I’m very excited to announce to everyone that the latest and greatest version of WordPress, version 2.8 “Baker,” is immediately available for download. 2.8 represents a nice fit and finish release for WordPress with improvements to themes, widgets, taxonomies, and overall speed. We also fixed over 790 bugs. This release is named in honor of noted trumpeter and vocalist Chet Baker. Here’s a quick video overview of everything in the new release:
The first thing you’ll notice is that visually 2.8 feels a lot like 2.7, just with some minor tweaks here and there. However once you’ll dig in you’ll begin to appreciate the changes.
First and foremost, 2.8 is way faster to use. We’ve changed the way WordPress does style and scripting.
The core and plugin updaters in previous versions of WordPress have been such a success we decided to bring the same to themes. You can now browse the entire theme directory and install a theme with one click from the comfort of your WordPress dashboard.
If you make edits or tweaks to themes or plugins from your dashboard, you’ll appreciate the new CodePress editor which gives syntax highlighting to the previously-plain editor. Also there is now contextual documentation for the functions in the file you’re editing linked right below the editor.
If you were ever frustrated with widgets before, this release should be your savior. We’ve completely redesigned the widgets interface (which we didn’t have time to in 2.7) to allow you to do things like edit widgets on the fly, have multiple copies of the same widget, drag and drop widgets between sidebars, and save inactive widgets so you don’t lose all their settings. Developers now have access to a much cleaner and robust API for creating widgets as well.
Finally you should explore the new Screen Options on every page. It’s the tab in the top right. Now, for example, if you have a wide monitor you could set up your dashboard to have four columns of widgets instead of the two it has by default. On other pages you can change how many items show per page.
You can read the full list of over 180 new features, changes, upgrades, and improvements on the Codex. The list is exhausting!
We’re already thinking hard about the next versions, 2.9 and 3.0. Keep an eye out for improved media handling, better dependency checking, versioning of templates and themes, and of course the fabled merging of WordPress and MU announced at WordCamp San Francisco two weeks ago.
We just announced the availability of MT 4.26 on the MovableType.com blog. This optional performance release includes bug fixes and feature improvements. No new features have been added.
MT 4.26 provides dramatically better search performance for large datasets and improves performance and reliability around TheSchwartz. More details can be found on the release notes, or you can just use the following direct download links to get MTOS 4.26:
We want to thank everyone in the community for your ongoing feedback and suggestions for improving Movable Type. While this release doesn't address every bug report and potential performance enhancement, we are continually working to improve your experience and satisfaction with Movable Type.
This post about using Hive and Hadoop for analytics comes straight from Facebook engineers.
Scalable analysis on large data sets has been core to the functions of a number of teams at Facebook - both engineering and non-engineering. Apart from ad hoc analysis and business intelligence applications used by analysts across the company, a number of Facebook products are also based on analytics.
These products range from simple reporting applications like Insights for the Facebook Ad Network, to more advanced kind such as Facebook's Lexicon product.
As a result a flexible infrastructure that caters to the needs of these diverse applications and users and that also scales up in a cost effective manner with the ever increasing amounts of data being generated on Facebook, is critical. Hive and Hadoop are the technologies that we have used to address these requirements at Facebook.
Read the rest of the article on Engineering @ Facebook's Notes page
I've been getting asked about this a lot lately so I figured I'd just blog about it. Products like WebSphere eXtreme Scale work by taking a dataset, partitioning it using a key and then assigning those partitions to a number of JVMs. Each partition usually has a primary and a replica. These 'shards' are assigned to JVMs. A transactional application typically interacts with the data on a single partition at a time. This means the transaction is executed in a single JVM. A server box will be able to do M of those transactions per second and it scales because N boxes does MN (M multiplied by N) transactions per second. Increase N, you get more transactions per second. Availability is very good because a transaction only depends on 1 of the N servers that are currently online. Any of the other (N-1) servers can go down or fail with no impact on the transaction. So, single partition transactions can scale indefinitely from a throughput point of view, offer very consistent response times and they are very available because they only point a small part of the grid at once.
Relational databases, document databases, and distributed hash tables get most of the hype these days, but there's another option: graph databases. Back to the future it seems. Here's a really interesting paper by Marko A. Rodriguez introducing the graph model and it's extension to representing the world wide web of data.
Modern day open source and commercial graph databases can store on the order of 1 billion relationships with some databases reaching the 10 billion mark. These developments are making the graph database practical for applications that require large-scale knowledge structures. Moreover, with
the Web of Data standards set forth by the Linked Data community, it is possible to interlink graph databases across the web into a giant global knowledge structure. This talk will discuss graph databases, their underlying data model, their querying mechanisms, and the benefits of the graph data structure for modeling and analysis.
I spend a blog entry discussing single partition and every partition transactions when using distributed KV systems and solutions for some common problems
计划在6月底发布新版本BugFree。主要内容包括
敬请期待。
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