‘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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