Apache Spark is a unified analytics engine for large-scale data processing.It provides high-level APIs in Java, Scala, Python and R,and an optimized engine that supports general execution graphs.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 Structured Streaming for incremental computation and stream processing.
Security in Spark is OFF by default. This could mean you are vulnerable to attack by default.Please see Spark Security before downloading and running Spark.
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Spark runs on Java 8/11, Scala 2.12, Python 2.7+/3.4+ and R 3.5+. Java 8 prior to version 8u92 support is deprecated as of Spark 3.0.0. Python 2 and Python 3 prior to version 3.6 support is deprecated as of Spark 3.0.0. RGB Color Examples 0/0/0 0/0/0.1 0/0/0.2 0/0/0.3 0/0/0.4 0/0/0.5 0/0/0.6 0/0/0.7 0/0/0.8 0/0/0.9. 0/0.9/0.6 0/0.9/0.7 0/0.9/0.8 0/0.9/0.9 0/0.9/1 0/1/0 0/1/0.1 0/1/0.
Get Spark from the downloads page of the project website. Soy luna 2 capitulo 1 luna patina. This documentation is for Spark version 3.0.1. Spark uses Hadoop’s client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions.Users can also download a “Hadoop free” binary and run Spark with any Hadoop versionby augmenting Spark’s classpath.Scala and Java users can include Spark in their projects using its Maven coordinates and Python users can install Spark from PyPI.
If you’d like to build Spark from source, visit Building Spark.
Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS), and it should run on any platform that runs a supported version of Java. This should include JVMs on x86_64 and ARM64. It’s easy to run locally on one machine — all you need is to have
java
installed on your system PATH
, or the JAVA_HOME
environment variable pointing to a Java installation.Spark runs on Java 8/11, Scala 2.12, Python 2.7+/3.4+ and R 3.5+.Java 8 prior to version 8u92 support is deprecated as of Spark 3.0.0.Python 2 and Python 3 prior to version 3.6 support is deprecated as of Spark 3.0.0.For the Scala API, Spark 3.0.1uses Scala 2.12. You will need to use a compatible Scala version(2.12.x).
For Java 11,
-Dio.netty.tryReflectionSetAccessible=true
is required additionally for Apache Arrow library. This prevents java.lang.UnsupportedOperationException: sun.misc.Unsafe or java.nio.DirectByteBuffer.(long, int) not available
when Apache Arrow uses Netty internally.Spark comes with several sample programs. Scala, Java, Python and R examples are in the
examples/src/main
directory. To run one of the Java or Scala sample programs, usebin/run-example <class> [params]
in the top-level Spark directory. (Behind the scenes, thisinvokes the more generalspark-submit
script forlaunching applications). For example,You can also run Spark interactively through a modified version of the Scala shell. This is agreat way to learn the framework.
The
--master
option specifies themaster URL for a distributed cluster, or local
to runlocally with one thread, or local[N]
to run locally with N threads. You should start by usinglocal
for testing. For a full list of options, run Spark shell with the --help
option.Spark also provides a Python API. To run Spark interactively in a Python interpreter, use
bin/pyspark
:Example applications are also provided in Python. For example,
Spark also provides an R API since 1.4 (only DataFrames APIs included).To run Spark interactively in an R interpreter, use
bin/sparkR
:Example applications are also provided in R. For example,
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The Spark cluster mode overview explains the key concepts in running on a cluster.Spark can run both by itself, or over several existing cluster managers. It currently provides severaloptions for deployment:
- Standalone Deploy Mode: simplest way to deploy Spark on a private cluster
Programming Guides:
- Quick Start: a quick introduction to the Spark API; start here!
- RDD Programming Guide: overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables
- Spark SQL, Datasets, and DataFrames: processing structured data with relational queries (newer API than RDDs)
- Structured Streaming: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)
- Spark Streaming: processing data streams using DStreams (old API)
- MLlib: applying machine learning algorithms
- GraphX: processing graphs
API Docs:
Deployment Guides:
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- Cluster Overview: overview of concepts and components when running on a cluster
- Submitting Applications: packaging and deploying applications
- Deployment modes:
- Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
- Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager
- Mesos: deploy a private cluster using Apache Mesos
- YARN: deploy Spark on top of Hadoop NextGen (YARN)
- Kubernetes: deploy Spark on top of Kubernetes
Other Documents:
- Configuration: customize Spark via its configuration system
- Monitoring: track the behavior of your applications
- Tuning Guide: best practices to optimize performance and memory use
- Job Scheduling: scheduling resources across and within Spark applications
- Security: Spark security support
- Hardware Provisioning: recommendations for cluster hardware
- Integration with other storage systems:
- Migration Guide: Migration guides for Spark components
- Building Spark: build Spark using the Maven system
- Third Party Projects: related third party Spark projects
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External Resources:
- Spark Community resources, including local meetups
- Mailing Lists: ask questions about Spark here
- AMP Camps: a series of training camps at UC Berkeley that featured talks andexercises about Spark, Spark Streaming, Mesos, and more. Videos,slides and exercises areavailable online for free.
- Code Examples: more are also available in the
examples
subfolder of Spark (Scala, Java, Python, R)
IO and Event Looping¶
As AMQP is a two-way RPC protocol where the client can send requests to the server and the server can send requests to a client, Pika implements or extends IO loops in each of its asynchronous connection adapters. These IO loops are blocking methods which loop and listen for events. Each asynchronous adapter follows the same standard for invoking the IO loop. The IO loop is created when the connection adapter is created. To start an IO loop for any given adapter, call the
connection.ioloop.start()
method.If you are using an external IO loop such as Tornado’s
IOLoop
you invoke it normally and then add the Pika Tornado adapter to it.Example:
Continuation-Passing Style¶
Interfacing with Pika asynchronously is done by passing in callback methods you would like to have invoked when a certain event completes. Sketch 55 2015. For example, if you are going to declare a queue, you pass in a method that will be called when the RabbitMQ server returns a Queue.DeclareOk response.
In our example below we use the following five easy steps:
- We start by creating our connection object, then starting our event loop.
- When we are connected, the on_connected method is called. In that method we create a channel.
- When the channel is created, the on_channel_open method is called. In that method we declare a queue.
- When the queue is declared successfully, on_queue_declared is called. In that method we call
channel.basic_consume
telling it to call the handle_delivery for each message RabbitMQ delivers to us. - When RabbitMQ has a message to send us, it calls the handle_delivery method passing the AMQP Method frame, Header frame, and Body.
Note
Step #1 is on line #28 and Step #2 is on line #6. This is so that Python knows about the functions we’ll call in Steps #2 through #5.
Example:
Credentials¶
The
pika.credentials
module provides the mechanism by which you pass the username and password to the ConnectionParameters
class when it is created.Example:
Connection Parameters¶
There are two types of connection parameter classes in Pika to allow you to pass the connection information into a connection adapter,
ConnectionParameters
and URLParameters
. Both classes share the same default connection values.Pikka 2 0 30
TCP Backpressure¶
As of RabbitMQ 2.0, client side Channel.Flow has been removed [1]. Instead, the RabbitMQ broker uses TCP Backpressure to slow your client if it is delivering messages too fast. If you pass in backpressure_detection into your connection parameters, Pika attempts to help you handle this situation by providing a mechanism by which you may be notified if Pika has noticed too many frames have yet to be delivered. By registering a callback function with the
add_backpressure_callback
method of any connection adapter, your function will be called when Pika sees that a backlog of 10 times the average frame size you have been sending has been exceeded. You may tweak the notification multiplier value by calling the set_backpressure_multiplier
method passing any integer value.Example:
Footnotes
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[1] | “more effective flow control mechanism that does not require cooperation from clients and reacts quickly to prevent the broker from exhausting memory - see http://lists.rabbitmq.com/pipermail/rabbitmq-announce/attachments/20100825/2c672695/attachment.txt |