Effortless ORC Data Integration: Reading ORC Files into PySpark DataFrames

PySpark @ Freshers.in

In the realm of big data processing, PySpark stands out for its ability to handle large datasets efficiently. One common task is reading data from various file formats. This article focuses on reading ORC (Optimized Row Columnar) files into PySpark DataFrames, a format known for its high performance and efficiency in storing Hive data.

Why ORC Files?

ORC files offer a highly efficient way to store Hive data. They improve performance by storing data in a columnar format, allowing for faster reads, better compression, and efficient data querying.

Setting Up the Environment

Before diving into the process, ensure you have the following:

  1. Apache Spark and PySpark installed and configured.
  2. Access to ORC files that you want to read into PySpark.

Reading ORC Files into PySpark DataFrames

Step 1: Initializing a SparkSession

Start by creating a SparkSession, the entry point for programming Spark with the Dataset and DataFrame API.

from pyspark.sql import SparkSession
spark = SparkSession.builder \
    .appName("ReadORC @ Freshers.in Learning") \

Step 2: Reading the ORC File

Use the read.orc method of the SparkSession to read ORC files.


orc_file_path = "/path/to/your/orcfile.orc"
df = spark.read.orc(orc_file_path)

Replace "/path/to/your/orcfile.orc" with the path to your ORC file.

Step 3: Displaying the DataFrame

After reading the ORC file, you can perform operations like showing the data or performing data transformations.



This command displays the contents of the DataFrame.

Suppose you have an ORC file with employee data containing columns id, name, and department.

Reading the ORC File:

df = spark.read.orc("employees.orc")

Expected Output:

| id|   name|department |
|  1| Alice |Engineering|
|  2|  Bob  |Marketing  |
|  3| Carol |HR         |

Advanced Usage: Data Manipulation

PySpark DataFrames offer various methods for data manipulation. For instance, you can filter data, perform aggregations, or join with other DataFrames.

Example: Filtering Data:

engineering_dept = df.filter(df.department == "Engineering")

This query filters out employees from the Engineering department.

Author: user