Table of Contents
Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
API Overview
Apache Spark is a unified analytics engine for large-scale data processing. It provides a powerful API that allows developers to build scalable, fault-tolerant applications that can run on a variety of distributed computing platforms. Spark's core features include support for memory computing, real-time stream processing, and machine learning. It also provides a rich set of libraries for data manipulation, statistical analysis, and machine learning algorithms. Spark is widely used in a variety of industries, including finance, healthcare, retail, and manufacturing.
The Apache Spark API provides a comprehensive set of tools for developing distributed applications. The documentation for the API is extensive and well-organized, making it easy for developers to get started. The API also includes a REST API and a set of Webhooks that allow developers to interact with Spark applications remotely. Additionally, the API includes rate limits to prevent applications from overloading the system.
API Request Limits
Free
100 requests per day
Shared
1000 requests per day
Dedicated
10000 requests per day
SDKs
APIs
Method: parallelize
Description: Distributes a local collection to form an RDD..
Endpoint:
Parameters: seq: collection to form an RDD
Method: textFile
Description: Loads a text file into an RDD..
Endpoint:
Parameters: path: Path to text file
Method: wholeTextFiles
Description: Loads a directory of text files into an RDD..
Endpoint:
Parameters: path: Path to a directory containing flat text files
Method: sequenceFile
Description: Loads a Hadoop SequenceFile into an RDD..
Endpoint:
Parameters: path: Path to SequenceFile
Method: objectFile
Description: Loads an RDD saved using <code>RDD.saveAsObjectFile</code> method..
Endpoint:
Parameters: path: Path to the previously saved object file
Method: parquetFile
Description: Loads a Parquet file into an RDD..
Endpoint:
Parameters: path: Path to a parquet file
Method: createDataFrame
Description: Creates a DataFrame from a local Seq of Product instances..
Endpoint:
Parameters: data: Seq of Product instances, rowRDD: RDD of Product instances
Method: createDataFrame
Description: Creates a DataFrame from an RDD of Product instances..
Endpoint:
Parameters: data: RDD of Product instances, schema: The schema of the DataFrame
Method: sql
Description: Returns a DataFrame representing the result of the given SQL query..
Endpoint:
Parameters: sql: SQL query string
Method: newAPIHadoopRDD
Description: Creates a new Hadoop RDD with the given key and value types, using the given Hadoop configuration..
Endpoint:
Parameters: config: Hadoop configuration, f: A function that returns an Iterator of HadoopRDD tuples (K, V)
Method: newAPIHadoopFile
Description: Create a Hadoop file-based RDD.
Endpoint:
Parameters: path: Path to file, keyClass: Class type of the key in Hadoop file, valueClass: Class type of the value in Hadoop file, inputFormatClass: InputFormat class to use for reading the file
Method: binaryRecords
Description: Creates a BinaryRecordsInputRDD..
Endpoint:
Parameters: path: Path to binary file, recordLength: Length of each record in the file
Method: sequenceFile
Description: Creates a Hadoop SequenceFile RDD..
Endpoint:
Parameters: path: Path to SequenceFile, keyClass: Class type of the key in Sequence file, valueClass: Class type of the value in Sequence file
Method: hadoopRDD
Description: Creates a HadoopRDD..
Endpoint:
Parameters: conf: Hadoop configuration, inputFormatClass: InputFormat class to use for reading the file, keyClass: Class type of the key in Hadoop file, valueClass: Class type of the value in Hadoop file
Method: parallelize
Description: Distributes a local collection to form an RDD..
Endpoint:
Parameters: seq: collection to form an RDD
Method: textFile
Description: Loads a text file into an RDD..
Endpoint:
Parameters: path: Path to text file
Method: wholeTextFiles
Description: Loads a directory of text files into an RDD..
Endpoint:
Parameters: path: Path to a directory containing flat text files
Method: sequenceFile
Description: Loads a Hadoop SequenceFile into an RDD..
Endpoint:
Parameters: path: Path to SequenceFile
Method: objectFile
Description: Loads an RDD saved using <code>RDD.saveAsObjectFile</code> method..
Endpoint:
Parameters: path: Path to the previously saved object file
Method: parquetFile
Description: Loads a Parquet file into an RDD..
Endpoint:
Parameters: path: Path to a parquet file
FAQ
How do I authenticate my application to use the Apache Spark APIs?
You can authenticate your application using OAuth 2.0. You will need to create a developer account and obtain a client ID and secret.
Are there any rate limits for using the Apache Spark APIs?
Yes, there are rate limits in place to prevent abuse of the APIs. The specific rate limits vary depending on the API.
How do I create a sandbox account to test the Apache Spark APIs?
You can create a sandbox account through the Apache Spark website. Sandbox accounts allow you to test the APIs without having to create a production account.
How do I get support for using the Apache Spark APIs?
You can get support for using the Apache Spark APIs through the Apache Spark community forums or by contacting Apache Spark support.
What are the best practices for using the Apache Spark APIs?
Some best practices for using the Apache Spark APIs include using the latest version of the APIs, using the correct authentication method, and using the APIs efficiently.