‘LOAD DATA LOCAL INPATH '{file_path}' into table {table_name}’ command is used to load the contents ofa file into HIVE table.
Step 1: Lets create employee table.
CREATE TABLE emp ( id INT, name STRING, hobbies ARRAY<STRING>, technology_experience MAP<STRING,STRING>, gender_age STRUCT<gender:STRING,age:INT> ) ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' COLLECTION ITEMS TERMINATED BY ',' MAP KEYS TERMINATED BY ':' STORED AS TEXTFILE;
hive> CREATE TABLE emp (
> id INT,
> name STRING,
> hobbies ARRAY<STRING>,
> technology_experience MAP<STRING,STRING>,
> gender_age STRUCT<gender:STRING,age:INT>
> )
> ROW FORMAT DELIMITED
> FIELDS TERMINATED BY '|'
> COLLECTION ITEMS TERMINATED BY ','
> MAP KEYS TERMINATED BY ':'
> STORED AS TEXTFILE;
OK
Time taken: 0.054 seconds
Step 2: Create empInfo.txt file with below content.
empInfo.txt
1|Hari|Football,Cricket|Java:3.4Yrs,C:4.5Yrs|Male,30 2|Chamu|Trekking,Watching movies|Selenium:5.6Yrs|Feale,38 3|Sailu|Chess,Listening to music|EmbeddedC:9Yrs|Femle,32 4|Gopi|Cricket|Datastage:11Yrs|Male,32
Step 3: load empInfo.txt file content to emp table.
hive> LOAD DATA LOCAL INPATH '/Users/krishna/Documents/empInfo.txt' into table emp; Loading data to table default.employee OK Time taken: 0.214 seconds
Select all the records from emp table.
hive> select * from emp;
OK
1 Hari ["Football","Cricket"] {"Java":"3.4Yrs","C":"4.5Yrs"} {"gender":"Male","age":30}
2 Chamu ["Trekking","Watching movies"] {"Selenium":"5.6Yrs"} {"gender":"Feale","age":38}
3 Sailu ["Chess","Listening to music"] {"EmbeddedC":"9Yrs"} {"gender":"Femle","age":32}
4 Gopi ["Cricket"] {"Datastage":"11Yrs"} {"gender":"Male","age":32}
Time taken: 0.096 seconds, Fetched: 4 row(s)If you reload the contents of empInfo.txt file, it recopies the content to emp table.
hive> LOAD DATA LOCAL INPATH '/Users/krishna/Documents/empInfo.txt' into table emp; Loading data to table default.employee OK Time taken: 0.214 seconds
Select all the records from emp table.
hive> select * from emp;
OK
1 Hari ["Football","Cricket"] {"Java":"3.4Yrs","C":"4.5Yrs"} {"gender":"Male","age":30}
2 Chamu ["Trekking","Watching movies"] {"Selenium":"5.6Yrs"} {"gender":"Feale","age":38}
3 Sailu ["Chess","Listening to music"] {"EmbeddedC":"9Yrs"} {"gender":"Femle","age":32}
4 Gopi ["Cricket"] {"Datastage":"11Yrs"} {"gender":"Male","age":32}
1 Hari ["Football","Cricket"] {"Java":"3.4Yrs","C":"4.5Yrs"} {"gender":"Male","age":30}
2 Chamu ["Trekking","Watching movies"] {"Selenium":"5.6Yrs"} {"gender":"Feale","age":38}
3 Sailu ["Chess","Listening to music"] {"EmbeddedC":"9Yrs"} {"gender":"Femle","age":32}
4 Gopi ["Cricket"] {"Datastage":"11Yrs"} {"gender":"Male","age":32}
Time taken: 0.095 seconds, Fetched: 8 row(s)
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