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provides unsafe low-level bindings for OpenGL Core 3.2 - 4.6, and GLFW 3.3.2. cl-glfw includes, in addition to the GLFW bindings, a set of automatically generated OpenGL bindings, and a hand-written interface to GLU. It works with any implementation that supports CFFI and also includes optional bindings to GLU and GLUT. cl-opengl is a set of CFFI bindings to OpenGL. Ada OpenGL binding GLOBE_3D is a free, open-source, real-time 3D Engine written in Ada, based on OpenGL. Ada OpenGL bindings supports GL & GLU and GLUT. OpenGLAda is a thick Ada binding for OpenGL and GLFW. It offers complete independence from network protocols and topologies.Īll OpenGL applications produce consistent visual display results on any OpenGL API-compliant hardware, regardless of operating system or windowing system. #JAVA 3D 1.6 SERIES#
AMP Camps: a series of training camps at UC Berkeley that featured talks andĮxercises about Spark, Spark Streaming, Mesos, and more.OpenGL is supported on every major operating system, it works with every major windowing system, and it is callable from most programming languages. Mailing Lists: ask questions about Spark here. Spark Community resources, including local meetups. Supplemental Projects: related third party Spark projects. Building Spark: build Spark using the Maven system. Integration with other storage systems:. Hardware Provisioning: recommendations for cluster hardware. Job Scheduling: scheduling resources across and within Spark applications. Tuning Guide: best practices to optimize performance and memory use. Monitoring: track the behavior of your applications. Configuration: customize Spark via its configuration system. YARN: deploy Spark on top of Hadoop NextGen (YARN). Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager. Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes. Submitting Applications: packaging and deploying applications. Cluster Overview: overview of concepts and components when running on a cluster. GraphX: Spark’s new API for graph processing. MLlib: built-in machine learning library. Spark SQL, Datasets, and DataFrames: support for structured data and relational queries. Spark Streaming: processing real-time data streams. In all supported languages (Scala, Java, Python, R) Spark Programming Guide: detailed overview of Spark. Quick Start: a quick introduction to the Spark API start here!. Standalone Deploy Mode: simplest way to deploy Spark on a private cluster. Amazon EC2: our EC2 scripts let you launch a cluster in about 5 minutes. Spark can run both by itself, or over several existing cluster managers. The Spark cluster mode overview explains the key concepts in running on a cluster. bin/spark-submit examples/src/main/r/dataframe.R bin/sparkR -master localĮxample applications are also provided in R. To run Spark interactively in a R interpreter, use bin/sparkR. 
Spark also provides an experimental R API since 1.4 (only DataFrames APIs included).
bin/spark-submit examples/src/main/python/pi.py 10 bin/pyspark -master localĮxample applications are also provided in Python. To run Spark interactively in a Python interpreter, useīin/pyspark.
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For a full list of options, run Spark shell with the -help option. Locally with one thread, or local to run locally with N threads. Master URL for a distributed cluster, or local to run Great way to learn the framework./bin/spark-shell -master local You can also run Spark interactively through a modified version of the Scala shell. To run one of the Java or Scala sample programs, useīin/run-example in the top-level Spark directory. Scala, Java, Python and R examples are in theĮxamples/src/main directory. Spark comes with several sample programs. You will need to use a compatible Scala version
Spark runs on Java 7+, Python 2.6+ and R 3.1+. Or the JAVA_HOME environment variable pointing to a Java installation. Locally on one machine - all you need is to have java installed on your system PATH,
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Spark runs on both Windows and UNIX-like systems (e.g.
#JAVA 3D 1.6 DOWNLOAD#
Users can also download a “Hadoop free” binary and run Spark with any Hadoop version Downloads are pre-packaged for a handful of popular Hadoop versions. Spark uses Hadoop’s client libraries for HDFS and YARN. This documentation is for Spark version 1.6.3. Get Spark from the downloads page of the project website. 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. It provides high-level APIs in Java, Scala, Python and R,Īnd an optimized engine that supports general execution graphs. Apache Spark is a fast and general-purpose cluster computing system.
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