Manual De Pentaho Etl Trans For Mac
Install the Web Application Server The BA Server can be deployed on either the Tomcat or JBoss web application server. By default, BA Server software is configured for Tomcat. This means that if you choose to use Tomcat, you will need to make fewer configuration changes than you would if you choose to use JBoss. You must install the web application server yourself. If you already have a Tomcat or JBoss web application server installed and you want to deploy the BA Server on it, please skip this step.
To download and install the web application software, use the instructions in the documentation for the web application server of your choice. We recommend that you install the web application server in the pentaho server biserver-ee directory.
Verify the web application server is installed correctly by starting it and viewing the default page. If the web application server does not start, troubleshoot it using the web application server's documentation before you continue with the BA Server installation process. Stop the web application server. Install the BA Repository Host Database MySQL The BA Repository houses data needed for Pentaho tools to provide scheduling and security functions. The repository also stores metadata and models for reports that you create. You can choose to host the BA Repository on these databases. PostgreSQL.
MySQL. Oracle. MS SQL Server To install the BA Repository's host database, do these things. Check the section to determine which versions of the databases Pentaho supports. Download and install the database of your choice. Verify that the BA Repository database is installed correctly.
Unpack BA Server Installation File. Unzip the BA Server Installation file. To unpack the file, run installer.sh.
The IZPak window appears. Read the license agreement, select I accept the terms of this license agreement, and click Next.
In the Select the installation path text box, enter the place where you want to create the pentaho directory, then click Next. A message indicating that a target directory will be created appears. When the installation progress is complete, click Quit. Navigate to the pentaho directory and create a server subdirectory. Move the biserver-ee directory into the server directory. When you are finished, the directory structure should look like this:.
pentaho/jdbc-distribution. pentaho/license-installer. pentaho/server/biserver-ee. Unpack Files.
Unzip the BA Server Installation file. To unpack the file, run install.sh. The IZPak window appears. If you are unpacking the file in a non-graphical environment, open a Terminal or Command Prompt and type java -jar installer.jar -console and follow the instructions presented in the window. Read the license agreement, select I accept the terms of this license agreement, and click Next. In the Select the installation path text box, enter the place where you want to create the pentaho directory, then click Next. A message indicating that a target directory will be created appears.
When the installation progress is complete, click Quit. Put Files in Directories. Navigate to the pentaho directory where you unpacked the files, unzip the zip files and place their contents in the appropriate directories listed below. File Unzip the Contents of the File to This Directory license-installer.zip pentaho/server pentaho-data.zip pentaho/server/biserver-ee pentaho-solutions.zip pentaho/server/biserver-ee. Copy these files to the following directories. File Copy Files to This Directory pentaho.war. Tomcat: pentaho/server/biserver-ee//webapps. JBoss: pentaho/server/biserver-ee//standalone/deployments pentaho-style.war.
Tomcat: pentaho/server/biserver-ee//webapps. JBoss: pentaho/server/biserver-ee//standalone/deployments PentahoBIPlatformOSSLicenses.html pentaho/server/biserver-ee.
Unpack and Unzip Plugin Files Do the following for each of the plugin files. To unpack the file, run install.sh.
The IZPak window appears. If you are unpacking the file in a non-graphical environment, open a Terminal or Command Prompt and type java -jar installer.jar -console and follow the instructions presented in the window. Read the license agreement, select I accept the terms of this license agreement, and click Next. In the Select the installation path text box, enter the pentaho server biserver-ee pentaho-solutions system directory, then click Next. A message appears. When the installation progress is complete, click Quit. Verify Directory Structure Verify that the files have been placed in the following places by comparing the following directory structure with yours.
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If your web application server is not in the pentaho server biserver-ee directory, the pentaho.war and pentaho-style.war files should appear where you've chosen to install your web application server. Set Environment Variables Set the PENTAHOJAVAHOME and PENTAHOINSTALLEDLICENSEPATH environment variables. If you do not set these variables, Pentaho will not start correctly. If you are using a JRE, set the JREHOME home environment variable as well. Set the path of the PENTAHOJAVAHOME variable to the path of your Java installation, like this.
Export PENTAHOJAVAHOME=/usr/lib/jvm/java-7-sun. Set the path of the PENTAHOINSTALLEDLICENSEPATH variable to the path of the installed licenses, like this. Export PENTAHOINSTALLEDLICENSEPATH=/home/pentaho/.pentaho/.installedLicenses.xml. Log out and in again, then verify the variables have been properly set. Prepare a Headless Linux or Solaris Server There are two headless server scenarios that require special procedures on Linux and Solaris systems. One is for a system that has no video card; the other is for a system that has a video card, but does not have an X server installed.
In some situations - particularly if your server doesn't have a video card - you will have to perform both procedures to properly generate reports with the BA Server. Systems without video cards The java.awt.headless option enables systems without video output and/or human input hardware to execute operations that require them. To set this application server option when the BA Server starts, you will need to modify the startup scripts for either the BA Server, or your Java application server.
Manual De Pentaho Etl Trans For Mac Free
You do not need to do this now, but you will near the end of these instruction when you perform the step. For now, add the following item to the list of CATALINAOPTS parameters: -Djava.awt.headless=true. The entire line should look something like this: export CATALINAOPTS='-Djava.awt.headless=true -Xms4096m -Xmx6144m -XX:MaxPermSize=256m -Dsun.rmi.dgc.client.gcInterval=3600000 -Dsun.rmi.dgc.server.gcInterval=3600000' If you intend to create a BA Server service control script, you must add this parameter to that script's CATALINAOPTS line.
If you do not have an X server installed, you must also follow the below instructions. Systems without X11 To generate charts, the Pentaho Reporting engine requires functionality found in X11. If you are unwilling or unable to install an X server, you can install the xvfb package instead. Xvfb provides X11 framebuffer emulation, which performs all graphical operations in memory instead of sending them to the screen. Use your operating system's package manager to properly install xvfb. Adjust Amount of Memory Mac OS Allocates for PostgreSQL If you plan to install the software on a Mac OS, and you choose to use PostgreSQL, you need to increase the amount of memory that the Mac OS allocates for PostgreSQL. You can skip these instructions if you plan to install the software on Windows or Linux.
PostgreSQL is the name of the default database that contains audit, schedule and other data that you create. PostgreSQL starts successfully only if your computer has allocated enough memory. Go to and follow the instructions there on how to adjust the memory settings on your computer.
In Pentaho Data Integration 6.0, we released a great new capability to collect data lineage for PDI transformations and jobs. Data lineage is an oft-overloaded term, but for the purposes of this blog I will be talking about the flow of data from external sources into steps/entries and possibly out to other external targets. Basically we keep track of all fields as they are created, split, aggregated, transformed, etc. Over the course of a transformation or job.
When jobs or transformations call other jobs or transformations, that relationship is also captured. So in that sense you can follow your data all the way through your PDI process. The code for the current data lineage capability is entirely open source and is available on GitHub here:. You may see the term 'metaverse' listed throughout the code and documentation (including the project name itself). The term was a pet name for what we envisioned the end product to be, a universe of metadata and relationships between all the artifacts and concepts in the Pentaho stack.
Whether that vision is realized the same way depends on the roadmap, it is very possible the needs of Pentaho's customers will drive the data lineage capabilities in a different direction. Approach Collecting lineage information for PDI is non-trivial. It may seem like the fields, steps, and operations are readily available such that the relationships could easily be discovered. However PDI is a very flexible and powerful tool. This includes APIs that are more general than uniform, as flexibility has seemed a more important goal than introspection. For example, getting the list of fields that are output from a transformation step involves calling the getStepFields API method. This lets the step report its own outgoing fields, and many times the step needs to know the incoming fields before it can properly report the output fields.
So the step in turn calls the previous steps' getStepFields methods. In the case of a Table Input step, the query is actually executed so the metadata of the ResultSet is available to determine the output fields. This requires a valid database connection and introduces extra latency into the lineage collection. Other considerations include variables and parameters. It is possible to parameterize things like table names, field names, etc. This makes it impossible to collect accurate data lineage based on a transformation 'at-rest', i.e.
During design time. Even using parameters' default values doesn't work as the default value may be meant to fail the transformation (to ensure valid parameter values are passed in). For this reason, data lineage collection is performed at run-time, such that the fields and values are all resolved and known. There is one caveat to this that I'll talk about at the end of the blog, but it is unsupported, undocumented, and more geared for the community for the time being. At present, the architecture and flow for collecting lineage is as follows: - Just before a transformation is executed, a graph model is created and associated with the running Trans object. We use 2.6 for this (more on that in a moment) - If a step/entry is data-driven, we collect lineage information as each row is processed. Data-driven means the step behaves differently based on values coming in via fields in each row.
For example, a Table Input step that gets parameters from a previous step, or a REST step that gets its URLs from an incoming field. This is contrast to a variable or parameter, which is defined and resolved once at the beginning of execution, and does not change over the execution. Once a transformation/job has finished, we iterate over each step/entry to collect the rest of the lineage information. This is done using a. These are individual objects responsible for collecting lineage information for a particular step (Table Output, e.g.), and there are generic versions of each in the event that no specific one exists.
There are also top-level analyzers for transformations and jobs, which actually perform the iteration and add top-level lineage information to the graph. The and make a best-guess effort to collect accurate lineage. There is no encompassing API to collect all the various operations and business logic performed in each step. So all fields are assumed to be pass-through (meaning the structure of a named field - type, e.g. hasn't changed as a result of the logic) and any new fields have no relationships to the incoming fields (as we don't know what those relationships are). To report full lineage, a step/entry needs a specific analyzer that understands the business logic of that step or entry. There are 'decorator' analyzer interfaces (and base implementations) that can be associated with step/entry analyzers, most notably and (and the JobEntry versions thereof).
These (when present for a step/entry analyzer) are called by the lineage collector to get relationships between steps (and/or their fields) to resources outside the transformation, such as text files, databases, etc. The workhorse in this situation (the 'lineage collector') is implemented in (there's a Job version too of course). This is the entry point to be called before a transformation starts, namely at the TransformationStartThreads extension point (see full list ). Instead of implementing multiple extension point plugins to be activated at the various points during execution, the TransListener interface provided the level of interaction we wanted, so the extension point plugin simply adds a interface (also implemented by the TransformationRuntimeExtensionPoint object) which will be called by the PDI internals. The transStarted method of TransformationRuntimeExtensionPoint creates a new 'root node' associated with the client (Spoon, e.g.). This provides a well-known entry point for querying the graph if no other information is known.
When a graph model is created, nodes for all the concepts (transformation, job, database connection, step, field, etc.) are added to the graph as well. The method also creates a future runner for the lineage analysis, which will be called when the transformation is complete.
The transFinished method spins off a new thread to perform the full lineage analysis. The Runnable, so a. The top-level analyzer adds its own lineage/metadata to the graph, then iterates over the steps/entries' so their analyzers can add their lineage information (see Graph Model and Usage below) NOTE: In the code you will see lots of references to ExecutionProfile. This may be tied to the lineage graph someday (and indeed there is some data common to both) but for now it is there to collect something like the PDI Operations Mart and logging do, but in a uniform fashion with a standard format (JSON).
Graph Model and Usage PDI's data lineage model is based on a. A graph is composed of nodes (concepts, entities, etc.) and edges connecting nodes (representing relationships between nodes). Nodes and edges can have properties (such as name = 'my transformation' or relationship = 'knows').
For our model, the nodes are things like the executed jobs/transformations, steps, stream fields, database connections, etc. Also the model includes 'concept nodes' that allow for more targeted graph queries. For example, the graph includes a concept node labelled 'Transformation', and all executed transformations in that graph have basically an 'is-a' relationship with that concept node. In practice, it is a 'parentconcept' edge from the concept node to the instance(s) of that concept. In our example, we could use it to start a query from the Transformation concept node and find all nodes connected to it via an outgoing 'parentconcept' edge.
This query returns nodes corresponding to all transformations executed for this lineage artifact. For our property graph model implementation, we chose the open-source project. The 3.x line of Tinkerpop has been accepted to the Apache Incubator, and I certainly congratulate them on that achievement! Tinkerpop 3.x has absorbed all the 2.x products into a single product, and represents an impressive leap forward in terms of graph planning/processing engines.
Having said that, Tinkerpop 3 requires Java 8, and since PDI 6.0 supports Java 7, we had to use the deprecated 2.6 version. However 2.x had more than enough functionality for us, we just had to bring in the pieces we needed. Those include the following:: This is the generic graph API, upon which all other Tinkerpop products are built, and useful in its own right to work at a low level with the graph model.: This is the dataflow framework to allow for graph traversal and processing using a pipeline architecture (hence the name).
The process pipeline(s) are themselves modelled as a graph (called a process graph): This is the actual data traversal language, available as a Java API as well as a fluent Groovy DSL. Graph queries in Pentaho data lineage are materialized as Gremlin statements, which are executed as a process graph using the Pipes framework: This is basically an ORM from the graph/process models to Java objects. It allows the lineage code to offer a Java method whose body is essentially a Gremlin query that returns the specified object. There is some overhead involved with this ORM (due to the amount of reflection and such that is needed), so we only use Frames at present for integration testing. However it did increase our productivity and made our tests much less verbose:) Viewing PDI Lineage Information There's already a on this subject by Pedro Alves, I highly recommend it as it explains where PDI stores lineage, as well as how to retrieve and display it using 3rd party tools such as. Design-time Lineage Information As I mentioned, the lineage capability for PDI 6.0 is first-and-foremost a runtime lineage collection engine. However there are some APIs and such for accessing the lineage of an active transformation in Spoon.
For example, the Data Services optimizations dialog uses something called the to determine the 'origin fields' for those fields exposed by a data service, in order to find possible push-down optimizations. LineageClient contains methods offering a domain-level API for querying the lineage graphs. Inside each of these methods you'll see the Gremlin-Java API at work.
Note: we decided not to include Groovy as an additional compile/runtime dependency to keep things simple and smaller in the build. This makes the usage more verbose (see the code) but there was no loss of functionality for us, there's a Tinkerpop on how to do Gremlin in Java. To actually build the lineage graph for the active transformation, PDI 6.0 has TransOpenedExtensionPoint and TransChangedExtensionPoint plugins, each of which will create and populate the lineage graph for the active TransMeta object.
It uses.addLineageGraph to achieve this. This didn't need to be in its own thread as we can't collect data-driven lineage and we don't dive all the way down into executed transformations. The latter is because some transformations are dynamically created (using metadata injection for example). So the extension points create and maintain the lineage graph for the active TransMeta, and the LineageClient can query (at the domain level) said graph. However the graph(s) are stored in the and are thus accessible by anybody (using the active TransMeta as the key). Similarly, the runtime graphs are available in the during their execution lifetime. Get Involved If you're looking to use the lineage capability from an external application, check out the for lineage.
If you'd like to get involved in the codebase, one great way is to add a StepAnalyzer or JobEntryAnalyzer for a step/entry that isn't covered yet. The documentation for how to contribute these is on. If you want to know which steps/entries have analyzers, start up PDI and (using the same HTTP API base as in the above link) point at cxf/lineage/info/steps (or./entries for Job Entry analyzers) Summary Hopefully this sheds some light on the innards of the Pentaho Data Integration 6.0 data lineage capability. This opens a world of opportunities both inside and outside Pentaho for maintaining provenance, determining change impact, auditing, and metadata management. I didn't bother with doing a 'Get Fields' automatically because I won't know if there's a header row, etc. Plus this is just a fun proof-of-concept, hopefully I/we will have a more robust Drag-n-Drop system in the future. The trick is getting your FileListener registered with Spoon.
There is no extension point directly for that purpose, but you can use a LifecycleListener plugin and implement the registration in your onStart callback. To get this going quickly, I wrote the CsvListener in Groovy, and put that in a file called onStart.groovy. I did that so I could leverage my (available on the Marketplace), then drop my onStart.groovy file into plugins/pdi-script-extension-points/ and start Spoon. The Groovy script is as follows, and is also available as a. The Pentaho Data Integration (PDI) is a great place to share your contributions with the community at-large. To add your plugin, you can pull down the marketplace.xml file (via our ) and add your own entry, then submit a pull-request to have the entry added to the master Marketplace. But did you know you could 'host' your own PDI Marketplace?
The Marketplace is designed to read in locations of marketplaces from anywhere you like, via a file at $KETTLEHOME/.kettle/marketplaces.xml (where KETTLEHOME can be your PDI/Kettle install directory and/or your user's home directory). Here's an example file on. The file contains a list of marketplace entries, which are locations of various lists (aka marketplaces) of PDI plugins. The URLs provided are used to read Marketplace XML files, which contain the PDI Marketplace entries.
This is how I test incoming pull-requests for PDI Marketplace plugins. I use the marketplaces.xml file from the Gist link above, then checkout the pull-request from GitHub. Then I start PDI, go to the Marketplace, find the proposed plugin, try to install, open the dialog (if appropriate), then uninstall (NOTE: reboots are required). Of course, support for the plugin itself is (perhaps) available via the submitter. These details are available in the PDI Marketplace UI before installation, and all licensing, usage, etc. Is provided by the submitter. The benefit of having a marketplaces.xml is that you can decide the list of PDI plugins available for download.
If your clients have a marketplaces.xml that only point at your own repositories / locations for plugins, then you can control which plugins can be downloaded by those clients. For developers (as I show above), you can use it for testing before submitting your pull-request.
For consultants / OEMs, you can decide which plugins should show up in the list. This mechanism is very flexible and should support most use cases. In closing, I personally review many of the PDI Marketplace entries (aka pull-requests in GitHub), please let me know if you have any issues with announcing your plugin or otherwise contributing to our community. In a, I announced my SuperScript step for PDI, which adds and enhances some capabilities of the built-in Script step. One notable addition is the ability to use AppleScript on a Mac, as the AppleScript script engine comes with the Mac JDK. However the implementation of the script engine is a bit different than most other script engines, especially in terms of getting data into the script as variables, arguments, etc.
If you just want to call a script for every row (without using incoming fields), you can just write straight AppleScript. However if you want to use incoming field(s), you have to do a little magic to get it all working. First, the AppleScript script engine in Java will not pass bindings to the script as variables. Instead they use a combination of bindings to achieve this: javaxscriptfunction: This variable is set to the name of a function to be invoked in AppleScript javax.script.argv: This variable is set to the value to be passed to the function. Since PDI doesn't have a List type, you can only pass one argument into your function in SuperScript. If you need multiple values, you'll have to concatenate them in PDI and pass them in as a single field value. To make matters worse, SuperScript only passes in 'used' fields to the script.
To determine used fields, it (like the Script step) simply looks for the field name in the script. In this case, the actual field name used in the function invocation is likely neither the above properties. To get around this, simply put both of the above variable names in a comment: (. uses javaxscriptfunction and javax.script.argv.) Then wrap your logic inside the function call: on testFunc(testVal) return testVal & ' World!' End testFunc In this example I used a Generate Rows step to set javaxscriptfunction to 'testFunc' and javax.script.argv to 'Hello'. Then I ran the following sample transformation. For my latest fun side project, I looked at the integration of Pentaho Data Integration (PDI).
From the website: 'Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs.' If you substitute 'graphical' for 'high-level' and 'PDI' for 'Apache Pig', you get a pretty accurate description of the Pentaho Data Integration product. For this reason I thought it natural to look at the ways PDI and Pig could play together in the same pigpen, so to speak:) Pentaho Data Integration has long offered an ' job entry, which allows the user to submit a Pig script to a Hadoop cluster (or a local Pig instance), which allows orchestration of data analysis programs written in Pig. However it doesn't integrate with other PDI capabilities (such as transformations) that are also data analysis programs. My idea was to kind of turn the integration idea inside-out, so instead of PDI orchestrating Pig jobs, I wanted to leverage PDI transformations as data analysis programs inside of Pig. I didn't want to have to include a PDI deployment inside a Pig deployment or vice versa; rather I envisioned a system where both Pig and PDI were installed, and the former could locate and use the latter. This involved creating the following:.
A Pig UDF that can inject data into a PDI transformation and collect it on the other side, without needing PDI as a compile-time dependency. A way to bridge the Pig UDF to a PDI deployment. A way to transform Pig data types/values to/from PDI data types/values For #2, I noticed that there are many places where this bridge could be leveraged (Hive, Spark, e.g.), so I created a project called that could be used generally in other places. The project does two things: First, it supplies classes that will run a transformation, inject rows, and collect result rows using an intermediate data model.
Second, there is a Java file (that is not compiled or included in the pdi-bridge JAR) called KettleBridge, this file needs to be copied into whatever integration project needs it, which in this case was my custom Pig UDF project. The KettleBridge looks for a system property (then an environment variable) called KETTLEHOME which needs to point at a valid PDI deployment. You can find the actual transformation on. As readers of my blog know, I'm a huge fan of scripting languages on the JVM (especially Groovy), and of course I'm a huge fan of Pentaho Data Integration:) While using the (experimental) Script step to do various things, I saw a few places where a script step could be improved for easier use and more powerful features.
Specifically I wanted:. A drop-down UI for selecting the scripting engine. Allow non-compilable JSR-223 scripting (such as AppleScript). Enable the use of the script's returned value as an output field.
Enable the use of the script step as an input step (doesn't need a trigger). A noticeable addition is the 'lastRow' variable, this will contain null (or be undefined) for the first row but will contain the previous row's data for all subsequent rows. This opens the door for more powerful processing, such as filling empty fields with the previous row's value, changing script behavior based on if a field value has changed since the last row, etc. UPDATE: Here is a screenshot of an example script that will fill the field (if null) with the previous field's value (if not null).
Perhaps the most fun and powerful addition is the ability of SuperScript to execute any JSR-223 Script Engine. The existing Script step requires that the Script Engine produce CompiledScript(s), which of course is the fastest but not always available. To that end, SuperScript will attempt to compile the script first, and if it cannot, it will fall back to evaluating (i.e. Interpreting) the script(s). This opens the door for a lot of new scripting languages, such as, and (an R ScriptEngine for the JVM).