Learning Objectives

Learn how to work with the Hive interactive shell.
Learn how to create tables in Hive.
Learn how to load data into Hive tables.
Learn how to run basic Hive querys.

This section shows the basic usage of Hadoop Hive. Hive uses a SQL-like language called HiveQL, and runs on top of Hadoop. Instead of writing raw MapReduce programs, Hive allows you to perform data warehouse tasks using a simple and familiar query language. After completing this section, you will be able to use HiveQL to query big data.

Interactive shell

In the sample code below we will continue to use the same event tuple patient data. Let's start the Hive CLI interactive shell first by typing hive in the command line.

> cd bigdata-bootcamp/sample/hive
> hive
...                                                                         
[info]
hive>

Create table

Before loading data, we first need to define a table just like we would if we were working with a database server such as SQL.

hive> CREATE TABLE events (
        patient_id STRING,
        event_name STRING,
        date_offset INT,
        value INT)
      ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
      STORED AS TEXTFILE;
OK
Time taken: 0.289 seconds
hive>

And you can check existing tables and schema with the commands SHOW TABLES; and DESCRIBE table_name; respectively.

hive> SHOW TABLES;
OK
events
Time taken: 0.022 seconds, Fetched: 1 row(s)
hive> DESCRIBE events;
OK
patient_id              string                                      
event_name              string                                      
date_offset             int                                         
value                   int                                         
Time taken: 0.221 seconds, Fetched: 4 row(s)

Load data

Next we'll insert data into the table.

hive> LOAD DATA LOCAL INPATH 'data'
      OVERWRITE INTO TABLE events;
Loading data to table default.events
Table default.events stats: [numFiles=2, numRows=0, totalSize=1208972, rawDataSize=0]
OK
Time taken: 0.521 seconds

Query

Basic

With the data loaded you can run familiar SQL statements like:

hive> SELECT patient_id, count(*) FROM events
      GROUP BY patient_id;

[info]...

F49EA945C42543C8        19
F4C0BFF334226C29        60
F560829E559E1FEB        13
...
FA4854797F48D537        88
FA831739B546F976        15
FAEF9F6E7AF1D99D        196
FBF4F34C7437373D        119
FBFD014814507B5C        28
Time taken: 20.351 seconds, Fetched: 300 row(s)

Save result

You can also save query results to local directory (in the local file system):

hive> INSERT OVERWRITE LOCAL DIRECTORY 'tmp_local_out'
      ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
      STORED AS TEXTFILE
      SELECT patient_id, count(*) 
      FROM events 
      GROUP BY patient_id;


[info]...
OK
Time taken: 17.034 seconds

You can learn more about Hive syntax from the language manual.

Besides shell

Besides running commands with the interactive shell, you can also run a script in batch mode automatically. For example, in the sample/hive folder, you can run the entire sample.hql script with the command:

> hive -f sample.hql

The contents of the script is simply all of the commands that we ran in the shell, with one additional statement to drop existing table if necessary:

DROP TABLE IF EXISTS events;

CREATE TABLE events (
  patient_id STRING,
  event_name STRING,
  date_offset INT,
  value INT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE;

SHOW TABLES;
DESCRIBE events;

LOAD DATA LOCAL INPATH 'data'
OVERWRITE INTO TABLE events;

INSERT OVERWRITE LOCAL DIRECTORY 'tmp_local_out'
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
SELECT patient_id, count(*) 
FROM events 
GROUP BY patient_id;

Furthermore, it's also possible to run hive as a server and connect to the server with JDBC or with its beeline client.

Hive translate queries into a series of MapReduce jobs, therefore it is not suitable for real-time use cases. Alternative tools inspired and influenced by Hive are getting more attention lately, for example, Cloudera Impala and Spark SQL.