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Structured Streaming Programming Guide Overview Quick Example Programming Model Basic Concepts Handling Event-time and Late Data Fault Tolerance Semantics API using Datasets and DataFrames Creating streaming DataFrames and streaming Datasets Input Sources Schema inference and partition of streaming DataFrames/Datasets Operations on streaming DataFrames/Datasets Basic Operations - Selection, Projec
Apache Spark 3.0.0 is the first release of the 3.x line. The vote passed on the 10th of June, 2020. This release is based on git tag v3.0.0 which includes all commits up to June 10. Apache Spark 3.0 builds on many of the innovations from Spark 2.x, bringing new ideas as well as continuing long-term projects that have been in development. With the help of tremendous contributions from the open-sour
Performance Tuning Caching Data In Memory Other Configuration Options Join Strategy Hints for SQL Queries Coalesce Hints for SQL Queries Adaptive Query Execution Coalescing Post Shuffle Partitions Spliting skewed shuffle partitions Converting sort-merge join to broadcast join Converting sort-merge join to shuffled hash join Optimizing Skew Join Misc For some workloads, it is possible to improve pe
Running Spark on Kubernetes Prerequisites How it works Submitting Applications to Kubernetes Docker Images Cluster Mode Dependency Management Using Remote Dependencies Secret Management Introspection and Debugging Accessing Logs Accessing Driver UI Debugging Kubernetes Features Namespaces RBAC Client Mode Future Work Configuration Spark Properties Spark can run on clusters managed by Kubernetes. T
Running Spark on Kubernetes Security User Identity Volume Mounts Prerequisites How it works Submitting Applications to Kubernetes Docker Images Cluster Mode Client Mode Client Mode Networking Client Mode Executor Pod Garbage Collection Authentication Parameters IPv4 and IPv6 Dependency Management Secret Management Pod Template Using Kubernetes Volumes PVC-oriented executor pod allocation Local Sto
Apache Spark 2.3.0 is the fourth release in the 2.x line. This release adds support for Continuous Processing in Structured Streaming along with a brand new Kubernetes Scheduler backend. Other major updates include the new DataSource and Structured Streaming v2 APIs, and a number of PySpark performance enhancements. In addition, this release continues to focus on usability, stability, and polish w
Apache Spark 2.0.0 is the first release on the 2.x line. The major updates are API usability, SQL 2003 support, performance improvements, structured streaming, R UDF support, as well as operational improvements. In addition, this release includes over 2500 patches from over 300 contributors. To download Apache Spark 2.0.0, visit the downloads page. You can consult JIRA for the detailed changes. We
Spark 3.5.3 ScalaDoc
Machine Learning Library (MLlib) Guide MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. At a high level, it provides tools such as: ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering Featurization: feature extraction, transformation, dimensionality reduction, and sele
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
Building Spark Building Apache Spark Apache Maven Setting up Maven’s Memory Usage build/mvn Building a Runnable Distribution Specifying the Hadoop Version and Enabling YARN Building With Hive and JDBC Support Packaging without Hadoop Dependencies for YARN Building with Mesos support Building with Kubernetes support Building submodules individually Building with Spark Connect support Continuous Com
GraphX is Apache Spark's API for graphs and graph-parallel computation. Flexibility Seamlessly work with both graphs and collections. GraphX unifies ETL, exploratory analysis, and iterative graph computation within a single system. You can view the same data as both graphs and collections, transform and join graphs with RDDs efficiently, and write custom iterative graph algorithms using the Pregel
Spark 1.3.0 is the fourth release on the 1.X line. This release brings a new DataFrame API alongside the graduation of Spark SQL from an alpha project. It also brings usability improvements in Spark’s core engine and expansion of MLlib and Spark Streaming. Spark 1.3 represents the work of 174 contributors from more than 60 institutions in more than 1000 individual patches. To download Spark 1.3 vi
Apache Spark™ examples This page shows you how to use different Apache Spark APIs with simple examples. Spark is a great engine for small and large datasets. It can be used with single-node/localhost environments, or distributed clusters. Spark’s expansive API, excellent performance, and flexibility make it a good option for many analyses. This guide shows examples with the following Spark APIs: D
Tuning Spark Data Serialization Memory Tuning Memory Management Overview Determining Memory Consumption Tuning Data Structures Serialized RDD Storage Garbage Collection Tuning Other Considerations Level of Parallelism Parallel Listing on Input Paths Memory Usage of Reduce Tasks Broadcasting Large Variables Data Locality Summary Because of the in-memory nature of most Spark computations, Spark prog
Machine Learning Library (MLlib) MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below: Data types Basic statistics summary statistics correlations stratified sampling hypothesis te
Collaborative Filtering - RDD-based API Collaborative filtering Explicit vs. implicit feedback Scaling of the regularization parameter Examples Tutorial Collaborative filtering Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix. spark.mllib currently supports model-based collaborative filtering, in
Submitting Applications The spark-submit script in Spark’s bin directory is used to launch applications on a cluster. It can use all of Spark’s supported cluster managers through a uniform interface so you don’t have to configure your application especially for each one. Bundling Your Application’s Dependencies If your code depends on other projects, you will need to package them alongside your ap
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 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
Naive Bayes - RDD-based API Naive Bayes is a simple multiclass classification algorithm with the assumption of independence between every pair of features. Naive Bayes can be trained very efficiently. Within a single pass to the training data, it computes the conditional probability distribution of each feature given label, and then it applies Bayes’ theorem to compute the conditional probability
RDD Programming Guide Overview Linking with Spark Initializing Spark Using the Shell Resilient Distributed Datasets (RDDs) Parallelized Collections External Datasets RDD Operations Basics Passing Functions to Spark Understanding closures Example Local vs. cluster modes Printing elements of an RDD Working with Key-Value Pairs Transformations Actions Shuffle operations Background Performance Impact
Job Scheduling Overview Scheduling Across Applications Dynamic Resource Allocation Caveats Configuration and Setup Resource Allocation Policy Request Policy Remove Policy Graceful Decommission of Executors Scheduling Within an Application Fair Scheduler Pools Default Behavior of Pools Configuring Pool Properties Scheduling using JDBC Connections Concurrent Jobs in PySpark Overview Spark has severa
MLlib: RDD-based API This page documents sections of the MLlib guide for the RDD-based API (the spark.mllib package). Please see the MLlib Main Guide for the DataFrame-based API (the spark.ml package), which is now the primary API for MLlib. Data types Basic statistics summary statistics correlations stratified sampling hypothesis testing streaming significance testing random data generation Class
Spark Structured Streaming makes it easy to build streaming applications and pipelines with the same and familiar Spark APIs. Easy to use Spark Structured Streaming abstracts away complex streaming concepts such as incremental processing, checkpointing, and watermarks so that you can build streaming applications and pipelines without learning any new concepts or tools. spark .readStream .select($"
Spark Configuration Spark Properties Dynamically Loading Spark Properties Viewing Spark Properties Available Properties Application Properties Runtime Environment Shuffle Behavior Spark UI Compression and Serialization Memory Management Execution Behavior Executor Metrics Networking Scheduling Barrier Execution Mode Dynamic Allocation Thread Configurations Spark Connect Server Configuration Securi
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
Running Spark on YARN Security Launching Spark on YARN Adding Other JARs Preparations Configuration Debugging your Application Spark Properties Available patterns for SHS custom executor log URL Resource Allocation and Configuration Overview Stage Level Scheduling Overview Important notes Kerberos YARN-specific Kerberos Configuration Troubleshooting Kerberos Configuring the External Shuffle Servic
GraphX Programming Guide Overview Background on Graph-Parallel Computation GraphX Replaces the Spark Bagel API Migrating from Spark 0.9.1 Workaround for Graph.partitionBy in Spark 1.0.0 Getting Started The Property Graph Example Property Graph Graph Operators Summary List of Operators Property Operators Structural Operators Join Operators Neighborhood Aggregation Map Reduce Triplets (mapReduceTrip
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