in a nutshell, grid computing is a way to distribute your computations across multiple computers (nodes). however, even jms does that, but jms is not a grid computing product - it's a messaging protocol. to correctly classify grid computing products we have to split them into 2 categories: compute grids and data grids. compute grid compute grids allow you to take a computation, optionally split it into multiple parts, and execute them on different grid nodes in parallel. the obvious benefit here is that your computation will perform faster as it now can use resources from all grid nodes in parallel. one of the most common design patterns for parallel execution is mapreduce . however, compute grids are useful even if you don't need to split your computation - they help you improve overall scalability and fault-tolerance of your system by offloading your computations onto most available nodes. some of the "must have" compute grid features are: automatic deployment - allows for automatic deployment of classes and resources onto grid without any extra steps from user. this feature alone provides one of the largest productivity boosts in distributed systems. users usually are able to simply execute a task from one grid node and as task execution penetrates the grid, all classes and resources are also automatically deployed. topology resolution - allows to provision nodes based on any node characteristic or user-specific configuration. for example, you can decide to only include linux nodes for execution, or to only include a certain group of nodes within certain time window. you should also be able to choose all nodes with cpu loaded, say, under 50% that have more than 2gb of available heap memory. collision resolution - allows users to control which jobs get executed, which jobs get rejected, how many jobs can be executed in parallel, order of overall execution, etc. load balancing - allows to balance properly balance your system load within grid. usually range of load balancing policies varies within products. some of the most common ones are round robin, random, or adaptive. more advanced vendors also provide affinity load balancing where grid jobs always end up on the same node based on job's affinity key. this policy works well with data grids described below. fail-over - grid jobs should automatically fail-over onto other nodes in case of node crash or some other job failure. checkpoints - long running jobs should be able to periodically store their intermediate state. this is useful for fail-overs, when a failed job should be able to pick up its execution from the latest checkpoint, rather than start from scratch. grid events - a querying mechanism for all grid events is essential. any grid node should be able to query all events that happened on remote grid nodes during grid task execution. node metrics - a good compute grid solution should be able to provide dynamic grid metrics for all grid nodes. metrics should include vital node statistics, from cpu load to average job execution time. this is especially useful for load balancing, when the system or user need to pick the least loaded node for execution. pluggability - in order to blend into any environment a good compute grid should have well thought out pluggability points. for example, if running on top of jboss, a compute grid should totally reuse jboss communication and discovery protocols. data grid integration - it is important that compute grid are able to natively integrate with data grids as quite often businesses will need both, computational and data features working within same application. some compute grid vendors: - gridgain - professional open source - jppf - open source data grid data grids allow you to distribute your data across the grid. most of us are used to the term distributed cache rather than data grid (data grid does sound more savvy though). the main goal of data grid is to provide as much data as possible from memory on every grid node and to ensure data coherency. some of the important data grid features include: data replication - all data is fully replicated to all nodes in the grid. this strategy consumes the most resources, however it is the most effective solution for read-mostly scenarios, as data is available everywhere for immediate access. data invalidation - in this scenario, nodes load data on demand. whenever data changes on one of the nodes, then the same data on all other nodes is purged (invalidated). then this data will be loaded on-demand the next time it is accessed. distributed transactions - transactions are required to ensure data coherency. cache updates must work just like database updates - whenever an update failed, then the whole transaction must be rolled back. most data grid support various transaction policies, such as read committed, write committed, serializable, etc... data backups - useful for fail-over. some data grid products provide ability to assign backup nodes for the data. this way whenever a node crashes, the data is immediately available from another node. data affinity/partitioning - data affinity allows you to split/partition your whole data set into multiple subsets and assign every subset to a grid node. in the purest form, data is not replicated between nodes at all, every node is only responsible for it's own subset of data. however, various data grid products may provide different flavors of data affinity, such as replication only to back up nodes for example. data affinity is one of the more advanced features, and is not provided by every vendor. to my knowledge, according to product websites, out of commercial vendors oracle coherence and gemstone have it (there may be others). in professional open source space you can take a look at combination of gridgain with affinity load balancing and jbosscache . some data grid/cache vendors: - oracle coherence - commercial - gemstone - commercial - gigaspaces - commercial - jbosscache - professional open source - ehcache - open source

The hardest part about learning Hbase (the open source implementation of Google's BigTable), is just wrapping your mind around the concept of what it actually is. I find it rather unfortunate that these two great systems contain the words table and base in their names, which tend to cause confusion among RDBMS indoctrinated individuals (like myself). This article aims to describe these distributed data storage systems from a conceptual standpoint. After reading it, you should be better able to make an educated decision regarding when you might want to use Hbase vs when you'd be better off with a "traditional" database. It's all in the terminology Fortunately, Google's BigTable Paper clearly explains what BigTable actually is. Here is the first sentence of the "Data Model" section: A Bigtable is a sparse, distributed, persistent multidimensional sorted map. Note: At this juncture I like to give readers the opportunity to collect any brain matter which may have left their skulls upon reading that last line. The BigTable paper continues, explaining that: The map is indexed by a row key, column key, and a timestamp; each value in the map is an uninterpreted array of bytes. Along those lines, the HbaseArchitecture page of the Hadoop wiki posits that: HBase uses a data model very similar to that of Bigtable. Users store data rows in labelled tables. A data row has a sortable key and an arbitrary number of columns. The table is stored sparsely, so that rows in the same table can have crazily-varying columns, if the user likes. Although all of that may seem rather cryptic, it makes sense once you break it down a word at a time. I like to discuss them in this sequence: map, persistent, distributed, sorted, multidimensional, and sparse. Rather than trying to picture a complete system all at once, I find it easier to build up a mental framework piecemeal, to ease into it... map At its core, Hbase/BigTable is a map. Depending on your programming language background, you may be more familiar with the terms associative array (PHP), dictionary (Python), Hash (Ruby), or Object (JavaScript). From the wikipedia article, a map is "an abstract data type composed of a collection of keys and a collection of values, where each key is associated with one value." Using JavaScript Object Notation, here's an example of a simple map where all the values are just strings: { "zzzzz" : "woot", "xyz" : "hello", "aaaab" : "world", "1" : "x", "aaaaa" : "y" } persistent Persistence merely means that the data you put in this special map "persists" after the program that created or accessed it is finished. This is no different in concept than any other kind of persistent storage such as a file on a filesystem. Moving along... distributed Hbase and BigTable are built upon distributed filesystems so that the underlying file storage can be spread out among an array of independent machines. Hbase sits atop either Hadoop's Distributed File System (HDFS) or Amazon's Simple Storage Service (S3), while a BigTable makes use of the Google File System (GFS). Data is replicated across a number of participating nodes in an analogous manner to how data is striped across discs in a RAID system. For the purpose of this article, we don't really care which distributed filesystem implementation is being used. The important thing to understand is that it is distributed, which provides a layer of protection against, say, a node within the cluster failing. sorted Unlike most map implementations, in Hbase/BigTable the key/value pairs are kept in strict alphabetical order. That is to say that the row for the key "aaaaa" should be right next to the row with key "aaaab" and very far from the row with key "zzzzz". Continuing our JSON example, the sorted version looks like this: { "1" : "x", "aaaaa" : "y", "aaaab" : "world", "xyz" : "hello", "zzzzz" : "woot" } Because these systems tend to be so huge and distributed, this sorting feature is actually very important. The spacial propinquity of rows with like keys ensures that when you must scan the table, the items of greatest interest to you are near each other. This is important when choosing a row key convention. For example, consider a table whose keys are domain names. It makes the most sense to list them in reverse notation (so "com.jimbojw.www" rather than "www.jimbojw.com") so that rows about a subdomain will be near the parent domain row. Continuing the domain example, the row for the domain "mail.jimbojw.com" would be right next to the row for "www.jimbojw.com" rather than say "mail.xyz.com" which would happen if the keys were regular domain notation. It's important to note that the term "sorted" when applied to Hbase/BigTable does not mean that "values" are sorted. There is no automatic indexing of anything other than the keys, just as it would be in a plain-old map implementation. multidimensional Up to this point, we haven't mentioned any concept of "columns", treating the "table" instead as a regular-old hash/map in concept. This is entirely intentional. The word "column" is another loaded word like "table" and "base" which carries the emotional baggage of years of RDBMS experience. Instead, I find it easier to think about this like a multidimensional map - a map of maps if you will. Adding one dimension to our running JSON example gives us this: { "1" : { "A" : "x", "B" : "z" }, "aaaaa" : { "A" : "y", "B" : "w" }, "aaaab" : { "A" : "world", "B" : "ocean" }, "xyz" : { "A" : "hello", "B" : "there" }, "zzzzz" : { "A" : "woot", "B" : "1337" } } In the above example, you'll notice now that each key points to a map with exactly two keys: "A" and "B". From here forward, we'll refer to the top-level key/map pair as a "row". Also, in BigTable/Hbase nomenclature, the "A" and "B" mappings would be called "Column Families". A table's column families are specified when the table is created, and are difficult or impossible to modify later. It can also be expensive to add new column families, so it's a good idea to specify all the ones you'll need up front. Fortunately, a column family may have any number of columns, denoted by a column "qualifier" or "label". Here's a subset of our JSON example again, this time with the column qualifier dimension built in: { // ... "aaaaa" : { "A" : { "foo" : "y", "bar" : "d" }, "B" : { "" : "w" } }, "aaaab" : { "A" : { "foo" : "world", "bar" : "domination" }, "B" : { "" : "ocean" } }, // ... } Notice that in the two rows shown, the "A" column family has two columns: "foo" and "bar", and the "B" column family has just one column whose qualifier is the empty string (""). When asking Hbase/BigTable for data, you must provide the full column name in the form ":". So for example, both rows in the above example have three columns: "A:foo", "A:bar" and "B:". Note that although the column families are static, the columns themselves are not. Consider this expanded row: { // ... "zzzzz" : { "A" : { "catch_phrase" : "woot", } } } In this case, the "zzzzz" row has exactly one column, "A:catch_phrase". Because each row may have any number of different columns, there's no built-in way to query for a list of all columns in all rows. To get that information, you'd have to do a full table scan. You can however query for a list of all column families since these are immutable (more-or-less). The final dimension represented in Hbase/BigTable is time. All data is versioned either using an integer timestamp (seconds since the epoch), or another integer of your choice. The client may specify the timestamp when inserting data. Consider this updated example utilizing arbitrary integral timestamps: { // ... "aaaaa" : { "A" : { "foo" : { 15 : "y", 4 : "m" }, "bar" : { 15 : "d", } }, "B" : { "" : { 6 : "w" 3 : "o" 1 : "w" } } }, // ... } Each column family may have its own rules regarding how many versions of a given cell to keep (a cell is identified by its rowkey/column pair) In most cases, applications will simply ask for a given cell's data, without specifying a timestamp. In that common case, Hbase/BigTable will return the most recent version (the one with the highest timestamp) since it stores these in reverse chronological order. If an application asks for a given row at a given timestamp, Hbase will return cell data where the timestamp is less than or equal to the one provided. Using our imaginary Hbase table, querying for the row/column of "aaaaa"/"A:foo" will return "y" while querying for the row/column/timestamp of "aaaaa"/"A:foo"/10 will return "m". Querying for a row/column/timestamp of "aaaaa"/"A:foo"/2 will return a null result. sparse The last keyword is sparse. As already mentioned, a given row can have any number of columns in each column family, or none at all. The other type of sparseness is row-based gaps, which merely means that there may be gaps between keys. This, of course, makes perfect sense if you've been thinking about Hbase/BigTable in the map-based terms of this article rather than perceived similar concepts in RDBMS's. And that's about it Well, I hope that helps you understand conceptually what the Hbase data model feels like. As always, I look forward to your thoughts, comments and suggestions.

JSR 310 aims to modernize the date and calendar classes. The goal is to provide a more advanced and comprehensive model for date and time than those found in the Date and Calendar APIs. The JSR's leaders, Stephen Colebourne and Michael Nascimento, are presenting their work at JavaOne and give an overview below. Firstly, please briefly introduce yourselves. Michael Nascimento. I'm a senior technical consultant at Summa technologies and the founder of the Genesis open source project. I have also served as an expert on a few JSRs, such as the Common Annotations for the Java Platform (JSR-250). Stephen Colebourne. I am employed building travel e-commerce booking engines and am involved with many open source projects, such as Apache Commons and JodaTime. I am involved in this JSR because of my JodaTime project. JodaTime? What's that? Stephen: JodaTime provides a complete replacement of the date and time classes in the JDK: public boolean isAfterPayDay(DateTime datetime) { if (datetime.getMonthOfYear() == 2) { // February is month 2!! return datetime.getDayOfMonth() > 26; } return datetime.getDayOfMonth() > 28; } public Days daysToNewYear(LocalDate fromDate) { LocalDate newYear = fromDate.plusYears(1).withDayOfYear(1); return Days.daysBetween(fromDate, newYear); } public boolean isRentalOverdue(DateTime datetimeRented) { Period rentalPeriod = new Period().withDays(2).withHours(12); return datetimeRented.plus(rentalPeriod).isBeforeNow(); } public String getBirthMonthText(LocalDate dateOfBirth) { return dateOfBirth.monthOfYear().getAsText(Locale.ENGLISH); } What are the main things that are wrong with the current date and time classes? Stephen: The existing classes are pretty bad—probably the worst APIs in the JDK. They're buggy, mutable, cumbersome, many bugs, and they tend not to be threadsafe. Michael: The original date class comes from JDK 1.0. At the time, James Gosling tried to follow the related functions in C and didn't put much force into designing them from scratch. For example, they can't be internationalized and only local timezones are supported. Stephen: Right. The Gregorian calendar class is a direct port of the C-class, such as "January = 0". So, if you enter the month "12", the month is January because the algorithm wraps around. The algorithm performs calculations such as this that you don't expect. For example, with the Gregorian calendar class, getYear(), getMonth(), and getDay() are quick, while if you call combinations of getyear(), setyear() (and getMonth() setMonth(), and so on), performance will be bad because lots of calculations are done unexpectedly. Politely put, one can describe these classes as exhibiting "unusual performance characteristics". Why has it taken so long to fix these various problems? Stephen: People have known of these problems for several years. Some attempts have been made to fix the Calendar class, but it only got worse. Fixing these issues once and for all has never been a high enough priority. So why now and why you? Stephen: I started JodaTime in 2000/2001 and gradually solved the standard date and time class problems, releasing it in 2003. My solution has been picked up across the board, from small applications to the largest advertizing systems in the world. The point is that I wanted the solution to exist a few years as JodaTime, before heading into a JSR so that all the issues would have been identified in preparation for the JSR. In a nutshell, what does JodaTime offer me? Michael: Firstly, a better quality API. Stephen: Secondly, JodaTime supports a number of additional concepts. Firstly, "periods", such as if you wanted to store the concept of 5 weeks and 3 days. Secondly, "intervals", so that you'll be able to store the interval between the start of JavaOne and its end, i.e., for example, from Monday May 5, 9 a.m. to May 9, 3 p.m. Thirdly, an updated timezone implementation to make it easy to pick up timezone changes, which could even be on an annual basis. Finally, handling of different calendar systems, such as Islamic calendar systems / Coptic calendar systems, and so on, which don't exist in the standard JDK. Michael: The third point is why I got interested in this JSR in the first place. I'm from Brazil where the daylight saving systems change each year and there's always one or two weeks of chaos. I asked myself why things go wrong every year around this issue. Stephen. Possibly we could offer a solution consisting of a JAR file with the latest set of rules, which you could then put on the classpath. However, sometimes you'd need both sets of rules at the same time. We're still thinking about these situations and ought to be able to come up with something. By the way, where does the name "Joda" come from? Stephen: "Joda" was a 4 letter domain name starting with "J" that was free in 2003. I simply typed random things beginning with "J" and found that that one was free... Where is the JSR process now? Michael: We are progressing it in an open manner. All discussions are on public mailing lists and Wikis. All repositories are open and Issuezilla is open. Stephen: We are using java.net to build a reference implementation and a testing kit in Subversion. People can go there and try it out. It is all "work in progress". The basic API is there. Right now, parsing needs to be finished and some loose ends need to be tidied up. Parsing, intervals, and multiple calendar systems are missing at the moment. Can you say something about the JSR's timeline? Stephen: We hope that we'll be in Java 7, but given that there's no date for it, there's no guarantee that we'll finish in time. We received a little bit of funding from the OpenJDK challenge to get to early draft review by August. Michael: It's really important that people get involved, the last chance to influence design aspects is the early draft review, scheduled for August, which is coming near. Two previous attempts have been made for rewriting these classes and it's unlikely there'll be another one after ours. So it is really important to let your voice be heard because the more feedback we get the better. Stephen: There's been good quality feedback. We've had suggestions consisting of sample implementations of intervals, people pointing to different ISO specifications, and suggestions to expand into areas outside our scope. People should take a look at the algorithms too. Maybe someone could come up with better algorithms than those that we already have. Michael: We've also been nominated for a JCP Program Award, probably because we're the main examples of individuals, rather than a company, leading a JSR. The results will be announced on Tuesday during JavaOne. Will you present something around your JSR at JavaOne? Michael: Our technical session on Thursday at 1.30 is completely full and there's a repeat session on Friday at the same time, that is, at 1.30. In the session, we will cover all the basic classes, show examples of the code and how to get started with it. Stephen: There'll be little bit of explanation around the design principles, with examples of how bad the current date and time classes are. There'll also be a small puzzler, asking participants to identify the number of bugs in an existing bit of JDK code... Further Reading JSR 310 JSR 310 Technical Sessions at JavaOne JSR 310 General Purpose Area for Anyone to Leave Messages JSR 310 Mailing List Stephen Colebourne's blog Michael Nascimento's blog JCP Program's Award Nominations

Today marks the availability of the first binary release of the open source JFugue Music NotePad. The core basic functionality of the tool is available and ready to be tried out. After downloading and unzipping the archive, you need to specify the location of the JDK (JDK 5 or above, so Mac users are welcome too), in etc/mnotepad.conf, which is in the unzipped archive's main directory. The binary can be downloaded here: mnotepad_9April2008.zip Once you have done so, you can launch it, which should result in the following main window at start up: Here it is on the Mac: Then choose File | New, to define a new composition: Click Finish and you can begin composing music. To do so, choose notes from the toolbar and point/click to add them to the composition. Currently you can't add notes between existing notes. However, select one/more notes with your mouse, the notes turn red to show they are selected, and then use up/down to move notes up the scale and left/right to decrease/increase their length. While doing this part of the work, compositions typically look something like this: You can change instruments by right-clicking one in the Instruments window and choosing "Select". Finally, click Play to play the music or Save to save it. Compositions are saved to disk in midi format, the status bar shows where they are saved to, in the installation directory. Architecture. Instead of dealing directly with the Midi API, the JFugue API is used instead. The JFugue API provides an extremely transparent, simple, yet surprisngly powerful layer of functionality on top of the complexities of typical Midi programming. The user interface is pure Swing on top of the NetBeans Platform, therefore the application has a very mature window system (max/minimize, dock/undock) and is pluggable out of the box, among other features. User Comments. One of the user comments thus far: "It's definitely very cool! And as promised, you already got a whole bunch of subtle features for free from the NetBeans Platform (windowing, favorites, etc). :-D It already looks more stable and trustworthy than any other app with the same amount of work invested, just because of the solid and consistent windowing system." Known Issues. Amongst others, the following: several usability issues, such as that it isn't easy to know/see from the ui that notes can be changed by selecting them and moving up/down/left/right. Should be able to specify where saved midi file should be saved to. Some users report not being able to change instruments. Keyboard window currently unused. Playing of tune blocks the ui. Feedback Welcome. The JFugue Music NotePad is an open source project at https://nbjfuguesupport.dev.java.net/. You are welcome to join, especially if you are able to offer time/insight to add missing features. Especially programmers who are also musicians are welcome. For example, this application could do with a metronome, which could be provided by a separate plugin...

John Wilson is mainly known to the Groovy community because of his work on XmlSlurper, one of the easiest ways to work with XML in the JVM. Continue reading to learn what inspired John to get into Groovy. Enjoy! [img_assist|nid=2119|title=|desc=|link=none|align=right|width=220|height=188]Q. John, you are the creator of XmlSlurper, what motivated you to make it ? A. I had a problem with processing very large XML documents. XmlParser uses a simple and robust way of implementing GPath expressions which involves building an array to hold the result of each term on the expression. Unfortunately this means that you can consume large amounts of memory if the original document is large. I was getting out of memory errors quite a bit so I wrote the original version of XmlSlurper to use Iterarors rather than arrays which cut down the memory footprint quite a bit. It had the happy side effect of being faster too. I have rewritten XmlSlurper a couple of times since then and it now plays very well with StreamingMarkupBuilder, handles namespaces nicely and has an interesting way of doing edits on the fly as the slurped document is written out. Q. XmlSluper and XmlParser are so similar, is there a reason to have both ? A. On the one hand it's a disadvantage because users are unsure which one to use (The answer is - for most things it doesn't matter). However they have differences which are significant and, in my view, valuable. The most significant difference is their approach to editing the document. XmlParser is very straightforward, you just change the in memory tree structure which represents the document. XmlSlurper does not let you have direct access to the in memory data structure. It forces you to put you editing code in closures and specify where in the document the closures should be applied. It then does the editing on the fly as the document is written out. The first mechanism is simple but limited the second is more complicated but very powerful. Most of us only want to do relatively simple operations on small XML documents and XmlParser is excellent for that. For those people who want to do rather complex operations on XMl documents which can be quite large then XmlSlurper is a better choice. Fortunately they support an almost identical GPath syntax so switching from one to the other is no big deal. This was not always so - the community owes a big debt of gratitude to Paul King for doing a large amount of work on documenting these implementations and aligning them. My long term aim is to be able to do away altogether with the need to hold the whole document in memory but to stream the document through memory whist executing the GPath expressions. Q. You are also the creator of the xmlrpc module, what can you tell us about it ? A. It's a module I'm very fond of. It's an excellent example of how Groovy can make something that is quite complex in Java completely trivial. I can build an XML-RPC server in 4 lines of Groovy and a client in 1 line. Performance is excellent and it interoperates well. It is based on code I wrote a few years ago to implement XML-RPC on the Dallas Semiconductor TINI. The TINI is an amazing device the size of a memory SIMM which runs Java on a 8051 (the processor which controlled the keyboard in the original IBM PC) with 1Mb of battery backed up RAM and an Ethernet port. One of my favourite TINI apps was a weather forecasting toaster! [it's true! check it out for yourselves here] Q. Have you participated in other Open Source projects ? A. Yes, mostly in the embedded Java arena. I wrote tiny XML parsers (MinML and MinML2) and a tiny XML-RPC server (MinMl-RPC) i have also contributed to VNC and the Snort intrusion detections system. In the background I'm working on Ng which is an attempt to build a runtime system for dynamic languages on the JVM which is both simple and fast. Q. Ng, can you share more about it? A. Ng is a solo (at the moment) project which tries to answer the question: How can we implement a fully dynamic language on the current JVM which runs no more than ten times slower than Java? This is looking for an improvement of one to two orders of magnitude over current implementations (Groovy, JRuby, Jython, etc.). The idea is to design a programming language "backwards". I start with a highly optimised runtime system and then derive a language which can be optimally compiled for that runtime system. I'm hoping that some of the insights I get whist doing this can be fed back into the Groovy 2.0 MOP redesign. I'm making good but slow progress. I have arithmetic operations executing at less than twice as slow as Java in some benchmarks and method calls are coming below ten times as slow. I have started to document some of the techniques I have developed http://docs.google.com/View?docid=ah76zbd6xsx2_9ck33c8dp Q. How did you get involved with Groovy ? A. I was looking for an Open Source project to get involved in. I have a long term interest in programming languages (my first paid job was as a compiler writer in 1971). I looked at Ruby and JRuby but it was too Perlish for my tastes and the JRuby project looked moribund. Google found me Groovy and I liked the feel of the language and the community was very lively so I stuck around. Q. Do you use Groovy at work ? A. Yes. If I have to mung XML I will always do it in Groovy. I also spend quite a bit of time building DSLs in Groovy. I think the return on investment in DSLs is huge if they are done properly. Q. Do you have a preferred technique for building DSLs (builders, metaprogramming, ... ) ? A. I like builders a lot. I think that the Builder concept is one of James Strachan's best ideas. I built a little DSL to allow people to specify arbitrary graphs - it took about an hour to develop and it's saved days in allowing us to specify complex graphs simply, clearly and reliably. I tend to override invokeMethod, etc. or use Categories rather than ExpandoMetaClass to do MetaPrograming magic. That's probably because to got into the habit before Graeme wrote ExpandoMetaClass. However I do like the fact that Categories allow me to limit the extent of the change to a single thread - they need to have less impact on performance, though. Q. Is there a specific feature you would like to see in a future version of Groovy ? A. I think Inner Classes need to be added. Other than that I don't see much urgent need for language extensions. Quite a lot of work has been done on making the run time system cleaner and that work needs to continue. The speed of the implementation has been improved in the last few months but there is more work needed there (especially with Categories). the big thing I'd like to see is the ability to not compile to class files but to execute the AST (Abstract Syntax Tree) directly. JRuby does this and it can be very useful in cases where you are generating code dynamically and executing it once or twice before discarding it (which is quite a common use). It would also help with the Groovy console. Thanks John! John's bio John Wilson has been a programmer, project manager, teacher, CTO and CEO. He's now CTO of an English engineering company and is enjoying working with a great crowd in the Groovy/Grails community.

SVNKitis an Open Source pure Java Subversion library. SVNKit literally brings Subversion, popular open source version control system, to the Java world. With SVNKit you can do the following: All standard Subversion operations: For instance, the following snipped checks out project from repository: File dstPath = new File("c:/svnkit"); SVNURL url = SVNURL. parseURIEncoded("http://svn.svnkit.com/repos/svnkit/branches/1.1.x/"); SVNClientManager cm = SVNClientManager.newInstance(); SVNUpdateClient uc = cm.getUpdateClient(); uc.doCheckout(url, dstPath, SVNRevision.UNDEFINED, SVNRevision.HEAD, true); Updates it to the latest revision: uc.doUpdate(dstPath, SVNRevision.HEAD, true); And finally commits local changes in "www" subdirectory if there are any: SVNCommitClient cc = cm.getCommitClient(); cc.doCommit(new File[] {new File(dstPath, "www")}, false, "message", false, true); SVNKit supports all standard Subversion operations and compatible with the latest version of Subversion. Access Subversion repository directly: Some applications will benefit from working with repository directly, without keeping working copy locally. Example below displays list of files in "www" directory. SVNURL url = SVNURL.parseURIEncoded("http://svn.svnkit.com/repos/svnkit/branches/1.1.x/"); SVNRepository repos = SVNRepositoryFactory.create(url); long headRevision = repos.getLatestRevision(); Collection entriesList = repos.getDir("www", headRevision, null, (Collection) null); for (Iterator entries = entriesList.iterator(); entries.hasNext();) { SVNDirEntry entry = (SVNDirEntry) entries.next(); System.out.println("entry: " + entry.getName()); System.out.println("last modified at revision: " + entry.getDate() + " by " + entry.getAuthor()); } Direct repository access API allows to perform operations like update, commit, diff and many other. Additionaly to the performance benefits of the direct access to repository, this API makes it possible to version arbitrary objects or object models within Subevrsion repository, not only files from the file system. Replace JNI Subversion bindings with SVNKit: Native Subversion provides Java interface that works with Subversion binaries through JNI. In case you already using it or would like to use as an option, you may also use SVNKit through exactly the same interface. This way you'll let your application dynamically switch between JNI and SVNKit implementation of the same API or let your application work on the platforms where there are no native Subversion binaries. For example: // pure Java implementation of the standard Subversion Java interface SVNClientInterface jniAPI = SVNClientImpl.newInstance(); byte[] contents = jniAPI.fileContent("http://svn.svnkit.com/repos/svnkit/branches/1.1.x/changelog.txt", Revision.HEAD); SVNKit is widely used in different applications, including IntelliJ IDEA, Eclipse Subversion integrations, SmartSVN, JDeveloper, bug tracking server side applications (e.g. Atlassian JIRA) and repository management and tracking tools (e.g. Atlassian FishEye) and many others. Where to get more information: Recently we've released SVNKit version 1.1.6 which is bugfix release. At http://svnkit.com/ you will find more information on that new version and, of course, downloads, documentation, source code example and articles explaining how to use SVNKit. In case of any questions you're welcome at our mailing list, or just contact us at [email protected] SVNKit is widely used in different applications, including IntelliJ IDEA, Eclipse Subversion integrations, SmartSVN, JDeveloper, bug tracking server side applications (e.g. Atlassian JIRA) and repository management and tracking tools (e.g. Atlassian FishEye) and many others. With best regards, TMate Software, http://svnkit.com/ - Java [Sub]Versioning Library!

Trying to troubleshoot Java? VisualVM is a great, free, open source tool.