How to Resolve the 'No FileSystem for scheme: https' Error When Loading Files from Amazon S3 in Apache Spark

Hello data scientists and software engineers out there

How to Resolve the ‘No FileSystem for scheme: https’ Error When Loading Files from Amazon S3 in Apache Spark

Hello data scientists and software engineers out there! If you’ve ever encountered the error message ‘No FileSystem for scheme: https’ while using Apache Spark to load files from Amazon S3, then you’re in the right place. This blog post will guide you on how to resolve this issue and ensure smooth data processing.

What is Apache Spark?

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

What is Amazon S3?

Amazon S3 (Simple Storage Service) is an object storage service that offers industry-leading scalability, data availability, security, and performance.

The Error: No FileSystem for scheme: https

When trying to read data from a secure Amazon S3 bucket via HTTPS in Spark, you might encounter an error message ‘No FileSystem for scheme: https’. This error arises when Spark is unable to recognize the ‘https’ file system scheme. But don’t worry, the solution is straightforward and involves configuring Hadoop properly in your Spark environment.

The Solution

To solve the ‘No FileSystem for scheme: https’ error, you need to ensure that the Hadoop-AWS package is correctly included in your Spark project. The Hadoop-AWS module provides support for AWS integration, including the S3A connector to Amazon S3.

Here’s the step-by-step guide:

Step 1: Include the Hadoop-AWS Package

For Maven projects, include the following in your pom.xml file:

    <version>[Your Hadoop version]</version>

For SBT projects, include this in your build.sbt file:

libraryDependencies += "org.apache.hadoop" % "hadoop-aws" % "[Your Hadoop version]"

Replace [Your Hadoop version] with the version of Hadoop you’re using.

Step 2: Configure the FileSystem

In your Spark code, add the following lines to configure the file system:

val spark: SparkSession = ...
spark.sparkContext.hadoopConfiguration.set("fs.s3a.access.key", "[Your AWS access key]")
spark.sparkContext.hadoopConfiguration.set("fs.s3a.secret.key", "[Your AWS secret key]")

Replace [Your AWS access key] and [Your AWS secret key] with your actual AWS credentials.

Step 3: Read Data from S3 in Spark

Now you can read data from your S3 bucket:

val df ="s3a://[Your bucket name]/[Your file path]")

Replace [Your bucket name] and [Your file path] with your actual bucket name and file path.


Dealing with errors can sometimes be frustrating, especially when working with big data tools like Apache Spark. However, understanding the cause of these errors and knowing how to resolve them is part of the journey every data scientist or software engineer undertakes. The ‘No FileSystem for scheme: https’ error is one of those hurdles we’ve successfully overcome today. Happy Sparking!

Keywords: Apache Spark, Amazon S3, No FileSystem for scheme: https, Hadoop-AWS, Data Science, Big Data, AWS, S3A Connector, Spark Error, Loading Files from S3 in Spark

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