Find the file name corresponding to a record in hive

Every table in hive has two virtual columns. They are


INPUT__FILE__NAME give the name of the file.

BLOCK__OFFSET__INSIDE__FILE is the current global file position.

Suppose if we want to find the name of the file corresponding to each record in a file. We can use the INPUT__FILE__NAME column. This feature is available from hive versions above 0.8. A small example is given below.

Table Customer Details

create table customer_details ( name string, phone_number string) row format delimited fields terminated by ',';

Sample data set






select INPUT__FILE__NAME, name from customer_data;

This will give us the file name corresponding to each record. If you want to get the file names corresponding to a hive table, the below query will help you.

select distinct(INPUT__FILE__NAME) from customer_data;

An Introduction to Apache Hive

Hive is an important member of hadoop ecosystem. It runs on top of hadoop.  Hive uses a SQL type query language to process the data in hdfs. Hive is very simple as compared to writing several lines of mapreduce codes using programming languages such as Java. Hive was developed by facebook in a vision to support their SQL experts to handle big data without much difficulty.  Hive queries are easy to learn for people who don’t know any programming languages.  People having experience in SQL can go straight forward with hive queries. The queries fired into hive will ultimately run as mapreduce.

Hive runs in two execution modes, local and distributed mode.

In local, the hive queries run as a single process and uses the local file system. In distributed mode, the mapper and reducer runs as different process and uses the hadoop distributed file system.

The installation of hive was explained well in my previous post Hive Installation.

Hive stores its contents in hdfs and table details (metadata) in some databases. By default the metadata is stored in derby database, but this is just for play around setups only and cannot be used for multiuser environments. For multiuser environments, we can use databases such as mysql, postgresql , oracle etc for storing the hive metadata. The data are stored in hdfs and it is contained in a location called hive warehouse directory which is defined by the property hive.metastore.warehouse.dir. By default this will be /user/hive/warehouse

We can fire queries into hive using a command line interface or using clients written in different programming languages. Hive server exposes a thrift service making hive accessible from various programming languages .

The simplicity and power of hive can be explained by comparing the word count program written in java program and in hive query.

The word count program written in java is well explained in my previous post A Simple Mapreduce Program – Wordcount . For that have to write a lot of lines of code and it will take time and it needs some good programming knowledge also.

The same word count can be done using hive query in a few lines of hive query.

CREATE TABLE word_counts AS
SELECT word, count(1) AS count FROM
(SELECT explode(split(line, '\s')) AS word FROM docs) word
ORDER BY word;

Hadoop commands in hive command line interface

We can execute hadoop commands in hive cli. It is very simple.
Just put an exclamation mark (!) before your hadoop command in hive cli and put a semicolon (;) after your command.


hive> !hadoop fs –ls / ;

drwxr-xr-x   - hdfs supergroup          0 2013-03-20 12:44 /app
drwxrwxrwx   - hdfs supergroup          0 2013-05-23 11:54 /tmp
drwxr-xr-x   - hdfs supergroup          0 2013-05-08 18:47 /user

Very simple.. 🙂