Exploring Pandas API on Spark: Load an ORC object from the file path : read_orc


Spark offers a Pandas API, bridging the gap between the two platforms. In this article, we’ll delve into the specifics of using the Pandas API on Spark for Input/Output operations, with a focus on reading ORC files using the read_orc function.

Understanding ORC Files:

ORC (Optimized Row Columnar) is a columnar storage file format, designed for efficient data processing in big data environments. It offers significant advantages in terms of compression, predicate pushdown, and schema evolution, making it a popular choice for data storage in Spark applications.

Using read_orc in Pandas API on Spark:

The read_orc function in the Pandas API on Spark allows users to load ORC files directly into Spark DataFrames, seamlessly integrating Pandas functionalities with Spark’s distributed computing capabilities.


import pandas as pd
# Load an ORC object from the file path
df = pd.read_orc(path)

Example: Loading an ORC File: Let’s demonstrate how to use read_orc to load an ORC file into a Spark DataFrame.

# Import necessary libraries
import pandas as pd

# Path to the ORC file
orc_path = "path/to/orc/file"

# Load ORC file into a Spark DataFrame using read_orc
spark_df = pd.read_orc(orc_path)

# Display the first few rows of the DataFrame


   col1  col2  col3
0   1     4     7
1   2     5     8
2   3     6     9

The read_orc function allows for seamless loading of ORC files into Spark DataFrames, enabling efficient data processing at scale.

Author: user