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.
Syntax:
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
print(spark_df.head())
Output:
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.
Spark important urls to refer
