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  • Introducing the Documentation Workflows

    - by Owen Allen
    The how-to documents  provide end to end examples of specific features, such as creating a new zone or discovering a new system. We are enhancing the individual how-tos with documents called Workflows. These workflows are each built around procedural flowcharts that show these larger and more complex tasks. The workflow indicates which how-tos or other workflows you should follow to complete a more complex process, and give you a flow for planning the execution of a process. Over the coming days I'll highlight each of these workflows, and talk about the tasks that each one guides you through.

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  • Testing Workflows – Test-After

    - by Timothy Klenke
    Originally posted on: http://geekswithblogs.net/TimothyK/archive/2014/05/30/testing-workflows-ndash-test-after.aspxIn this post I’m going to outline a few common methods that can be used to increase the coverage of of your test suite.  This won’t be yet another post on why you should be doing testing; there are plenty of those types of posts already out there.  Assuming you know you should be testing, then comes the problem of how do I actual fit that into my day job.  When the opportunity to automate testing comes do you take it, or do you even recognize it? There are a lot of ways (workflows) to go about creating automated tests, just like there are many workflows to writing a program.  When writing a program you can do it from a top-down approach where you write the main skeleton of the algorithm and call out to dummy stub functions, or a bottom-up approach where the low level functionality is fully implement before it is quickly wired together at the end.  Both approaches are perfectly valid under certain contexts. Each approach you are skilled at applying is another tool in your tool belt.  The more vectors of attack you have on a problem – the better.  So here is a short, incomplete list of some of the workflows that can be applied to increasing the amount of automation in your testing and level of quality in general.  Think of each workflow as an opportunity that is available for you to take. Test workflows basically fall into 2 categories:  test first or test after.  Test first is the best approach.  However, this post isn’t about the one and only best approach.  I want to focus more on the lesser known, less ideal approaches that still provide an opportunity for adding tests.  In this post I’ll enumerate some test-after workflows.  In my next post I’ll cover test-first. Bug Reporting When someone calls you up or forwards you a email with a vague description of a bug its usually standard procedure to create or verify a reproduction plan for the bug via manual testing and log that in a bug tracking system.  This can be problematic.  Often reproduction plans when written down might skip a step that seemed obvious to the tester at the time or they might be missing some crucial environment setting. Instead of data entry into a bug tracking system, try opening up the test project and adding a failing unit test to prove the bug.  The test project guarantees that all aspects of the environment are setup properly and no steps are missing.  The language in the test project is much more precise than the English that goes into a bug tracking system. This workflow can easily be extended for Enhancement Requests as well as Bug Reporting. Exploratory Testing Exploratory testing comes in when you aren’t sure how the system will behave in a new scenario.  The scenario wasn’t planned for in the initial system requirements and there isn’t an existing test for it.  By definition the system behaviour is “undefined”. So write a new unit test to define that behaviour.  Add assertions to the tests to confirm your assumptions.  The new test becomes part of the living system specification that is kept up to date with the test suite. Examples This workflow is especially good when developing APIs.  When you are finally done your production API then comes the job of writing documentation on how to consume the API.  Good documentation will also include code examples.  Don’t let these code examples merely exist in some accompanying manual; implement them in a test suite. Example tests and documentation do not have to be created after the production API is complete.  It is best to write the example code (tests) as you go just before the production code. Smoke Tests Every system has a typical use case.  This represents the basic, core functionality of the system.  If this fails after an upgrade the end users will be hosed and they will be scratching their heads as to how it could be possible that an update got released with this core functionality broken. The tests for this core functionality are referred to as “smoke tests”.  It is a good idea to have them automated and run with each build in order to avoid extreme embarrassment and angry customers. Coverage Analysis Code coverage analysis is a tool that reports how much of the production code base is exercised by the test suite.  In Visual Studio this can be found under the Test main menu item. The tool will report a total number for the code coverage, which can be anywhere between 0 and 100%.  Coverage Analysis shouldn’t be used strictly for numbers reporting.  Companies shouldn’t set minimum coverage targets that mandate that all projects must have at least 80% or 100% test coverage.  These arbitrary requirements just invite gaming of the coverage analysis, which makes the numbers useless. The analysis tool will break down the coverage by the various classes and methods in projects.  Instead of focusing on the total number, drill down into this view and see which classes have high or low coverage.  It you are surprised by a low number on a class this is an opportunity to add tests. When drilling through the classes there will be generally two types of reaction to a surprising low test coverage number.  The first reaction type is a recognition that there is low hanging fruit to be picked.  There may be some classes or methods that aren’t being tested, which could easy be.  The other reaction type is “OMG”.  This were you find a critical piece of code that isn’t under test.  In both cases, go and add the missing tests. Test Refactoring The general theme of this post up to this point has been how to add more and more tests to a test suite.  I’ll step back from that a bit and remind that every line of code is a liability.  Each line of code has to be read and maintained, which costs money.  This is true regardless whether the code is production code or test code. Remember that the primary goal of the test suite is that it be easy to read so that people can easily determine the specifications of the system.  Make sure that adding more and more tests doesn’t interfere with this primary goal. Perform code reviews on the test suite as often as on production code.  Hold the test code up to the same high readability standards as the production code.  If the tests are hard to read then change them.  Look to remove duplication.  Duplicate setup code between two or more test methods that can be moved to a shared function.  Entire test methods can be removed if it is found that the scenario it tests is covered by other tests.  Its OK to delete a test that isn’t pulling its own weight anymore. Remember to only start refactoring when all the test are green.  Don’t refactor the tests and the production code at the same time.  An automated test suite can be thought of as a double entry book keeping system.  The unchanging, passing production code serves as the tests for the test suite while refactoring the tests. As with all refactoring, it is best to fit this into your regular work rather than asking for time later to get it done.  Fit this into the standard red-green-refactor cycle.  The refactor step no only applies to production code but also the tests, but not at the same time.  Perhaps the cycle should be called red-green-refactor production-refactor tests (not quite as catchy).   That about covers most of the test-after workflows I can think of.  In my next post I’ll get into test-first workflows.

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  • Workflows in SharePoint 2013, Part 1

    - by Sahil Malik
    SharePoint 2010 Training: more information Hooray! My latest article is now online on code-magazine. And this time, it’s about “Workflows in SharePoint 2013” – and there will be a part 2 of this next month. Here is a starter .. If we have been friends for a while, you must know my opinions about workflows in SharePoint 2010 and SharePoint 2007. I didn’t think they were very good, especially from a performance and scalability point of view. Frankly I think Microsoft should have called them “workslows.” Though, I don’t think it was the implementation in SharePoint that was the issue, it was fundamental issues with Workflow Foundation, compounded by the nature of SharePoint that acerbated the issues. Well, I am happy to say that Workflows in SharePoint 2013 are something I feel quite comfortable recommending to anyone, and I hope to make that case in this article. Read more .. Read full article ....

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  • Programming user interfaces using F# workflows

    F# asynchronous workflows can be used to solve a wide range of programming problems. In this article we'll look how to use asynchronous workflows for elegantly expressing the control flow of interaction with the user. We'll also look at clear functional way for encoding drag&drop-like algorithm.

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  • How to Create Custom SharePoint Workflows in Visual Studio 2008

    Whereas simple workflows are possible using Microsoft Office SharePoint Designer, you will soon reach the point where you will need to use Visual Studio. In the third article in Charles' introduction to Workflows in Sharepoint, he demonstrates how to create a workflow from scratch using Visual Studio, and discusses the relative merits of the two tools for this sort of development work.

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  • Building Simple Workflows in Oozie

    - by dan.mcclary
    Introduction More often than not, data doesn't come packaged exactly as we'd like it for analysis. Transformation, match-merge operations, and a host of data munging tasks are usually needed before we can extract insights from our Big Data sources. Few people find data munging exciting, but it has to be done. Once we've suffered that boredom, we should take steps to automate the process. We want codify our work into repeatable units and create workflows which we can leverage over and over again without having to write new code. In this article, we'll look at how to use Oozie to create a workflow for the parallel machine learning task I described on Cloudera's site. Hive Actions: Prepping for Pig In my parallel machine learning article, I use data from the National Climatic Data Center to build weather models on a state-by-state basis. NCDC makes the data freely available as gzipped files of day-over-day observations stretching from the 1930s to today. In reading that post, one might get the impression that the data came in a handy, ready-to-model files with convenient delimiters. The truth of it is that I need to perform some parsing and projection on the dataset before it can be modeled. If I get more observations, I'll want to retrain and test those models, which will require more parsing and projection. This is a good opportunity to start building up a workflow with Oozie. I store the data from the NCDC in HDFS and create an external Hive table partitioned by year. This gives me flexibility of Hive's query language when I want it, but let's me put the dataset in a directory of my choosing in case I want to treat the same data with Pig or MapReduce code. CREATE EXTERNAL TABLE IF NOT EXISTS historic_weather(column 1, column2) PARTITIONED BY (yr string) STORED AS ... LOCATION '/user/oracle/weather/historic'; As new weather data comes in from NCDC, I'll need to add partitions to my table. That's an action I should put in the workflow. Similarly, the weather data requires parsing in order to be useful as a set of columns. Because of their long history, the weather data is broken up into fields of specific byte lengths: x bytes for the station ID, y bytes for the dew point, and so on. The delimiting is consistent from year to year, so writing SerDe or a parser for transformation is simple. Once that's done, I want to select columns on which to train, classify certain features, and place the training data in an HDFS directory for my Pig script to access. ALTER TABLE historic_weather ADD IF NOT EXISTS PARTITION (yr='2010') LOCATION '/user/oracle/weather/historic/yr=2011'; INSERT OVERWRITE DIRECTORY '/user/oracle/weather/cleaned_history' SELECT w.stn, w.wban, w.weather_year, w.weather_month, w.weather_day, w.temp, w.dewp, w.weather FROM ( FROM historic_weather SELECT TRANSFORM(...) USING '/path/to/hive/filters/ncdc_parser.py' as stn, wban, weather_year, weather_month, weather_day, temp, dewp, weather ) w; Since I'm going to prepare training directories with at least the same frequency that I add partitions, I should also add that to my workflow. Oozie is going to invoke these Hive actions using what's somewhat obviously referred to as a Hive action. Hive actions amount to Oozie running a script file containing our query language statements, so we can place them in a file called weather_train.hql. Starting Our Workflow Oozie offers two types of jobs: workflows and coordinator jobs. Workflows are straightforward: they define a set of actions to perform as a sequence or directed acyclic graph. Coordinator jobs can take all the same actions of Workflow jobs, but they can be automatically started either periodically or when new data arrives in a specified location. To keep things simple we'll make a workflow job; coordinator jobs simply require another XML file for scheduling. The bare minimum for workflow XML defines a name, a starting point, and an end point: <workflow-app name="WeatherMan" xmlns="uri:oozie:workflow:0.1"> <start to="ParseNCDCData"/> <end name="end"/> </workflow-app> To this we need to add an action, and within that we'll specify the hive parameters Also, keep in mind that actions require <ok> and <error> tags to direct the next action on success or failure. <action name="ParseNCDCData"> <hive xmlns="uri:oozie:hive-action:0.2"> <job-tracker>localhost:8021</job-tracker> <name-node>localhost:8020</name-node> <configuration> <property> <name>oozie.hive.defaults</name> <value>/user/oracle/weather_ooze/hive-default.xml</value> </property> </configuration> <script>ncdc_parse.hql</script> </hive> <ok to="WeatherMan"/> <error to="end"/> </action> There are a couple of things to note here: I have to give the FQDN (or IP) and port of my JobTracker and NameNode. I have to include a hive-default.xml file. I have to include a script file. The hive-default.xml and script file must be stored in HDFS That last point is particularly important. Oozie doesn't make assumptions about where a given workflow is being run. You might submit workflows against different clusters, or have different hive-defaults.xml on different clusters (e.g. MySQL or Postgres-backed metastores). A quick way to ensure that all the assets end up in the right place in HDFS is just to make a working directory locally, build your workflow.xml in it, and copy the assets you'll need to it as you add actions to workflow.xml. At this point, our local directory should contain: workflow.xml hive-defaults.xml (make sure this file contains your metastore connection data) ncdc_parse.hql Adding Pig to the Ooze Adding our Pig script as an action is slightly simpler from an XML standpoint. All we do is add an action to workflow.xml as follows: <action name="WeatherMan"> <pig> <job-tracker>localhost:8021</job-tracker> <name-node>localhost:8020</name-node> <script>weather_train.pig</script> </pig> <ok to="end"/> <error to="end"/> </action> Once we've done this, we'll copy weather_train.pig to our working directory. However, there's a bit of a "gotcha" here. My pig script registers the Weka Jar and a chunk of jython. If those aren't also in HDFS, our action will fail from the outset -- but where do we put them? The Jython script goes into the working directory at the same level as the pig script, because pig attempts to load Jython files in the directory from which the script executes. However, that's not where our Weka jar goes. While Oozie doesn't assume much, it does make an assumption about the Pig classpath. Anything under working_directory/lib gets automatically added to the Pig classpath and no longer requires a REGISTER statement in the script. Anything that uses a REGISTER statement cannot be in the working_directory/lib directory. Instead, it needs to be in a different HDFS directory and attached to the pig action with an <archive> tag. Yes, that's as confusing as you think it is. You can get the exact rules for adding Jars to the distributed cache from Oozie's Pig Cookbook. Making the Workflow Work We've got a workflow defined and have collected all the components we'll need to run. But we can't run anything yet, because we still have to define some properties about the job and submit it to Oozie. We need to start with the job properties, as this is essentially the "request" we'll submit to the Oozie server. In the same working directory, we'll make a file called job.properties as follows: nameNode=hdfs://localhost:8020 jobTracker=localhost:8021 queueName=default weatherRoot=weather_ooze mapreduce.jobtracker.kerberos.principal=foo dfs.namenode.kerberos.principal=foo oozie.libpath=${nameNode}/user/oozie/share/lib oozie.wf.application.path=${nameNode}/user/${user.name}/${weatherRoot} outputDir=weather-ooze While some of the pieces of the properties file are familiar (e.g., JobTracker address), others take a bit of explaining. The first is weatherRoot: this is essentially an environment variable for the script (as are jobTracker and queueName). We're simply using them to simplify the directives for the Oozie job. The oozie.libpath pieces is extremely important. This is a directory in HDFS which holds Oozie's shared libraries: a collection of Jars necessary for invoking Hive, Pig, and other actions. It's a good idea to make sure this has been installed and copied up to HDFS. The last two lines are straightforward: run the application defined by workflow.xml at the application path listed and write the output to the output directory. We're finally ready to submit our job! After all that work we only need to do a few more things: Validate our workflow.xml Copy our working directory to HDFS Submit our job to the Oozie server Run our workflow Let's do them in order. First validate the workflow: oozie validate workflow.xml Next, copy the working directory up to HDFS: hadoop fs -put working_dir /user/oracle/working_dir Now we submit the job to the Oozie server. We need to ensure that we've got the correct URL for the Oozie server, and we need to specify our job.properties file as an argument. oozie job -oozie http://url.to.oozie.server:port_number/ -config /path/to/working_dir/job.properties -submit We've submitted the job, but we don't see any activity on the JobTracker? All I got was this funny bit of output: 14-20120525161321-oozie-oracle This is because submitting a job to Oozie creates an entry for the job and places it in PREP status. What we got back, in essence, is a ticket for our workflow to ride the Oozie train. We're responsible for redeeming our ticket and running the job. oozie -oozie http://url.to.oozie.server:port_number/ -start 14-20120525161321-oozie-oracle Of course, if we really want to run the job from the outset, we can change the "-submit" argument above to "-run." This will prep and run the workflow immediately. Takeaway So, there you have it: the somewhat laborious process of building an Oozie workflow. It's a bit tedious the first time out, but it does present a pair of real benefits to those of us who spend a great deal of time data munging. First, when new data arrives that requires the same processing, we already have the workflow defined and ready to run. Second, as we build up a set of useful action definitions over time, creating new workflows becomes quicker and quicker.

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  • Testing Workflows &ndash; Test-First

    - by Timothy Klenke
    Originally posted on: http://geekswithblogs.net/TimothyK/archive/2014/05/30/testing-workflows-ndash-test-first.aspxThis is the second of two posts on some common strategies for approaching the job of writing tests.  The previous post covered test-after workflows where as this will focus on test-first.  Each workflow presented is a method of attack for adding tests to a project.  The more tools in your tool belt the better.  So here is a partial list of some test-first methodologies. Ping Pong Ping Pong is a methodology commonly used in pair programing.  One developer will write a new failing test.  Then they hand the keyboard to their partner.  The partner writes the production code to get the test passing.  The partner then writes the next test before passing the keyboard back to the original developer. The reasoning behind this testing methodology is to facilitate pair programming.  That is to say that this testing methodology shares all the benefits of pair programming, including ensuring multiple team members are familiar with the code base (i.e. low bus number). Test Blazer Test Blazing, in some respects, is also a pairing strategy.  The developers don’t work side by side on the same task at the same time.  Instead one developer is dedicated to writing tests at their own desk.  They write failing test after failing test, never touching the production code.  With these tests they are defining the specification for the system.  The developer most familiar with the specifications would be assigned this task. The next day or later in the same day another developer fetches the latest test suite.  Their job is to write the production code to get those tests passing.  Once all the tests pass they fetch from source control the latest version of the test project to get the newer tests. This methodology has some of the benefits of pair programming, namely lowering the bus number.  This can be good way adding an extra developer to a project without slowing it down too much.  The production coder isn’t slowed down writing tests.  The tests are in another project from the production code, so there shouldn’t be any merge conflicts despite two developers working on the same solution. This methodology is also a good test for the tests.  Can another developer figure out what system should do just by reading the tests?  This question will be answered as the production coder works there way through the test blazer’s tests. Test Driven Development (TDD) TDD is a highly disciplined practice that calls for a new test and an new production code to be written every few minutes.  There are strict rules for when you should be writing test or production code.  You start by writing a failing (red) test, then write the simplest production code possible to get the code working (green), then you clean up the code (refactor).  This is known as the red-green-refactor cycle. The goal of TDD isn’t the creation of a suite of tests, however that is an advantageous side effect.  The real goal of TDD is to follow a practice that yields a better design.  The practice is meant to push the design toward small, decoupled, modularized components.  This is generally considered a better design that large, highly coupled ball of mud. TDD accomplishes this through the refactoring cycle.  Refactoring is only possible to do safely when tests are in place.  In order to use TDD developers must be trained in how to look for and repair code smells in the system.  Through repairing these sections of smelly code (i.e. a refactoring) the design of the system emerges. For further information on TDD, I highly recommend the series “Is TDD Dead?”.  It discusses its pros and cons and when it is best used. Acceptance Test Driven Development (ATDD) Whereas TDD focuses on small unit tests that concentrate on a small piece of the system, Acceptance Tests focuses on the larger integrated environment.  Acceptance Tests usually correspond to user stories, which come directly from the customer. The unit tests focus on the inputs and outputs of smaller parts of the system, which are too low level to be of interest to the customer. ATDD generally uses the same tools as TDD.  However, ATDD uses fewer mocks and test doubles than TDD. ATDD often complements TDD; they aren’t competing methods.  A full test suite will usually consist of a large number of unit (created via TDD) tests and a smaller number of acceptance tests. Behaviour Driven Development (BDD) BDD is more about audience than workflow.  BDD pushes the testing realm out towards the client.  Developers, managers and the client all work together to define the tests. Typically different tooling is used for BDD than acceptance and unit testing.  This is done because the audience is not just developers.  Tools using the Gherkin family of languages allow for test scenarios to be described in an English format.  Other tools such as MSpec or FitNesse also strive for highly readable behaviour driven test suites. Because these tests are public facing (viewable by people outside the development team), the terminology usually changes.  You can’t get away with the same technobabble you can with unit tests written in a programming language that only developers understand.  For starters, they usually aren’t called tests.  Usually they’re called “examples”, “behaviours”, “scenarios”, or “specifications”. This may seem like a very subtle difference, but I’ve seen this small terminology change have a huge impact on the acceptance of the process.  Many people have a bias that testing is something that comes at the end of a project.  When you say we need to define the tests at the start of the project many people will immediately give that a lower priority on the project schedule.  But if you say we need to define the specification or behaviour of the system before we can start, you’ll get more cooperation.   Keep these test-first and test-after workflows in your tool belt.  With them you’ll be able to find new opportunities to apply them.

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  • What makes for a good JIRA workflow with a software development team?

    - by Hari Seldon
    I am migrating my team from a snarl of poorly managed excel documents, individual checklists, and personal emails to manage our application issues and development tasks to a new JIRA project. My team and I are new to JIRA (and issue tracking software in general). My team is skeptical of the transition at best, so I am also trying not to scare them off by introducing something overly complex at the start. I understand one of JIRA's strengths to be the customized workflows that can be created for a project. I've looked over the JIRA documentation and a number of tutorials, and am comfortable with the how in creating workflows, but I need some contextual What to go along with it. What makes a particular workflow work well? What does a poorly designed workflow look like? What are the benefits/drawbacks of a strict workflow with very specific states and transitions to a looser workflow, with fewer, broader defined states and transitions

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  • CRM 2011 - Workflows Vs JavaScripts

    - by Kanini
    In the Contact entity, I have the following attributes Preferred email - A read only field of type Email Personal email 1 - An email field Personal email 2 - An email field Work email 1 - An email field Work email 2 - An email field School email - An email field Other email - An email field Preferred email option - An option set with the following values {Personal email 1, Personal email 2, Work email 1, Work email 2, School email and Other email). None of the above mentioned fields are required. Requirement When user picks a value from Preferred email option, we copy the email address available in that field and apply the same in the Preferred email field. Implementation The Solution Architect suggested that we implement the above requirement as a Workflow. The reason he provided was - most of the times, these values are to be populated by an external website and the data is then fed into CRM 2011 system. So, when they update Preferred email option via a Web Service call to CRM, the WF will run and updated the Preferred email field. My argument / solution What will happen if I do not pick a value from the Preferred email Option Set? Do I set it to any of the email addresses that has a value in it? If so, what if there is more than one of the email address fields are populated, i.e., what if Personal email 1 and Work email 1 is populated but no value is picked in the Option Set? What if a value existed in the Preferred email Option Set and I then change it to NULL? Should the field Preferred email (where the text value of email address is stored) be set to Read Only? If not, what if I have picked Personal email 1 in the Option Set and then edit the Preferred email address text field with a completely new email address If yes, then we are enforcing that the preferred email should be one among Personal email 1, Personal email 2, Work email 1, Work email 2, School email or Other email [My preference would be this] What if I had a value of [email protected] in the personal email 1 field and personal email 2 is empty and choose value of Personal email 1 in the drop down for Preferred email (this will set the Preferred email field to [email protected]) and later, I change the value to Personal email 2 in the Preferred email. It overwrites a valid email address with nothing. I agree that it would be highly unlikely that a user will pick Preferred email as Personal email 2 and not have a value in it but nevertheless it is a possible scenario, isn’t it? What if users typed in a value in Personal email 1 but by mistake picked Personal email 2 in the option set and Personal email 2 field had no value in it. Solution The field Preferred email option should be a required field A JS should run whenever Preferred email option is changed. That JS function should set the relevant email field as required (based on the option chosen) and another JS function should be called (see step 3). A JS function should update the value of Preferred email with the value in the email field (as picked in the option set). The JS function should also be run every time someone updates the actual email field which is chosen in the option set. The guys who are managing the external website should update the Preferred email field - surely, if they can update Preferred email option via a Web Service call, it is easy enough to update the Preferred email right? Question Which is a better method? Should it be written as a JS or a WorkFlow? Also, whose responsibility is it to update the Preferred email field when the data flows from an external website? I am new to CRM 2011 but have around 6 years of experience as a CRM consultant (with other products). I do not come from a development background as I started off as a Application Support Engineer but have picked up development in the last couple of years.

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  • Trade-offs of local vs remote development workflows for a web development team

    - by lamp_scaler
    We currently have SVN setup on a remote development server. Developers SSH into the server and develops on their sandbox environment on the server. Each one has a virtual host pointed to their sandbox so they can preview their changes via the web browser by connecting to developer-sandbox1.domain.com. This has worked well so far because the team is small and everyone uses computers with varying specs and OSs. I've heard some web shops are using a workflow that has the developers work off of a VM on their local machine and then finally push changes to the remote server that hosts SVN. The downside to this is that everyone will need to make sure their machine is powerful enough to run both the VM and all their development tools. This would also mean creating images that mirror the server environment (we use CentOS) and have them install it into their VMs. And this would mean creating new images every time there is an update to the server environment. What are some other trade-offs? Ultimately, why did you choose one workflow over the other?

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  • Recommended workflows for Apache virtual hosts?

    - by craig zheng
    I do a lot of local web development work on my Ubuntu machine, and I'm constantly setting up virtual hosts in Apache. I don't need to do hard core server management, but I am getting tired of the repetitive process of manually adding config directives to files in /etc/apache2/sites-available/ and then updating the /etc/hosts file. Is there a more efficient or more automated way to do all this that I'm missing? Maybe something like rapache but that's actually working?

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  • Tools for modelling data and workflows using structured text files

    - by Alexey
    Consider a case when I want to try some idea of an application. But I want to avoid investing a lot of effort in coding UI/work flows/database schema etc before I see that it's going to be useful to me (as example of potential user). My idea is stay lightweight and put all the data in text files. So the components could be following: Domain objects are represented by text files or their fragments Domain objects are grouped by their type using directories Structure the files using some both human- and machine-friendly format, e.g. YAML Use some smart text editor (e.g. vim, emacs, rubymine) to edit and navigate those files Use color schemes and macros/custom commands of the text editor to effectively manipulate those files Use scripts (or a lightweight web framework like Sinatra) to try some business logic ideas on top of the data model The question is: Are there tools or toolkits that support or can be adopted to this approach? Also any ideas, links to articles/other knowledge sources are very welcome. And more specific question: What is the simplest way to index and update index of files with YAML files?

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  • How to fix “Add Host to Workflow Farm problem” when installing Windows Azure Workflow in SharePoint2013 Preview

    - by ybbest
    Problem: When I try to configure the windows Azure workflow in SharePoint2013 preview, I got the following error see screenshot below. Detailed log can be found here. Solution: I asked the question in SharePoint StackExchange , Rajat’s help me to fix the problem .The solution for this is quite simple, instead of using the short form for your RunAs account, you should use the fully qualified name. So change administrator@YBBEST to [email protected] make the problem go away as shown below. References: How to: Set up and configure SharePoint 2013 workflows

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  • Git workflow for small teams

    - by janos
    I'm working on a git workflow to implement in a small team. The core ideas in the workflow: there is a shared project master that all team members can write to all development is done exclusively on feature branches feature branches are code reviewed by a team member other than the branch author the feature branch is eventually merged into the shared master and the cycle starts again The article explains the steps in this cycle in detail: https://github.com/janosgyerik/git-workflows-book/blob/small-team-workflow/chapter05.md Does this make sense or am I missing something?

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  • Oracle Primavera Partner Programs

    - by mark.kromer
    Here is the slide presentation with only the slides that can be shared at this time, for our Oracle Primavera partner programs focusing on expanding P6's workflows and reporting capabilities. By leveraging Oracle's BPM & BI Publisher products, you can build exciting new workflow & enhanced reports to expand the capabilities of Primavera applications.

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  • How to fix “Add Host to Workflow Farm problem” when installing Windows Azure Workflow in SharePoint2013 Preview

    - by ybbest
    Problem: When I try to configure the windows Azure workflow in SharePoint2013 preview, I got the following error see screenshot below. Detailed log can be found here. Solution: I asked the question in SharePoint StackExchange , Rajat’s help me to fix the problem .The solution for this is quite simple, instead of using the short form for your RunAs account, you should use the fully qualified name. So change administrator@YBBEST to [email protected] make the problem go away as shown below. Having other problems , check out AC’S blog on trouble-shooting the installation. References: How to: Set up and configure SharePoint 2013 workflows

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  • How to Implement a Parallel Workflow

    - by Paul
    I'm trying to implement a parallel split task using a workflow system. I'm using .NET but my process is very simple and I don't want to use WF or anything heavy like that. I've tried using Stateless. So far is was easy to set up and run, but I may be using the wrong tool for the job because I'm not sure how you're supposed to model parallel split workflows, where you have multiple sub-tasks required before you can advance to the next state, but the steps don't require being performed in any particular order. I can easily use the dynamic configuration options to check my data model manually to see if the model is in the correct state (all sub-tasks completed) and can transition to the next state, but this seems to completely break the workflow paradigm. What is the proper, orthodox way to implement a parallel split process? Thanks

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  • Simple task framework - building software from reusable pieces

    - by RuslanD
    I'm writing a web service with several APIs, and they will be sharing some of the implementation code. In order not to copy-paste, I would like to ideally implement each API call as a series of tasks, which are executed in a sequence determined by the business logic. One obvious question is whether that's the best strategy for code reuse, or whether I can look at it in a different way. But assuming I want to go with tasks, several issues arise: What's a good task interface to use? How do I pass data computed in one task to another task in the sequence that might need it? In the past, I've worked with task interfaces like: interface Task<T, U> { U execute(T input); } Then I also had sort of a "task context" object which had getters and setters for any kind of data my tasks needed to produce or consume, and it gets passed to all tasks. I'm aware that this suffers from a host of problems. So I wanted to figure out a better way to implement it this time around. My current idea is to have a TaskContext object which is a type-safe heterogeneous container (as described in Effective Java). Each task can ask for an item from this container (task input), or add an item to the container (task output). That way, tasks don't need to know about each other directly, and I don't have to write a class with dozens of methods for each data item. There are, however, several drawbacks: Each item in this TaskContext container should be a complex type that wraps around the actual item data. If task A uses a String for some purpose, and task B uses a String for something entirely different, then just storing a mapping between String.class and some object doesn't work for both tasks. The other reason is that I can't use that kind of container for generic collections directly, so they need to be wrapped in another object. This means that, based on how many tasks I define, I would need to also define a number of classes for the task items that may be consumed or produced, which may lead to code bloat and duplication. For instance, if a task takes some Long value as input and produces another Long value as output, I would have to have two classes that simply wrap around a Long, which IMO can spiral out of control pretty quickly as the codebase evolves. I briefly looked at workflow engine libraries, but they kind of seem like a heavy hammer for this particular nail. How would you go about writing a simple task framework with the following requirements: Tasks should be as self-contained as possible, so they can be composed in different ways to create different workflows. That being said, some tasks may perform expensive computations that are prerequisites for other tasks. We want to have a way of storing the results of intermediate computations done by tasks so that other tasks can use those results for free. The task framework should be light, i.e. growing the code doesn't involve introducing many new types just to plug into the framework.

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  • Simplifying the process of compiling and running objective-c apps in GNUstep

    - by Matthew
    I just installed GNUstep (following this post: http://www.jaysonjc.com/programming/objective-c-programming-in-windows-gnustep-projectcenter.html) It says to run this code: gcc -o helloworld helloworld.m -I /GNUstep/System/Library/Headers -L /GNUstep/System/Library/Libraries -lobjc -lgnustep-base -fconstant-string-class=NSConstantString every time I want to compile. It works just fine for me. However as I'm learning and will be compiling/running apps way often (making little changes and trying again), I'd like a simpler way to do this. Is there an easier way to compile and then run the app? Or am I just being lazy?

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  • Is a code review which uses only code comments a good idea?

    - by gaRex
    Preconditions Team uses DVCS IDE supports comments parsing (like TODO and etc.) Tools like CodeCollaborator are expensive for budget Tools like gerrit are too complex for install or not usable Workflow Author publishes somewhere on central repo feature branch Reviewer fetch it and start review In case of some question/issue reviewer create comment with special label, like "REV". Such label MUST not be in production code -- only on review stage: $somevar = 123; // REV Why do echo this here? echo $somevar; When reviewer finish post comments -- it just commits with stupid message "comments" and pushes back Author pulls feature branch back and answer comments in similar way or improve code and push it back When "REV" comments have gone we can think, that review has successfully finished. Author interactively rebases feature branch, squashes it to remove those "comment" commits and now is ready to merge feature to develop or make any action that usualy could be after successful internal review IDE support I know, that custom comment tags are possible in eclipse & netbeans. Sure it also should be in blablaStorm family. Questions Do you think this methodology is viable? Do you know something similar? What can be improved in it?

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  • Advice Needed: Developers blocked by waiting on code to merge from another branch using GitFlow

    - by fogwolf
    Our team just made the switch from FogBugz & Kiln/Mercurial to Jira & Stash/Git. We are using the Git Flow model for branching, adding subtask branches off of feature branches (relating to Jira subtasks of Jira features). We are using Stash to assign a reviewer when we create a pull request to merge back into the parent branch (usually develop but for subtasks back into the feature branch). The problem we're finding is that even with the best planning and breakdown of feature cases, when multiple developers are working together on the same feature, say on the front-end and back-end, if they are working on interdependent code that is in separate branches one developer ends up blocking the other. We've tried pulling between each others' branches as we develop. We've also tried creating local integration branches each developer can pull from multiple branches to test the integration as they develop. Finally, and this seems to work possibly the best for us so far, though with a bit more overhead, we have tried creating an integration branch off of the feature branch right off the bat. When a subtask branch (off of the feature branch) is ready for a pull request and code review, we also manually merge those change sets into this feature integration branch. Then all interested developers are able to pull from that integration branch into other dependent subtask branches. This prevents anyone from waiting for any branch they are dependent upon to pass code review. I know this isn't necessarily a Git issue - it has to do with working on interdependent code in multiple branches, mixed with our own work process and culture. If we didn't have the strict code-review policy for develop (true integration branch) then developer 1 could merge to develop for developer 2 to pull from. Another complication is that we are also required to do some preliminary testing as part of the code review process before handing the feature off to QA.This means that even if front-end developer 1 is pulling directly from back-end developer 2's branch as they go, if back-end developer 2 finishes and his/her pull request is sitting in code review for a week, then front-end developer 2 technically can't create his pull request/code review because his/her code reviewer can't test because back-end developer 2's code hasn't been merged into develop yet. Bottom line is we're finding ourselves in a much more serial rather than parallel approach in these instance, depending on which route we go, and would like to find a process to use to avoid this. Last thing I'll mention is we realize by sharing code across branches that haven't been code reviewed and finalized yet we are in essence using the beta code of others. To a certain extent I don't think we can avoid that and are willing to accept that to a degree. Anyway, any ideas, input, etc... greatly appreciated. Thanks!

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  • Project Management - Asana / activeCollab / basecamp / alternative / none

    - by rickyduck
    I don't know whether this should be on programmers - I've been looking at the above three apps over the past few weeks just for myself and I'm in two minds. All three look good, are easy to use, and I came to this conclusion; Asana is the easiest to use ActiveCollab is the feature rich and easiest flow BaseCamp is the best UX / design But I didn't really find my workflow was any more quicker / efficient, in fact it was a bit slower and organized. Is there a realistic place for them in workflow - should programmers use them for themselves, or only when a project manager can take control of it?

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