pyspark.sql module — PySpark 3.0.1 documentation
Module Contents¶ Important classes of Spark SQL and DataFrames: pyspark.sql.SparkSession Main entry point for DataFrame and SQL functionality. pyspark.sql.DataFrame A distributed collection of data grouped into named columns. pyspark.sql.Column A column expression in a DataFrame. pyspark.sql.Row A row of data in a DataFrame. pyspark.sql.GroupedData Aggregation methods, returned by DataFrame.groupB
Spark Streaming + Kafka Integration Guide - Spark 3.5.1 Documentation
Spark Streaming + Kafka Integration Guide Apache Kafka is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Please read the Kafka documentation thoroughly before starting an integration using Spark. At the moment, Spark requires Kafka 0.10 and higher. See Kafka 0.10 integration documentation for details.
Running Spark on Mesos - Spark 3.5.1 Documentation
Running Spark on Mesos Security How it Works Installing Mesos From Source Third-Party Packages Verification Connecting Spark to Mesos Authenticating to Mesos Credential Specification Preference Order Deploy to a Mesos running on Secure Sockets Uploading Spark Package Using a Mesos Master URL Client Mode Cluster mode Mesos Run Modes Coarse-Grained Fine-Grained (deprecated) Mesos Docker Support Runn
Spark Release 1.2.0 | Apache Spark
Spark 1.2.0 is the third release on the 1.X line. This release brings performance and usability improvements in Spark’s core engine, a major new API for MLlib, expanded ML support in Python, a fully H/A mode in Spark Streaming, and much more. GraphX has seen major performance and API improvements and graduates from an alpha component. Spark 1.2 represents the work of 172 contributors from more tha
Spark SQL and DataFrames - Spark 3.5.1 Documentation
Spark SQL, DataFrames and Datasets Guide Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations. There are several ways to interact wit
Spark Release 1.1.0 | Apache Spark
Spark 1.1.0 is the first minor release on the 1.X line. This release brings operational and performance improvements in Spark core along with significant extensions to Spark’s newest libraries: MLlib and Spark SQL. It also builds out Spark’s Python support and adds new components to the Spark Streaming module. Spark 1.1 represents the work of 171 contributors, the most to ever contribute to a Spar
Spark Standalone Mode - Spark 3.5.1 Documentation
Spark Standalone Mode Security Installing Spark Standalone to a Cluster Starting a Cluster Manually Cluster Launch Scripts Resource Allocation and Configuration Overview Connecting an Application to the Cluster Client Properties Launching Spark Applications Spark Protocol REST API Resource Scheduling Executors Scheduling Stage Level Scheduling Overview Caveats Monitoring and Logging Running Alongs
Spark Streaming - Spark 3.5.1 Documentation
Spark Streaming Programming Guide Note Overview A Quick Example Basic Concepts Linking Initializing StreamingContext Discretized Streams (DStreams) Input DStreams and Receivers Transformations on DStreams Output Operations on DStreams DataFrame and SQL Operations MLlib Operations Caching / Persistence Checkpointing Accumulators, Broadcast Variables, and Checkpoints Deploying Applications Monitorin
Running Spark on EC2 - Spark 1.0.0 Documentation
Running Spark on EC2 The spark-ec2 script, located in Spark’s ec2 directory, allows you to launch, manage and shut down Spark clusters on Amazon EC2. It automatically sets up Spark, Shark and HDFS on the cluster for you. This guide describes how to use spark-ec2 to launch clusters, how to run jobs on them, and how to shut them down. It assumes you’ve already signed up for an EC2 account on the Ama
Quick Start - Spark 1.3.0 Documentation
Quick Start Interactive Analysis with the Spark Shell Basics More on Dataset Operations Caching Self-Contained Applications Where to Go from Here This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write applications in Java, Scala, and Python. To follow along with this guide, first, do
Spark Release 1.0.0 | Apache Spark
Spark 1.0.0 is a major release marking the start of the 1.X line. This release brings both a variety of new features and strong API compatibility guarantees throughout the 1.X line. Spark 1.0 adds a new major component, Spark SQL, for loading and manipulating structured data in Spark. It includes major extensions to all of Spark’s existing standard libraries (ML, Streaming, and GraphX) while also
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