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Posted to commits@apex.apache.org by th...@apache.org on 2016/03/02 02:40:32 UTC
[6/8] incubator-apex-core git commit: Migrating docs
Migrating docs
Project: http://git-wip-us.apache.org/repos/asf/incubator-apex-core/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-apex-core/commit/44f220fd
Tree: http://git-wip-us.apache.org/repos/asf/incubator-apex-core/tree/44f220fd
Diff: http://git-wip-us.apache.org/repos/asf/incubator-apex-core/diff/44f220fd
Branch: refs/heads/APEXCORE-293
Commit: 44f220fd221bbd4ace04943e1d2e9bc275391923
Parents: c07663b
Author: sashadt <sa...@datatorrent.com>
Authored: Fri Jan 29 18:39:20 2016 -0800
Committer: Thomas Weise <th...@datatorrent.com>
Committed: Sun Feb 28 22:46:41 2016 -0800
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apex.md | 14 -
apex_development_setup.md | 151 -
apex_malhar.md | 65 -
application_development.md | 2934 ------------------
application_packages.md | 669 ----
autometrics.md | 311 --
configuration_packages.md | 242 --
docs/apex.md | 14 +
docs/apex_development_setup.md | 151 +
docs/apex_malhar.md | 65 +
docs/application_development.md | 2934 ++++++++++++++++++
docs/application_packages.md | 669 ++++
docs/autometrics.md | 311 ++
docs/configuration_packages.md | 242 ++
docs/dtcli.md | 273 ++
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http://git-wip-us.apache.org/repos/asf/incubator-apex-core/blob/44f220fd/apex.md
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diff --git a/apex.md b/apex.md
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-Apache Apex
-================================================================================
-
-Apache Apex (incubating) is the industry’s only open source, enterprise-grade unified stream and batch processing engine. Apache Apex includes key features requested by open source developer community that are not available in current open source technologies.
-
-* Event processing guarantees
-* In-memory performance & scalability
-* Fault tolerance and state management
-* Native rolling and tumbling window support
-* Hadoop-native YARN & HDFS implementation
-
-For additional information visit [Apache Apex](http://apex.incubator.apache.org/).
-
-[](http://apex.incubator.apache.org/)
http://git-wip-us.apache.org/repos/asf/incubator-apex-core/blob/44f220fd/apex_development_setup.md
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diff --git a/apex_development_setup.md b/apex_development_setup.md
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-Apache Apex Development Environment Setup
-=========================================
-
-This document discusses the steps needed for setting up a development environment for creating applications that run on the Apache Apex or the DataTorrent RTS streaming platform.
-
-
-Microsoft Windows
-------------------------------
-
-There are a few tools that will be helpful when developing Apache Apex applications, some required and some optional:
-
-1. *git* -- A revision control system (version 1.7.1 or later). There are multiple git clients available for Windows (<http://git-scm.com/download/win> for example), so download and install a client of your choice.
-
-2. *java JDK* (not JRE). Includes the Java Runtime Environment as well as the Java compiler and a variety of tools (version 1.7.0\_79 or later). Can be downloaded from the Oracle website.
-
-3. *maven* -- Apache Maven is a build system for Java projects (version 3.0.5 or later). It can be downloaded from <https://maven.apache.org/download.cgi>.
-
-4. *VirtualBox* -- Oracle VirtualBox is a virtual machine manager (version 4.3 or later) and can be downloaded from <https://www.virtualbox.org/wiki/Downloads>. It is needed to run the Data Torrent Sandbox.
-
-5. *DataTorrent Sandbox* -- The sandbox can be downloaded from <https://www.datatorrent.com/download>. It is useful for testing simple applications since it contains Apache Hadoop and Data Torrent RTS 3.1.1 pre-installed with a time-limited Enterprise License. If you already installed the RTS Enterprise Edition (evaluation or production license) on a cluster, you can use that setup for deployment and testing instead of the sandbox.
-
-6. (Optional) If you prefer to use an IDE (Integrated Development Environment) such as *NetBeans*, *Eclipse* or *IntelliJ*, install that as well.
-
-
-After installing these tools, make sure that the directories containing the executable files are in your PATH environment; for example, for the JDK executables like _java_ and _javac_, the directory might be something like `C:\\Program Files\\Java\\jdk1.7.0\_80\\bin`; for _git_ it might be `C:\\Program Files\\Git\\bin`; and for maven it might be `C:\\Users\\user\\Software\\apache-maven-3.3.3\\bin`. Open a console window and enter the command:
-
- echo %PATH%
-
-to see the value of the `PATH` variable and verify that the above directories are present. If not, you can change its value clicking on the button at _Control Panel_ ⇨ _Advanced System Settings_ ⇨ _Advanced tab_ ⇨ _Environment Variables_.
-
-
-Now run the following commands and ensure that the output is something similar to that shown in the table below:
-
-
-<table>
-<colgroup>
-<col width="30%" />
-<col width="70%" />
-</colgroup>
-<tbody>
-<tr class="odd">
-<td align="left"><p>Command</p></td>
-<td align="left"><p>Output</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p><tt>javac -version</tt></p></td>
-<td align="left"><p>javac 1.7.0_80</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p><tt>java -version</tt></p></td>
-<td align="left"><p>java version "1.7.0_80"</p>
-<p>Java(TM) SE Runtime Environment (build 1.7.0_80-b15)</p>
-<p>Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)</p></td>
-</tr>
-<tr class="even">
-<td align="left"><p><tt>git --version</tt></p></td>
-<td align="left"><p>git version 2.6.1.windows.1</p></td>
-</tr>
-<tr class="odd">
-<td align="left"><p><tt>mvn --version</tt></p></td>
-<td align="left"><p>Apache Maven 3.3.3 (7994120775791599e205a5524ec3e0dfe41d4a06; 2015-04-22T06:57:37-05:00)</p>
-<p>Maven home: C:\Users\ram\Software\apache-maven-3.3.3\bin\..</p>
-<p>Java version: 1.7.0_80, vendor: Oracle Corporation</p>
-<p>Java home: C:\Program Files\Java\jdk1.7.0_80\jre</p>
-<p>Default locale: en_US, platform encoding: Cp1252</p>
-<p>OS name: "windows 8", version: "6.2", arch: "amd64", family: "windows"</p></td>
-</tr>
-</tbody>
-</table>
-
-
-To install the sandbox, first download it from <https://www.datatorrent.com/download> and import the downloaded file into VirtualBox. Once the import completes, you can select it and click the Start button to start the sandbox.
-
-
-The sandbox is configured with 6GB RAM; if your development machine has 16GB or more, you can increase the sandbox RAM to 8GB or more using the VirtualBox console. This will yield better performance and support larger applications. Additionally, you can change the network adapter from **NAT** to **Bridged Adapter**; this will allow you to login to the sandbox from your host machine using an _ssh_ tool like **PuTTY** and also to transfer files to and from the host using `pscp` on Windows. Of course all such configuration must be done when when the sandbox is not running.
-
-
-You can choose to develop either directly on the sandbox or on your development machine. The advantage of the former is that most of the tools (e.g. _jdk_, _git_, _maven_) are pre-installed and also the package files created by your project are directly available to the Data Torrent tools such as **dtManage** and **dtcli**. The disadvantage is that the sandbox is a memory-limited environment so running a memory-hungry tool like a Java IDE on it may starve other applications of memory.
-
-
-You can now use the maven archetype to create a basic Apache Apex project as follows: Put these lines in a Windows command file called, for example, `newapp.cmd` and run it:
-
- @echo off
- @rem Script for creating a new application
- setlocal
- mvn archetype:generate ^
- -DarchetypeRepository=https://www.datatorrent.com/maven/content/repositories/releases ^
- -DarchetypeGroupId=com.datatorrent ^
- -DarchetypeArtifactId=apex-app-archetype ^
- -DarchetypeVersion=3.1.1 ^
- -DgroupId=com.example ^
- -Dpackage=com.example.myapexapp ^
- -DartifactId=myapexapp ^
- -Dversion=1.0-SNAPSHOT
- endlocal
-
-
-
-The caret (^) at the end of some lines indicates that a continuation line follows. When you run this file, the properties will be displayed and you will be prompted with `` Y: :``; just press **Enter** to complete the project generation.
-
-
-This command file also exists in the Data Torrent _examples_ repository which you can check out with:
-
- git clone https://github.com/DataTorrent/examples
-
-You will find the script under `examples\tutorials\topnwords\scripts\newapp.cmd`.
-
-You can also, if you prefer, use an IDE to generate the project as described in Section 3 of [Application Packages](application_packages.md) but use the archetype version 3.1.1 instead of 3.0.0.
-
-
-When the run completes successfully, you should see a new directory named `myapexapp` containing a maven project for building a basic Apache Apex application. It includes 3 source files:**Application.java**, **RandomNumberGenerator.java** and **ApplicationTest.java**. You can now build the application by stepping into the new directory and running the appropriate maven command:
-
- cd myapexapp
- mvn clean package -DskipTests
-
-The build should create the application package file `myapexapp\target\myapexapp-1.0-SNAPSHOT.apa`. This file can then be uploaded to the Data Torrent GUI tool on the sandbox (called **dtManage**) and launched from there. It generates a stream of random numbers and prints them out, each prefixed by the string `hello world: `. If you built this package on the host, you can transfer it to the sandbox using the `pscp` tool bundled with **PuTTY** mentioned earlier.
-
-
-If you want to checkout the Apache Apex source repositories and build them, you can do so by running the script `build-apex.cmd` located in the same place in the examples repository described above. The source repositories contain more substantial demo applications and the associated source code. Alternatively, if you do not want to use the script, you can follow these simple manual steps:
-
-
-1. Check out the source code repositories:
-
- git clone https://github.com/apache/incubator-apex-core
- git clone https://github.com/apache/incubator-apex-malhar
-
-2. Switch to the appropriate release branch and build each repository:
-
- pushd incubator-apex-core
- git checkout release-3.1
- mvn clean install -DskipTests
- popd
- pushd incubator-apex-malhar
- git checkout release-3.1
- mvn clean install -DskipTests
- popd
-
-The `install` argument to the `mvn` command installs resources from each project to your local maven repository (typically `.m2/repository` under your home directory), and **not** to the system directories, so Administrator privileges are not required. The `-DskipTests` argument skips running unit tests since they take a long time. If this is a first-time installation, it might take several minutes to complete because maven will download a number of associated plugins.
-
-After the build completes, you should see the demo application package files in the target directory under each demo subdirectory in `incubator-apex-malhar\demos\`.
-
-Linux
-------------------
-
-Most of the instructions for Linux (and other Unix-like systems) are similar to those for Windows described above, so we will just note the differences.
-
-
-The pre-requisites (such as _git_, _maven_, etc.) are the same as for Windows described above; please run the commands in the table and ensure that appropriate versions are present in your PATH environment variable (the command to display that variable is: `echo $PATH`).
-
-
-The maven archetype command is the same except that continuation lines use a backslash (``\``) instead of caret (``^``); the script for it is available in the same location and is named `newapp` (without the `.cmd` extension). The script to checkout and build the Apache Apex repositories is named `build-apex`.
http://git-wip-us.apache.org/repos/asf/incubator-apex-core/blob/44f220fd/apex_malhar.md
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diff --git a/apex_malhar.md b/apex_malhar.md
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-Apache Apex Malhar
-================================================================================
-
-Apache Apex Malhar is an open source operator and codec library that can be used with the Apache Apex platform to build real-time streaming applications. As part of enabling enterprises extract value quickly, Malhar operators help get data in, analyze it in real-time and get data out of Hadoop in real-time with no paradigm limitations. In addition to the operators, the library contains a number of demos applications, demonstrating operator features and capabilities.
-
-
-
-# Capabilities common across Malhar operators
-
-For most streaming platforms, connectors are afterthoughts and often end up being simple ‘bolt-ons’ to the platform. As a result they often cause performance issues or data loss when put through failure scenarios and scalability requirements. Malhar operators do not face these issues as they were designed to be integral parts of apex*.md RTS. Hence, they have following core streaming runtime capabilities
-
-1. **Fault tolerance** – Apache Apex Malhar operators where applicable have fault tolerance built in. They use the checkpoint capability provided by the framework to ensure that there is no data loss under ANY failure scenario.
-2. **Processing guarantees** – Malhar operators where applicable provide out of the box support for ALL three processing guarantees – exactly once, at-least once & at-most once WITHOUT requiring the user to write any additional code. Some operators like MQTT operator deal with source systems that cant track processed data and hence need the operators to keep track of the data. Malhar has support for a generic operator that uses alternate storage like HDFS to facilitate this. Finally for databases that support transactions or support any sort of atomic batch operations Malhar operators can do exactly once down to the tuple level.
-3. **Dynamic updates** – Based on changing business conditions you often have to tweak several parameters used by the operators in your streaming application without incurring any application downtime. You can also change properties of a Malhar operator at runtime without having to bring down the application.
-4. **Ease of extensibility** – Malhar operators are based on templates that are easy to extend.
-5. **Partitioning support** – In streaming applications the input data stream often needs to be partitioned based on the contents of the stream. Also for operators that ingest data from external systems partitioning needs to be done based on the capabilities of the external system. E.g. With the Kafka or Flume operator, the operator can automatically scale up or down based on the changes in the number of Kafka partitions or Flume channels
-
-# Operator Library Overview
-
-## Input/output connectors
-
-Below is a summary of the various sub categories of input and output operators. Input operators also have a corresponding output operator
-
-* **File Systems** – Most streaming analytics use cases we have seen require the data to be stored in HDFS or perhaps S3 if the application is running in AWS. Also, customers often need to re-run their streaming analytical applications against historical data or consume data from upstream processes that are perhaps writing to some NFS share. Hence, it’s not just enough to be able to save data to various file systems. You also have to be able to read data from them. RTS supports input & output operators for HDFS, S3, NFS & Local Files
-* **Flume** – NOTE: Flume operator is not yet part of Malhar
-
-Many customers have existing Flume deployments that are being used to aggregate log data from variety of sources. However Flume does not allow analytics on the log data on the fly. The Flume input/output operator enables RTS to consume data from flume and analyze it in real-time before being persisted.
-
-* **Relational databases** – Most stream processing use cases require some reference data lookups to enrich, tag or filter streaming data. There is also a need to save results of the streaming analytical computation to a database so an operational dashboard can see them. RTS supports a JDBC operator so you can read/write data from any JDBC compliant RDBMS like Oracle, MySQL etc.
-* **NoSQL databases** –NoSQL key-value pair databases like Cassandra & HBase are becoming a common part of streaming analytics application architectures to lookup reference data or store results. Malhar has operators for HBase, Cassandra, Accumulo (common with govt. & healthcare companies) MongoDB & CouchDB.
-* **Messaging systems** – JMS brokers have been the workhorses of messaging infrastructure in most enterprises. Also Kafka is fast coming up in almost every customer we talk to. Malhar has operators to read/write to Kafka, any JMS implementation, ZeroMQ & RabbitMQ.
-* **Notification systems** – Almost every streaming analytics application has some notification requirements that are tied to a business condition being triggered. Malhar supports sending notifications via SMTP & SNMP. It also has an alert escalation mechanism built in so users don’t get spammed by notifications (a common drawback in most streaming platforms)
-* **In-memory Databases & Caching platforms** - Some streaming use cases need instantaneous access to shared state across the application. Caching platforms and in-memory databases serve this purpose really well. To support these use cases, Malhar has operators for memcached & Redis
-* **Protocols** - Streaming use cases driven by machine-to-machine communication have one thing in common – there is no standard dominant protocol being used for communication. Malhar currently has support for MQTT. It is one of the more commonly, adopted protocols we see in the IoT space. Malhar also provides connectors that can directly talk to HTTP, RSS, Socket, WebSocket & FTP sources
-
-
-
-## Compute
-
-One of the most important promises of a streaming analytics platform like Apache Apex is the ability to do analytics in real-time. However delivering on the promise becomes really difficult when the platform does not provide out of the box operators to support variety of common compute functions as the user then has to worry about making these scalable, fault tolerant etc. Malhar takes this responsibility away from the application developer by providing a huge variety of out of the box computational operators. The application developer can thus focus on the analysis.
-
-Below is just a snapshot of the compute operators available in Malhar
-
-* Statistics & Math - Provide various mathematical and statistical computations over application defined time windows.
-* Filtering & pattern matching
-* Machine learning & Algorithms
-* Real-time model scoring is a very common use case for stream processing platforms. Malhar allows users to invoke their R models from streaming applications
-* Sorting, Maps, Frequency, TopN, BottomN, Random Generator etc.
-
-
-## Query & Script invocation
-
-Many streaming use cases are legacy implementations that need to be ported over. This often requires re-use some of the existing investments and code that perhaps would be really hard to re-write. With this in mind, Malhar supports invoking external scripts and queries as part of the streaming application using operators for invoking SQL query, Shell script, Ruby, Jython, and JavaScript etc.
-
-## Parsers
-
-There are many industry vertical specific data formats that a streaming application developer might need to parse. Often there are existing parsers available for these that can be directly plugged into an Apache Apex application. For example in the Telco space, a Java based CDR parser can be directly plugged into Apache Apex operator. To further simplify development experience, Malhar also provides some operators for parsing common formats like XML (DOM & SAX), JSON (flat map converter), Apache log files, syslog, etc.
-
-## Stream manipulation
-
-Streaming data aka ‘stream’ is raw data that inevitably needs processing to clean, filter, tag, summarize etc. The goal of Malhar is to enable the application developer to focus on ‘WHAT’ needs to be done to the stream to get it in the right format and not worry about the ‘HOW’. Hence, Malhar has several operators to perform the common stream manipulation actions like – DeDupe, GroupBy, Join, Distinct/Unique, Limit, OrderBy, Split, Sample, Inner join, Outer join, Select, Update etc.
-
-## Social Media
-
-Malhar includes an operator to connect to the popular Twitter stream fire hose.