I have talked about human filters and my plan for digital curation. These items are the fruits of those ideas, the items I deemed worthy from my Google Reader feeds. These items are a combination of tech business news, development news and programming tools and techniques. Dew Drop – June 7, 2013 (#1,563) (Alvin Ashcraft's Morning Dew) On friction in software (Ayende @ Rahien) Caching, jQuery Ajax and Other IE Fun (HTML5 Zone) IndexedDB and Date Example (HTML5 Zone) DevOps Scares Me – Part 1 (Architects Zone – Architectural Design Patterns & Best Practices) Visualizing the News with Vivagraph.js (Architects Zone – Architectural Design Patterns & Best Practices) My First Clojure Workflow (Javalobby – The heart of the Java developer community) Helping an ISV Look at Their Cloud Options (Architects Zone – Architectural Design Patterns & Best Practices) Ignore Requirements to Gain Flexibility, Value, Insights! The Power of Why (Javalobby – The heart of the Java developer community) Estimating the Unknown: Dates or Budgets, Part 1 (Agile Zone – Software Methodologies for Development Managers) Team Decision Making Techniques – Fist to Five and others (Agile Zone – Software Methodologies for Development Managers) The Daily Six Pack: June 7, 2013 (Dirk Strauss) Pastime (xkcd.com) The Affect Heuristic (Mark Needham) Every great company has been built the same way: bit by bit (Hacker News) Under the Hood: The entities graph (Facebook Engineering's Facebook Notes) Entrepreneurship With a Family is for Crazy People (Stay N Alive) Thinking Together for Release Planning (Javalobby – The heart of the Java developer community) I hope you enjoy today’s items, and please participate in the discussions on those sites.
I have talked about human filters and my plan for digital curation. These items are the fruits of those ideas, the items I deemed worthy from my Google Reader feeds. These items are a combination of tech business news, development news and programming tools and techniques. Real-Time Ad Impression Bids Using DynamoDB (Amazon Web Services Blog) The mother of all M&A rumors: AT&T, Verizon to jointly buy Vodafone (GigaOM) Is this the future of memory? A Hybrid Memory Cube spec makes its debut. (GigaOM) Dew Drop – April 2, 2013 (#1,518) (Alvin Ashcraft's Morning Dew) Rosetta Stone acquires Livemocha for $8.5m to move its language learning platform into the cloud (The Next Web) Double Shot #1098 (A Fresh Cup) Extending git (Atlassian Blogs) A Thorough Introduction To Backbone.Marionette (Part 2) (Smashing Magazine Feed) 60 Problem Solving Strategies (Javalobby – The heart of the Java developer community) Why asm.js is a big deal for game developers (HTML5 Zone) Implementing DAL in Play 2.x (Scala), Slick, ScalaTest (Javalobby – The heart of the Java developer community) “It’s Open Source, So the Source is, You Know, Open.” (Javalobby – The heart of the Java developer community) How to Design a Good, Regular API (Javalobby – The heart of the Java developer community) Scalding: Finding K Nearest Neighbors for Fun and Profit (Javalobby – The heart of the Java developer community) The Daily Six Pack: April 2, 2013 (Dirk Strauss) Usually When Developers Are Mean, It Is About Power (Agile Zone – Software Methodologies for Development Managers) Do Predictive Modelers Need to Know Math? (Data Mining and Predictive Analytics) Heroku Forces Customer Upgrade To Fix Critical PostgreSQL Security Hole (TechCrunch) DYNAMO (Lambda the Ultimate – Programming Languages Weblog) FitNesse your ScalaTest with custom Scala DSL (Java Code Geeks) LinkBench: A database benchmark for the social graph (Facebook Engineering's Facebook Notes) Khan Academy Checkbook Scaling to 6 Million Users a Month on GAE (High Scalability) Famo.us, The Framework For Fast And Beautiful HTML5 Apps, Will Be Free Thanks To “Huge Hardware Vendor Interest” (TechCrunch) Why We Need Lambda Expressions in Java – Part 2 (Javalobby – The heart of the Java developer community) I hope you enjoy today’s items, and please participate in the discussions on those sites.
Poor Oddjob, I thought as I read Craig Flichel’s Feature Comparison of Java Job Schedulers featuring Obsidian, Quartz, Cron4j and Spring. Yet again it hasn’t made the grade, it’s been passed over for the scheduling team. Never mind I say, you’re just a little bit different and misunderstood. Let’s have a kick about in the back yard and see what you can do… Real-time Schedule Changes / Real-time Job Configuration Oddjob: Yes Here is Oddjob’s Client GUI, connecting to an instance of Oddjob running as a Windows Service on my home PC. My Oddob instance sends me a reminder email when it’s someone’s birthday, and also tells me when it’s going to rain. The swing UI allows complete configuration of the server. With it I can configure the jobs and their schedules, but unfortunately I can’t control the weather! Ad-hoc Job Submission: Yes Configurable Job Conflicts: Not Really Applicable Ad-hoc job submission is really what Oddjob is all about. Many jobs won’t be scheduled at all and will sit in a folder to be manually run as required. To run a job, scheduled or not, just click ‘run’ from the job menu. Job conflicts aren’t really a problem for Oddjob because it won’t reschedule a job again until it’s finished. If a job’s next slot has passed, you have the choice to run immediately or skip missed runs and reschedule from the current time. If you want concurrent execution you can configure different jobs to run at the same time or use a single schedule and launch the jobs in parallel. Manually Stopping a job is just as easy as running it. Click ‘stop’ from the job menu. Code- and XML-Free Job Configuration Oddjob: Yes You saw this in the first section. Oddjob’s configuration is via a UI and is done in real time. In fact I often start one job as I’m configuring the next. It’s all very interactive. Oddjob uses XML behind the scenes for those that like to see under the hood. Job Event Subscription/Notification Oddjob: Yes It’s very easy to trigger a job based on the completion state of another job. You would have to write code to listen to configuration changes though. Custom Listeners: Undocumented Job Chaining: Yes There’s lots of options for job chaining, sequential, parallel, or cascade, and any nested combinations thereof. Adding a custom Stateful listener would be easy enough, and might be useful if embedding Oddjob but this isn’t the normal use case. The unit tests do this extensively however. Monitoring & Management UI Oddjob: Yes The same UI allows you to see the job state, job by job log messages, the console of an Exec Job, and the run time properties of all the jobs. Zero Configuration Clustering and Load Sharing Oddjob: Kind Of Oddjob has a Grab Job so you can run the same configuration on several servers and have them compete for work. I wrote it as a proof of concept but I’ve never had cause to use it in the field and I haven’t had any reports that others have either. Job Execution Host Affinity: Kind Of In the same way that you add the ‘Grab job’ to many servers to share work, you could in theory just add Grab for a particular job to only certain servers. I guess this is server Affinity? Scripting Language Support in Jobs Oddjob: Yes Oddjob has a Script Job for any language that supports the Java Scripting Framework. JavaScript is shipped by default.With the Script Job you can also interact with Oddjob to use the properties of other jobs, and set variables for other jobs to use. Scheduling Precision Oddjob: Millisecond In theory Oddjob can schedule with millisecond precision, but this isn’t usual practice. Polling for a file every 30 seconds, for instance, is normally fine. Job Scheduling & Management REST API Oddjob: JMX Only No REST API. You can embed the JMX Client easily enough and control everything from Java, but not for other languages. Not yet. Custom Calendar Support Oddjob: Yes Oddjob has the concept of breaking a schedule with another. The breaks can be as flexible as the job schedule itself – define days, weeks or just a few hours off for a task. The Scheduling section of the User Guide has much more on Oddjob’s flexible scheduling capabilities. Conclusion Oddjob has many other features to make automating repetitive tasks easy. One noteworthy feature is the ease of adding custom jobs by just implementing java.lang.Runnable. Oddjob is undeniably an amateur player in the Scheduler league, and one that is often overlooked. With its Apache licence it is completely free and open. Why not check it out when you have an hour or two? You might be pleasantly surprised by the quality of play.
There were several posts lately critical of Scala language, specifically this onehttps://dzone.com/articles/i-dont-scala. It is a well written, critical of Scala post by someone who clearly prefers other languages (i.e. Java) at this point. However, having used Scala exclusively for the last 4 years and having led the company (GridGain Systems) that has been one of the pioneers in Scala adoption boasting one of the largest production code based in Scala across many projects – I can see all too familiar “reasoning” in that post… The biggest issue with Scala’s perception is the deeply varying quality of frameworks and tools that significantly affect one’s perception of the Scala language itself. I’ve been saying openly that SBT and Scalaz projects, for example, have had the cumulatively negative impact on Scala’s initial adoption. While poor engineering behind SBT and colossal snobbism of Scalaz have been well understood – I can addSpray.io to this list as well now. The engineering ineptitude of people behindSpray.io (and the Spray.routing specifically) is worrisome to say the least. When someone takes a test run with Scala and gets exposed to SBT, Scalaz andSpray.io – on top of the existing growing pains of binary compatibly, IDE support and slow compilation – I’m surprised we have even that small community around Scala as it is today. Yet – remove these engineering warts – and Scala provides brilliantly simple, extremely productive and intellectually sutisfying world in which we can express our algorithms. What attracted me the most to Scala almost 5 years ago is its engineering pragmatisms vs. hopeless academic idealism of Haskell or intellectual laziness of dynamically typed languages. That engineering pragmatism coupled with an almost algebraic elegance and simplicity is what makes Scala probably the best general purpose language available today. So, I say to my engineers to look at Scala holistically, away from sub-standard projects, tools and individual snobs. There are plenty of good examples in Scala eco-system where one can learn how to think and ultimately write quality code in Scala: For sane type-level programming look at latest Scala collections For DLS look at ScalaTest For concurrency look at Akka For boundary pushing yet still useful type-level programming look at Shapeless and the list can go on. When tinkering with Scala for the first time always remember that Scala the language is much bigger than the sum of many of its projects – and there are few of them that you probably should stay clear off anyways.
see also: part i: when to build your data warehouse part ii: building a new schema part iii: location of your data warehouse part iv: extraction, transformation, and load in part i we looked at the advantages of building a data warehouse independent of cubes/a bi system and in part ii we looked at how to architect a data warehouse’s table schema. in part iii, we looked at where to put the data warehouse tables. in part iv, we are going to look at how to populate those tables and keep them in sync with your oltp system. today, our last part in this series, we will take a quick look at the benefits of building the data warehouse before we need it for cubes and bi by exploring our reporting and other options. as i said in part i, you should plan on building your data warehouse when you architect your system up front. doing so gives you a platform for building reports, or even application such as web sites off the aggregated data. as i mentioned in part ii, it is much easier to build a query and a report against the rolled up table than the oltp tables. to demonstrate, i will make a quick pivot table using sql server 2008 r2 powerpivot for excel (or just powerpivot for short!). i have showed how to use powerpivot before on this blog , however, i usually was going against a sql server table, sql azure table, or an odata feed. today we will use a sql server table, but rather than build a powerpivot against the oltp data of northwind, we will use our new rolled up fact table. to get started, i will open up powerpivot and import data from the data warehouse i created in part ii. i will pull in the time, employee, and product dimension tables as well as the fact table. once the data is loaded into powerpivot, i am going to launch a new pivottable. powerpivot understands the relationships between the dimension and fact tables and places the tables in the designed shown below. i am going to drag some fields into the boxes on the powerpivot designer to build a powerful and interactive pivot table. for rows i will choose the category and product hierarchy and sum on the total sales. i’ll make the columns (or pivot on this field) the month from the time dimension to get a sum of sales by category/product by month. i will also drag in year and quarter in my vertical and horizontal slicers for interactive filtering. lastly i will place the employee field in the report filter pane, giving the user the ability to filter by employee. the results look like this, i am dynamically filtering by 1997, third quarter and employee name janet leverling. this is a pretty powerful interactive report build in powerpivot using the four data warehouse tables. if there was no data warehouse, this pivot table would have been very hard for an end user to build. either they or a developer would have to perform joins to get the category and product hierarchy as well as more joins to get the order details and sum of the sales. in addition, the breakout and dynamic filtering by year and quarter, and display by month, are only possible by the dimtime table, so if there were no data warehouse tables, the user would have had to parse out those dateparts. just about the only thing the end user could have done without assistance from a developer or sophisticated query is the employee filter (and even that would have taken some powerpivot magic to display the employee name, unless the user did a join.) of course pivot tables are not the only thing you can create from the data warehouse tables you can create reports, ad hoc query builders, web pages, and even an amazon style browse application. (amazon uses its data warehouse to display inventory and oltp to take your order.) i hope you have enjoyed this series, enjoy your data warehousing.
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