#Setting up your build environment You will need to increase your maven memory settings before running the maven build.

export MAVEN_OPTS="-Xmx512m -XX:PermSize=256m"

#Building After cloning the repository (to a directory we'll refer to as 'atk'), cd to atk/misc/titan-shaded and install the module.

cd atk/misc/titan-shaded/
mvn install

go back to the root of the repository

cd ../../

Start the zinc server for incremental compilation

bin/zinc.sh start

Compile source

mvn compile -P events

mvn package -P events -DskipTests

If you want to run all the test run the maven package without skipTests option

mvn package -P events

You can add -T option to run maven with parallel execution except when running tests.

#OK
mvn -T 4 package -P events -DskipTests
#OK
mvn -T 4 compile -P events


#Not OK, will fail
mvn -T 4 package -P events

This is an overview. Additional details and instructions are in the user documentation.

• Pre-requisites
  • Java 7
  • Access to Cloudera Hadoop cluster with Yarn, HDFS, HBase, Zookeeper
  • PostgreSQL
  • python 2.7
  • Python depedencies installed (numpy, bson, requests, etc)
• Build the project jars
• Setup conf/ folder
  • Create a conf/application.conf based on the templates under conf/examples
    • Add cluster settings in application.conf
  • Create a conf/logback.xml if you want to configure logging
• Use bin/rest-server.sh to start the server
• cd /python-client
• Open ipython or python shell
  • Verify you are runing a python 2.7
  • Run these commands first
    • import trustedanalytics as ta
    • ta.connect()

• Bug Tracking
• Look at our end user docs
• Look at our wiki docs

• bin/ - scripts for starting REST server and scoring engine, etc.
• conf/ - configuration templates for setting up a system, put your application.conf here for running out of source code
• deploy/ - a module for creating an uber jar needed for deployment on Analytics PaaS.
• doc/ - end user docs for the system (except doc for specific plugins goes in the plugins themselves)
• doc-api-examples/ - examples of api usage for plugins
• engine/
  • engine-core/ - loads and executes plugins and provides the basic services that plugins need.
  • interfaces/ - interfaces the Engine exposes to the REST server. (we should move plugin args/return values out of here)
  • graphbuilder - Titan graph construction and reading
  • meta-store/ - code that interacts with the meta-store database repository including SQL scripts
• engine-plugins/ - plugins use engine services to implement user visible operations
  • frame-plugins/ - frame related plugins, e.g. frame.add_columns()
  • graph-plugins/ - graph related plugins that run on Spark and GraphX
  • model-plugins/ - model related plugins, e.g. LinearRegressionModel
  • giraph-plugins/ - a few algorithms that run on Giraph
• integration-tests/ - developer written, build time integration tests in python, these run against a minimal version of our product
• misc/ - miscellaneous items that aren't really part of our product
  • launcher/ - starts up our application, launches parts of our app
• module-loader/ - future replacement for launcher, starts application and sets up ClassLoaders appropriately.
• package/ - packaging for VM's, RPM's
• python-client/ - python client code (talks with rest-server)
  • examples/ - example code for users of how to use the API
• rest-server/ - the rest server converts HTTP requests to messages that are sent to the Engine
• scoring-engine/ - a small lightweight REST server that can score against trained models
• scoring-interfaces/ - interfaces the scoring-engine depends on. Other model implementations could be plugged in as long as they implement these interfaces.
• scoring-models/ - implementations of models the scoring-engine uses.
• testutils/ - some test utility code that gets reused between tests in different modules

• Most items under misc should move to separate repos
• Enable lazy execution and delayed execution. We have a plan where SparkContexts can be re-used and only shutdown when needed.
• Properly support directed and undirected graphs (get rid of "bi-directional")
• Data types in graphs/frames needs to be extended greatly
• Improve Plugins
  • Further separation between framework and plugins (ideally these are even separate projects)
  • Possibly separate plugin from yarn job
  • Dependency Injection
  • Need many more extension points for 3rd parties
  • Meta-programming needs to be expanded to support more kinds of objects
  • Nicer interfaces for plugin authors
• All plugins should move to plugin modules (a few lingering plugins under engine-core)
• Add support for Spark's dataframes
• Replace Slick with something simpler (even straight JDBC would be better, everyone already knows SQL and we aren't getting enough value to justify the learning curve for Slick)
• Replace Spray DSL with something simpler (the DSL is confusing to use and our REST layer is too thin to make people learn a DSL)
• Integration tests need support added to be able to test Giraph and Titan functions
• testutils should probably merge into engine-core
• giraph-plugins needs refactoring of packages (the current hierarchy is very poorly organized)
• Need Maven profiles to make it easier for developers to build only part of the project
• Break up CommandExecutor
• Move args classes out of interfaces next to their associated plugin (possibly get rid of args classes all together)
• Auto-conversion of return types
• Frames should go back to mutable and immutability should be re-implemented but where frames can keep stable ids
• Launcher code needs simplification and tests (it doesn't seem to setup classloaders all of the way how we want)