Python to Hive Connection: A Comprehensive Guide to Analyzing Big Data with Ease

Learn how to establish a seamless connection between Python and Hive, and harness the power of Python to analyze massive datasets stored in Hadoop Distributed File System

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What is Python?

Python is a powerful programming language that is widely used in various industries for data analysis, machine learning, and scientific computing

What is Hive?

Hive is a data warehouse infrastructure that is built on top of Hadoop. It provides an SQL-like interface to query data stored in Hadoop Distributed File System (HDFS).

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In this blog, we will discuss how to establish a connection between Python and Hive.

Before we start, we need to make sure that we have installed the necessary packages. We need to install the following packages:

• pyhive: It is a Python package that provides a Python DB-API 2.0-compliant interface to Hive.

• thrift: It is a Python package that provides a Python implementation of the Thrift protocol.

• sasl: It is a Python package that provides a Python implementation of the Simple Authentication and Security Layer (SASL) protocol.

• thrift-sasl: It is a Python package that provides a SASL transport for Thrift.

To install these packages, we can use the pip command in the terminal or command prompt:

On Windows

pip install pyhive thrift sasl thrift-sasl

On Linux:

pip3 install pyhive thrift sasl thrift-sasl

Note: For running pyhive properly Python version should be greater than 3.6 at least .

Now that we have installed the necessary packages, let's establish a connection between Python and Hive. We need to follow the following steps:

Step 1: Import the required packages

from pyhive import hive

Step 2: Create a connection object

conn = hive.Connection(host='localhost', port=10000, username='hive')

In the Connection constructor, we need to provide the host and port of the Hive server and the username to authenticate the connection.

Connection arguments:

Host: It can be of either some IP/URL address without Http. e.g. “192.168.0.141” or “localhost“ or “your_hive.com“, etc.

Port: Specify your hive port which can be found in the Hive-site.xml file.

Username: mention your name for connecting to the hive. In my case, the username is “hive”

Note: If you want to connect Hive from Python using HTTPS Hostname then it’s not possible with pyhive. You should go for Impyla python lib.

Impyla Installation link: https://pypi.org/project/impyla/

Step 3: Create a cursor object

cursor = conn.cursor()

The cursor object is used to execute SQL queries on the Hive server.

Step 4: Execute SQL queries

cursor.execute('SELECT * FROM my_table')

We can execute any SQL query using the execute() method.

Step 5: Fetch the results

results = cursor.fetchall()

The fetchall() method returns all the rows of the result set as a list of tuples.

Step 6: Close the connection

conn.close()

It is good practice to close the connection after using it.

Here is the complete code:

from pyhive import hive
conn = hive.Connection(host='localhost', port=10000, username='your_username')
cursor = conn.cursor()
cursor.execute('SELECT * FROM my_table')
results = cursor.fetchall()
conn.close()

Conclusion:

In conclusion, connecting Python to Hive is a straightforward process. With the pyhive package, we can quickly establish a connection and execute SQL queries on the Hive server. This allows us to analyze and manipulate large datasets stored in Hadoop using the powerful capabilities of Python.

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