Apache Spark™ - Unified Engine for large-scale data analytics
网页Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Overview - Spark 3.5.3 Documentation - Apache Spark
网页It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for incremental computation and stream processing.
Documentation - Apache Spark
网页The documentation linked to above covers getting started with Spark, as well the built-in components MLlib, Spark Streaming, and GraphX. In addition, this page lists other resources for learning Spark.
Quick Start - Spark 3.5.3 Documentation - Apache Spark
网页Unlike the earlier examples with the Spark shell, which initializes its own SparkSession, we initialize a SparkSession as part of the program. To build the program, we also write a Maven pom.xml file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.
PySpark Overview — PySpark 3.5.3 documentation - Apache Spark
网页PySpark combines Python’s learnability and ease of use with the power of Apache Spark to enable processing and analysis of data at any size for everyone familiar with Python. PySpark supports all of Spark’s features such as Spark SQL, DataFrames, Structured Streaming, Machine Learning (MLlib) and Spark Core.
Spark SQL & DataFrames - Apache Spark
网页Seamlessly mix SQL queries with Spark programs. Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Usable in Java, Scala, Python and R.
Examples - Apache Spark
网页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: DataFrames; SQL; Structured Streaming; RDDs
Downloads - Apache Spark
网页Download Spark: Verify this release using the and project release KEYS by following these procedures . Note that Spark 3 is pre-built with Scala 2.12 in general and Spark 3.2+ provides additional pre-built distribution with Scala 2.13.
Spark SQL and DataFrames - Spark 3.5.3 Documentation - Apache …
网页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.
Getting Started — PySpark 3.5.3 documentation - Apache Spark
网页Quickstart: Spark Connect. Launch Spark server with Spark Connect; Connect to Spark Connect server; Create DataFrame; Quickstart: Pandas API on Spark. Object Creation; Missing Data; Operations; Grouping; Plotting; Getting data in/out; Testing PySpark. Build a PySpark Application; Testing your PySpark Application; Putting It All Together!