduce operator
Starting Job = job_1438754688191_0137, --分布式MR任务的JobId
Tracking URL = http://hadoop01:8088/proxy/application_1438754688191_0137/
Kill Command = /hadoop/hadoop-2.2.0/bin/hadoop job -kill job_1438754688191_0137 --kill hadoop job的命令
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0 --map任务1个，reduce任务0个
2015-08-19 12:17:18,571 Stage-1 map = 0%, reduce = 0%
2015-08-19 12:17:22,757 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.55 sec
MapReduce Total cumulative CPU time: 550 msec
Ended Job = job_1438754688191_0137
MapReduce Jobs Launched:
Job 0: Map: 1 Cumulative CPU: 0.55 sec --1个map任务
HDFS Read: 284 HDFS Write: 592 --HDFS读取284字节，HDFS写出592字节
SUCCESS Total MapReduce CPU Time Spent: 550 msec
OK
name age input__file__name
zhangsan 1 hdfs://hadoop01:9000/user/hive/warehouse/student/student
lisi 2 hdfs://hadoop01:9000/user/hive/warehouse/student/student
wangwu 3 hdfs://hadoop01:9000/user/hive/warehouse/student/student
zhaoba 4 hdfs://hadoop01:9000/user/hive/warehouse/student/student
liliuy 5 hdfs://hadoop01:9000/user/hive/warehouse/student/student
jiujiu 9 hdfs://hadoop01:9000/user/hive/warehouse/student/student
shiba 18 hdfs://hadoop01:9000/user/hive/warehouse/student/student
xiyita 28 hdfs://hadoop01:9000/user/hive/warehouse/student/student
NULL NULL hdfs://hadoop01:9000/user/hive/warehouse/student/student
Time taken: 9.902 seconds, Fetched: 9 row(s)
9.hive是否支持自动Map-Join？
支持，设置hive.auto.convert.join=true，根据输入表的文件大小，自动将reduce端common join转为mapjoin，从而加快大表关联小表的速度。（建议开启）