Today we get on with our series that will connect our Android applications to internet webservices! Next up in line: from JSON to a Listview. A lot of this project is identical to the previous post in this series so try to look there first if you have any problems. On the bottom of the post ill add the Eclipse project with the source. For this example i made use of an already existing JSON webservice located here. This is a piece of the JSON array that gets returned: {"earthquakes": [ { "eqid": "c0001xgp", "magnitude": 8.8, "lng": 142.369, "src": "us", "datetime": "2011-03-11 04:46:23", "depth": 24.4, "lat": 38.322 }, { "eqid": "2007hear", "magnitude": 8.4, "lng": 101.3815, "src": "us", "datetime": "2007-09-12 09:10:26", "depth": 30, "lat": -4.5172 }<--more -->]} So how do we get this data into our application! Behold our getJSON class! getJSON(String url) public static JSONObject getJSONfromURL(String url){//initializeInputStream is = null;String result = "";JSONObject jArray = null;//http posttry{HttpClient httpclient = new DefaultHttpClient();HttpPost httppost = new HttpPost(url);HttpResponse response = httpclient.execute(httppost);HttpEntity entity = response.getEntity();is = entity.getContent();}catch(Exception e){Log.e("log_tag", "Error in http connection "+e.toString());}//convert response to stringtry{BufferedReader reader = new BufferedReader(new InputStreamReader(is,"iso-8859-1"),8);StringBuilder sb = new StringBuilder();String line = null;while ((line = reader.readLine()) != null) {sb.append(line + "\n");}is.close();result=sb.toString();}catch(Exception e){Log.e("log_tag", "Error converting result "+e.toString());}//try parse the string to a JSON objecttry{ jArray = new JSONObject(result);}catch(JSONException e){Log.e("log_tag", "Error parsing data "+e.toString());}return jArray;} The code above can be divided in 3 parts. the first part makes the HTTP call the second part converts the stream into a String the third part converts the string to a JSPNObject Now we only have to implement this into out ListView. We can use the same method as in the XML tutorial. We make a HashMap that stores our data and we put JSON values in the HashMap. After that we will bind that HashMap to a SimpleAdapter. Here is how its done: Implementation ArrayList> mylist = new ArrayList>();//Get the data (see above)JSONObject json =JSONfunctions.getJSONfromURL("http://api.geonames.org/postalCodeSearchJSON?formatted=true&postalcode=9791&maxRows=10&username=demo&style=full"); try{//Get the element that holds the earthquakes ( JSONArray )JSONArray earthquakes = json.getJSONArray("earthquakes"); //Loop the Array for(int i=0;i < earthquakes.length();i++){ HashMap map = new HashMap(); JSONObject e = earthquakes.getJSONObject(i); map.put("id", String.valueOf(i)); map.put("name", "Earthquake name:" + e.getString("eqid")); map.put("magnitude", "Magnitude: " + e.getString("magnitude")); mylist.add(map);} }catch(JSONException e) { Log.e("log_tag", "Error parsing data "+e.toString()); } After this we only need to make up the Simple Adapter ListAdapter adapter = new SimpleAdapter(this, mylist , R.layout.main, new String[] { "name", "magnitude" }, new int[] { R.id.item_title, R.id.item_subtitle }); setListAdapter(adapter); final ListView lv = getListView(); lv.setTextFilterEnabled(true); lv.setOnItemClickListener(new OnItemClickListener() { public void onItemClick(AdapterView parent, View view, int position, long id) { @SuppressWarnings("unchecked") Toast.makeText(Main.this, "ID '" + o.get("id") + "' was clicked.", Toast.LENGTH_SHORT).show(); }); Now we have a ListView filled with JSON data! Here is the Eclipse project: source code Have fun playing around with it.

In one of my projects I have to read FoxPro DBF-files and import data from them. As this code must run in server and customer doesn’t want to install FoxPro there we found another solution that seems at least to me way better. In this posting I will show you how to read DBF-files using Sybase Advantage data providers. Getting Advantage data providers Here are the download links to data providers: Advantage .NET Data Provider Release 10.1 for Windows (32-bit and 64-bit) Platforms for Advantage OLE DB Provider Release 10.1 Platforms for Advantage ODBC Driver Release 10.1 I downloaded and installed .NET data provider and my example here is fully based on this. Configuring application If you run application without configuring some data providers stuff before you will get the following error: Error 5185: Local server connections are restricted in this environment. See the 5185 error code documentation for details. Go to your application bin folder and add there usual text file called ads.ini. Here is the content for this file: [SETTINGS] MTIER_LOCAL_CONNECTIONS=1 Make sure you add reference to Advantage data provider assembly and include ads.ini to your project like shown on image above. Getting data to DataTable Here is short code example about how to get data from DBF-file to DataTable. static void Main(string[] args) { var tableName = "TABLENAME_WITHOUT_EXTENSION"; var connStr = "data source={0};tabletype=vfp;servertype= local;"; connStr = string.Format(connStr, "c:\\temp\\"); var table = new DataTable(); using (var conn = new AdsConnection(connStr)) using (var adapter = new AdsDataAdapter()) using (var cmd = new AdsCommand()) { cmd.Connection = conn; cmd.CommandText = "select * from " + tableName; adapter.SelectCommand = cmd; conn.Open(); adapter.Fill(table); conn.Close(); } Console.WriteLine("Table fields:"); foreach (DataColumn col in table.Columns) Console.WriteLine(col.ColumnName); Console.WriteLine(" "); Console.WriteLine("Rows: " + table.Rows.Count); Console.Read(); } If Advantage data providers were installed correctly and there are no errors in table names, locations and your SQL query then you should see list of table column names and row count on console window when you run the application.

We will discuss how to iterate over a collection of String objects in Struts2 tag libraries and then a List of custom class objects. It looks as if iterating a list of string objects is easier than iterating over a list of custom class objects in Struts 2. But the reality is that iterating a list of custom class objects is also equally easier. By custom class we mean the User, Employee, Department, Products, Vehicles classes that are created in any web application. Download Working Sample Here Usually it happens that one needs to fetch a list of records from database/files and then display it in the JSP. The module requiring this functionality could be Search, Listing users/departments/products etc. The basic flow of struts2 web application goes like: The user initiates the request from one page. This request is received by the interceptor which further invokes the Struts2 action. The action class fetches the records and stores in a list. This list is available to the next JSP using the public getter method. Please note that the public getter method for the List is mandatory. Once the List has been populated by Struts2 action class, the JSP then iterates over this List and displays the corresponding information. In the days gone by, one would store the List as a session attribute and then access the list in JSP using the scriptlets to display appropriate output to the users. Here is a Struts2 sample application to iterate one String and one custom class objects List. Though we are using the Struts2 tag library to iterate the list but JSTL can also be used for iteration. Also if you are going to use the code examples given below, use the following URL's to access the application: http://localhost:8080//index.action Iterate a Custom class ArrayList in Struts2 web.xml struts2 org.apache.struts2.dispatcher.ng.filter.StrutsPrepareAndExecuteFilter struts2 *.action struts.xml /home.jsp /success.jsp /failure.jsp home.jsp Enter a user name to get the documents uploaded by that user. Username success.jsp Documents uploaded by the user are: failure.jsp FileAction.java package com.example; import java.util.ArrayList; import java.util.List; public class FetchAction { private String username; private String message; private List documents = new ArrayList(); public List getDocuments() { return documents; } public String getMessage() { return message; } public void setMessage(String message) { this.message = message; } public String getUsername() { return username; } public void setUsername(String username) { this.username = username; } public String execute() { if( username != null) { //logic to fetch the document list (say from database) Document d1 = new Document(); d1.setName("user.doc"); Document d2 = new Document(); d2.setName("office.doc"); Document d3 = new Document(); d3.setName("transactions.doc"); documents.add(d1); documents.add(d2); documents.add(d3); return "success"; } else { message="Unable to fetch"; return "failure"; } } } Document.java package com.example; public class Document { private String name; public String getName() { return name; } public void setName(String name) { this.name = name; } } Iterate String List in Struts2 The way to iterate the a String list is similar with the only difference that the action class FetchAction.java now populates the name of documents into an ArrayList of String objects. The code zip file containing the iteration over an ArrayList of custom class object or bean can be downloaded at: http://www.fileserve.com/file/QmrsJ7k The URL to access this application will be: http://localhost:8080/IteratorExample/index.action The code zip file containing the iteration over an ArrayList of string class object or bean can be downloaded at: http://www.fileserve.com/file/V2kXkfx The URL to access this application will be: http://localhost:8080/StringIteratorExample/index.action From http://extreme-java.blogspot.com/2011/05/how-to-iterate-arraylist-in-struts2.html

Learn what is a DAO and how to create a DAO, as well as the significance of creating Data Access Objects.

in this post i’m going to talk about a set of benchmarks that i’ve done with solr. the goal behind it is to see how each parameter defined in the schema affects the size of the index and the performance of the system. the first step was to fetch the set of documents that i was going to use in the tests. i wanted the documents to be composed of real text, so i started to look for sources in internet. the first one that i really liked was twitter. they provide a rest api that allows you to read a continuous stream of tweets, composed of approximately 1% of all the public tweets. each tweet is expressed as a json object, and carries meta-data about the message and the author. while this source allowed me to get a good number of documents in a short time (about 1.7 million tweets in 2 days), they were really small, so i started to look for a source of bigger documents, finally choosing wikipedia. i downloaded the documents through http using the “random article” feature in their site, obtaining about 160,000 articles in a couple of days. at the time of writting, the site download.wikipedia.org , which provides an easy way of downloading a bunch of articles, was out of service. the next step was to design the schema. because one of the objectives is to see how each change in the schema affects the size of the index, i used many different combination of parameters, as to measure the influence of each one of them. on each case, the database of stop-words was populated using the top 100 terms of each set of documents, obtained from the administration panel of solr. for both datasets, the “omitnorms”, “termvectors” and “stopwords” parameters are referred to the “text” field. in all cases, the value of the parameters “termoffsets” and “termpositions” is the same as “termvectors”. in the first figure you can see the size of the index for each schema for the twitter data-set, and which proportion of the index corresponds to each parameter. remember that this data-set has lots of documents (about 1.7 million) but each one is small (240 bytes on average). there are many remarkable things here. the first one is that the space occupied by the term vectors (~280 mib when not using stop words) is almost equal to the space occupied by the inverted index itself (~240 mib). in second place, the space saved by omitting norms is almost negligible (~2 mib). third, the space saved by using stop word is doubled when storing term vectors, going from about 4% of the index to about 10%. finally, the space occupied by the stored fields (~340 mib) is considerably bigger than the space occupied by the inverted index itself. in the second figure you can see the same information for the wikipedia data-set. the size occupied by the norms is still negligible (< 1mib), however, the size occupied by the stop words has increased to about 22% of the index size when not storing term vectors, and about 25% when storing them. this time, the size occupied by the term vectors (~1067 mib) is almost three times the space occupied by the inverted index itself (~380 mib). finally, the size of the stored documents (~6330 mib) is more than four times the size of the index with term vectors stored. at this point, we can state some conclusions concerning the size of the index: when the number of fields is small, the size of the norms is negligible, independently of the size and number of documents. when the documents are large, the stop words help reducing the size of the index significantly. maybe here is important to note two things. in first place, the documents fetched from wikipedia are writen using traditional language, and are all writen in english, while the documents fetched from twitter are writen using modern language, and in many different languages. in second place, i didn’t measure the precision and recall of the system when using stop words, so it is possible that the findability in a real scenario won’t be good. if you’re storing the documents, and they are big enough, it’s not so important if you store the term vectors or not, so if you’re using a feature such as highlighting and you are looking for good performance, you should store them. if you’re not storing documents, or your documents are small, you should think twice before storing the term vectors, because they’re going to increase significantly your index’s size. i hope you find this post useful. currently i’m working on a set of benchmarks to measure the influence of each one of these parameters in the performance of the system, so if you liked this post, stay tuned!

Bixo Labs shows how to use Solr as a NoSQL solution for big data Many people use the Hadoop open source project to process large data sets because it’s a great solution for scalable, reliable data processing workflows. Hadoop is by far the most popular system for handling big data, with companies using massive clusters to store and process petabytes of data on thousands of servers. Since it emerged from the Nutch open source web crawler project in 2006, Hadoop has grown in every way imaginable – users, developers, associated projects (aka the “Hadoop ecosystem”). Starting at roughly the same time, the Solr open source project has become the most widely used search solution on planet Earth. Solr wraps the API-level indexing and search functionality of Lucene with a RESTful API, GUI, and lots of useful administrative and data integration functionality. The interesting thing about combining these two open source projects is that you can use Hadoop to crunch the data, and then serve it up in Solr. And we’re not talking about just free-text search; Solr can be used as a key-value store (i.e. a NoSQL database) via its support for range queries. Even on a single server, Solr can easily handle many millions of records (“documents” in Lucene lingo). Even better, Solr now supports sharding and replication via the new, cutting-edge SolrCloud functionality. Background I started using Hadoop & Solr about five years ago, as key pieces of the Krugle code search startup I co-founded in 2005. Back then, Hadoop was still part of the Nutch web crawler we used to extract information about open source projects. And Solr was fresh out of the oven, having just been released as open source by CNET. At Bixo Labs we use Hadoop, Solr, Cascading, Mahout, and many other open source technologies to create custom data processing workflows. The web is a common source of our input data, which we crawl using the Bixo open source project. The Problem During a web crawl, the state of the crawl is contained in something commonly called a “crawl DB”. For broad crawls, this has to be something that works with billions of records, since you need one entry for each known URL. Each “record” has the URL as the key, and contains important state information such as the time and result of the last request. For Hadoop-based crawlers such as Nutch and Bixo, the crawl DB is commonly kept in a set of flat files, where each file is a Hadoop “SequenceFile”. These are just a packed array of serialized key/value objects. Sometimes we need to poke at this data, and here’s where the simple flat-file structure creates a problem. There’s no easy way run queries against the data, but we can’t store it in a traditional database since billions of records + RDBMS == pain and suffering. Here is where scalable NoSQL solutions shine. For example, the Nutch project is currently re-factoring this crawl DB layer to allow plugging in HBase. Other options include Cassandra, MongoDB, CouchDB, etc. But for simple analytics and exploration on smaller datasets, a Solr-based solution works and is easier to configure. Plus you get useful and surprising fun functionality like facets, geospatial queries, range queries, free-form text search, and lots of other goodies for free. Architecture So what exactly would such a Hadoop + Solr system look like? As mentioned previously, in this example our input data comes from a Bixo web crawler’s CrawlDB, with one entry for each known URL. But the input data could just as easily be log files, or records from a traditional RDBMS, or the output of another data processing workflow. The key point is that we’re going to take a bunch of input data, (optionally) munge it into a more useful format, and then generate a Lucene index that we access via Solr. Hadoop For the uninitiated, Hadoop implements both a distributed file system (aka “HDFS”) and an execution layer that supports the map-reduce programming model. Typically data is loaded and transformed during the map phase, and then combined/saved during the reduce phase. In our example, the map phase reads in Hadoop compressed SequenceFiles that contain the state of our web crawl, and our reduce phase write out Lucene indexes. The focus of this article isn’t on how to write Hadoop map-reduce jobs, but I did want to show you the code that implements the guts of the job. Note that it’s not typical Hadoop key/value manipulation code, which is painful to write, debug, and maintain. Instead we use Cascading, which is an open source workflow planning and data processing API that creates Hadoop jobs from shorter, more representative code. The snippet below reads SequenceFiles from HDFS, and pipes those records into a sink (output) that stores them using a LuceneScheme, which in turn saves records as Lucene documents in an index. Tap source = new Hfs(new SequenceFile(CRAWLDB_FIELDS), inputDir); Pipe urlPipe = new Pipe("crawldb urls"); urlPipe = new Each(urlPipe, new ExtractDomain()); Tap sink = new Hfs(new LuceneScheme(SOLR_FIELDS, STORE_SETTINGS, INDEX_SETTINGS, StandardAnalyzer.class, MAX_FIELD_LENGTH), outputDir, true); FlowConnector fc = new FlowConnector(); fc.connect(source, sink, urlPipe).complete(); We defined CRAWLDB_FIELDS and SOLR_FIELDS to be the set of input and output data elements, using names like “url” and “status”. We take advantage of the Lucene Scheme that we’ve created for Cascading, which lets us easily map from Cascading’s view of the world (records with fields) to Lucene’s index (documents with fields). We don’t have a Cascading Scheme that directly supports Solr (wouldn’t that be handy?), but we can make-do for now since we can do simple analysis for this example. We indexed all of the fields so that we can perform queries against them. Only the status message contains normal English text, so that’s the only one we have to analyze (i.e., break the text up into terms using spaces and other token delimiters). In addition, the ExtractDomain operation pulls the domain from the URL field and builds a new Solr field containing just the domain. This will allow us to do queries against the domain of the URL as well as the complete URL. We could also have chosen to apply a custom analyzer to the URL to break it into several pieces (i.e., protocol, domain, port, path, query parameters) that could have been queried individually. Running the Hadoop Job For simplicity and pay-as-you-go, it’s hard to beat Amazon’s EC2 and Elastic Mapreduce offerings for running Hadoop jobs. You can easily spin up a cluster of 50 servers, run your job, save the results, and shut it down – all without needing to buy hardware or pay for IT support. There are many ways to create and configure a Hadoop cluster; for us, we’re very familiar with the (modified) EC2 Hadoop scripts that you can find in the Bixo distribution. Step-by-step instructions are available at http://openbixo.org/documentation/running-bixo-in-ec2/ The code for this article is available via GitHub, at http://github.com/bixolabs/hadoop2solr. The README displayed on that page contains step-by-step instructions for building and running the job. After the job is done, we’ll copy the resulting index out of the Hadoop distributed file system (HDFS) and onto the Hadoop cluster’s master server, then kill off the one slave we used. The Hadoop master is now ready to be configured as our Solr server. Solr On the Solr side of things, we need to create a schema that matches the index we’re generating. The key section of our schema.xml file is where we define the fields. These fields have a one-to-one correspondence with the SOLR_FIELDS we defined in our Hadoop workflow. They also need to use the same Lucene settings as what we defined in the static IndexWorkflow.java STORE_SETTINGS and INDEX_SETTINGS. Once we have this defined, all that’s left is to set up a server that we can use. To keep it simple, we’ll use the single EC2 instance in Amazon’s cloud (m1.large) that we used as our master for the Hadoop job, and run the simple Solr search server that relies on embedded Jetty to provide the webapp container. Similar to the Hadoop job, step-by-step instructions are in the README for the hadoop2solr project on GitHub. But in a nutshell, we’ll copy and unzip a Solr 1.4.1 setup on the EC2 server, do the same for our custom Solr configuration, create a symlink to the index, and then start it running with: Giving it a Try Now comes the interesting part. Since we opened up the default Jetty port used by Solr (8983) on this EC2 instance, we can directly access Solr’s handy admin console by pointing our browser at http://:8983/solr/admin % cd solr % java -Dsolr.solr.home=../solr-conf -Dsolr.data.dir=../solr-data -jar start.jar From here we can run queries against Solr: We can also use curl to talk to the server via HTTP requests: curl http://:8983/solr/select/?q=-status%3AFETCHED+and+-status%3AUNFETCHED The response is XML by default. Below is an example of the response from the above request, where we found 2,546 matches in 94ms. Now here’s what I find amazing. For an index of 82 million documents, running on a fairly wimpy box (EC2 m1.large = 2 virtual cores), the typical response time for a simple query like “status:FETCHED” is only 400 milliseconds, to find 9M documents. Even a complex query such as (status not FETCHED and not UNFETCHED) only takes six seconds. Scaling Obviously we could use beefier boxes. If we switched to something like m1.xlarge (15GB of memory, 4 virtual cores) then it’s likely we could handle upwards of 200M “records” in our Solr index and still get reasonable response times. If we wanted to scale beyond a single box, there are a number of solutions. Even out of the box Solr supports sharding, where your HTTP request can specify multiple servers to use in parallel. More recently, the Solr trunk has support for SolrCloud. This uses the ZooKeeper open source project to simplify coordination of multiple Solr servers. Finally, the Katta open source project supports Lucene-level distributed search, with many of the features needed for production quality distributed search that have not yet been added to SolrCloud. Summary The combination of Hadoop and Solr makes it easy to crunch lots of data and then quickly serve up the results via a fast, flexible search & query API. Because Solr supports query-style requests, it’s suitable as a NoSQL replacement for traditional databases in many situations, especially when the size of the data exceeds what is reasonable with a typical RDBMS. Solr has some limitations that you should be aware of, specifically: · Updating the index works best as a batch operation. Individual records can be updated, but each commit (index update) generates a new Lucene segment, which will impact performance. · Current support for replication, fail-over, and other attributes that you’d want in a production-grade solution aren’t yet there in SolrCloud. If this matters to you, consider Katta instead. · Many SQL queries can’t be easily mapped to Solr queries. The code for this article is available via GitHub, at http://github.com/bixolabs/hadoop2solr. The README displayed on that page contains additional technical details.

I’ve written a couple of posts (here and here) about Java and the JDBC Driver for SQL Server with the promise of eventually writing about how to get a Java application running on the Windows Azure platform. In this post, I’ll deliver on that promise. Specifically, I’ll show you two things: 1) how to connect to a SQL Azure Database from a Java application running locally, and 2) how to connect to a SQL Azure database from an application running in Windows Azure. You should consider these as two ordered steps in moving an application from running locally against SQL Server to running in Windows Azure against SQL Azure. In both steps, connection to SQL Azure relies on the JDBC Driver for SQL Server and SQL Azure. The instructions below assume that you already have a Windows Azure subscription. If you don’t already have one, you can create one here: http://www.microsoft.com/windowsazure/offers/. (You’ll need a Windows Live ID to sign up.) I chose the Free Trial Introductory Special, which allows me to get started for free as long as keep my usage limited. (This is a limited offer. For complete pricing details, see http://www.microsoft.com/windowsazure/pricing/.) After you purchase your subscription, you will have to activate it before you can begin using it (activation instructions will be provided in an email after signing up). Connecting to SQL Azure from an application running locally I’m going to assume you already have an application running locally and that it uses the JDBC Driver for SQL Server. If that isn’t the case, then you can start from scratch by following the steps in this post: Getting Started with the SQL Server JDBC Driver. Once you have an application running locally, then the process for running that application with a SQL Azure back-end requires two steps: 1. Migrate your database to SQL Azure. This only takes a couple of minutes (depending on the size of your database) with the SQL Azure Migration Wizard - follow the steps in the Creating a SQL Azure Server and Creating a SQL Azure Database sections of this post. 2. Change the database connection string in your application. Once you have moved your local database to SQL Azure, you only have to change the connection string in your application to use SQL Azure as your data store. In my case (using the Northwind database), this meant changing this… String connectionUrl = "jdbc:sqlserver://serverName\\sqlexpress;" + "database=Northwind;" + "user=UserName;" + "password=Password"; …to this… String connectionUrl = "jdbc:sqlserver://xxxxxxxxxx.database.windows.net;" + "database=Northwind;" + "user=UserName@xxxxxxxxxx;" + "password=Password"; (where xxxxxxxxxx is your SQL Azure server ID). Connecting to SQL Azure from an application running in Windows Azure The heading for this section might be a bit misleading. Once you have a locally running application that is using SQL Azure, then all you have to do is move your application to Windows Azure. The connecting part is easy (see above), but moving your Java application to Windows Azure takes a bit more work. Fortunately, Ben Lobaugh has written a great post that that shows how to use the Windows Azure Starter Kit for Java to get a Java application (a JSP application, actually) running in Windows Azure: Deploying a Java application to Windows Azure with Command-Line Ant. (If you are using Eclipse, see Ben’s related post: Deploying a Java application to Windows Azure with Eclipse.) I won’t repeat his work here, but I will call out the steps I took in modifying his instructions to deploy a simple JSP page that connects to SQL Azure. 1. Add the JDBC Driver for SQL Server to the Java archive. One step in Ben’s tutorial (see the Select the Java Runtime Environment section) requires that you create a .zip file from your local Java installation and add it to your Java/Azure application. Most likely, your local Java installation references the JDBC driver by setting the classpath environment variable. When you create a .zip file from your java installation, the JDBC driver will not be included and the classpath variable will not be set in the Azure environment. I found the easiest way around this was to simply add the sqljdbc4.jar file (probably located in C:\Program Files\Microsoft SQL Server JDBC Driver\sqljdbc_3.0\enu) to the \lib\ext directory of my local Java installation before creating the .zip file. Note: You can put the JDBC driver in a separate directory, include it when you create the .zip folder, and set the classpath environment variable in the startup.bat script. But, I found the above approach to be easier. 2. Modify the JSP page. Instead of the code Ben suggests for the HelloWorld.jsp file (see the Prepare your Java Application section), use code from your locally running application. In my case, I just used the code from this post after changing the connection string and making a couple minor JSP-specific changes: Northwind Customers That’s it!. To summarize the steps… Migrate your database to SQL Azure with the SQL Azure Migration Wizard. Change the database connection in your locally running application. Use the Windows Azure Starter Kit for Java to move your application to Windows Azure. (You’ll need to follow instructions in this post and instructions above.) Thanks. -Brian

One of the new features added in Java 7 is the capability to switch on a String. With Java 6, or less String color = "red"; if (color.equals("red")) { System.out.println("Color is Red"); } else if (color.equals("green")) { System.out.println("Color is Green"); } else { System.out.println("Color not found"); } String color = "red"; if (color.equals("red")) { System.out.println("Color is Red"); } else if (color.equals("green")) { System.out.println("Color is Green"); } else { System.out.println("Color not found"); } With Java 7: String color = "red"; switch (color) { case "red": System.out.println("Color is Red"); break; case "green": System.out.println("Color is Green"); break; default: System.out.println("Color not found"); } Conclusion The switch statement when used with a String uses the equals() method to compare the given expression to each value in the case statement and is therefore case-sensitive and will throw a NullPointerException if the expression is null. It is a small but useful feature which not only helps us write more readable code but the compiler will likely generate more efficient bytecode as compared to the if-then-else statement. From http://www.vineetmanohar.com/2011/03/new-java-7-feature-string-in-switch-support/

Both Java SE and the NetBeans Platform have default property editors for several primitive and common data types. These are suitable for most cases and most of us almost never need to worry about it. There are, however, those few moments where the one-size-fits-all approach does not actually fit. For instance, the default float editor is a text editor: For general-purpose cases, this should be fine. Now, what if you want to restrict the values your property can have or have other control over it? Or simply give the user a more comfortable control for data input, like a JSpinner: Most of what I'll show was taken from the NetBeans tutorials and Javadoc, so I will not extend in details what you can find easily there. First, lets implement the PropertyEditor and the InlineEditor (don't forget to to fix imports): public abstract class FloatPropertyEditor extends PropertyEditorSupport implements ExPropertyEditor, InplaceEditor.Factory{ protected InplaceEditor ed = null; protected SpinnerNumberModel model; public FloatPropertyEditor(Object source, SpinnerNumberModel model) { super(source); this.model = model; } public FloatPropertyEditor(SpinnerNumberModel model) { this.model = model; } @Override public String getAsText() { Float d = (Float)getValue(); if (d == null) { return "0.0"; } return NumberFormat.getNumberInstance().format(d.floatValue()); } @Override public void setAsText(String s) { try { setValue(new Float( NumberFormat.getNumberInstance().parse(s).floatValue())); } catch (ParseException ex) { setValue(Float.valueOf(0.0f)); } } @Override public void attachEnv(PropertyEnv env) { env.registerInplaceEditorFactory(this); } @Override public InplaceEditor getInplaceEditor() { if (ed == null) { ed = new FloatInplaceEditor(model); } return ed; } protected static class FloatInplaceEditor implements InplaceEditor { private final JSpinner spinner; private PropertyEditor editor = null; private PropertyModel model; public FloatInplaceEditor(SpinnerNumberModel model) { this.spinner = new JSpinner(model); } @Override public void connect(PropertyEditor propertyEditor, PropertyEnv env) { editor = propertyEditor; reset(); } @Override public JComponent getComponent() { return spinner; } @Override public void clear() { //avoid memory leaks: editor = null; model = null; } @Override public Object getValue() { return spinner.getValue(); } @Override public void setValue(Object object) { try { spinner.setValue(object); } catch (IllegalArgumentException e) { spinner.setValue(null); } } @Override public boolean supportsTextEntry() { return true; } @Override public void reset() { Float d = (Float) editor.getValue(); if (d != null) { setValue(d); } } @Override public KeyStroke[] getKeyStrokes() { return new KeyStroke[0]; } @Override public PropertyEditor getPropertyEditor() { return editor; } @Override public PropertyModel getPropertyModel() { return model; } @Override public void setPropertyModel(PropertyModel propertyModel) { this.model = propertyModel; } @Override public boolean isKnownComponent(Component component) { return component == spinner || spinner.isAncestorOf(component); } @Override public void addActionListener(ActionListener actionListener) { //do nothing - not needed for this component } @Override public void removeActionListener(ActionListener actionListener) { //do nothing - not needed for this component } } } So far, we've been closely following the tutorials. Notice that the FloatPropertyEditor class is abstract. This is because PropertyEditor classes should have a default constructor (more on this below). Not much of a gain, maybe, but now you have a JSpinner as an editor. Now imagine you are developing a 3D "bodies in space" application. You can select an item and you can change its dimensions and coordinates at will. Coordinates can have any real value, whether negative, zero, or positive (besides the practical dimensional limits your universe might have), while dimensions must be at least be zero. This is where the abstract plays its role. By inheriting the class above, you can decide which values our properties can have. For coordinate properties, set this class as PropertyEditor: public class CoordinateFloatPropertyEditor extends FloatPropertyEditor { public CoordinateFloatPropertyEditor() { super(new SpinnerNumberModel(0.0, -100000.0, 100000.0, 0.5)); } } And, for dimension properties, use this one, instead: public class DimensionFloatPropertyEditor extends FloatPropertyEditor { public DimensionFloatPropertyEditor() { super(new SpinnerNumberModel(0.0, 0.09999, 100000.0, 0.5)); } } It's up to you to decide the maximum, minimum, and step values, and tweak this sample for your needs. I want also to add that this is part of a real application, from where I was inspired to write this article. I'll be further extending this subject in the next posts. Thanks for you attention. Muchas gracias por su atención.

If you create small per-test datasets, as DBUnit advises, you’ll get intermittent build failures due to foreign-key violations. This post explains (1) why this happens, (2) why small per-test datasets are still a good idea, and (3) one simple way to get around the problem. NB When I searched for solutions to this problem, I discovered that other kinds of foreign-key problem come up with DBUnit. Some people have circular dependencies in their relational database schemas, which stops DBUnit from loading the test data. If such is your case, I’m sorry to say that this post won’t help you with it, and your best option is probably to just take yourself outside and shoot yourself now. (Although some people seem to chosen instead to disable foreign key checking during test runs.) What causes the foreign-key violations The cause of the problem is simple, and illustrated by a trivial example. Suppose you have two entity classes, HitchHiker and SpaceShip. The HitchHiker table has a foreign key that references SpaceShip. The test data for HitchHikerDaoTest contains lines from both tables, whereas the test data for SpaceShipDaoTest contains only lines from SpaceShip. DBUnit’s default setup operation, CLEAN_INSERT, wipes data from every table occurring in the test dataset and then inserts the lines listed in that dataset. When SpaceShipDaoTest runs, DBUnit will start by deleting everything in the SpaceShip table. If any HitchHikers are currently riding in the SpaceShips that are about to be deleted, the database will object to their untimely eviction (I’m not sure whether the error message will read like Vogon poetry, though). If you start from an empty database, and execute SpaceShipDaoTest and then HitchHikerDaoTest, you’ll be fine; but if you do it in the other order, your build will fail. It’s that second-worst kind of bug, the unpredictable kind, since you don’t (usually) specify the order in which tests run. After all, they’re supposed to be independent! So you may well find that you have no problems for months on end, until one day you get an error running individual tests in a particular sequence, or Maven changes the order in which it runs your tests on the CI server, and BOOM! Why you should still use small independent datasets It’s tempting to circumvent the problem by using a single monolithic dataset for all your integration tests. I’ve tried this, and I advise against it. A big data file is hard to work with: you waste a lot of time scrolling around looking for the line you need, and it’s very hard to follow and understand foreign-key relations. Worse still: by modifying the data to make one test pass, you can easily accidentally break another one. The larger the dataset and the test suite become, the more fragile they get, and the more painstaking it becomes to modify them. How to avoid the foreign-key problem with small independent datasets One working but unsatisfactory solution would be to pad out every XML dataset with the list of all tables touched in the test suite. It’s unsatisfactory because the only way to add a table into a FlatXmlDataSet is to list a line of that table — a FlatXmlDataSet can’t contain empty tables — and there’s no justification for polluting the test data with lines from tables that are not part of the test. The solution I found was to use a DTD to clean tables before tests. Every XML file has different contents, but they all reference a single DTD which lists all the tables involved in the test suite. The DTD is easy to generate from the database schema, and useful for auto-complete and catching typos in column names, so you should probably already be using one. The code to exploit its contents is very simple: private IDataSet loadTestDataWithDtdTableList(String dtdFilename) throws IOException, DataSetException, SQLException { Reader dtdReader = new FileReader(new ClassPathResource(dtdFilename).getFile()); IDataSet dtdDataset = new FlatDtdDataSet(dtdReader); FlatXmlDataSetBuilder builder = new FlatXmlDataSetBuilder(); builder.setMetaDataSet(new DatabaseDataSet(dbUnitConnection, false)); IDataSet xmlDataset = builder.build(asFile(xmlFilename)); return new CompositeDataSet(dtdDataset, xmlDataset);} How it works: DBUnit provides a facility to load a dataset from a DTD. This dataset contains all the tables listed in the DTD, but of course empty of data. The DTD dataset is then combined with a FlatXmlDataSet representing your test data. The graphic below illustrates the composite dataset that would be produced for the SpaceShip example. If you have dictionary tables whose contents never change, you can and should leave them out of the DTD as well as out of the XML datasets, to improve test performance a little. One further detail: you should close the FileReader after test setup. I couldn’t find a hook into the end of the test setup operation (short of writing my own DatabaseOperation), so I saved the reference as a member variable and hooked the close() call into the tear-down phase of the test. NB For a more complete code example, see this Gist snippet of a base class for TestNG+Spring+DBUnit tests that adds the above-described DBUnit setup operation to Spring’s TestNG helper class. Happy database testing! From http://www.andrewspencer.net/2011/solve-foreign-key-problems-in-dbunit-test-data/

i started to write a new article series about ibatis / mybatis . this is the first article and it will walk you through what is ibatis / mybatis and why you should use it. for those who does not know ibatis / mybatis yet, it is a persistence framework – an alternative to jdbc and hibernate , available for java and .net platforms. i’ve been working with it for almost two years, and i am enjoying it! the first thing you may notice in this and following articles about ibatis/mybatis is that i am using both ibatis and mybatis terms. why? until june 2010, ibatis was under apache license and since then, the framework founders decided to move it to google code and they renamed it to mybatis. the framework is still the same though, it just has a different name now. i gathered some resources, so i am just going to quote them: what is mybatis/ibatis? the mybatis data mapper framework makes it easier to use a relational database with object-oriented applications. mybatis couples objects with stored procedures or sql statements using a xml descriptor. simplicity is the biggest advantage of the mybatis data mapper over object relational mapping tools.to use the mybatis data mapper, you rely on your own objects, xml, and sql. there is little to learn that you don’t already know. with the mybatis data mapper, you have the full power of both sql and stored procedures at your fingertips. ( www.mybatis.org ) ibatis is based on the idea that there is value in relational databases and sql, and that it is a good idea to embrace the industrywide investment in sql. we have experiences whereby the database and even the sql itself have outlived the application source code, and even multiple versions of the source code. in some cases we have seen that an application was rewritten in a different language, but the sql and database remained largely unchanged. it is for such reasons that ibatis does not attempt to hide sql or avoid sql. it is a persistence layer framework that instead embraces sql by making it easier to work with and easier to integrate into modern object-oriented software. these days, there are rumors that databases and sql threaten our object models, but that does not have to be the case. ibatis can help to ensure that it is not. ( ibatis in action book) so… what is ibatis ? a jdbc framework developers write sql, ibatis executes it using jdbc. no more try/catch/finally/try/catch. an sql mapper automatically maps object properties to prepared statement parameters. automatically maps result sets to objects. support for getting rid of n+1 queries. a transaction manager ibatis will provide transaction management for database operations if no other transaction manager is available. ibatis will use external transaction management (spring, ejb cmt, etc.) if available. great integration with spring, but can also be used without spring (the spring folks were early supporters of ibatis). what isn’t ibatis ? an orm does not generate sql does not have a proprietary query language does not know about object identity does not transparently persist objects does not build an object cache essentially, ibatis is a very lightweight persistence solution that gives you most of the semantics of an o/r mapping toolkit, without all the drama. in other words ,ibatis strives to ease the development of data-driven applications by abstracting the low-level details involved in database communication (loading a database driver, obtaining and managing connections, managing transaction semantics, etc.), as well as providing higher-level orm capabilities (automated and configurable mapping of objects to sql calls, data type conversion management, support for static queries as well as dynamic queries based upon an object’s state, mapping of complex joins to complex object graphs, etc.). ibatis simply maps javabeans to sql statements using a very simple xml descriptor. simplicity is the key advantage of ibatis over other frameworks and object relational mapping tools.( http://www.developersbook.com ) who is using ibatis/mybatis? see the list in this link: http://www.apachebookstore.com/confluence/oss/pages/viewpage.action?pageid=25 i think the biggest case is myspace , with millions of users. very nice! this was just an introduction, so in next articles i will show how to create an application using ibatis/mybatis – step-by-step. enjoy! from http://loianegroner.com/2011/02/introduction-to-ibatis-mybatis-an-alternative-to-hibernate-and-jdbc/

The Spring Data project provides a solution for accessing data stored in new emerging technologies like NoSQL databases and cloud based services. When we look into the SpringSource git repository we see a lot of spring-data sub-projects: spring-data-commons: common interfaces and utility class for other spring-data projects. spring-data-column: support for column based databases. It has not started yet, but there will be support for Cassandra and HBase spring-data-document: support for document databases. Currently MongoDB and CouchDB are supported. spring-data-graph: support for graph based databases. Currently Neo4j is supported. spring-data-keyvalue: support for key-value databases. Currently Redis and Riak are supported and probably Membase will be supported in future. spring-data-jdbc-ext: JDBC extensions, as example Oracle RAC connection failover is implemented. spring-data-jpa: simplifies JPA based data access layer. I would like to share with you how you can use Redis. The first step is to download it from the redis.io web page. try.redis-db.com is a useful site where we can run Redis commands. It also provides a step by step tutorial. This tutorial shows us all structures that Redis supports (list, set, sorted set and hashes) and some useful commands. A lot of reputable sites use Redis today. After download and unpacking we should compile Redis (version 2.2, the release candidate is the preferable one to use since some commands do not work in version 2.0.4). make sudo make install Once we run these commands we are all set to run the following five commands: redis-benchmark - for benchmarking Redis server redis-check-aof - check the AOF (Aggregate Objective Function), and it can repair that. redis-check-dump - check rdb files for unprocessable opcodes. redis-cli - Redis client. redis-server - Redis server. We can test Redis server. redis-server [1055] 06 Jan 18:19:15 # Warning: no config file specified, using the default config. In order to specify a config file use 'redis-server /path/to/redis.conf' [1055] 06 Jan 18:19:15 * Server started, Redis version 2.0.4 [1055] 06 Jan 18:19:15 * The server is now ready to accept connections on port 6379 [1055] 06 Jan 18:19:15 - 0 clients connected (0 slaves), 1074272 bytes in use and Redis client. redis-cli redis> set my-super-key "my-super-value" OK Now we create a simple Java project in order to show how simple a spring-data-redis module really is. mvn archetype:create -DgroupId=info.pietrowski -DpackageName=info.pietrowski.redis -DartifactId=spring-data-redis -Dpackage=jar Next we have to add in pom.xml milestone spring repository, and add spring-data-redis as a dependency. After that all required dependencies will be fetched. Next we create a resources folder under the main folder, and create application.xml which will have all the configuration. We can configure the JedisConnectionFactory, in two different ways, One - we can provide a JedisShardInfo object in shardInfo property. Two - we can provide host (default localhost), port (default 6379), password (default empty) and timeout (default 2000) properties. One thing to keep in mind is that the JedisShardInfo object has precedence and allows to setup weight, but only allows constructor injection. We can setup the factory to use connection pooling by setting the value of the pooling property to 'true' (default). See application.xml comments to see three different way of configuration. Note: There are two different libraries supported: Jedis and JRedis. They have very similar names and both have the same factory name. See the difference: org.springframework.data.keyvalue.redis.connection.jedis.JedisConnectionFactory org.springframework.data.keyvalue.redis.connection.jredis.JredisConnectionFactory Similar to what we do in Spring, we configure the template object by providing it with a connection factory. We will perform all the operations through this template object. By default we need to provide only Connection Factory, but there are more properties we can provide: exposeConnection (default false) - if we return real connection or proxy object. keySerializer, hashKeySerializer, valueSerializer, hashValueSerializer (default JdkSerializationRedisSerializer) which delegates serialization to Java serialization mechanism. stringSerializer (default StringRedisSerializer) which is simple String to byte[] (and back) serializer with UTF8 encoding. We are ready to execute some code which will be cooperating with the Redis instance. Spring-Data provides us with two ways of interaction, First is by using the execute method and providing a RedisCallback object. Second is by using *Operations helpers (these will be explained later) When we are using RedisCallback we have access to low level Redis commands, see this list of interfaces (I won't put all the methods here because it is huge list): RedisConnection - gathers all Redis commands plus connection management. RedisCommands - gathers all Redis commands (listed beloved). RedisHashCommands - Hash-specific Redis commands. RedisListCommands - List-specific Redis commands. RedisSetCommands - Set-specific Redis commands. RedisStringCommands - key/value specific Redis commands. RedisTxCommands - Transaction/Batch specific Redis commands. RedisZSetCommands - Sorted Set-specific Redis commands. Check RedisCallbackExample class, this was the hard way and the problem is we have to convert our objects into byte arrays in both directions, the second way is easier. Spring Data provides for us with Operations objects, so we have much more simpler API and all byte<->object conversion is made by serializer we setup (or the default one). Higher level API (you will easily recognize *Operation *Commands equivalents): HashOperations - Redis hash operations. ListOperations - Redis list operations. SetOperations - Redis set operations. ValueOperations - Redis 'string' operations. ZSetOperations - Redis sorted set operations. Most of methods get key as first parameters so we have an even better API for multiple operations on the same key: BoundHashOperations - Redis hash operations for specific key. BoundListOperations - Redis list operations for specific key. BoundSetOperations - Redis set operations for specific key. BoundValueOperations - Redis 'string' operations for specific key. BoundZSetOperations - Redis sorted set operations for specific key. Check RedisCallbackExample class to see some easy examples of *Operations usage. One important thing to mention is that you should use stringSerializers for keys, otherwise you will have problems from other clients, because standard serialization adds class information. Otherwise you end up keys such as: "\xac\xed\x00\x05t\x00\x05atInt" "\xac\xed\x00\x05t\x00\nmySuperKey" "\xac\xed\x00\x05t\x00\bsuperKey" Up until now we have just checked the API for Redis, but Spring Data offers more for us. All the cool stuff is in org.springframework.data.keyvalue.redis.support package and all sub-packages. We have: RedisAtomicInteger - Atomic integer (CAS operation) backed by Redis. RedisAtomicLong - Same as previous for Long. RedisList - Redis extension for List, Queue, Deque, BlockingDeque and BlockingQueue with two additional methods List range(start, end) and RedisList trim(start, end). RedisSet - Redis extension for Set with additional methods: diff, diffAndStore, intersect, intersectAndStore, union, unionAndStore. RedisZSet - Redis extension for SortedSet. Note that Comparator is not applicable here so this interface extends normal Set and provide proper methods similar to SortedSet. RedisMap - Redis extension for Map with additional Long increment(key, delta) method Every interface currently has one Default implementation. Check application-support.xml for examples of configuration and RedisSupportClassesExample for examples of use. There is lot of useful information in the comments as well. Summary The library is a first milestone release so there are minor bugs, the documentation isn't as perfect as we used to and the current version needs no stable Redis server. But this is definitely a great library which allows us to use all this cool NoSQL stuff in a "standard" Spring Data Access manner. Awesome job! This post is only useful if you checkout the code: from bitbucket , for the lazy ones here is spring-data-redis zip file as well. This post is originally from http://pietrowski.info/2011/01/spring-data-redis-tutorial/

we've all seen them on various websites. crappy search utilities. they are a constant reminder that search is not something you should take lightly when building a website or application. search is not just google's game anymore. when a java library called lucene was introduced into the apache ecosystem, and then solr was built on top of that, open source developers began to wield some serious power when it came to customizing search features. in this article you'll be introduced to apache solr and a wealth of applications that have been built with it. the content is divided as follows: introduction setup solr applications summary 1. introduction apache solr is an open source search server. it is based on the full text search engine called apache lucene . so basically solr is an http wrapper around an inverted index provided by lucene. an inverted index could be seen as a list of words where each word-entry links to the documents it is contained in. that way getting all documents for the search query "dzone" is a simple 'get' operation. one advantage of solr in enterprise projects is that you don't need any java code, although java itself has to be installed. if you are unsure when to use solr and when lucene, these answers could help. if you need to build your solr index from websites, you should take a look into the open source crawler called apache nutch before creating your own solution. to be convinced that solr is actually used in a lot of enterprise projects, take a look at this amazing list of public projects powered by solr . if you encounter problems then the mailing list or stackoverflow will help you. to make the introduction complete i would like to mention my personal link list and the resources page which lists books, articles and more interesting material. 2. setup solr 2.1. installation as the very first step, you should follow the official tutorial which covers the basic aspects of any search use case: indexing - get the data of any form into solr. examples: json, xml, csv and sql-database. this step creates the inverted index - i.e. it links every term to its documents. querying - ask solr to return the most relevant documents for the users' query to follow the official tutorial you'll have to download java and the latest version of solr here . more information about installation is available at the official description . next you'll have to decide which web server you choose for solr. in the official tutorial, jetty is used, but you can also use tomcat. when you choose tomcat be sure you are setting the utf-8 encoding in the server.xml . i would also research the different versions of solr, which can be quite confusing for beginners: the current stable version is 1.4.1. use this if you need a stable search and don't need one of the latest features. the next stable version of solr will be 3.x the versions 1.5 and 2.x will be skipped in order to reach the same versioning as lucene. version 4.x is the latest development branch. solr 4.x handles advanced features like language detection via tika, spatial search , results grouping (group by field / collapsing), a new "user-facing" query parser ( edismax handler ), near real time indexing, huge fuzzy search performance improvements, sql join-a like feature and more. 2.2. indexing if you've followed the official tutorial you have pushed some xml files into the solr index. this process is called indexing or feeding. there are a lot more possibilities to get data into solr: using the data import handler (dih) is a really powerful language neutral option. it allows you to read from a sql database, from csv, xml files, rss feeds, emails, etc. without any java knowledge. dih handles full-imports and delta-imports. this is necessary when only a small amount of documents were added, updated or deleted. the http interface is used from the post tool, which you have already used in the official tutorial to index xml files. client libraries in different languages also exist. (e.g. for java (solrj) or python ). before indexing you'll have to decide which data fields should be searchable and how the fields should get indexed. for example, when you have a field with html in it, then you can strip irrelevant characters , tokenize the text into 'searchable terms', lower case the terms and finally stem the terms . in contrast, if you would have a field with text in it that should not be interpreted (e.g. urls) you shouldn't tokenize it and use the default field type string. please refer to the official documentation about field and field type definitions in the schema.xml file. when designing an index keep in mind the advice from mauricio : "the document is what you will search for. " for example, if you have tweets and you want to search for similar users, you'll need to setup a user index - created from the tweets. then every document is a user. if you want to search for tweets, then setup a tweet index; then every document is a tweet. of course, you can setup both indices with the multi index options of solr. please also note that there is a project called solr cell which lets you extract the relevant information out of several different document types with the help of tika. 2.3. querying for debugging it is very convenient to use the http interface with a browser to query solr and get back xml. use firefox and the xml will be displayed nicely: you can also use the velocity contribution , a cross-browser tool, which will be covered in more detail in the section about 'search application prototyping' . to query the index you can use the dismax handler or standard query handler . you can filter and sort the results: q=superman&fq=type:book&sort=price asc you can also do a lot more ; one other concept is boosting. in solr you can boost while indexing and while querying. to prefer the terms in the title write: q=title:superman^2 subject:superman when using the dismax request handler write: q=superman&qf=title^2 subject check out all the various query options like fuzzy search , spellcheck query input , facets , collapsing and suffix query support . 3. applications now i will list some interesting use cases for solr - in no particular order. to see how powerful and flexible this open source search server is. 3.1. drupal integration the drupal integration can be seen as generic use case to integrate solr into php projects. for the php integration you have the choice to either use the http interface for querying and retrieving xml or json. or to use the php solr client library . here is a screenshot of a typical faceted search in drupal : for more information about faceted search look into the wiki of solr . more php projects which integrates solr: open source typo3- solr module magento enterprise - solr module . the open source integration is out dated. oxid - solr module . no open source integration available. 3.2. hathi trust the hathi trust project is a nice example that proves solr's ability to search big digital libraries. to quote directly from the article : "... the index for our one million book index is over 200 gigabytes ... so we expect to end up with a two terabyte index for 10 million books" other examples for libraries: vufind - aims to replace opac internet archive national library of australia 3.3. auto suggestions mainly, there are two approaches to implement auto-suggestions (also called auto-completion) with solr: via facets or via ngramfilterfactory . to push it to the extreme you can use a lucene index entirely in ram. this approach is used in a large music shop in germany. live examples for auto suggestions: kaufda.de 3.4. spatial search applications when mentioning spatial search, people have geographical based applications in mind. with solr, this ordinary use case is attainable . some examples for this are : city search - city guides yellow pages kaufda.de spatial search can be useful in many different ways : for bioinformatics, fingerprints search, facial search, etc. (getting the fingerprint of a document is important for duplicate detection). the simplest approach is implemented in jetwick to reduce duplicate tweets, but this yields a performance of o(n) where n is the number of queried terms. this is okay for 10 or less terms, but it can get even better at o(1)! the idea is to use a special hash set to get all similar documents. this technique is called local sensitive hashing . read this nice paper about 'near similarity search and plagiarism analysis' for more information. 3.5. duckduckgo duckduckgo is made with open source and its "zero click" information is done with the help of solr using the dismax query handler: the index for that feature contains 18m documents and has a size of ~12gb. for this case had to tune solr: " i have two requirements that differ a bit from most sites with respect to solr: i generally only show one result, with sometimes a couple below if you click on them. therefore, it was really important that the first result is what people expected. false positives are really bad in 0-click, so i needed a way to not show anything if a match wasn't too relevant. i got around these by a) tweaking dismax and schema and b) adding my own relevancy filter on top that would re-order and not show anything in various situations. " all the rest is done with tuned open source products. to quote gabriel again: "the main results are a hybrid of a lot of things, including external apis, e.g. bing, wolframalpha, yahoo, my own indexes and negative indexes (spam removal), etc. there are a bunch of different types of data i'm working with. " check out the other cool features such as privacy or bang searches . 3.6. clustering support with carrot2 carrot2 is one of the "contributed plugins" of solr. with carrot2 you can support clustering : " clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. " see some research papers regarding clustering here . here is one visual example when applying clustering on the search "pannous" - our company : 3.7. near real time search solr isn't real time yet, but you can tune solr to the point where it becomes near real time, which means that the time ('real time latency') that a document takes to be searchable after it gets indexed is less than 60 seconds even if you need to update frequently. to make this work, you can setup two indices. one write-only index "w" for the indexer and one read-only index "r" for your application. index r refers to the same data directory of w, which has to be defined in the solrconfig.xml of r via: /pathto/indexw/data/ to make sure your users and the r index see the indexed documents of w, you have to trigger an empty commit every 60 seconds: wget -q http://localhost:port/solr/update?stream.body=%3ccommit/%3e -o /dev/null everytime such a commit is triggered a new searcher without any cache entries is created. this can harm performance for visitors hitting the empty cache directly after this commit, but you can fill the cache with static searches with the help of the newsearcher entry in your solrconfig.xml. additionally, the autowarmcount property needs to be tuned, which fills the cache with a newsearcher from old entries. also, take a look at the article 'scaling lucene and solr' , where experts explain in detail what to do with large indices (=> 'sharding') and what to do for high query volume (=> 'replicating'). 3.8. loggly = full text search in logs feeding log files into solr and searching them at near real-time shows that solr can handle massive amounts of data and queries the data quickly. i've setup a simple project where i'm doing similar things , but loggly has done a lot more to make the same task real-time and distributed. you'll need to keep the write index as small as possible otherwise commit time will increase too great. loggly creates a new solr index every 5 minutes and includes this when searching using the distributed capabilities of solr ! they are merging the cores to keep the number of indices small, but this is not as simple as it sounds. watch this video to get some details about their work. 3.9. solandra = solr + cassandra solandra combines solr and the distributed database cassandra , which was created by facebook for its inbox search and then open sourced. at the moment solandra is not intended for production use. there are still some bugs and the distributed limitations of solr apply to solandra too. tthe developers are working very hard to make solandra better. jetwick can now run via solandra just by changing the solrconfig.xml. solandra also has the advantages of being real-time (no optimize, no commit!) and distributed without any major setup involved. the same is true for solr cloud. 3.10. category browsing via facets solr provides facets , which make it easy to show the user some useful filter options like those shown in the "drupal integration" example. like i described earlier , it is even possible to browse through a deep category tree. the main advantage here is that the categories depend on the query. this way the user can further filter the search results with this category tree provided by you. here is an example where this feature is implemented for one of the biggest second hand stores in germany. a click on 'schauspieler' shows its sub-items: other shops: game-change 3.11. jetwick - open twitter search you may have noticed that twitter is using lucene under the hood . twitter has a very extreme use case: over 1,000 tweets per second, over 12,000 queries per second, but the real-time latency is under 10 seconds! however, the relevancy at that volume is often not that good in my opinion. twitter search often contains a lot of duplicates and noise. reducing this was one reason i created jetwick in my spare time. i'm mentioning jetwick here because it makes extreme use of facets which provides all the filters to the user. facets are used for the rss-alike feature (saved searches), the various filters like language and retweet-count on the left, and to get trending terms and links on the right: to make jetwick more scalable i'll need to decide which of the following distribution options to choose: use solr cloud with zookeeper use solandra move from solr to elasticsearch which is also based on apache lucene other examples with a lot of facets: cnet reviews - product reviews. electronics reviews, computer reviews & more. shopper.com - compare prices and shop for computers, cell phones, digital cameras & more. zappos - shoes and clothing. manta.com - find companies. connect with customers. 3.12. plaxo - online address management plaxo.com , which is now owned by comcast, hosts web addresses for more than 40 million people and offers smart search through the addresses - with the help of solr. plaxo is trying to get the latest 'social' information of your contacts through blog posts, tweets, etc. plaxo also tries to reduce duplicates . 3.13. replace fast or google search several users report that they have migrated from a commercial search solution like fast or google search appliance (gsa) to solr (or lucene). the reasons for that migration are different: fast drops linux support and google can make integration problems. the main reason for me is that solr isn't a black box —you can tweak the source code, maintain old versions and fix your bugs more quickly! 3.14. search application prototyping with the help of the already integrated velocity plugin and the data import handler it is possible to create an application prototype for your search within a few hours. the next version of solr makes the use of velocity easier. the gui is available via http://localhost:port/solr/browse if you are a ruby on rails user, you can take a look into flare. to learn more about search application prototyping, check out this video introduction and take a look at these slides. 3.15. solr as a whitelist imagine you are the new google and you have a lot of different types of data to display e.g. 'news', 'video', 'music', 'maps', 'shopping' and much more. some of those types can only be retrieved from some legacy systems and you only want to show the most appropriated types based on your business logic . e.g. a query which contains 'new york' should result in the selection of results from 'maps', but 'new yorker' should prefer results from the 'shopping' type. with solr you can set up such a whitelist-index that will help to decide which type is more important for the search query. for example if you get more or more relevant results for the 'shopping' type then you should prefer results from this type. without the whitelist-index - i.e. having all data in separate indices or systems, would make it nearly impossible to compare the relevancy. the whitelist-index can be used as illustrated in the next steps. 1. query the whitelist-index, 2. decide which data types to display, 3. query the sub-systems and 4. display results from the selected types only. 3.16. future solr is also useful for scientific applications, such as a dna search systems. i believe solr can also be used for completely different alphabets so that you can query nucleotide sequences - instead of words - to get the matching genes and determine which organism the sequence occurs in, something similar to blast . another idea you could harness would be to build a very personalized search. every user can drag and drop their websites of choice and query them afterwards. for example, often i only need stackoverflow, some wikis and some mailing lists with the expected results, but normal web search engines (google, bing, etc.) give me results that are too cluttered. my final idea for a future solr-based app could be a lucene/solr implementation of desktop search. solr's facets would be especially handy to quickly filter different sources (files, folders, bookmarks, man pages, ...). it would be a great way to wade through those extra messy desktops. 4. summary the next time you think about a problem, think about solr! even if you don't know java and even if you know nothing about search: solr should be in your toolbox. solr doesn't only offer professional full text search, it could also add valuable features to your application. some of them i covered in this article, but i'm sure there are still some exciting possibilities waiting for you!

Almost all .NET developers who have been working in several applications up to date are probably familiar with writing SQL queries for specific needs within the application. Before LINQ as a technology came on scene, my daily programming life was about 60-70% of the day writing code either in the front-end (ASPX, JavaScript, jQuery, HTML/CSS etc…) or in the back-end (C#, VB.NET etc…), and about 30-40% writing SQL queries for specific needs used within the application. Now, when LINQ is there, I feel that the percentage for writing SQL queries got down to about 10% per day. I don’t say it won’t change with time depending what technology I use within the projects or what way would be better, but since I’m writing a lot LINQ code in the latest projects, I thought to see if there is a tool that can automatically translate SQL to LINQ so that I can transfer many queries as a LINQ statements within the code. Linqer is a tool that I have tested in the previous two weeks and I see it works pretty good. Even I’m not using it yet to convert SQL to LINQ code because I did it manually before I discovered that Linqer could have really helped me, I would recommend it for those who are just starting with LINQ and have knowledge of writing SQL queries. Let’s pass through several steps so that I will help you get started faster… 1. Go to http://www.sqltolinq.com/ website and download the version you want. There is a Linqer Version 4.0.1 for .NET 4.0 or Linqer Version 3.5.1 for .NET 3.5. 2. Once you download the zip file, extract it and launch the Linqer4Inst.exe then add install location. In the location you will add, the Linqer.exe will be created. 3. Launch the Linqer.exe. Once you run it for first time, the Linqer Connection Pool will be displayed so that you can create connection to your existing Model Click the Add button Right after this, the following window will appear #1 – The name of the connection string you are creating #2 – Click “…” to construct your connection string using Wizard window #3 – Chose your language, either C# or VB #4 – Model LINQ to SQL or LINQ to Entities Right after you select LINQ to SQL, the options to select the files for the Model will be displayed. In our case I will select LINQ to SQL, and here is the current progress So, you can select existing model from your application or you can Generate LINQ to SQL Files so that the *.dbml and *.designer.cs will be automatically filled #5 – At the end, you can chose your context name of the model which will be used when generating the LINQ code Once you are done, click OK. You will get back to the parent window filled with all needed info and click Close. Note: You can later add additional connections in your Linqer Connections Pool from Tools –> Linqer Connections In the root folder where your Linqer.exe is placed, now you have Linqer.ini file containing the Connection string settings. Ok, now lets go to the interesting part. Lets create one (first) simple SQL query and try to translate it to LINQ statement. SQL Query select * from authors a where a.city = 'Oakland' If we add this query to Linqer, here is the result: So, the LINQ code is similar to the SQL code and is easy to read since it’s simple. Also, if you notice, the tool generates class (you can add class name) with prepared code for using in your project. Perfect! Now, lets try to translate a query with two joined tables (little bit more complex): SQL Query select * from employee left join publishers on employee.pub_id = publishers.pub_id where employee.fname like '%a' The LINQ generated code is: from employee in db.Employee join publishers in db.Publishers on employee.Pub_id equals publishers.Pub_id into publishers_join from publishers in publishers_join.DefaultIfEmpty() where employee.Fname.EndsWith("a") select new { employee.Emp_id, employee.Fname, employee.Minit, employee.Lname, employee.Job_id, employee.Job_lvl, employee.Pub_id, employee.Hire_date, Column1 = publishers.Pub_id, Pub_name = publishers.Pub_name, City = publishers.City, State = publishers.State, Country = publishers.Country } So, if you can notice the where clause, we said in the SQL query: ... like "%a" and the corresponding LINQ code in C# is ... EndsWith("a"); - Excellent! And the Class automatically generated by the tool is public class EmployeePubClass { private String _Emp_id; private String _Fname; private String _Minit; private String _Lname; private Int16? _Job_id; private Byte? _Job_lvl; private String _Pub_id; private DateTime? _Hire_date; private String _Column1; private String _Pub_name; private String _City; private String _State; private String _Country; public EmployeePubClass( String AEmp_id, String AFname, String AMinit, String ALname, Int16? AJob_id, Byte? AJob_lvl, String APub_id, DateTime? AHire_date, String AColumn1, String APub_name, String ACity, String AState, String ACountry) { _Emp_id = AEmp_id; _Fname = AFname; _Minit = AMinit; _Lname = ALname; _Job_id = AJob_id; _Job_lvl = AJob_lvl; _Pub_id = APub_id; _Hire_date = AHire_date; _Column1 = AColumn1; _Pub_name = APub_name; _City = ACity; _State = AState; _Country = ACountry; } public String Emp_id { get { return _Emp_id; } } public String Fname { get { return _Fname; } } public String Minit { get { return _Minit; } } public String Lname { get { return _Lname; } } public Int16? Job_id { get { return _Job_id; } } public Byte? Job_lvl { get { return _Job_lvl; } } public String Pub_id { get { return _Pub_id; } } public DateTime? Hire_date { get { return _Hire_date; } } public String Column1 { get { return _Column1; } } public String Pub_name { get { return _Pub_name; } } public String City { get { return _City; } } public String State { get { return _State; } } public String Country { get { return _Country; } } } public class List: List { public List(Pubs db) { var query = from employee in db.Employee join publishers in db.Publishers on employee.Pub_id equals publishers.Pub_id into publishers_join from publishers in publishers_join.DefaultIfEmpty() where employee.Fname.EndsWith("a") select new { employee.Emp_id, employee.Fname, employee.Minit, employee.Lname, employee.Job_id, employee.Job_lvl, employee.Pub_id, employee.Hire_date, Column1 = publishers.Pub_id, Pub_name = publishers.Pub_name, City = publishers.City, State = publishers.State, Country = publishers.Country }; foreach (var r in query) Add(new EmployeePubClass( r.Emp_id, r.Fname, r.Minit, r.Lname, r.Job_id, r.Job_lvl, r.Pub_id, r.Hire_date, r.Column1, r.Pub_name, r.City, r.State, r.Country)); } } Great! We have ready-to-use class for our application and we don't need to type all this code. Besides this way to generate code, you can in same time use this tool to see the db results I like this tool because mainly it’s very easy to use, lightweight and does the job pretty straight forward. You can try the tool and send me feedback using the comments in this blog post.

This article considers the issues of one to many relationships from the JPA model, and looks at an alternative strategy to provide more efficient and fine grained data access, to build more robust and lightweight applications and web services. A fairly typical use is to have one entity ‘owned’ by the other in such a way that one entity is said to ‘have’ many instances of the other one. A typical example would be customer and orders : class Customer { @OneToMany(mappedBy="customer") private Set orders; } class Order { @ManyToOne private Customer customer; } In this trivial example, the order belongs to a customer, and the customer has a set of orders. We don’t have a problem with the ManyToOne relationship, especially as it is required in order to map the order back to the customer. When we load an order we will at most get a single reference to a customer. No, our problem is with the value we get from customer.getOrders() as this set of order entities doesn’t really serve any useful purpose and can cause more problems than it solves for the following reasons : Dumb Relationship – It will contain every order for this particular customer when you usually only want a subset of the orders that match a set of criteria. You either have to read them all and filter the ones you don’t want manually (which is what SQL is for) or you end up having to make a call to a method to get the specific entities you are interested in. Unbounded dataset – How many orders a customer has could vary and you could end up with a customer with thousands of orders. Combined with accidental eager fetching and loading a simple list of 10 people could mean loading thousands of entities. Unsecured Access – Sometimes we may want to restrict the items visible to the user based on their security rights. By making it available as a property controlled by JPA we lose that ability or have to implement it further down in the application stack. No Pagination – Similar to the unbounded dataset, you end up throwing the whole list into the pagination components and letting them sort out what to display. In most cases, you need to treat each dataset like it will eventually contain more than 30 records so you really need to consider pagination early. Overgrown object graph – When you request an entity, how much of the object graph do you need? How do you know which pieces to initialize so you can avoid LIEs? This is often the case with JPA, but is also more relevant when you take account of the needs to serialize object graphs to XML or JSON. Sometimes you might need the relationships and sometimes you do not depending on the context you will be using the data in. Rife with pitfalls – Who saves and cascades what and how do you bind one to the other? You create an order, and assign the customer, do you need to then add it to the customers list of orders or not. What happens if you forget to add it to the customer and you save the customer? Whatever strategy you pick for dealing with this will no doubt end up being implemented inconsistently. (Ok, the first four are really different facets of the same problem, that you can’t control the data you are getting back.) So what use are they? Well, they make it really tempting just to use customer.orders in the view which is suitable for some sets of data. They also allow the relationship to be used in ejbql statements, although the inverse of the relationship can also be used in most cases. Specifying this relationship can also allow you to cascade updates/deletes from the customer to the order, but then so can your database. Going Granular The best alternative I’ve found is to provide additional methods to obtain the relational information separate from the model. This more granular approach gives you plenty of ways of obtaining data from the database without the dangers and temptations of bad practices. For example, the Order object still has the Customer reference on it, which we use to obtain lists of orders from the data access layer which can be constrained by customer, time frame, or other criteria depending on where it is being used. Also, it allows data to be fetched when needed without having to define a single initialization strategy using annotations or mapping files. The code that knows what pieces of data it needs will have access to facilities to fetch the specific data it needs. Alternatively, the methods to fetch the data can either be exposed as web services directly or DTO objects can be used to build a data payload to be returned from a single web service that consolidates the calls. Regardless, you don’t need to worry about setting the JPA fetch or XML/JSON serialization policy permanently in the model. Some examples might be to fetch orders for a customer in different ways. public List getOrders(Long customerId) {...} public List getOrders(Long customerId,Date startDate,Date endDate) {...} public List getOrders(SearchCriteria searchCriteria,int firstResult,int pageSize) {...} What about @ManyToMany Good question. In most cases I find that what starts as a many to many relationship can usually be modeled as a separate entity because when you create a many to many relationship, there is usually additional information stored with that relationship. For example, a Users and Groups ManyToMany relationship has many users belonging to many groups and vice versa. The membership however also probably has start and end dates and also maybe a role within that group. This also exhibits one of the earlier problems in that user.getGroupMemberships() would return all group memberships past and present whereas you probably only want the active ones. Modeling it as a separate entity means it becomes an entity with two OneToMany relationships. While there are cases where the many to many relationship is literally just a pair of ids (think blog post tags, many tags to many posts), you could benefit at a later date by using an entity if you decide to add additional information into the relationship. In summary, moving relational fetches out of the data model and into the data layer means you remove some of the temptations of bad practices and create a library of reusable functions for fetching the data that can be used from different code points. From http://www.andygibson.net/blog/article/deterring-tomany-relationships-in-jpa-models/

Two tips for the Eclipse Modeling Framework (EMF) 2.2.1: Accessing model’s meta model – accessing EClass/attribute by name – so that you can set an attribute when you only know its name and haven’t its EAttribute How to force EMF to serialize an object as an XML element instead of an XML attribute Excuse my rather liberal and slightly confusing use of the terms model, model instance and meta model. Tip 1: Accessing model’s meta model – accessing EClass/attribute by name Normally you cannot do any operation on a dynamic EMF model instance – such as instantiating an EObject or settings its property via eSet – without the corresponding meta model objects such as EClass and EAttribute. But there is a solution – you can build an ExtendedMetaData instance and use its methods to find the meta model objects based on search criteria such as element name and namespace. Examples Create ExtendedMetaData from a model One way to build a meta data instance is to instantiate BasicExtendedMetaData based on a Registry, containing all registered packages. This is usually available either via ResourceSet.getPackageRegistry() or globally, via EPackage.Registry.INSTANCE. import org.eclipse.emf.ecore.util.*; ... ExtendedMetaData modelMetaData = new BasicExtendedMetaData(myResourceSet.getPackageRegistry()); Get EClass for a namespace and name Provided that your model contains an element with the name Book and namespace http://example.com/book: EClass bookEClass = (EClass) modelMetaData.getType("http://example.com/book", "Book"); Get EClass’ attribute by name Beware: Some properties (such as those described by named complex types) are not represented by an EAttribute but an EReference (both extend EStructuralFeature) and are accessible as the EClass’ elements, not attributes, even though from developer’s points of view they’re attributes of the owning class. Let’s suppose that the book has the attribute name: EStructuralFeature nameAttr = modelMetaData.getElement(bookEClass, null, "name"); The namespace is null because normally attributes/nested elements are not classified with a schema. Here is how you would print the name and namespace URI of an attribute/element: System.out.println("attr: " + modelMetaData.getNamespace(nameAttr) + ":" + nameAttr.getName()); //prints "null:name" Tip 2: How to force EMF to serialize an object as an XML element instead of an XML attribute Normally EMF stores simple Java properties as attributes of the XML element representing the owning class: but you might prefer to have it rather as a nested element: The Book of Songs To achieve that: Enable the save option OPTION_EXTENDED_META_DATA (so that extended meta data such as annotations and an XML map aren’t ignored) Tell EMF that you want this property to be stored as an element By attaching an eAnnotation to it (not shown) By supplying a XML Map with this information upon save To enable the extended metadata: Map saveOptions = new HashMap(); saveOptions.put(XMLResource.OPTION_EXTENDED_META_DATA, Boolean.TRUE); According to some documentation the value should be an implementation of ExtendedMetaData, according to others Boolean.TRUE is the correct choice – I use the latter for it’s easier and works for me. To tell EMF to write a property as an element when serailizing to XML: import org.eclipse.emf.ecore.xmi.impl.*; ... EAttribute bookNameEAttribute = ...; // retrieved e.g. from meta data, see Tip 1 XMLMapImpl map = new XMLMapImpl(); XMLInfoImpl x = new XMLInfoImpl(); x.setXMLRepresentation(XMLInfoImpl.ELEMENT); map.add(bookNameEAttribute, x); saveOptions.put(XMLResource.OPTION_XML_MAP, map); The XMLInfoImpl enables you to customize the namespace, name and representation of the element. When saving you then just supply the save options: EObject target = ...; org.eclipse.emf.ecore.resource.Resource outResource = ...; outResource.getContents().add(target); outResource.save(saveOptions); Reference: the Redbook Eclipse Development using the Graphical Editing Framework and the Eclipse Modeling Framework, page 74, section 2.3.4 From http://theholyjava.wordpress.com/2011/01/11/emf-tips-accessing-model-meta-data-serializing-into-elementattribute/

Does MySQL work with Entity Framework 4.0? The answer is: yes, it works! I just put up one experimental project to play with MySQL and Entity Framework 4.0 and in this posting I will show you how to get MySQL data to EF. Also I will give some suggestions how to deploy your applications to hosting and cloud environments. MySQL stuff As you may guess you need MySQL running somewhere. I have MySQL installed to my development machine so I can also develop stuff when I’m offline. The other thing you need is MySQL Connector for .NET Framework. Currently there is available development version of MySQL Connector/NET 6.3.5 that supports Visual Studio 2010. Before you start download MySQL and Connector/NET: MySQL Community Server Connector/NET 6.3.5 If you are not big fan of phpMyAdmin then you can try out free desktop client for MySQL – HeidiSQL. I am using it and I am really happy with this program. NB! If you just put up MySQL then create also database with couple of table there. To use all features of Entity Framework 4.0 I suggest you to use InnoDB or other engine that has support for foreign keys. Connecting MySQL to Entity Framework 4.0 Now create simple console project using Visual Studio 2010 and go through the following steps. 1. Add new ADO.NET Entity Data Model to your project. For model insert the name that is informative and that you are able later recognize. Now you can choose how you want to create your model. Select “Generate from database” and click OK. 2. Set up database connection Change data connection and select MySQL Database as data source. You may also need to set provider – there is only one choice. Select it if data provider combo shows empty value. Click OK and insert connection information you are asked about. Don’t forget to click test connection button to see if your connection data is okay. If everything works then click OK. 3. Insert context name Now you should see the following dialog. Insert your data model name for application configuration file and click OK. Click next button. 4. Select tables for model Now you can select tables and views your classes are based on. I have small database with events data. Uncheck the checkbox “Include foreign key columns in the model” – it is damn annoying to get them away from model later. Also insert informative and easy to remember name for your model. Click finish button. 5. Define your classes Now it’s time to define your classes. Here you can see what Entity Framework generated for you. Relations were detected automatically – that’s why we needed foreign keys. The names of classes and their members are not nice yet. After some modifications my class model looks like on the following diagram. Note that I removed attendees navigation property from person class. Now my classes look nice and they follow conventions I am using when naming classes and their members. NB! Don’t forget to see properties of classes (properties windows) and modify their set names if set names contain numbers (I changed set name for Entity from Entity1 to Entities). 6. Let’s test! Now let’s write simple testing program to see if MySQL data runs through Entity Framework 4.0 as expected. My program looks for events where I attended. using(var context = new MySqlEntities()) { var myEvents = from e in context.Events from a in e.Attendees where a.Person.FirstName == "Gunnar" && a.Person.LastName == "Peipman" select e; Console.WriteLine("My events: "); foreach(var e in myEvents) { Console.WriteLine(e.Title); } } Console.ReadKey(); And when I run it I get the result shown on screenshot on right. I checked out from database and these results are correct. At first run connector seems to work slow but this is only the effect of first run. As connector is loaded to memory by Entity Framework it works fast from this point on. Now let’s see what we have to do to get our program work in hosting and cloud environments where MySQL connector is not installed. Deploying application to hosting and cloud environments If your hosting or cloud environment has no MySQL connector installed you have to provide MySQL connector assemblies with your project. Add the following assemblies to your project’s bin folder and include them to your project (otherwise they are not packaged by WebDeploy and Azure tools): MySQL.Data MySQL.Data.Entity MySQL.Web You can also add references to these assemblies and mark references as local so these assemblies are copied to binary folder of your application. If you have references to these assemblies then you don’t have to include them to your project from bin folder. Also add the following block to your application configuration file. ... ... Conclusion It was not hard to get MySQL connector installed and MySQL connected to Entity Framework 4.0. To use full power of Entity Framework we used InnoDB engine because it supports foreign keys. It was also easy to query our model. To get our project online we needed some easy modifications to our project and configuration files.

As promised I am going to provide an article on how we can use Sphinx with Java to perform a full text search. I will begin the article with an introduction to Sphinx. Introduction to Sphinx Databases are continually growing and sometimes tend to hold about 100M records and need an external solution for full text search to be performed. I have picked Sphinx, an open source full-text search engine, distributed under GPL version 2 to perform a full text search on such a huge amount of data. Generally, it's a standalone search engine meant to provide fast, size-efficient and relevant full-text search functions to other applications very much compatible with an SQL Database. So my example will be based on the MySQL database, as we cannot produce millions of data to evaluate the real power of Sphinx, we will have a small amount of data and I think that should not be a problem. Here are few Sphinx Unique Features: high indexing speed (up to 10 MB/sec on modern CPUs) high search speed (avg query is under 0.1 sec on 2-4 GB text collections) high scalability (up to 100 GB of text, upto 100 M documents on a single CPU) provides distributed searching capabilities provides searching from within MySQL through pluggable storage engine supports boolean, phrase, and word proximity queries supports multiple full-text fields per document (upto 32 by default) supports multiple additional attributes per document (ie. groups, timestamps, etc) supports MySQL natively (MyISAM and InnoDB tables are both supported) The important features which have been adopted to perform a full text search are the provision of the Java API to integrate easily with the web application and considerably high indexing and searching speed with an average of 4-10 MB/sec & 20-30 ms/q @5GB,3.5M docs(wikipedia) Sphinx Terms & How It Works The fist principle part of sphinx is indexer. It is solely responsible for gathering the data that will be searchable. From the Sphinx point of view, the data it indexes is a set of structured documents, each of which has the same set of fields. This is biased towards SQL, where each row corresponds to a document, and each column to a field. Sphinx builds a special data structure optimized for our queries from the data provided. This structure is called index; and the process of building index from data is called indexing and the element of sphinx which carries out these tasks is called indexer. Indexer can be executed either from a regular script or command-line interface. Sphinx documents are equal to records in DB. Document is set of text fields and number attributes + unique ID – similar to row in DB Set of fields and attributes is constant for index – similar to table in DB Fields are searchable for FullText queries Attributes may be used for filtering, sorting, grouping searchd is the second principle tools as part of Sphinx. It is the part of the system which actually handles searches; it functions as a server and is responsible for receiving queries, processing them and returning a dataset back to the different APIs for client applications. Unlike indexer, searchd is not designed to be run either from a regular script or command-line calling, but instead either as a daemon to be called from init.d (on Unix/Linux type systems) or to be called as a service (on Windows-type systems). I am going to focus on Windows environment so later I will show you how we can install sphinx on windows as a service. Finally search is one of the helper tools within the Sphinx package. Whereas searchd is responsible for searches in a server-type environment, search is aimed at testing the index quickly without building a framework to make the connection to the server and process its response. This will only be used for testing sphinx from command – line and with respect to application’s requirement; searchd service will be used to query the MySql Server with a pre created index. Installation on Windows So now we come to the part of installing Sphinx on Windows: Download Sphinx from the official Sphinx download site i.e http://sphinxsearch.com (I downloaded Win32 release binaries with MySQL support: sphinx-0.9.9-win32.zip) Unzip the file to some folder, I unzipped to C:\devel\sphinx-0.9.9-win32 and added the bin directory to the windows path variable Well Sphinx is installed. Nice, simple, easy. Later I will tell how to set up indexes and search. Sample Application Till now I guess the whole motto of this article is clear to you, let's move ahead to define our sample application. We all use the Address Book to search for people by using their name or e-mail address when we want to immediately address an e-mail message to a specific person, people, or distribution list. We also search for people by using other basic information, such as e-mail alias, office location, and telephone number etc. I think most of the people on this planet are quire familiar with this kind of search, so let's make outlook address book as our sample database schema. Most of the fields are mapped from microsoft outlook, the only additional column is date of joining so that we can filter our queries based on joining dates of the employees. The example that I am going to put forth will use Sphinx to search for a particluar address entry using free text search, meaning the user is free to type in anything, here is our search screen, the DOJ (date of joining) search parameter is optional. The screen is self explanatory, let's move ahead and define our database. As Sphinx works well with MySQL and MySQL being free also, lets create our db scripts around mysql database (Those who wish to install MySQL can dowload it from http://www.mysql.com) Let's create our sample database 'addressbook' mysql> create database addressbook; Query OK, 1 row affected (0.03 sec) mysql> use addressbook; Database changed Note: The fields defined in the following tables are for the purpose of learning only and may not contain a complete set of fields that microsoft address book or any similar software may provide. mysql> CREATE TABLE addressbook ( Id int(11) NOT NULL, FirstName varchar(30) NOT NULL, LastName varchar(30) NOT NULL, OfficeId int(11) DEFAULT NULL, Title varchar(20) DEFAULT NULL, Alias varchar(20) NOT NULL, Email varchar(50) NOT NULL, DOJ date NOT NULL, PhoneNo varchar(20) DEFAULT NULL ) ENGINE=InnoDB DEFAULT CHARSET=utf8; mysql> CREATE TABLE CompanyLocations ( Id int(11) NOT NULL, Location varchar(60) NOT NULL, Country varchar(20) NOT NULL ) ENGINE=InnoDB DEFAULT CHARSET=utf8; It's time to put some dummy data into the table, so let's fill our tables. Our virtual company 'gogs.it' has six offices across India and Singapore as defined in the following insert script. mysql> insert into CompanyLocations (Id, Location, Country) VALUES (1, 'Tower One, Harbour Front, Singapore', 'SG'); insert into CompanyLocations (Id, Location, Country) VALUES (2, 'DLF Phase 3, Gurgaon, India', 'IN'); insert into CompanyLocations (Id, Location, Country) VALUES (3, 'Hiranandani Gardens, Powai, Mumbai, India', 'IN'); insert into CompanyLocations (Id, Location, Country) VALUES (4, 'Hinjwadi, Pune, India', 'IN'); insert into CompanyLocations (Id, Location, Country) VALUES (5, 'Toll Post, Nagrota, Jammu, India', 'IN'); insert into CompanyLocations (Id, Location, Country) VALUES (6, 'Bani (Kathua), India', 'IN'); Now comes the real stuff... The data sphinx is going to index, let's populate that as well...wooooo mysql> INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (1,'Aabheer','Kumar',1,'Mr','u534','[email protected]','2008-9-3', '+911234599990'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (2,'Aadarsh','Gupta',6,'Mr','u668','[email protected]','2007-2-23','+911234599991'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (3,'Aachman','Singh',5,'Mr','u2766','[email protected]','2006-12-18','+911234599992'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (4,'Aadesh','Shrivastav',5,'Mr','u3198','[email protected]','2007-11-23','+911234599993'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (5,'Aadi','manav',1,'Mr','u2686','[email protected]','2010-7-20','+911234599994'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (6,'Aadidev','singh',4,'Mr','u572','[email protected]','2010-8-18','+911234599995'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (7,'Aafreen','sheikh',4,'Smt','u1092','[email protected]','2007-7-11','+911234599996'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (8,'Aakar','Sherpa',5,'Mr','u1420','[email protected]','2009-10-3','+911234599997'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (9,'Aakash','Singh',4,'Mrs','u2884','[email protected]','2008-6-11','+911234599998'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (10,'Aalap','Singhania',4,'Mrs','u609','[email protected]','2010-10-8','+911234599999'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (11,'Aandaleeb','mahajan',1,'Smt','u131','[email protected]','2010-10-21','+911234580001'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (12,'Mamata','kumari',5,'Sh','u2519','[email protected]','2009-6-12','+911234580002'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (13,'Mamta','sharma',6,'Smt','u4123','[email protected]','2009-2-8','+911234580003'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (14,'Manali','singh',6,'Mr','u1078','[email protected]','2008-6-14','+911234580004'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (15,'Manda','saxena',1,'Mrs','u196','[email protected]','2010-9-4','+911234580005'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (16,'Salila','shetty',3,'Miss','u157','[email protected]','2009-11-15','+911234580006'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (17,'Salima','happy',3,'Mrs','u3445','[email protected]','2006-7-14','+911234580007'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (18,'Salma','haik',5,'Sh','u4621','[email protected]','2008-6-23','+911234580008'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (19,'Samita','patil',3,'Smt','u3156','[email protected]','2006-6-7','+911234580009'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (20,'Sameena','sheikh',5,'Mrs','u952','[email protected]','2008-8-13','+911234580010'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (21,'Ranita','gupta',5,'Mrs','u2664','[email protected]','2008-10-20','+911234580011'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (22,'Ranjana','sharma',1,'Sh','u3085','[email protected]','2010-6-21','+911234580012'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (23,'Ranjini','singh',6,'Mrs','u4200','[email protected]','2007-4-13','+911234580013'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (24,'Ranjita','vyapari',2,'Smt','u1109','[email protected]','2008-1-22','+911234580014'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (25,'Rashi','gupta',6,'Mrs','u3492','[email protected]','2006-2-2','+911234580015'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (26,'Rashmi','sehgal',3,'Mr','u3248','[email protected]','2008-9-9','+911234580016'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (27,'Rashmika','sexy',1,'Mrs','u4599','[email protected]','2009-3-12','+911234580017'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (28,'Rasika','dulari',3,'Smt','u2089','[email protected]','2009-1-24','+911234580018'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (29,'Dilber','lover',6,'Mr','u4241','[email protected]','2007-10-11','+911234580019'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (30,'Dilshad','happy',1,'Mr','u1564','[email protected]','2007-4-8','+911234580020'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (31,'Dipali','lights',5,'Sh','u1127','[email protected]','2006-11-1','+911234580021'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (32,'Dipika','lamp',1,'Sh','u2271','[email protected]','2010-12-17','+911234580022'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (33,'Dipti','brightness',5,'Smt','u422','[email protected]','2010-9-25','+911234580023'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (34,'Disha','singh',3,'Sh','u4604','[email protected]','2006-5-2','+911234580024'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (35,'Maadhav','Krishna',1,'Miss','u2561','[email protected]','2007-11-6','+911234580025'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (36,'Maagh','month',5,'Miss','u874','[email protected]','2008-5-8','+911234580026'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (37,'Maahir','Skilled',4,'Mr','u3372','[email protected]','2007-8-4','+911234580027'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (38,'Maalolan','Ahobilam',5,'Mrs','u3498','[email protected]','2007-7-9','+911234580028'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (39,'Maandhata','King',1,'Smt','u2089','[email protected]','2009-9-3','+911234580029'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (40,'Maaran','Brave',2,'Miss','u4020','[email protected]','2008-4-5','+9112345606001'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (41,'Maari','Rain',2,'Sh','u3593','[email protected]','2007-12-5','+9112345606002'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (42,'Madan','Cupid',4,'Mrs','u795','[email protected]','2007-11-11','+9112345606003'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (43,'Madangopal','Krishna',3,'Sh','u438','[email protected]','2007-2-19','+9112345606004'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (44,'sahil','gogna',1,'Sh','u2273','[email protected]','2007-10-7','+9112345606005'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (45,'nikhil','gogna',2,'Mr','u1240','[email protected]','2009-9-14','+9112345606006'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (46,'amit','gogna',5,'Sh','u3879','[email protected]','2006-2-8','+9112345606007'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (47,'krishan','gogna',4,'Miss','u3632','[email protected]','2010-9-20','+9112345606008'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (48,'anil','kashyap',4,'Smt','u3939','[email protected]','2010-3-15','+9112345606009'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (49,'sunil','kashyap',5,'Mrs','u3493','[email protected]','2008-3-16','+9112345606010'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (50,'sandy','singh',6,'Mrs','u4691','[email protected]','2009-6-2','+9112345606011'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (51,'vishal','kapoor',3,'Mr','u1087','[email protected]','2010-5-13','+9112345606012'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (52,'bala','ji',5,'Mrs','u4762','[email protected]','2007-8-9','+9112345606013'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (53,'karan','sarin',4,'Miss','u3030','[email protected]','2008-4-8','+9112345606014'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (54,'abhishek','kumar',4,'Miss','u1093','[email protected]','2008-12-21','+9112345605001'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (55,'babu','the',1,'Miss','u1055','[email protected]','2008-7-2','+9112345506001'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (56,'sandeep','gainda',3,'Miss','u1320','[email protected]','2010-5-14','+9112345606301'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (57,'dheeraj','kumar',3,'Miss','u3685','[email protected]','2007-10-14','+9112345606091'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (58,'dharmendra','chauhan',1,'Smt','u3235','[email protected]','2008-8-1','+9112345806001'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (59,'max','alan',3,'Smt','u3465','[email protected]','2009-5-5','+9112345608011'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (60,'hidayat','khan',3,'Smt','u958','[email protected]','2007-11-18','+911234599101'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (61,'himnashu','singh',4,'Miss','u2027','[email protected]','2008-3-2','+911234599102'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (62,'dinesh','kumar',6,'Sh','u3233','[email protected]','2008-5-9','+911234599103'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (63,'toshi','prakash',1,'Mr','u3766','[email protected]','2010-9-17','+911234599104'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (64,'niti','puri',3,'Mr','u3575','[email protected]','2009-11-15','+911234599105'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (65,'pawan','tikki',3,'Sh','u3919','[email protected]','2006-3-19','+911234599106'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (66,'gaurav','sharma',2,'Sh','u413','[email protected]','2010-4-2','+911234599107'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (67,'himanshu','verma',2,'Mrs','u4732','[email protected]','2009-3-20','+911234599108'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (68,'priyanshu','verma',3,'Sh','u183','[email protected]','2010-8-12','+911234599109'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (69,'nitika','luthra',2,'Mrs','u4259','[email protected]','2010-7-12','+911234599110'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (70,'neeru','gogna',2,'Sh','u1633','[email protected]','2010-6-23','+91532110000'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (71,'bindu','gupta',1,'Sh','u1859','[email protected]','2006-11-10','+91532110001'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (72,'gurleen','bakshi',5,'Miss','u1423','[email protected]','2007-7-1','+91532110003'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (73,'rahul','gupta',3,'Sh','u1223','[email protected]','2009-8-11','+91532110004'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (74,'jagdish','salgotra',3,'Mr','u12','[email protected]','2008-5-19','+91532110005'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (75,'vikas','sharma',3,'Smt','u465','[email protected]','2006-6-2','+91532110006'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (76,'poonam','mahendra',2,'Sh','u1744','[email protected]','2009-12-2','+91532110007'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (77,'pooja','kulkarni',3,'Mrs','u1903','[email protected]','2008-10-6','+91532110008'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (78,'priya','mahajan',6,'Sh','u4205','[email protected]','2010-8-5','+91532110009'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (79,'manoj','zerger',1,'Mrs','u3369','[email protected]','2009-12-4','+91532110010'); INSERT INTO AddressBook(Id, FirstName, LastName, OfficeId, Title, Alias, Email, DOJ, PhoneNo) VALUES (80,'mohan','master',5,'Mr','u2841','[email protected]','2010-10-7','+91532110011'); Please note that above employee data is just a data *only data* I created using a small java programme using random number generators and reading some names file, so you may find titles getting messed up :( We next create a procedure that we will use from java to fetch records that we just inserted. DROP PROCEDURE IF EXISTS search_address_book; CREATE PROCEDURE search_address_book(IN address_ids VARCHAR(1000) ) BEGIN DECLARE search_address_query VARCHAR(2000) DEFAULT ''; SET address_ids = CONCAT('\'', REPLACE(address_ids, ',', '\',\''), '\''); SET search_address_query = CONCAT(search_address_query, ' select ab.Id as Id , ab.FirstName as FName, ab.LastName as LName, cl.Location as Location, ab.Title as Title, ab.Alias as Alias, ab.Email as Email, ab.DOJ as DOJ, ab.PhoneNo as PhoneNo ' ); SET search_address_query = CONCAT(search_address_query, ' from AddressBook ab left join CompanyLocations cl on ab.OfficeId=cl.Id '); SET search_address_query = CONCAT(search_address_query, ' where ab.id IN (', address_ids ,') '); SET @statement = search_address_query; PREPARE dynquery FROM @statement; EXECUTE dynquery; DEALLOCATE PREPARE dynquery; END; # To get records for ids 1, 6 and 7, we run following commands: call search_address_book('1,6,7'); Configuring Sphinx It turns out that it was not terribly difficult to setup sphinx, but I had a hard time finding instructions on the web, so I'll post my steps here. By default Sphinx looks for 'sphinx.co.in' configuration file to come with indexes and other stuff, lets create and define source and index for our sample application addressbook.conf (read between the lines) ############################################################################# ## data source definition ############################################################################# source addressBookSource { ## SQL settings for 'mysql' ## type = mysql # some straightforward parameters for SQL source types sql_host = localhost sql_user = root sql_pass = root sql_db = addressbook sql_port = 3306 # optional, default is 3306 # pre-query, executed before the main fetch query sql_query_pre = SET NAMES utf8 # main document fetch query, integer document ID field MUST be the first selected column sql_query = \ select ab.Id as Id , ab.FirstName as FName, ab.LastName as LName, cl.Location as Location, \ ab.Title as Title, ab.Alias as Alias, ab.Email as Email, UNIX_TIMESTAMP(ab.DOJ) as DOJ, ab.PhoneNo as PhoneNo \ from AddressBook ab left join CompanyLocations cl on ab.OfficeId=cl.Id sql_attr_timestamp = DOJ # document info query, ONLY for CLI search (ie. testing and debugging) , optional, default is empty must contain $id macro and must fetch the document by that id sql_query_info = SELECT * FROM AddressBook WHERE id=$id } ############################################################################# ## index definition ############################################################################# # local index example, this is an index which is stored locally in the filesystem index addressBookIndex { # document source(s) to index source = addressBookSource # index files path and file name, without extension, make sure you have this folder path = C:\devel\sphinx-0.9.9-win32\data\addressBookIndex # document attribute values (docinfo) storage mode docinfo = extern # memory locking for cached data (.spa and .spi), to prevent swapping mlock = 0 morphology = none # make sure this file exists exceptions =C:\devel\sphinx-0.9.9-win32\data\exceptions.txt enable_star = 1 } ############################################################################# ## indexer settings ############################################################################# indexer { # memory limit, in bytes, kiloytes (16384K) or megabytes (256M) # optional, default is 32M, max is 2047M, recommended is 256M to 1024M mem_limit = 32M # maximum IO calls per second (for I/O throttling) # optional, default is 0 (unlimited) # # max_iops = 40 # maximum IO call size, bytes (for I/O throttling) # optional, default is 0 (unlimited) # # max_iosize = 1048576 # maximum xmlpipe2 field length, bytes # optional, default is 2M # # max_xmlpipe2_field = 4M # write buffer size, bytes # several (currently up to 4) buffers will be allocated # write buffers are allocated in addition to mem_limit # optional, default is 1M # # write_buffer = 1M } ############################################################################# ## searchd settings ############################################################################# searchd { # hostname, port, or hostname:port, or /unix/socket/path to listen on listen = 9312 # log file, searchd run info is logged here # optional, default is 'searchd.log' log = C:\devel\sphinx-0.9.9-win32\data\log\searchd.log # query log file, all search queries are logged here # optional, default is empty (do not log queries) query_log = C:\devel\sphinx-0.9.9-win32\data\log\query.log # client read timeout, seconds # optional, default is 5 read_timeout = 5 # request timeout, seconds # optional, default is 5 minutes client_timeout = 300 # maximum amount of children to fork (concurrent searches to run) # optional, default is 0 (unlimited) max_children = 30 # PID file, searchd process ID file name # mandatory pid_file = C:\devel\sphinx-0.9.9-win32\data\log\searchd.pid # max amount of matches the daemon ever keeps in RAM, per-index # WARNING, THERE'S ALSO PER-QUERY LIMIT, SEE SetLimits() API CALL # default is 1000 (just like Google) max_matches = 1000 # seamless rotate, prevents rotate stalls if precaching huge datasets # optional, default is 1 seamless_rotate = 1 # whether to forcibly preopen all indexes on startup # optional, default is 0 (do not preopen) preopen_indexes = 0 } # --eof-- Once the configuration is done, its time to index our sql data, the command to use is 'indexer' as shown below. C:\devel\sphinx-0.9.9-win32\bin>indexer.exe --all --config C:\devel\sphinx-0.9.9-win32\addressbook.conf CONSOLE: Sphinx 0.9.9-release (r2117) Copyright (c) 2001-2009, Andrew Aksyonoff using config file 'C:\devel\sphinx-0.9.9-win32\addressbook.conf'... indexing index 'addressBookIndex'... collected 80 docs, 0.0 MB sorted 0.0 Mhits, 100.0% done total 80 docs, 5514 bytes total 0.057 sec, 96386 bytes/sec, 1398.43 docs/sec total 2 reads, 0.000 sec, 3.5 kb/call avg, 0.0 msec/call avg total 7 writes, 0.000 sec, 2.5 kb/call avg, 0.0 msec/call avg Note: As I told earlier that Sphinx creates 1 document for each row, as we had 80 rows in the database so a total of 80 docs are created. Time taken is also very very small, believe me I tried with half million rows and it took around 3-4 seconds :) cool isn't it? Once the index is up let's try to search few records, the utility command to perform search is 'search'. Ok Sphinx maharaj* please search for employee whose alias is u4732 C:\devel\sphinx-0.9.9-win32\bin>search.exe --config C:\devel\sphinx-0.9.9-win32\addressbook.conf u4732 CONSOLE: Sphinx 0.9.9-release (r2117) Copyright (c) 2001-2009, Andrew Aksyonoff using config file 'C:\devel\sphinx-0.9.9-win32\addressbook.conf'... index 'addressBookIndex': query 'u4732 ': returned 1 matches of 1 total in 0.001 sec displaying matches: 1. document=67, weight=1, doj=Fri Mar 20 00:00:00 2009 Id=67 FirstName=himanshu LastName=verma OfficeId=2 Title=Mrs Alias=u4732 [email protected] DOJ=2009-03-20 PhoneNo=+911234599108 words: 1. 'u4732': 1 documents, 1 hits words: 1. 'u4732': 1 documents, 1 hits As you can see above this is a unique record for Himanshu. Note: You see a lot of information for the result, this is because of following line in our configuration file sql_query_info = SELECT * FROM AddressBook WHERE id=$id If you want to see less columns you need to change the sql_query_info in configuration file. Let's try another search, sphinx maharaj* please tell me which all rows have gurleen or toshi in them. C:\devel\sphinx-0.9.9-win32\bin>search.exe --config C:\devel\sphinx-0.9.9-win32\addressbook.conf --any toshi gurleen CONSOLE: displaying matches: 1. document=63, weight=2, doj=Fri Sep 17 00:00:00 2010 Id=63 FirstName=toshi LastName=prakash OfficeId=1 Title=Mr Alias=u3766 [email protected] DOJ=2010-09-17 PhoneNo=+911234599104 2. document=72, weight=2, doj=Sun Jul 01 00:00:00 2007 Id=72 FirstName=gurleen LastName=bakshi OfficeId=5 Title=Miss Alias=u1423 [email protected] DOJ=2007-07-01 PhoneNo=+91532110003 Exactly two records were returned and this is what we were expecting. The following special operators and modifiers can be used when using the extended matching mode: operator OR: nikhil | sahil operator NOT: hello -sandy hello !sandy field search operator: @Email [email protected] For a complete set of search features , I advise you to go through http://sphinxsearch.com/docs/manual-0.9.9.html#searching link. Sphinx as Windows Service Now our main aim is to use sphinx with JAVA API, so let's move towards that now, before java can utilize the true power of Sphinx, we need to start 'searchd' as a windows service so that our java programme can connect to sphinx search engine. Let's install Sphinx as a windows service so that our java program can use this daemon service to query the index that we just created, the command is : C:\devel\sphinx-0.9.9-win32\bin>searchd.exe --install --config C:\devel\sphinx-0.9.9-win32\addressbook.conf --servicename --port 9312 SphinxSearch CONSOLE: Sphinx 0.9.9-release (r2117) Copyright (c) 2001-2009, Andrew Aksyonoff Installing service... Service 'SphinxSearch' installed succesfully. Well now the sphinx is ready to serve us on port 9312 Note: If you try to install Sphinx without admin rights, you may get following error messages. C:\devel\sphinx-0.9.9-win32\bin>searchd.exe --install --config C:\devel\sphinx-0.9.9-win32\addressbook.conf --servicename --port 9312 SphinxSearch CONSOLE: Installing service... FATAL: OpenSCManager() failed: code=5, error=Access is denied. Once done you can start the service as: c:\>sc start SphinxSearch (or alternatively from the services screen, start 'services.msc' in windows Run) If some how you want to delete the service , use c:\>sc delete SphinxSearch Let's create an adapter to fetch data from the database. package it.gogs.sphinx.util; import it.gogs.sphinx.AddressBoook; import it.gogs.sphinx.exception.AddressBookBizException; import it.gogs.sphinx.exception.AddressBookTechnicalException; import java.sql.CallableStatement; import java.sql.Connection; import java.sql.DriverManager; import java.sql.ResultSet; import java.sql.SQLException; import java.util.ArrayList; import java.util.List; import org.apache.log4j.Logger; /** * Adapter to fetch data from the database. * * @author Munish Gogna * */ public class AddressBookAdapter { private static Logger logger = Logger.getLogger(AddressBookAdapter.class); private AddressBookAdapter() { // use in static way.. } private static Connection getConnection() throws AddressBookTechnicalException { String userName = "root"; String password = "root"; String url = "jdbc:mysql://localhost/addressbook"; try { Class.forName("com.mysql.jdbc.Driver").newInstance(); return DriverManager.getConnection(url, userName, password); } catch (Exception e) { throw new AddressBookTechnicalException("could not get connection"); } } public static List getAddressBookList(List addressIds) throws AddressBookTechnicalException, AddressBookBizException { List addressBoookList = new ArrayList(); if (addressIds == null || addressIds.size() == 0){ logger.error("AddressIds was null or empty, returning empty list"); return addressBoookList; } Connection connection = null; CallableStatement callableStatement = null; try { connection = getConnection(); callableStatement = connection.prepareCall("{ call search_address_book(?)}"); callableStatement.setString(1, Utils.toCommaString(addressIds)); callableStatement.execute(); ResultSet resultSet = callableStatement.getResultSet(); prepareResults(resultSet, addressBoookList); connection.close(); } catch (SQLException e) { logger.error("Problem connecting MYSQL - " + e.getMessage()); throw new AddressBookTechnicalException(e.getMessage()); } catch (AddressBookTechnicalException e) { logger.error("Problem connecting MYSQL - " + e.getMessage()); throw e; } finally{ if(connection != null){ try { connection.close(); } catch (SQLException e) { logger.error("Problem closing conection - " + e.getMessage()); e.printStackTrace(); } } } return addressBoookList; } private static void prepareResults(ResultSet resultSet, List addressBoookList) throws SQLException { AddressBoook addressBoook; while (resultSet.next()) { addressBoook = new AddressBoook(); addressBoook.setAlias(resultSet.getString("Alias")); addressBoook.setEmail(resultSet.getString("Email")); addressBoook.setfName(resultSet.getString("FName")); addressBoook.setlName(resultSet.getString("LName")); addressBoook.setOfficeLocation(resultSet.getString("Location")); addressBoook.setPhoneNo(resultSet.getString("PhoneNo")); addressBoook.setTitle(resultSet.getString("Title")); addressBoook.setDateOfJoining(resultSet.getDate("DOJ")); addressBoook.setId(resultSet.getLong("Id")); addressBoookList.add(addressBoook); } } } Next we create the SphinxInstance that will parse the keywords and date range and provide us a list of Ids that matches the search. package it.gogs.sphinx.util; import it.gogs.sphinx.DateRange; import it.gogs.sphinx.SearchCriteria; import it.gogs.sphinx.api.SphinxClient; import it.gogs.sphinx.api.SphinxException; import it.gogs.sphinx.api.SphinxMatch; import it.gogs.sphinx.api.SphinxResult; import it.gogs.sphinx.exception.AddressBookBizException; import java.util.ArrayList; import java.util.Date; import java.util.List; import org.apache.log4j.Logger; /** * Instance that will parse our free text and provide the results. * * Note: Make sure that 'searchd' is up and running before you use this class * @author Munish Gogna * */ public class SphinxInstance { private static String SPHINX_HOST = "localhost"; private static String SPHINX_INDEX = "addressBookIndex"; private static int SPHINX_PORT = 9312; private static SphinxClient sphinxClient; private static Logger logger = Logger.getLogger(SphinxInstance.class); static { sphinxClient = new SphinxClient(SPHINX_HOST, SPHINX_PORT); } public static List getAddressBookIds(SearchCriteria criteria) throws AddressBookBizException, SphinxException { List addressIdsList = new ArrayList(); try { if (Utils.isNull(criteria)) { logger.error("criteria is null"); throw new AddressBookBizException("criteria is null"); } if (Utils.isNull(criteria.getKeywords())) { logger.error("keyword is a required field"); throw new AddressBookBizException("keyword is a required field"); } DateRange dateRange = criteria.getDateRage(); if (!Utils.isNull(dateRange)) { if (Utils.isDateRangeValid(dateRange)) { // this is to filter results based on joining dates if they are provided sphinxClient.SetFilterRange("DOJ", getTimeInSeconds(dateRange.getFromDate()), getTimeInSeconds(dateRange.getToDate()), false); } else { logger.error(" fromDate/toDate should not be empty and 'fromDate' should be less than equal to 'toDate'"); throw new AddressBookBizException("fromDate/toDate should not be empty and 'fromDate' should be less than equal to 'toDate'"); } } sphinxClient.SetMatchMode(SphinxClient.SPH_MATCH_EXTENDED2); sphinxClient.SetSortMode(SphinxClient.SPH_SORT_RELEVANCE, ""); SphinxResult result = sphinxClient.Query(buildSearchQuery(criteria), SPHINX_INDEX, "buidling query for address book search"); SphinxMatch[] matches = result.matches; for (SphinxMatch match : matches) { addressIdsList.add(String.valueOf(match.docId)); } } catch (SphinxException e) { throw e; } catch (AddressBookBizException e) { throw e; } logger.info("Total record(s):" + addressIdsList.size()); return addressIdsList; } private static long getTimeInSeconds(Date time) { return time.getTime()/1000; } private static String buildSearchQuery(SearchCriteria criteria) throws AddressBookBizException { String keywords[] = criteria.getKeywords().split(" "); StringBuilder searchFor = new StringBuilder(); for (String key : keywords) { if (!Utils.isEmpty(key)) { searchFor.append(key); if (searchFor.length() > 1) { searchFor.append("*|*"); } } } searchFor.delete(searchFor.lastIndexOf("|*"), searchFor.length()); StringBuilder queryBuilder = new StringBuilder(); String query = searchFor.toString(); queryBuilder.append("@FName *" + query + " | "); queryBuilder.append("@LName *" + query + " | "); queryBuilder.append("@Title *" + query + " | "); queryBuilder.append("@Location *"+ query + " | "); queryBuilder.append("@Alias *" + query + " | "); queryBuilder.append("@Email *" + query + " | "); queryBuilder.append("@PhoneNo *" + query); logger.info("Sphinx Query: " + queryBuilder.toString()); return queryBuilder.toString(); } } Here is the interface that I will expose to the outside world (in my future article I will expose this interface as Web Service) import it.gogs.sphinx.AddressBoook; import it.gogs.sphinx.SearchCriteria; import it.gogs.sphinx.api.SphinxException; import it.gogs.sphinx.exception.AddressBookBizException; import it.gogs.sphinx.exception.AddressBookTechnicalException; import java.util.List; /** * * @author Munish Gogna * */ public interface AddressBook { /** * Returns the list of AddressBook objects based on search criteria. * * @param criteria * @throws AddressBookTechnicalException * @throws AddressBookBizException * @throws SphinxException */ public List getAddressBookList(SearchCriteria criteria) throws AddressBookTechnicalException, AddressBookBizException, SphinxException; } and here is the implementation class for the same. package it.gogs.sphinx.addressbook.impl; import java.util.List; import it.gogs.sphinx.AddressBoook; import it.gogs.sphinx.SearchCriteria; import it.gogs.sphinx.addressbook.AddressBook; import it.gogs.sphinx.api.SphinxException; import it.gogs.sphinx.exception.AddressBookBizException; import it.gogs.sphinx.exception.AddressBookTechnicalException; import it.gogs.sphinx.util.AddressBookAdapter; import it.gogs.sphinx.util.SphinxInstance; /** * Implementation for our Address Book example * * @author Munish Gogna * */ public class AddressBookImpl implements AddressBook{ public List getAddressBookList(SearchCriteria criteria) throws AddressBookTechnicalException, AddressBookBizException, SphinxException { List addressIds= SphinxInstance.getAddressBookIds(criteria); return AddressBookAdapter.getAddressBookList(addressIds); } } ok so far so good, let's run some tests now ............ package it.gogs.sphinx.test; import java.util.Calendar; import java.util.GregorianCalendar; import java.util.List; import it.gogs.sphinx.AddressBoook; import it.gogs.sphinx.DateRange; import it.gogs.sphinx.SearchCriteria; import it.gogs.sphinx.addressbook.AddressBook; import it.gogs.sphinx.addressbook.impl.AddressBookImpl; import it.gogs.sphinx.api.SphinxException; import it.gogs.sphinx.exception.AddressBookBizException; import it.gogs.sphinx.exception.AddressBookTechnicalException; import junit.framework.TestCase; /** * * @author Munish Gogna * */ public class AddressBookTest extends TestCase { private AddressBook addressBook; @Override protected void setUp() throws Exception { super.setUp(); addressBook = new AddressBookImpl(); } @Override protected void tearDown() throws Exception { super.tearDown(); } /** this should be a unique record for Himanshu */ public void test_search_for_himanshu() throws Exception { SearchCriteria criteria = new SearchCriteria(); // remember the first 'search' example?? criteria.setKeywords("u4732"); List addressList = addressBook.getAddressBookList(criteria); assertTrue(addressList.size() == 1); assertTrue("expecting himanshu here", "himanshu".equals(addressList.get(0).getfName())); } /** only two employees have name gurleen or toshi */ public void test_search_for_gurleen_or_toshi() throws Exception { SearchCriteria criteria = new SearchCriteria(); // remember the second 'search' example?? criteria.setKeywords("gurleen toshi"); List addressList = addressBook.getAddressBookList(criteria); assertTrue(addressList.size() == 2); assertTrue("expecting toshi here", "toshi".equals(addressList.get(0).getfName())); assertTrue("expecting gurleen here", "gurleen".equals(addressList.get(1).getfName())); } /** there are 16 people from jammu location */ public void test_search_for_people_from_jammu_location() throws Exception { SearchCriteria criteria = new SearchCriteria(); criteria.setKeywords("jammu"); List addressList = addressBook.getAddressBookList(criteria); assertTrue(addressList.size() == 16); } /** only Aalap, Manda and nitika are having title as Mrs and joined in 2010 */ public void test_joined_in_2010_with_title_Mrs() throws Exception { DateRange dateRange = new DateRange(); GregorianCalendar calendar1 = new GregorianCalendar(); calendar1.set(Calendar.YEAR, 2010); calendar1.set(Calendar.MONTH, Calendar.JANUARY); calendar1.set(Calendar.DAY_OF_MONTH, 1); dateRange.setFromDate(calendar1.getTime()); GregorianCalendar calendar2 = new GregorianCalendar(); calendar2.set(Calendar.YEAR, 2010); calendar2.set(Calendar.MONTH, Calendar.DECEMBER); calendar2.set(Calendar.DAY_OF_MONTH, 31); dateRange.setToDate(calendar2.getTime()); SearchCriteria criteria = new SearchCriteria(); criteria.setKeywords("Mrs"); criteria.setDateRage(dateRange); List addressList = addressBook.getAddressBookList(criteria); assertTrue("expecting 3 records here", addressList.size() == 3); } /** should get a business exception here */ public void test_without_specifying_keywords(){ SearchCriteria criteria = new SearchCriteria(); //criteria.setKeywords("Mrs"); try { addressBook.getAddressBookList(criteria); } catch (Exception e) { assertTrue(e instanceof AddressBookBizException); assertTrue(e.getMessage().indexOf("keyword is a required field") >-1); } } } How we update the Index once database changes? For these kinds of requirements, we can set up two sources and two indexes, with one "main" index for the data which only changes rarely (if ever), and one "delta" for the new documents. First Time data will go in the "main" index and the newly inserted address book entries will go into "delta". Delta index could then be reindexed very frequently, and the documents can be made available to search in a matter of minutes. Also one thing to take from this article is once 'searchd' daemon is running we can't index the data in normal way,we have to use --rotate option in such cases. For some applications where there is a timely batch update for the data, we can configure some cron job to reindex our documents in Sphinx as shown below. C:\devel\sphinx-0.9.9-win32\bin>indexer.exe --all --config C:\devel\sphinx-0.9.9-win32\addressbook.conf --rotate Capsule We asked Sphinx to provide us the Document Ids corresponding to our search parameters and then we used those Ids to fire database query. In case the data we want to return is included in Index (DOJ attribute for example in our case) we can skip the database portion, so choose wisely how much information (attributes) you want to include while you index your sql data. Well that's all ... it's time to say good bye. Take good care of your health and don't forget to vote, its a must :) - Munish Gogna

Setting the mouse cursor position on a Windows machine with the help of .NET Framework shouldn't be that big of a problem. After all, there is the built-in Cursor class that lets you do that by executing a simple line of code: Cursor.Position = new System.Drawing.Point(0, 0); Of course, here 0 and 0 are the absolute coordinates for the mouse cursor on the screen. One thing to mention about this type of position setting is that Cursor requires a reference to System.Windows.Forms. And in some cases you don't want this extra reference. If that's the case, WinAPI is your solution. It requires some more work compared to the regular .NET way (class instance -> method call) but at the end you get more control than you would expect. When using WinAPI to set the cursor position, there are two ways you can go: mouse_event SendInput mouse_event is the very basic function that is only able to set the mouse coordinates. It was superseded and Microsoft recomends using SendInput instead. Nonetheless, it still works (although I cannot say for sure whether it will be working in future releases of Windows). So to start, I have a very basic class: class WINAPI_SUPERSEDED { [DllImport("user32.dll",SetLastError=true)] public static extern void mouse_event(uint dwFlags, uint dx, uint dy, uint dwData, int dwExtraInfo); public enum MouseFlags { MOUSEEVENTF_ABSOLUTE = 0x8000, MOUSEEVENTF_LEFTDOWN = 0x0002, MOUSEEVENTF_LEFTUP = 0x0004, MOUSEEVENTF_MIDDLEDOWN = 0x0020, MOUSEEVENTF_MIDDLEUP = 0x0040, MOUSEEVENTF_MOVE = 0x0001, MOUSEEVENTF_RIGHTDOWN = 0x0008, MOUSEEVENTF_RIGHTUP = 0x0010, MOUSEEVENTF_WHEEL = 0x0800, MOUSEEVENTF_XDOWN = 0x0080, MOUSEEVENTF_XUP = 0x0100 } public enum DataFlags { XBUTTON1 = 0x0001, XBUTTON2 = 0x0002 } } So to set the cursor position to 0,0 I would use this: WINAPI_SUPERSEDED.mouse_event((int)WINAPI_SUPERSEDED.MouseFlags.MOUSEEVENTF_MOVE | (int)WINAPI_SUPERSEDED.MouseFlags.MOUSEEVENTF_ABSOLUTE, 0, 0, 0, 0); If you go through the documentation, you will notice that in fact, dx and dy are in no way direct coordinates but rather normalized values ranged between 0 and 65,535 - that is only when the MOUSEEVENTF_ABSOLUTE flag is present. Otherwise, the position will be adjusted according to the current mouse cursor position. This method doesn't return any value so I cannot be informed whether it was successful or not. GetLastError won't give much information either. In this case, SendInput comes to the rescue. Here is what I have defined as the main class: class WINAPI { [DllImport("kernel32.dll")] public static extern uint GetLastError(); public enum MouseData { XBUTTON1 = 0x0001, XBUTTON2 = 0x0002 } public enum MouseFlags { MOUSEEVENTF_ABSOLUTE = 0x8000, MOUSEEVENTF_HWHEEL = 0x01000, MOUSEEVENTF_MOVE = 0x0001, MOUSEEVENTF_MOVE_NOCOALESCE = 0x2000, MOUSEEVENTF_LEFTDOWN = 0x0002, MOUSEEVENTF_LEFTUP = 0x0004, MOUSEEVENTF_RIGHTDOWN = 0x0008, MOUSEEVENTF_RIGHTUP = 0x0010, MOUSEEVENTF_MIDDLEDOWN = 0x0020, MOUSEEVENTF_MIDDLEUP = 0x0040, MOUSEEVENTF_VIRTUALDESK = 0x4000, MOUSEEVENTF_WHEEL = 0x0800, MOUSEEVENTF_XDOWN = 0x0080, MOUSEEVENTF_XUP = 0x0100 } [DllImport("user32.dll", SetLastError=true)] public static extern uint SendInput(uint nInputs, ref INPUT pInputs, int cbSize); [StructLayout (LayoutKind.Explicit)] public struct INPUT { [FieldOffset(0)] public int type; [FieldOffset(4)] public MOUSEINPUT mi; } public struct MOUSEINPUT { public int dx; public int dy; public int mouseData; public int dwFlags; public int time; public int extraInfo; } } This class is a bit more complicated, but at the same time you have to understand that in some cases, SendInput is used for hardware and keyboard input as well. Of course, for experimentation purposes I removed those parts in the sample class. Here you have the same MouseFlags enum that will let you pass custom flags defining the mouse behavior. Notice, that SendInput has SetLastError set to true, therefore if something wrong happens via this method, the error will be easily obtained via GetLastError, that is implemented as a helper method in the same class. VERY IMPORTANT: When you define the INPUT struct, make sure you use LayoutKind.Explicit since when passed to an unamanaged call, a specific field layout is required - as you can see, every field is decorated with a FieldOffset attribute. Also taking about StructLayout, you don't have to set StructLayout.Sequential to MOUSEINPUT since it is setautomatically by CLR. When I want to call the method above, I can simply use this snippet: WINAPI.MOUSEINPUT mouseInput = new WINAPI.MOUSEINPUT(); mouseInput.dx = 100; mouseInput.dy = 10; mouseInput.dwFlags = (int)WINAPI.MouseFlags.MOUSEEVENTF_ABSOLUTE | (int)WINAPI.MouseFlags.MOUSEEVENTF_MOVE; WINAPI.INPUT input = new WINAPI.INPUT(); input.type = 0; input.mi = mouseInput; uint x = WINAPI.SendInput(1, ref input, Marshal.SizeOf(input)); Console.WriteLine(x); Console.WriteLine(WINAPI.GetLastError()); Console.ReadLine(); Notice that I have to call the unmanaged version of sizeof and pass the INPUT struct to it in order for the method to correctly execute. The regular C# sizeof won't cut it here. When I am defining the type of input, 0 is repesenting the INPUT_MOUSE flag, since I am only handling the mouse here. Of course, I can re-organize my method to accept a set of INPUT instances - the native call itself allows this by requesting an array of INPUT and the correct indication of the number of INPUT instances passed, but that is not required for testing purposes.

Hadoop Map/Reduce model is very good in processing large amount of data in parallel. It provides a general partitioning mechanism (based on the key of the data) to distribute aggregation workload across different machines. Basically, map/reduce algorithm design is all about how to select the right key for the record at different stage of processing. However, "time dimension" has a very different characteristic compared to other dimensional attributes of data, especially when real-time data processing is concerned. It presents a different set of challenges to the batch oriented, Map/Reduce model. Real-time processing demands a very low latency of response, which means there isn't too much data accumulated at the "time" dimension for processing. Data collected from multiple sources may not have all arrived at the point of aggregation. In the standard model of Map/Reduce, the reduce phase cannot start until the map phase is completed. And all the intermediate data is persisted in the disk before download to the reducer. All these added to significant latency of the processing. Here is a more detail description of this high latency characteristic of Hadoop. Although Hadoop Map/Reduce is designed for batch-oriented work load, certain application, such as fraud detection, ad display, network monitoring requires real-time response for processing large amount of data, have started to looked at various way of tweaking Hadoop to fit in the more real-time processing environment. Here I try to look at some technique to perform low-latency parallel processing based on the Map/Reduce model. General stream processing model In this model, data are produced at various OLTP system, which update the transaction data store and also asynchronously send additional data for analytic processing. The analytic processing will write the output to a decision model, which will feed back information to the OLTP system for real-time decision making. Notice the "asynchronous nature" of the analytic processing which is decoupled from the OLTP system, this way the OLTP system won't be slow down waiting for the completion of the analytic processing. Nevetheless, we still need to perform the analytic processing ASAP, otherwise the decision model will not be very useful if it doesn't reflect the current picture of the world. What latency is tolerable is application specific. Micro-batch in Map/Reduce One approach is to cut the data into small batches based on time window (e.g. every hour) and submit the data collected in each batch to the Map Reduce job. Staging mechanism is needed such that the OLTP application can continue independent of the analytic processing. A job scheduler is used to regulate the producer and consumer so each of them can proceed independently. Continuous Map/Reduce Here lets imagine some possible modification of the Map/Reduce execution model to cater for real-time stream processing. I am not trying to worry about the backward compatibility of Hadoop which is the approach that Hadoop online prototype (HOP) is taking. Long running The first modification is to make the mapper and reducer long-running. Therefore, we cannot wait for the end of the map phase before starting the reduce phase as the map phase never ends. This implies the mapper push the data to the reducer once it complete its processing and let the reducer to sort the data. A downside of this approach is that it offers no opportunity to run the combine() function on the map side to reduce the bandwidth utilization. It also shift more workload to the reducer which now needs to do the sorting. Notice there is a tradeoff between latency and optimization. Optimization requires more data to be accumulated at the source (ie: the Mapper) so local consolidation (ie: combine) can be performed. Unfortunately, low latency requires the data to be sent ASAP so not much accumulation can be done. HOP suggest an adaptive flow control mechanism such that data is pushed out to reducer ASAP until the reducer is overloaded and push back (using some sort of flow control protocol). Then the mapper will buffer the processed message and perform combine() before it send to the reducer. This approach automatically shift back and forth the aggregation workload between the reducer and the mapper. Time Window: Slice and Range This is a "time slice" concept and a "time range" concept. "Slice" defines a time window where result is accumulated before the reduce processing is executed. This is also the minimum amount of data that the mapper should accumulate before sending to the reducer. "Range" defines the time window where results are aggregated. It can be a landmark window where it has a well-defined starting point, or a jumping window (consider a moving landmark scenario). It can also be a sliding window where is a fixed size window from the current time is aggregated. After receiving a specific time slice from every mapper, the reducer can start the aggregation processing and combine the result with the previous aggregation result. Slice can be dynamically adjusted based on the amount of data sent from the mapper. Incremental processing Notice that the reducer need to compute the aggregated slice value after receive all records of the same slice from all mappers. After that it calls the user-defined merge() function to merge the slice value with the range value. In case the range need to be refreshed (e.g. reaching a jumping window boundary), the init() functin will be called to get a refreshed range value. If the range value need to be updated (when certain slice value falls outside a sliding range), the unmerge() function will be invoked. Here is an example of how we keep tracked of the average hit rate (ie: total hits per hour) within a 24 hour sliding window with update happens per hour (ie: an one-hour slice). # Call at each hit record map(k1, hitRecord) { site = hitRecord.site # lookup the slice of the particular key slice = lookupSlice(site) if (slice.time - now > 60.minutes) { # Notify reducer whole slice of site is sent advance(site, slice) slice = lookupSlice(site) } emitIntermediate(site, slice, 1) } combine(site, slice, countList) { hitCount = 0 for count in countList { hitCount += count } # Send the message to the downstream node emitIntermediate(site, slice, hitCount) } # Called when reducer receive full slice from all mappers reduce(site, slice, countList) { hitCount = 0 for count in countList { hitCount += count } sv = SliceValue.new sv.hitCount = hitCount return sv } # Called at each jumping window boundary init(slice) { rangeValue = RangeValue.new rangeValue.hitCount = 0 return rangeValue } # Called after each reduce() merge(rangeValue, slice, sliceValue) { rangeValue.hitCount += sliceValue.hitCount } # Called when a slice fall out the sliding window unmerge(rangeValue, slice, sliceValue) { rangeValue.hitCount -= sliceValue.hitCount }