Oracle Linux builds have a list of public yum repositories on the Oracle website, and they don't come configured with the builds, so if you're trying to install Puppet, you'll want to avoid this pitfall, along with a few others. We’ve been spending some time trying to setup our developer environment on a Oracle Linux 5.7 build and one of the first steps was to install Puppet as we’ve already created scripts which automate the installation of most things. Unfortunately Oracle Linux builds don’t come with any yum repos configured so when you run the following command… ls -alh /etc/yum.repos.d/ …you don’t see anything We eventually realised that there are a list of public yum repositories on the Oracle website, of which we needed to download the definition for Oracle Linux 5 like so: cd /etc/yum.repos.d wget http://public-yum.oracle.com/public-yum-el5.repo We then need to edit that file to enable the appropriate repository. In this case we want to enable ol5_u7_base: [ol5_u7_base] name=Oracle Linux $releasever - U7 - $basearch - base baseurl=http://public-yum.oracle.com/repo/OracleLinux/OL5/7/base/$basearch/ gpgkey=http://public-yum.oracle.com/RPM-GPG-KEY-oracle-el5 gpgcheck=1 enabled=1 I made the mistake of enabling ol5_u5_base which led to us getting some really weird problems whereby yum got confused as to which version of libselinux we had installed and was therefore unable to install libselinux-ruby as its dependencies weren’t being properly satisfied. Calling ‘yum list installed’ suggested that we had libselinux 1.33.4.5-7 installed but if we ran ‘yum install libselinux’ then it suggested we already had 1.33.4.5-5 installed. Very confusing! After trying to uninstall and downgrade libselinux and pretty much destroying the installation in the process, another colleague spotted my mistake. We also found that we had to add the epel repo which gave us access to some other packages that we needed: rpm -Uvh http://download.fedora.redhat.com/pub/epel/5/x86_64/epel-release-5-4.noarch.rpm After all that was done we were able to run the command to install puppet: yum install puppet That installs puppet 2.6.12 as that’s the latest version in that repo. The latest stable version is 2.7.9 but I think we’ll need to hook up a puppet specific repo to get that working. Source: http://www.markhneedham.com/blog/2012/01/18/installing-puppet-on-oracle-linux

VNC is just a special case of client-server, though perhaps an especially cool one. Quite a few rising web technologies do robust client-server work extra well (Node.js, WebSockets, etc.) -- and in-browser VNC is nothing new. Here are two (open-source, of course): noVNC is more ambitiously HTML5-duplexed, using WebSockets as well as Canvas. It's quite popular, and has its own 10-page Github wiki. Also supports wss:// encryption. Use this if you want a reliable, battle-tested HTML5 client. (WebSocket fallback is provided by web-socket-js.) vnc.js was written in 24 hours, during LinkedIn's first public Intern Hackday. So of course it hasn't been tested thoroughly, and probably could be written a little more cleanly. But there's something beautifully coherent about an app written in a single session. If the app really does work, then some of the decisions will make a little more sense -- it's possible to get into the developer's mind a little more easily -- and breaking down the code doesn't result in as many 'why did they do this??' moments, because the developers' minds were never far from any part of the project, at any moment during development. vnc.js doesn't use WebSockets (it uses Socket.io instead), but that's fine -- a little less HTML5, a little more slick JavaScript doesn't hurt anyone. Plus the marathoning hackers behind vnc.js put together a sweet little tutorial detailing the decisions made that 24-hour period, emphasizing the rapid thought-process behind the architecture (in clear diagrams), and a very practical abstraction for easier in-browser work with TCP (using Node.js and Socket.io) and RFB. Both packages are worth checking out; the hacking tutorial is a fun read for any web developer interested in coding a VNC client, or even just sophisticated with with different network protocols in the browser.

Neo4j is a high-performance, NOSQL graph database with all the features of a mature and robust database. In this post I will explain how to deploy a neo4j instance in Amazon EC2 web service. For this tutorial to take you no more than 10 minutes you should be able to execute properly some bash commands like mv, tar, ssh and scp (secure copy). I also assume that you have an account in Amazon Web Services and you are familiar to the process of launching instances. If not, I strongly recommend you to follow this starting guide and complete it till you manage to connect to your instance with ssh. Start downloading the latest stable version of neo4j. Which you can find here. The “Community Edition” fits well for development purposes. Do not forget to select the Unix version of the server. This will download a tar.gz file which you will copy to your EC2 instance later. While you download the neo4j server open the AWS Management Console and launch a Basic 32-bit Amazon Linux AMI. If you want to launch an Ubuntu AMI please notice that it doesn’t ship with Java, which is required for running neo4j. If you are not familiar with key pairs, pem files or security groups I insist you to follow the EC2 starting guide I mentioned above. You can either create a new security group or use the default, but you will need to configure a new security rule for the neo4j server port. After launching the instance, create a TCP rule on port 7474 with source 0.0.0.0/0. Here you are opening port 7474 for anyone. If you are planning to use the neo4j REST API and remotely call it from another server, for example a Rails application hosted in Heroku, for security reasons, you may want to change the source field to the address of your Heroku server. Do not forget to open port 22 (SSH), this is typically the first rule normal people create after launching an instance. You are almost done! You should now install neo4j in your instance. Open a terminal in your localhost and navigate to the path where you downloaded neo4j. Copy the file to your Amazon instance by using the scp command: scp -i your_pem_file.pem neo4j-community-1.6.M01-unix.tar.gz ec2-user@YOUR_PUBLIC_INSTANCE_DNS:/home/ec2-user Please notice that you will need to change the path to your pem file, typically placed in ~/.ssh, the filename of the neo4j server you just downloaded and the plublic DNS of your instance. Now connect to your instance with SSH: ssh -i your_pem_file.pem ec2-user@YOUR_PUBLIC_INSTANCE_DNS Untar the neo4j server: tar xvfz neo4j-community-1.6.M01-unix.tar.gz.tar.gz Move it to /usr/local and rename the folder to neo4j: sudo mv neo4j-community-1.6.M01 /usr/local/neo4j Almost done!!! You should now open neo4j-server.properties under the conf directory and add the following line: org.neo4j.server.webserver.address=0.0.0.0 This lines allows anyone to connect remotely to your neo4j database server. Now run the start script. From the neo4j server folder. sudo ./bin/neo4j start Finally, open a browser and access the webadmin interface of your neo4j database by typing http://YOUR_PUBLIC_INSTANCE_DNS:7474. You should see the Neo4j Monitoring and Management Tool, pretty cool! If not, ask me You can now try using the REST API and the curl bash command to insert nodes and relationships. I hope this post helped you, good luck! Follow me on Twitter @negarnil Source: http://www.cloudtmp.com/java/how-to-deploy-a-neo4j-instance-in-amazon-ec2-in-10-minutes/

Recently I ran across Rawkets, a slick site taking two emerging web technologies -- HTML5 Canvas and WebSockets -- and combining them in the most obvious way possible: a multiplayer space shooter. Why Canvas? No plugins -- graphical Yes; and why WebSockets? Low latency -- multiplayer Yes. Sadly, every time I join the game, nobody else is there. If I wanted single-player HTML5 gaming, I could check out another project by Rawkets' creator, Rob Hawkes: straight-up Asteroids, using the HTML5 game engine Impact. But WebSockets won't help Asteroids, because Asteroids runs totally on just one client. Rawkets, on the other hand, has multiple clients running Canvas, with their own JavaScript, connecting via WebSockets, all taking through Node.js on the server, producing something like this: I can't tell whether the game is any fun, because I've never seen anyone else in there. (Also, it doesn't seem to work in Chrome). But as a tech demo it's a cool idea, and conceptually straightforward enough to inspire. (If you're impressed, Rob also links from the game site to to his HTML5 Canvas book -- though apparently the book assumes virtually no knowledge of Canvas or JavaScript, and doesn't progress all that far.) Check it out, and maybe shoot someone else's ship down -- fairly fairly, of course, because WebSockets will keep multiplex channels persistently open...

this post presents an analysis of mysql (a relational database) and neo4j (a graph database) in a side-by-side comparison on a simple graph traversal. the data set that was used was an artificially generated graph with natural statistics. the graph has 1 million vertices and 4 million edges. the degree distribution of this graph on a log-log plot is provided below. a visualization of a 1,000 vertex subset of the graph is diagrammed above. loading the graph the graph data set was loaded both into mysql and neo4j. in mysql a single table was used with the following schema. create table graph ( outv int not null, inv int not null ); create index outv_index using btree on graph (outv); create index inv_index using btree on graph (inv); after loading the data, the table appears as below. the first line reads: “vertex 0 is connected to vertex 1.” mysql> select * from graph limit 10; +------+-----+ | outv | inv | +------+-----+ | 0 | 1 | | 0 | 2 | | 0 | 6 | | 0 | 7 | | 0 | 8 | | 0 | 9 | | 0 | 10 | | 0 | 12 | | 0 | 19 | | 0 | 25 | +------+-----+ 10 rows in set (0.04 sec) the 1 million vertex graph data set was also loaded into neo4j. in gremlin , the graph edges appear as below. the first line reads: “vertex 0 is connected to vertex 992915.” gremlin> g.e[1..10] ==>e[183][0-related->992915] ==>e[182][0-related->952836] ==>e[181][0-related->910150] ==>e[180][0-related->897901] ==>e[179][0-related->871349] ==>e[178][0-related->857804] ==>e[177][0-related->798969] ==>e[176][0-related->773168] ==>e[175][0-related->725516] ==>e[174][0-related->700292] warming up the caches before traversing the graph data structure in both mysql and neo4j, each database had a “ warm up ” procedure run on it. in mysql, a “select * from graph” was evaluated and all of the results were iterated through. in neo4j, every vertex in the graph was iterated through and the outgoing edges of each vertex were retrieved. finally, for both mysql and neo4j, the experiment discussed next was run twice in a row and the results of the second run were evaluated. traversing the graph the traversal that was evaluated on each database started from some root vertex and emanated n-steps out. there was no sorting, no distinct-ing, etc. the only two variables for the experiments are the length of the traversal and the root vertex to start the traversal from. in mysql, the following 5 queries denote traversals of length 1 through 5. note that the “?” is a variable parameter of the query that denotes the root vertex. select a.inv from graph as a where a.outv=? select b.inv from graph as a, graph as b where a.inv=b.outv and a.outv=? select c.inv from graph as a, graph as b, graph as c where a.inv=b.outv and b.inv=c.outv and a.outv=? select d.inv from graph as a, graph as b, graph as c, graph as d where a.inv=b.outv and b.inv=c.outv and c.inv=d.outv and a.outv=? select e.inv from graph as a, graph as b, graph as c, graph as d, graph as e where a.inv=b.outv and b.inv=c.outv and c.inv=d.outv and d.inv=e.outv and a.outv=? for neo4j, the blueprints pipes framework was used. a pipe of length n was constructed using the following static method. public static pipeline createpipeline(final integer steps) { final arraylist pipes = new arraylist(); for (int i = 0; i < steps; i++) { pipe pipe1 = new vertexedgepipe(vertexedgepipe.step.out_edges); pipe pipe2 = new edgevertexpipe(edgevertexpipe.step.in_vertex); pipes.add(pipe1); pipes.add(pipe2); } return new pipeline(pipes); } for both mysql and neo4j, the results of the query (sql and pipes) were iterated through. thus, all results were retrieved for each query. in mysql, this was done as follows. while (resultset.next()) { resultset.getint(finalcolumn); } in neo4j, this is done as follows. while (pipeline.hasnext()) { pipeline.next(); } experimental results the artificial graph dataset was constructed with a “ rich get richer “, preferential attachment model . thus, the vertices created earlier are the most dense (i.e. highest number of adjacent vertices). this property was used to limit the amount of time it would take to evaluate the tests for each traversal. only the first 250 vertices were used as roots of the traversals. before presenting timing results, note that all of these experiments were run on a macbook pro with a 2.66ghz intel core 2 duo and 4gigs of ram at 1067 mhz ddr3. the packages used were java 1.6, mysql jdbc 5.0.8, and blueprints pipes 0.1.2. java version "1.6.0_17" java(tm) se runtime environment (build 1.6.0_17-b04-248-10m3025) java hotspot(tm) 64-bit server vm (build 14.3-b01-101, mixed mode) the following java virtual machine parameters were used: -xmx1000m -xms500m below are the total running times for both mysql (red) and neo4j (blue) for traversals of length 1, 2, 3, and 4. the raw data is presented below along with the total number of vertices returned by each traversal—which, of course, is the same for both mysql and neo4j given that its the same graph data set being processed. also realize that traversals can loop and thus, many of the same vertices are returned multiple times. finally, note that only neo4j has the running time for a traversal of length 5. mysql did not finish after waiting 2 hours to complete. in comparison, neo4j took 14.37 minutes to complete a 5 step traversal. [mysql steps-1] time(ms):124 -- vertices_returned:11360 [mysql steps-2] time(ms):922 -- vertices_returned:162640 [mysql steps-3] time(ms):8851 -- vertices_returned:2206437 [mysql steps-4] time(ms):112930 -- vertices_returned:28125623 [mysql steps-5] n/a [neo4j steps-1] time(ms):27 -- vertices_returned:11360 [neo4j steps-2] time(ms):474 -- vertices_returned:162640 [neo4j steps-3] time(ms):3366 -- vertices_returned:2206437 [neo4j steps-4] time(ms):49312 -- vertices_returned:28125623 [neo4j steps-5] time(ms):862399 -- vertices_returned:358765631 next, the individual data points for both mysql and neo4j are presented in the plot below. each point denotes how long it took to return n number of vertices for the varying traversal lengths. finally, the data below provides the number of vertices returned per millisecond (on average) for each of the traversals. again, mysql did not finish in its 2 hour limit for a traversal of length 5. [mysql steps-1] vertices/ms:91.6128847554668 [mysql steps-2] vertices/ms:176.399127537985 [mysql steps-3] vertices/ms:249.286746556076 [mysql steps-4] vertices/ms:249.053599519823 [mysql steps-5] n/a [neo4j steps-1] vertices/ms:420.740351166341 [neo4j steps-2] vertices/ms:343.122344772028 [neo4j steps-3] vertices/ms:655.507125256186 [neo4j steps-4] vertices/ms:570.360621871775 [neo4j steps-5] vertices/ms:416.00886711325 conclusion in conclusion, given a traversal of an artificial graph with natural statistics, the graph database neo4j is more optimal than the relational database mysql. however, no attempts have been made to optimize the java vm, the sql queries, etc. these experiments were run with both neo4j and mysql “out of the box” and with a “natural syntax” for both types of queries. source: http://markorodriguez.com/2011/02/18/mysql-vs-neo4j-on-a-large-scale-graph-traversal/

Lynden is a family of transportation and logistics companies specialized in shipping to Alaska and other locations worldwide. Over land, on the water, in the air - or in any combination - Lynden has been helping customers solve transportation problems for over a century. The Lynden Freight Management System is a NetBeans Platform application which serves a dual purpose as both a planning and freight tracking tool. The Planning module allows terminal managers to see all freight that is currently inbound to their location as well as freight that is scheduled to depart from their location so they can make the most efficient use of their dock space and resources as possible. The Trace module allows customer service personnel to search for customer account information, view the tracking history of any given freight item in the system as well as display any documents related to the shipment, such as bills of lading or delivery receipts. NetBeans Platform Lynden has benefited from the NetBeans Platform as it allows developers to focus on the business logic of our applications rather than the underlying "plumbing". We are able to leverage built-in support for event handling, enable/disable functionality on UI controls, dockable windows, and automatic updates for our application with minimal work compared to rolling our own framework. We chose to go the desktop application route as we have a number of existing desktop applications here that this application will likely need to interface with at some point, as well as a commercial set of rich UI components that we have been using for some time now. For the initial deployment, we will be pushing the installer out to employee PCs via the Landesk remote desktop administration tool. Future updates to various modules within the application will be done via the update center functionality built into the NetBeans Platform. Screenshots

My previous blog post discussed the use of Neo4J as a RDF triple store. Michael Hunger however informed me that the neo-rdf-sail component is no longer under active development and advised me to have a look at Tinkerpop’s Sail implementation. As mentioned in my previous blog post, I recently got asked to implement a storage and querying platform for biological RDF (Resource Description Framework) data. Traditional RDF stores are not really an option as my solution should also provide the ability to calculate shortest paths between random subjects. Calculating shortest path is however one of the strong selling points of Graph Databases and more specifically Neo4J. Unfortunately, the neo-rdf-sail component, which suits my requirements perfectly, is no longer under active development. Tinkerpop’s Sail implementation however, fills the void with an even better alternative! 1. What is Tinkerpop? Tinkerpop is an open source project that provides an entire stack of technologies within the Graph Database space. At the core of this stack is the Blueprints framework. Blueprints can be considered as the JDBC of Graph Databases. By providing a collection of generic interfaces, it allows to develop graph-based applications, without introducing explicit dependencies on concrete Graph Database implementations. Additionally, Blueprints provides concrete bindings for the Neo4J, OrientDB and Dex Graph Databases. On top of Blueprints, the Tinkerpop team developed an entire range of graph technologies, including Gremlin, a powerful, domain-specific language designed for traversing graphs. Hence, once a Blueprints binding is available for a particular Graph Database, an entire range of technologies can be leveraged. 2. Tinkerpop and Sail Last time, I talked about exposing a Neo4J Graph Database (containing RDF triples) through the Sail interface, which is part of the openrdf.org project. By doing so, we can reuse an entire range of RDF utilities (parsers and query evaluators) that are part of the openrdf.org project. The Blueprints framework provides us with a similar ability: each Graph Database binding that implements the Tinkerpop TransactionalGraph and IndexableGraph interfaces can be exposed as a GraphSail, which is Tinkerpop’s implementation of the Sail interface. Once you have your Sail available, storing and querying RDF is analogous to the piece of code shown in my previous blog article. // Create the sail graph database graph = new MyNeo4jGraph("var/flights", 100000); graph.setTransactionMode(TransactionalGraph.Mode.MANUAL); sail = new GraphSail(graph); // Initialize the sail store sail.initialize(); // Get the sail repository connection connection = new SailRepository(sail).getConnection(); // Import the data connection.add(getResource("sneeair.rdf"), null, RDFFormat.RDFXML); // Execute SPARQL query TupleQuery durationquery = connection.prepareTupleQuery(QueryLanguage.SPARQL, "PREFIX io: " + "PREFIX fl: " + "SELECT ?number ?departure ?destination " + "WHERE { " + "?flight io:flight ?number . " + "?flight fl:flightFromCityName ?departure . " + "?flight fl:flightToCityName ?destination . " + "?flight io:duration \"1:35\" . " + "}"); TupleQueryResult result = durationquery.evaluate(); The two first lines of code require some more clarification. A TransactionalGraph can be run in MANUAL or AUTOMATIC transaction mode. In AUTOMATIC mode, transactions are basically ignored, in the sense that each item that gets created is immediately persisted in the underlying Graph Database. Although this fits my needs, AUTOMATIC mode is extremely slow in case of Neo4J because of the continuous IO access. MANUAL mode on the other hand is very fast; a new transaction is created at the moment the import of the RDF data file starts and is only committed to the Neo4J data store once all RDF triples are parsed and created. Unfortunately, MANUAL mode does not scale either in my specific situation; as some of my RDF data files contain over 50 million RDF triples, they can not fit into memory (i.e. Java heap space error). Requiring fast imports, I extended the default Neo4J Blueprints binding to support intermediate commits. I based my implementation on Neo4J’s best practices for big transactions. The idea is rather simple: you specify the maximum number of items that can be kept in memory, before they should be committed to the Neo4J data store. Once this number is reached, the current transaction is committed and a new one is automatically started. Simple, but very effective! public class MyNeo4jGraph extends Neo4jGraph { private long numberOfItems = 0; private long maxNumberOfItems = 1; public MyNeo4jGraph(final String directory, long maxNumberOfItems) { super(directory, null); this.maxNumberOfItems = maxNumberOfItems; } public MyNeo4jGraph(final String directory, final Map configuration, long maxNumberOfItems) { super(directory, configuration); this.maxNumberOfItems = maxNumberOfItems; } public Vertex addVertex(final Object id) { Vertex vertex = super.addVertex(id); commitIfRequired(); return vertex; } public Edge addEdge(final Object id, final Vertex outVertex, final Vertex inVertex, final String label) { Edge edge = super.addEdge(id, outVertex, inVertex, label); commitIfRequired(); return edge; } private void commitIfRequired() { // Check whether commit should be executed if (++numberOfItems % maxNumberOfItems == 0) { // Stop the transaction stopTransaction(Conclusion.SUCCESS); // Immediately start a new one startTransaction(); } } } 3. Shortest path calculation Although Blueprints allows you to abstract away the Neo4J implementation details, it still provides you with access to the raw Neo4J data store if needed. Hence, one can still use the graph algorithms provided in the neo4j-graph-algo component to calculate shortest paths between random subjects. The complete source code can be found on the Datablend public GitHub repository.

Version 2.3 of the Java servlet spec introduced the concept of filters. According to the documentation from Oracle’s site: “A filter dynamically intercepts requests and responses to transform or use the information contained in the requests or responses”. Today I’ll show you how to build a simple filter to intercept the response HTTP response code using annotations introduced in the Servlet 3.0 specification. With NetBeans IDE 7 create a new Maven Java Web Application called: Intercept Delete the index.jsp file under the Web Pages folder. Right-click on the project and add a new servlet called: MainServlet Since we are using the new Servlet 3 annotations we don’t need to set a whole lot of properties. Maven generates a decent MainServlet.java file for us, I just removed the comments for the output. My file looks like this: package com.giantflyingsaucer.intercept; import java.io.IOException; import java.io.PrintWriter; import javax.servlet.ServletException; import javax.servlet.annotation.WebServlet; import javax.servlet.http.HttpServlet; import javax.servlet.http.HttpServletRequest; import javax.servlet.http.HttpServletResponse; @WebServlet(name = "MainServlet", urlPatterns = {"/"}) public class MainServlet extends HttpServlet { protected void processRequest(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { response.setContentType("text/html;charset=UTF-8"); PrintWriter out = response.getWriter(); try { out.println(""); out.println(""); out.println(""); out.println(""); out.println(""); out.println("Servlet MainServlet"); out.println(""); out.println(""); } finally { out.close(); } } // /** * Handles the HTTP GET method. * @param request servlet request * @param response servlet response * @throws ServletException if a servlet-specific error occurs * @throws IOException if an I/O error occurs */ @Override protected void doGet(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { processRequest(request, response); } /** * Handles the HTTP POST method. * @param request servlet request * @param response servlet response * @throws ServletException if a servlet-specific error occurs * @throws IOException if an I/O error occurs */ @Override protected void doPost(HttpServletRequest request, HttpServletResponse response) throws ServletException, IOException { processRequest(request, response); } /** * Returns a short description of the servlet. * @return a String containing servlet description */ @Override public String getServletInfo() { return "Short description"; }// } Right-click on the project and add a Filter called: InterceptFilter We will add the following two lines to the doFilter method. HttpServletResponse hsr = (HttpServletResponse) response; System.out.println("HTTP Status: " + hsr.getStatus()); My doFilter method looks like this: @Override public void doFilter(ServletRequest request, ServletResponse response, FilterChain chain) throws IOException, ServletException { if (debug) { log("InterceptFilter:doFilter()"); } doBeforeProcessing(request, response); HttpServletResponse hsr = (HttpServletResponse) response; System.out.println("HTTP Status: " + hsr.getStatus()); Throwable problem = null; try { chain.doFilter(request, response); } catch (Throwable t) { problem = t; t.printStackTrace(); } doAfterProcessing(request, response); if (problem != null) { if (problem instanceof ServletException) { throw (ServletException) problem; } if (problem instanceof IOException) { throw (IOException) problem; } sendProcessingError(problem, response); } } Clean and Build the project and deploy it to Apache Tomcat. Access the URL with a browser and take a look at your catalina.out file and you should see the HTTP response code. Note: You shouldn’t need to do any changes to the web.xml file for this project to work. From http://www.giantflyingsaucer.com/blog/?p=3279

One of the challenges in building a distributed web application is in handling sessions. When you have multiple instances of an application running and session data is written to local files (as is the default behavior for the session handling functions in PHP) a user session can be lost when a session is started on one instance but subsequent requests are directed (via a load balancer) to other instances. To successfully manage sessions across multiple instances, you need a common data store. In this post I’ll show you how the Windows Azure SDK for PHP makes this easy by storing session data in Windows Azure Table storage. In the 4.0 release of the Windows Azure SDK for PHP, session handling via Windows Azure Table and Blob storage was included in the newly added SessionHandler class. Note: The SessionHandler class supports storing session data in Table storage or Blob storage. I will focus on using Table storage in this post largely because I haven’t been able to come up with a scenario in which using Blob storage would be better (or even necessary). If you have ideas about how/why Blob storage would be better, I’d love to hear them. The SessionHandler class makes it possible to write code for handling sessions in the same way you always have, but the session data is stored on a Windows Azure Table instead of local files. To accomplish this, precede your usual session handling code with these lines: require_once 'Microsoft/WindowsAzure/Storage/Table.php'; require_once 'Microsoft/WindowsAzure/SessionHandler.php'; $storageClient = new Microsoft_WindowsAzure_Storage_Table('table.core.windows.net', 'your storage account name', 'your storage account key'); $sessionHandler = new Microsoft_WindowsAzure_SessionHandler($storageClient , 'sessionstable'); $sessionHandler->register(); Now you can call session_start() and other session functions as you normally would. Nicely, it just works. Really, that’s all there is to using the SessionHandler, but I found it interesting to take a look at how it works. The first interesting thing to note is that the register method is simply calling the session_set_save_handler function to essentially map the session handling functionality to custom functions. Here’s what the method looks like from the source code: public function register() { return session_set_save_handler(array($this, 'open'), array($this, 'close'), array($this, 'read'), array($this, 'write'), array($this, 'destroy'), array($this, 'gc') ); } The reading, writing, and deleting of session data is only slightly more complicated. When writing session data, the key-value pairs that make up the data are first serialized and then base64 encoded. The serialization of the data allows for lots of flexibility in the data you want to store (i.e. you don’t have to worry about matching some schema in the data store). When storing data in a table, each entry must have a partition key and row key that uniquely identify it. The partition key is a string (“sessions” by default, but this is changeable in the class constructor) and the the row key is the session ID. (For more information about the structure of Tables, see this post.) Finally, the data is either updated (it it already exists in the Table) or a new entry is inserted. Here’s a portion of the write function: $serializedData = base64_encode(serialize($serializedData)); $sessionRecord = new Microsoft_WindowsAzure_Storage_DynamicTableEntity($this->_sessionContainerPartition, $id); $sessionRecord->sessionExpires = time(); $sessionRecord->serializedData = $serializedData; try { $this->_storage->updateEntity($this->_sessionContainer, $sessionRecord); } catch (Microsoft_WindowsAzure_Exception $unknownRecord) { $this->_storage->insertEntity($this->_sessionContainer, $sessionRecord); } Not surprisingly, when session data is read from the table, it is retrieved by session ID, base64 decoded, and unserialized. Again, here’s a snippet that show’s what is happening: $sessionRecord = $this->_storage->retrieveEntityById( $this->_sessionContainer, $this->_sessionContainerPartition, $id ); return unserialize(base64_decode($sessionRecord->serializedData)); As you can see, the SessionHandler class makes good use of the storage APIs in the SDK. To learn more about the SessionHandler class (and the storage APIs), check out the documentation on Codeplex. You can, of course, get the complete source code here: http://phpazure.codeplex.com/SourceControl/list/changesets. As I investigated the session handling in the Windows Azure SDK for PHP, I noticed that the absence of support for SQL Azure as a session store was conspicuous. I’m curious about how many people would prefer to use SQL Azure over Azure Tables as a session store. If you have an opinion on this, please let me know in the comments.

We recently wanted to get the Git history of a file which we knew existed but had now been deleted so we could find out what had happened to it. Using a simple git log didn’t work: git log deletedFile.txt fatal: ambiguous argument 'deletedFile.txt': unknown revision or path not in the working tree. We eventually came across Francois Marier’s blog post which points out that you need to use the following command instead: git log -- deletedFile.txt I’ve tried reading through the man page but I’m still not entirely sure what the distinction between using – and not using it is supposed to be. If someone could explain it that’d be cool… From http://www.markhneedham.com/blog/2011/10/04/git-getting-the-history-of-a-deleted-file

If you're looking for a cloud expert, specifically someone who knows Amazon Web Services and EC2, you'll want to have a battery of questions to assess their knowledge.

The integration framework Apache Camel already supports several important cloud services (see my overview article at http://www.kai-waehner.de/blog/2011/07/09/cloud-computing-heterogeneity-will-require-cloud-integration-apache-camel-is-already-prepared for more details). This article describes the combination of Apache Camel and the Amazon Web Services (AWS) interfaces of Simple Storage Service (S3), Simple Queue Service (SQS) and Simple Notification Service (SNS). Thus, The concept of Infrastructure as a Service (IaaS) is used to access messaging systems and data storage without any need for configuration. Registration to AWS and Setup of Camel First, you have to register to the Amazon Web Services (for free). Most AWS services include a free monthly quota, which is absolutely sufficient to play around and develop some simple applications. As its name states, AWS uses technology-independent web services. Besides, APIs for several different programming languages are available to ease development. By the way, Camel uses the AWS SDK for Java (http://aws.amazon.com/sdkforjava), of course. The documentation is detailed and easy to understand, including tutorials, screenshots and code examples . Hint 1: You should read the introductions to S3, SQS and SNS (go to http://aws.amazon.com and click on „products“) and play around with the AWS Management Console (http://aws.amazon.com/console) before you continue. This step is very easy and takes less than one hour. Then, you will have a much better understanding about AWS and where Camel can help you! Hint 2: It really helps to look at the source code of the camel-aws component, It helps you to understand how Camel uses the AWS Java API internally. If you want to write tests, you can do it the same way. In the past, I was afraid of looking at „complex“ source code of open source frameworks. But there is no need to be scared! The camel-aws component (and most other camel components) contain only of a few classes. Everything is easy to understand. It helps you to understand Camel internals, the AWS API, and to spot and solve errors due to exceptions in your code. In the meanwhile, the current Camel version 2.8 supports three AWS services: S3, SQS and SNS. All of them use similar concepts. Therefore, they are included in one single camel component: „camel-aws“. You have to add the libraries to your existing Camel project. As always, the simplest way is to use Maven and add the following dependency to the pom.xml: org.apache.camel camel-aws ${camel-version} Configuration of the Camel Endpoint The implementation and configuration of all three services is very similar. The URI looks like this (the code shows the SQS service): aws-sqs://queue-name[?options] There are two alternatives to configure your endpoint. Using Parameters The easy way is to use two paramters in the URI of your endpoint: „accessKey“ and „secretKey“ (you receive both after your AWS registration). “aws-sqs://unique-queue-name?accessKey=“INSERT_ME“&secretKey=INSERT_ME” Be aware of the following problem, which can result in a strange, non-speaking exception (thanks to Brendan Long): You’ll need to URL encode any +’s in your secret key (otherwise, they’ll be treated as spaces). + = %2B, so if your secretkey was “my+secret\key”, your Camel URL should have “secretKey=my%2Bsecret\key”. “Within the query string, the plus sign is reserved as shorthand notation for a space. Therefore, real plus signs must be encoded. This method was used to make query URIs easier to pass in systems which did not allow spaces.” Source: WC3 URI Recommendations Adding a configured AmazonClient to the Registry If you need to do more configuration (e.g. because your system is behind a firewall), you have to add an AmazonClient object to your registry. The following code shows an example using SQS, but SNS and S3 use exactly the same concept. @Override protected JndiRegistry createRegistry() throws Exception { JndiRegistry registry = super.createRegistry(); AWSCredentials awsCredentials = new BasicAWSCredentials(“INSERT_ME”, “INSERT_ME”); ClientConfiguration clientConfiguration = new ClientConfiguration(); clientConfiguration.setProxyHost(“http://myProxyHost”); clientConfiguration.setProxyPort(8080); AmazonSQSClient client = new AmazonSQSClient(awsCredentials, clientConfiguration); registry.bind(“amazonSQSClient”, client); return registry; } This example overwrites the createRegistry() method of a JUnit test (extending CamelTestSupport). You can also add this information to your runtime Camel application, of course. Apache Camel and the Simple Storage Service (S3) Simple Storage Service (S3) is a key-value-store. You can store small to very large data. The usage is very easy. You create buckets and put key-value data into these buckets. You can also create folders within buckets to organize your data. That’s it. You can monitor your buckets using the AWS Management Console – an intuitive GUI supporting most AWS services. The following example shows both alternatives for accessing the Amazon services (as described above): Paramenters and the AmazonClient. // Transfer data from your file inbox to the AWS S3 service from(“file:files/inbox”) // This is the key of your key-value data .setHeader(S3Constants.KEY, simple(“This is a static key”)) // Using parameters for accessing the AWS service .to(“aws-s3://camel-integration-bucket-mwea-kw?accessKey=INSERT_ME&secretKey=INSERT_ME&region=eu-west-1″); // Transfer data from the AWS S3 service to your file outbox from(“aws-s3://camel-integration-bucket-mwea-kw?amazonS3Client=#amazonS3Client&region=eu-wes”) .to(“file:files/outbox”); There are some additional parameters, for instance you can submit the desired AWS region or delete data after receiving it (see http://camel.apache.org/aws-s3.html and the corresponding SQS and SNS sites for more details about parameters and message headers). As you see in the code, you can use the AWS-S3 endpoint for producing and for consuming messages. Each bucket must be unique, thus you have to add some specific information such as your company to its name. Hint: If a bucket does not exist, Camel is creating it automatically (as the AWS API does). This concept is also used for SQS queues and SNS topics. Apache Camel and the Simple Queue Service (SQS) The Simple Queue Service (SQS) is similar to a JMS provider such as WebSphere MQ or ActiveMQ (but with some differences). You create queues and send messages to them. Consumers receive the messages. Contrary to most other AWS services, you cannot monitor queues by using the AWS management console directly. You have to use the service „Cloudwatch“ (http://aws.amazon.com/cloudwatch) and start an EC2 instance to monitor queues and its content. As you can see in the following code example, the syntax and concepts are almost the same as for the S3 service: from(“file:inbox”) .to(“aws-sqs://camel-integration-queue-mwea-kw?accessKey=INSERT_ME&secretKey=INSERT_ME”); from(“aws-sqs://camel-integration-queue-mwea-kw?amazonSQSClient=#amazonSQSClient”) .to(“file:outbox?fileName=sqs-${date:now:yyyy.MM.dd-hh:mm:ss:SS}”); Again, you can use the AWS-SQS endpoint for producing and for consuming messages. Each queue name must be unique. There exist two important differences to JMS (copy & paste from the AWS documentation): Q: How many times will I receive each message? Amazon SQS is engineered to provide “at least once” delivery of all messages in its queues. Although most of the time each message will be delivered to your application exactly once, you should design your system so that processing a message more than once does not create any errors or inconsistencies. Q: Why are there separate ReceiveMessage and DeleteMessage operations? When Amazon SQS returns a message to you, that message stays in the queue, whether or not you actually received the message. You are responsible for deleting the message; the delete request acknowledges that you’re done processing the message. If you don’t delete the message, Amazon SQS will deliver it again on another receive request. Apache Camel and the Simple Notification Service (SNS) The Simple Notification Service (SNS) acts like JMS topics. You create a topic, consumers subscribe to the topic and then receive notifications. Several transport protocols are supported: HTTP(S), Email and SQS. Further interfaces will be added in the future, e.g. the Short Message Service (SMS) for mobile phones. Contrary to S3 and SQS, Camel only offers a producer endpoint for this AWS service. You can only create topics and send messages via Camel. The reason is simple: Camel already offers endpoints for consuming these messages: HTTP, Email and SQS are already available. There is one tradeoff: A consumer cannot subscribe to topics using Camel – at the moment. The AWS Management Console has to be used. A very interesting discussion can be read on the Camel JIRA issue regarding the following questions: Should Camel be able to subscribe to topics? Should the producer contain this feature or should there be a consumer? In my opinion, there should be a consumer which is able to subscribe to topics, otherwise Camel is missing a key part of the AWS SNS service! Please read the discussion and contribute your opinion: https://issues.apache.org/jira/browse/CAMEL-3476. Apache Camel is already ready for the Cloud Computing Era AWS offers many more services for the cloud. Probably, it does not make sense to integrate everyone into Camel, but more AWS services will be supported in the future. For instance, SimpleDB and the Relational Database Service (RDS) are already planned and make sende, too: http://camel.apache.org/aws.html. The conclusion is easy: Apache Camel is already ready for the cloud computing era. Several important cloud services are already supported. Cloud integration will become very important in the future. Thus, Camel is on a very good way. Hopefully, we will see more cloud components, soon. I will continue to write articles about other Camel cloud components (and new AWS addons, ouf course). For instance, a component for the Platform as a Service (PaaS) product Google App Engine (GAE) is already available. If you have any additional important information, questions or other feedback, please write a comment. Thank you in advance… Best regards, Kai Wähner (Twitter: @KaiWaehner) [Content from my Blog: Cloud Integration with Apache Camel and Amazon Web Services (AWS): S3, SQS and SNS]

The "WZL Gear Toolbox", by the Laboratory for Machine Tools and Production Engineering at RWTH Aachen University, represents a unified graphical user interface containing different simulation programs for gear applications. It enables the usage of the following simulations: manufacturing simulation "GearGenerator" process simulation "SPARTApro" process simulation "KegelSpan" tooth contact analysis "ZaKo3D" The uniform graphical user interface enables the user to realize the simulations and to analyze the calculation results in a comfortable way. Thus, the "WZL Gear Toolbox" enables the analysis of the running behavior of gears by means of tooth contact analysis of the manufacturing-related deviations from the generating grinding. The long-term goal of the uniform graphical user interface is to provide a software tool containing the whole production chain of gear manufacturing. The "WZL Gear Toolbox" is funded by the WZL Gear Research Circle. Screenshot

I am Angelo D'Agnano and currently I work at the NATO Programming Centre as Software Architect.

This article describes how to create a simple HTML5 chat application using WebSockets to connect to a Java back-end.

The most common scenario to use diff is to see what changes you made after your last commit. Let’s see how to do it.

I am an experienced Java developer who has used various IDEs and prefer NetBeans IDE over all others by a long shot. I am also very fond of Maven as the tool to simplify and automate nearly every aspect of the development of my Java project throughout its lifecycle. Recently, I started developing Android applications and naturally I looked for a Maven plugin that would manage my Android projects. Luckily I found the maven-android-plugin which worked like a charm and allowed me to use Maven for developing my Android projects. The Android Emulator from the Android SDK seemed unusably slow. Lucklily, I found a way to use an Android Virtual Machine for VirtualBox that worked nearly as fast as my native computer! This page documents my experiences. Tested Environment Dev machine: Ubuntu 11.04 Linux IDE: NetBeans VirtualBox: 4.0.8 r71778 Android SDK Revision 11, Add on XML Schema #1, Repository XML Schema #3 (from About in SDK and AVD Manager) Android Version: 2.2 Overview of Steps Download and install the Android SDK on your dev machine Attach an Android Device to dev machine Configure and load your device for development and other use Create an initial Android maven project Connect Android Device to Android SDK Debug Android app using NetBeans Graphical Debuger Download and Install Android SDK Download and install the Android SDK on your dev machine as described here. Make sure to set the following in dev machine ~/.bashrc file: export ANDROID_HOME=$HOME/android-sdk-linux_x86 #Change as needed export PATH="$ANDROID_HOME/tools:$ANDROID_HOME/platform-tools:$PATH" Attaching an Android Device to Dev Machine If you have an actual device that is usually always best. If not, you must use a virtual Android device which usually has various limitations (e.g. no GPS, Camera etc.). The Android SDK makes it easy to create a new Virtual Device but the resulting device is painfully slow in my experience and not usable. Do not bother with this. Instead, create a virtual Android device using VirtualBox as described in the following steps: Install virtual box and initial Android VM as described here: http://androidspin.com/2011/01/24/howto-install-android-x86-2-2-in-virtualbox/ http://geeknizer.com/how-to-run-google-android-in-virtualbox-vmware-on-netbooks/ Configure Android VM so it is connected bidirectionally with your dev machine over TCP as described here: http://stackoverflow.com/questions/61156/virtualbox-host-guest-network-setup I used the approach of configuring a HOST ONLY network adapater and a second NAT adapter on the Android VM within virtual box. Configuring your Android Device This section describes various things I did to setup a dev environment for my Android device: Root the device. I used Universal AndRoot Install ConnectBot so you have ssh and related network utilities Creating Initial Android Maven Application Create initial project using instructions here. I found it best to create stub project structure using the maven-archtype-plugin and the archtypes at https://github.com/akquinet/android-archetypes/wiki Connecting Android VM Device to Android SDK In order for your code to be deployed from NetBeans IDE to Android Device and in order for you to monitor your deployed app from the Dalvik Debug Monitor (ddms) you need to connect your android VM device to the android sdk over TCP as described in the following steps. On Android Device open the Terminal Emulator Type su to become root (your device must be rooted for this Type following commands in root shell: setprop service.adb.tcp.port 5555 stop adbd start adbd Type the following commands on dev machine shell. TODO: Note that IP address below is whatever is the ip address associated with the device (see ifconfig on linux for device vboxnet0) adb tcpip 5555 adb connect 192.168.0.101:5555 For details on above steps see: http://stackoverflow.com/questions/2604727/how-can-i-connect-to-android-with-adb-over-tcp Set up port forwarding as described here http://redkrieg.com/2010/10/11/adb-over-ssh-fun-with-port-forwards/ (this is where I am most fuzzy) Build your maven android project using Right-Click / Clean and Build Now for the acid test whether you can deploy your app to the device from NetBeans IDE! Right-click / Custom / Goal to show Run Maven dialog. Enter android:deploy in Goals field. Select Remember As button and enter android:deploy for its text field. If all is well, the app will deploy to the device and will show up in its "Applications" screen. Debugging Android App Using NetBeans Graphical Debugger Once you can build and deploy your app to the real or virtual Android device, here are the steps to debug the app using NetBeans debugger: On Device: Start the app (TODO: determine how to start app on device with JVM options so it can wait for debugger connection. This should be easy) On Dev Machine run Dalvik Debug Monitor (ddms) in background: $ANDROID_HOME/tools/ddms & Lookup your app in ddms and get its debug port. This is described here but does not address NetBeans specifically In NetBeans do: Debug / Attach Debugger and specify the port looked up in ddms in previous step. You may leave rest of the fields with defaults. Click OK

A little tip that can be very useful, how to restore a deleted Git tag. If you juste deleted a tag by error, you can easily restore it following these steps. First, use git fsck --unreachable | grep tag then, you will see the unreachable tag. If you have several tags on the list, use git show KEY to found the good tag and finally, when you know which tag to restore, use git update-ref refs/tags/NAME KEY and the previously deleted tag with restore with NAME. Thanks to Shawn Pearce for the tip. From http://www.baptiste-wicht.com/2011/06/git-tip-restore-a-deleted-tag/

Git is a backup system itself: for example, you can version your .txt folders containing TODO lists. Since Git version your files just like it does for code, after accidental deletion or modifications it will be able to bring you back. Yet, if you do not regularly push your commits, a problem with the drive containing the repository may cause the loss of all your work. You can put the repository in Dropbox or on a similar service, but I don't trust it. Dropbox syncs files in .git independently from the rest and from one another, and it may break temporarily or for good the repository. By the way, I only want to snapshot a backup at specific points in time, not always occupying my connection by instant mirroring. A note before beginning: with binary data Git is not proficient as a backup tool: text works a lot better (it's like code). This article is dedicated to the backup of code and textual content. Push is not a backup For example, because it may lack branches. In general, pushing to origin is not even an option as you may not want to push your changes yet, but still perform a backup. It's only in the open source world that backup corresponds to publishing online. However, thanks to decentralization there are some simple solutions, involving the creation of repositorite different from origin: git clone /path/to/working/copy #creates the backup git pull #origin master of course, updates the backup # you can specify better branches via the local configuration of the backup copy (git config) The inverse solution, involging pulling, is also possible: git init . #in the folder of your backup, or you can use a remote repository git remote add backup_repo /path/to/backup/repo #or a git:// repo git push backup #master usually, but also multiple branches git push --all backup #an alternative that pushes all branches All the commands, also the one that will follow, are just bash commands: it's easy to create a script and automate its execution with cron, anacron or whatever you want. The Force"del" Unix is powerful in you. git bundle git bundle is another command that may be used for backup purposes. It will create a single file containing all the refs you need to export from your local repository. It's often used for publishing commits via USB keys or other media in absence of a connection. For one branch, it's simple. This command will create a myrepo.bundle file. git bundle create myrepo.bundle master For more branches or tags, it's still simple: git bundle create myrepo.bundle master other_branch Restoring the content of the bundle is a single comment. Inside an empty repo, type: git bundle unbundle myrepo.bundle Instead if you do not have a repo, and just want to recreate the old one: git clone myrepo.bundle -b master myrepo_folder In emergency situations, bundle comes handy. But my issue with that command is that I always forget something when I use it: for example in my tutorial repository I had a lot of tags, but bundle did not include them by default (you have to specify the whole references list like for master other_branch.) Tarballs An alternative is just to archive the repository in a tar.gz or tar.bz file. tar -cf repository.tar repository/ gzip repository.tar # or bzip2 repository.tar After that, you can use scp or even rsync (but I don't think it will speed up much) to put repository.tar.gz on another medium. The weight is higher in this case, since the repository contains also the checked out working copy. But you don't have to learn new commands: apart from the weight and the lack of incremental updates, this solution works fine. Bare repositories You can use git clone --bare repository/ backup_folder/ to create a bare copy of the repository, as a backup. The bare repository does not maintain a checked out working tree, and as so saves space and time for its transferral. This method can be used in conjunction with the pull/push or the tarball method. For restoring the backup: git clone backup_folder/ new_repository/ will recreate the original situation in new_repository. In any of the cases the new folders are created automatically. I won't advise to just copy the folder as often on other backup filesystems (like an USB key's vfat) permissions, owner and other metadata are lost. Conclusion So now you have some alternatives for backing up your repositories or transporting them without setting up a server like Gitosis or passing from the publicly available Github. In fact, I researched this techniques for transporting my tutorial code to phpDay 2011 and the Dutch PHP Conference, and they have worked pretty well.

The inspection of the error logs is a common way to detect errors and bugs. We also can show errors on-screen within our developement server, or we even can use great tools like firePHP to show our PHP errors and warnings inside our firebug console. That’s cool, but we only can see our session errors/warnings. If we want to see another’s errors we need to inspect the error log. tail -f is our friend, but we need to surf against all the warnings of all sessions to see our desired ones. Because of that I want to build a tool to monitor my PHP applications in real-time. Let’s start: What’s the idea? The idea is catch all PHP’s errors and warnings at run time and send them to a node.js HTTP server. This server will work similar than a chat server but our clients will only be able to read the server’s logs. Basically the applications have three parts: the node.js server, the web client (html5) and the server part (PHP). Let me explain a bit each part: The node Server Basically it has two parts: a http server to handle the PHP errors/warnings and a websocket server to manage the realtime communications with the browser. When I say that I’m using websockets that’s means the web client will only work with a browser with websocket support like chrome. Anyway it’s pretty straightforward swap from a websocket sever to a socket.io server to use it with every browser. But websockets seems to be the future, so I will use websockets in this example. The http server: http.createServer(function (req, res) { var remoteAdrress = req.socket.remoteAddress; if (allowedIP.indexOf(remoteAdrress) >= 0) { res.writeHead(200, { 'Content-Type': 'text/plain' }); res.end('Ok\n'); try { var parsedUrl = url.parse(req.url, true); var type = parsedUrl.query.type; var logString = parsedUrl.query.logString; var ip = eval(parsedUrl.query.logString)[0]; if (inspectingUrl == "" || inspectingUrl == ip) { clients.forEach(function(client) { client.write(logString); }); } } catch(err) { console.log("500 to " + remoteAdrress); res.writeHead(500, { 'Content-Type': 'text/plain' }); res.end('System Error\n'); } } else { console.log("401 to " + remoteAdrress); res.writeHead(401, { 'Content-Type': 'text/plain' }); res.end('Not Authorized\n'); } }).listen(httpConf.port, httpConf.host); and the web socket server: var inspectingUrl = undefined; ws.createServer(function(websocket) { websocket.on('connect', function(resource) { var parsedUrl = url.parse(resource, true); inspectingUrl = parsedUrl.query.ip; clients.push(websocket); }); websocket.on('close', function() { var pos = clients.indexOf(websocket); if (pos >= 0) { clients.splice(pos, 1); } }); }).listen(wsConf.port, wsConf.host); If you want to know more about node.js and see more examples, have a look to the great site: http://nodetuts.com/. In this site Pedro Teixeira will show examples and node.js tutorials. In fact my node.js http + websoket server is a mix of two tutorials from this site. The web client. The web client is a simple websockets application. We will handle the websockets connection, reconnect if it dies and a bit more. I’s based on node.js chat demo Real time monitor Socket status: Conecting ...IP: [all]" ?>count: 0 And the javascript magic var timeout = 5000; var wsServer = '192.168.2.2:8880'; var unread = 0; var focus = false; var count = 0; function updateCount() { count++; $("#count").text(count); } function cleanString(string) { return string.replace(/&/g,"&").replace(//g,">"); } function updateUptime () { var now = new Date(); $("#uptime").text(now.toRelativeTime()); } function updateTitle(){ if (unread) { document.title = "(" + unread.toString() + ") Real time " + selectedIp + " monitor"; } else { document.title = "Real time " + selectedIp + " monitor"; } } function pad(n) { return ("0" + n).slice(-2); } function startWs(ip) { try { ws = new WebSocket("ws://" + wsServer + "?ip=" + ip); $('#toolbar').css('background', '#65A33F'); $('#socketStatus').html('Connected to ' + wsServer); //console.log("startWs:" + ip); //listen for browser events so we know to update the document title $(window).bind("blur", function() { focus = false; updateTitle(); }); $(window).bind("focus", function() { focus = true; unread = 0; updateTitle(); }); } catch (err) { //console.log(err); setTimeout(startWs, timeout); } ws.onmessage = function(event) { unread++; updateTitle(); var now = new Date(); var hh = pad(now.getHours()); var mm = pad(now.getMinutes()); var ss = pad(now.getSeconds()); var timeMark = '[' + hh + ':' + mm + ':' + ss + '] '; logString = eval(event.data); var host = logString[0]; var line = "" + timeMark + "" + host + ""; line += "" + logString[1]; + ""; if (logString[2]) { line += " " + logString[2] + ""; } $('#log').append(line); updateCount(); window.scrollBy(0, 100000000000000000); }; ws.onclose = function(){ //console.log("ws.onclose"); $('#toolbar').css('background', '#933'); $('#socketStatus').html('Disconected'); setTimeout(function() {startWs(selectedIp)}, timeout); } } $(document).ready(function() { startWs(selectedIp); }); The server part: The server part will handle silently all PHP warnings and errors and it will send them to the node server. The idea is to place a minimal PHP line of code at the beginning of the application that we want to monitor. Imagine the following piece of PHP code $a = $var[1]; $a = 1/0; class Dummy { static function err() { throw new Exception("error"); } } Dummy1::err(); it will throw: A notice: Undefined variable: var A warning: Division by zero An Uncaught exception ‘Exception’ with message ‘error’ So we will add our small library to catch those errors and send them to the node server include('client/NodeLog.php'); NodeLog::init('192.168.2.2'); $a = $var[1]; $a = 1/0; class Dummy { static function err() { throw new Exception("error"); } } Dummy1::err(); The script will work in the same way than the fist version but if we start our node.js server in a console: $ node server.js HTTP server started at 192.168.2.2::5672 Web Socket server started at 192.168.2.2::8880 We will see those errors/warnings in real-time when we start our browser Here we can see a small screencast with the working application: This is the server side library: class NodeLog { const NODE_DEF_HOST = '127.0.0.1'; const NODE_DEF_PORT = 5672; private $_host; private $_port; /** * @param String $host * @param Integer $port * @return NodeLog */ static function connect($host = null, $port = null) { return new self(is_null($host) ? self::$_defHost : $host, is_null($port) ? self::$_defPort : $port); } function __construct($host, $port) { $this->_host = $host; $this->_port = $port; } /** * @param String $log * @return Array array($status, $response) */ public function log($log) { list($status, $response) = $this->send(json_encode($log)); return array($status, $response); } private function send($log) { $url = "http://{$this->_host}:{$this->_port}?logString=" . urlencode($log); $ch = curl_init(); curl_setopt($ch, CURLOPT_URL, $url); curl_setopt($ch, CURLOPT_NOBODY, true); curl_setopt($ch, CURLOPT_RETURNTRANSFER, true); $response = curl_exec($ch); $status = curl_getinfo($ch, CURLINFO_HTTP_CODE); curl_close($ch); return array($status, $response); } static function getip() { $realip = '0.0.0.0'; if ($_SERVER) { if ( isset($_SERVER['HTTP_X_FORWARDED_FOR']) && $_SERVER['HTTP_X_FORWARDED_FOR'] ) { $realip = $_SERVER["HTTP_X_FORWARDED_FOR"]; } elseif ( isset($_SERVER['HTTP_CLIENT_IP']) && $_SERVER["HTTP_CLIENT_IP"] ) { $realip = $_SERVER["HTTP_CLIENT_IP"]; } else { $realip = $_SERVER["REMOTE_ADDR"]; } } else { if ( getenv('HTTP_X_FORWARDED_FOR') ) { $realip = getenv('HTTP_X_FORWARDED_FOR'); } elseif ( getenv('HTTP_CLIENT_IP') ) { $realip = getenv('HTTP_CLIENT_IP'); } else { $realip = getenv('REMOTE_ADDR'); } } return $realip; } public static function getErrorName($err) { $errors = array( E_ERROR => 'ERROR', E_RECOVERABLE_ERROR => 'RECOVERABLE_ERROR', E_WARNING => 'WARNING', E_PARSE => 'PARSE', E_NOTICE => 'NOTICE', E_STRICT => 'STRICT', E_DEPRECATED => 'DEPRECATED', E_CORE_ERROR => 'CORE_ERROR', E_CORE_WARNING => 'CORE_WARNING', E_COMPILE_ERROR => 'COMPILE_ERROR', E_COMPILE_WARNING => 'COMPILE_WARNING', E_USER_ERROR => 'USER_ERROR', E_USER_WARNING => 'USER_WARNING', E_USER_NOTICE => 'USER_NOTICE', E_USER_DEPRECATED => 'USER_DEPRECATED', ); return $errors[$err]; } private static function set_error_handler($nodeHost, $nodePort) { set_error_handler(function ($errno, $errstr, $errfile, $errline) use($nodeHost, $nodePort) { $err = NodeLog::getErrorName($errno); /* if (!(error_reporting() & $errno)) { // This error code is not included in error_reporting return; } */ $log = array( NodeLog::getip(), "{$err} {$errfile}:{$errline}", nl2br($errstr) ); NodeLog::connect($nodeHost, $nodePort)->log($log); return false; }); } private static function register_exceptionHandler($nodeHost, $nodePort) { set_exception_handler(function($exception) use($nodeHost, $nodePort) { $exceptionName = get_class($exception); $message = $exception->getMessage(); $file = $exception->getFile(); $line = $exception->getLine(); $trace = $exception->getTraceAsString(); $msg = count($trace) > 0 ? "Stack trace:\n{$trace}" : null; $log = array( NodeLog::getip(), nl2br("Uncaught exception '{$exceptionName}' with message '{$message}' in {$file}:{$line}"), nl2br($msg) ); NodeLog::connect($nodeHost, $nodePort)->log($log); return false; }); } private static function register_shutdown_function($nodeHost, $nodePort) { register_shutdown_function(function() use($nodeHost, $nodePort) { $error = error_get_last(); if ($error['type'] == E_ERROR) { $err = NodeLog::getErrorName($error['type']); $log = array( NodeLog::getip(), "{$err} {$error['file']}:{$error['line']}", nl2br($error['message']) ); NodeLog::connect($nodeHost, $nodePort)->log($log); } echo NodeLog::connect($nodeHost, $nodePort)->end(); }); } private static $_defHost = self::NODE_DEF_HOST; private static $_defPort = self::NODE_DEF_PORT; /** * @param String $host * @param Integer $port * @return NodeLog */ public static function init($host = self::NODE_DEF_HOST, $port = self::NODE_DEF_PORT) { self::$_defHost = $host; self::$_defPort = $port; self::register_exceptionHandler($host, $port); self::set_error_handler($host, $port); self::register_shutdown_function($host, $port); $node = self::connect($host, $port); $node->start(); return $node; } private static $time; private static $mem; public function start() { self::$time = microtime(TRUE); self::$mem = memory_get_usage(); $log = array(NodeLog::getip(), "Start >>>> {$_SERVER['REQUEST_URI']}"); $this->log($log); } public function end() { $mem = (memory_get_usage() - self::$mem) / (1024 * 1024); $time = microtime(TRUE) - self::$time; $log = array(NodeLog::getip(), "End <<<< mem: {$mem} time {$time}"); $this->log($log); } } And of course the full code on gitHub: RealTimeMonitor