Welcome to part 5 of “Be a lazy but a productive android developer” series. If you are a lazy Android developer and looking for image loading library, which could help you to load image(s) asynchronously without writing a logic for downloading and caching images then this article is for you. This series so far: Part 1: We looked at RoboGuice, a dependency injection library by which we can reduce the boiler plate code, save time and there by achieve productivity during Android app development. Part 2: We saw and explored about Genymotion, which is a rocket speed emulator and super-fast emulator as compared to native emulator. And we can use Genymotion while developing apps and can quickly test apps and there by can achieve productivity. Part 3: We understood and explored about JSON Parsing libraries (GSON and Jackson), using which we can increase app performance, we can decrease boilerplate code and there by can optimize productivity. Part 4: We talked about Card UI and explored card library, also created a basic card and simple card list demo. In this part In this part, we are going to talk about some image libraries using which we can load image(s) asynchronously, can cache images and also can download images into the local storage. Required features for loading images Almost every android app has a need to load remote images. While loading remote images, we have to take care of below things: Image loading process must be done in background (i.e. asynchronously) to avoid blocking UI main thread. Image recycling image should be done. Image should be displayed once its loaded successfully. Images should be cached in local memory for the later use. If remote image gets failed (due to network connection or bad url or any other reasons) to load then it should be managed perfectly for avoiding duplicate requests to load the same again, instead it should load if and only if net connection is available. Memory management should be done efficiently. In short, we have to write a code to manage each and every aspects of image loading but there are some awesome libraries available, using which we can load/download image asynchronously. We just have to call the load image method and success/failure callbacks. Asynchronous image loading Consider a case where we are having 50 images and 50 titles and we try to load all the images/text into the listview, it won’t display anything until all the images get downloaded. Here Asynchronous image loading process comes in picture. Asynchronous image loading is nothing but a loading process which happens in background so that it doesn’t block main UI thread and let user to play with other loaded data on the screen. Images will be getting displayed as and when it gets downloaded from background threads. Asynchronous image loading libraries Nostra’s Universal Image loader – https://github.com/nostra13/Android-Universal-Image-Loader Picasso – http://square.github.io/picasso/ UrlImageViewHelper by Koush Volley - By Android team members @ Google Novoda’s Image loader – https://github.com/novoda/ImageLoader Let’s have a look at examples using Picasso and Universal Image loader libraries. Example 1: Nostra’s Universal Image loader Step 1: Initialize ImageLoader configuration ? public class MyApplication extends Application{ @Override public void onCreate() { // TODO Auto-generated method stub super.onCreate(); // Create global configuration and initialize ImageLoader with this configuration ImageLoaderConfiguration config = new ImageLoaderConfiguration.Builder(getApplicationContext()).build(); ImageLoader.getInstance().init(config); } } Step 2: Declare application class inside Application tag in AndroidManifest.xml file ? Step 3: Load image and display into ImageView ? ImageLoader.getInstance().displayImage(objVideo.getThumb(), holder.imgVideo); Now, Universal Image loader also provides a functionality to implement success/failure callback to check whether image loading is failed or successful. ? ImageLoader.getInstance().displayImage(photoUrl, imgView, new ImageLoadingListener() { @Override public void onLoadingStarted(String arg0, View arg1) { // TODO Auto-generated method stub findViewById(R.id.EL3002).setVisibility(View.VISIBLE); } @Override public void onLoadingFailed(String arg0, View arg1, FailReason arg2) { // TODO Auto-generated method stub findViewById(R.id.EL3002).setVisibility(View.GONE); } @Override public void onLoadingComplete(String arg0, View arg1, Bitmap arg2) { // TODO Auto-generated method stub findViewById(R.id.EL3002).setVisibility(View.GONE); } @Override public void onLoadingCancelled(String arg0, View arg1) { // TODO Auto-generated method stub findViewById(R.id.EL3002).setVisibility(View.GONE); } }); Example 2: Picasso Image loading straight way: ? Picasso.with(context).load("http://postimg.org/image/wjidfl5pd/").into(imageView); Image re-sizing: ? Picasso.with(context) .load(imageUrl) .resize(100, 100) .centerCrop() .into(imageView) Example 3: UrlImageViewHelper library It’s an android library that sets an ImageView’s contents from a url, manages image downloading, caching, and makes your coffee too. UrlImageViewHelper will automatically download and manage all the web images and ImageViews. Duplicate urls will not be loaded into memory twice. Bitmap memory is managed by using a weak reference hash table, so as soon as the image is no longer used by you, it will be garbage collected automatically. Image loading straight way: ? UrlImageViewHelper.setUrlDrawable(imgView, "http://yourwebsite.com/image.png"); Placeholder image when image is being downloaded: ? UrlImageViewHelper.setUrlDrawable(imgView, "http://yourwebsite.com/image.png", R.drawable.loadingPlaceHolder); Cache images for a minute only: ? UrlImageViewHelper.setUrlDrawable(imgView, "http://yourwebsite.com/image.png", null, 60000); Example 4: Volley library Yes Volley is a library developed and being managed by some android team members at Google, it was announced by Ficus Kirkpatrick during the last I/O. I wrote an article about Volley library 10 months back , read it and give it a try if you haven’t used it yet. Let’s look at an example of image loading using Volley. Step 1: Take a NetworkImageView inside your xml layout. ? Step 2: Define a ImageCache class Yes you are reading title perfectly, we have to define an ImageCache class for initializing ImageLoader object. ? public class BitmapLruCache extends LruCache implements ImageLoader.ImageCache { public BitmapLruCache() { this(getDefaultLruCacheSize()); } public BitmapLruCache(int sizeInKiloBytes) { super(sizeInKiloBytes); } @Override protected int sizeOf(String key, Bitmap value) { return value.getRowBytes() * value.getHeight() / 1024; } @Override public Bitmap getBitmap(String url) { return get(url); } @Override public void putBitmap(String url, Bitmap bitmap) { put(url, bitmap); } public static int getDefaultLruCacheSize() { final int maxMemory = (int) (Runtime.getRuntime().maxMemory() / 1024); final int cacheSize = maxMemory / 8; return cacheSize; } } Step 3: Create an ImageLoader object and load image Create an ImageLoader object and initialize it with ImageCache object and RequestQueue object. ? ImageLoader.ImageCache imageCache = new BitmapLruCache(); ImageLoader imageLoader = new ImageLoader(Volley.newRequestQueue(context), imageCache); Step 4: Load an image into ImageView ? NetworkImageView imgAvatar = (NetworkImageView) findViewById(R.id.imgDemo); imageView.setImageUrl(url, imageLoader); Which library to use? Can you decide which library you would use? Let us know which and what are the reasons? Selection of the library is always depends on the requirement. Let’s look at the few fact points about each library so that you would able to compare exactly and can take decision. Picasso: It’s just a one liner code to load image using Picasso. No need to initialize ImageLoader and to prepare a singleton instance of image loader. Picasso allows you to specify exact target image size. It’s useful when you have memory pressure or performance issues, you can trade off some image quality for speed. Picasso doesn’t provide a way to prepare and store thumbnails of local images. Sometimes you need to check image loading process is in which state, loading, finished execution, failed or cancelled image loading. Surprisingly It doesn’t provide a callback functionality to check any state. “fetch()” dose not pass back anything. “get()” is for synchronously read, and “load()” is for asynchronously draw a view. Universal Image loader (UIL): It’s the most popular image loading library out there. Actually, it’s based on the Fedor Vlasov’s project which was again probably a very first complete solution and also a most voted answer (for the image loading solution) on Stackoverflow. UIL library is better in documentation and even there’s a demo example which highlights almost all the features. UIL provides an easy way to download image. UIL uses builders for customization. Almost everything can be configured. UIL doesn’t not provide a way to specify image size directly you want to load into a view. It uses some rules based on the size of the view. Indirectly you can do it by mentioning ImageSize argument in the source code and bypass the view size checking. It’s not as flexible as Picasso. Volley: It’s officially by Android dev team, Google but still it’s not documented. It’s just not an image loading library only but an asynchronous networking library Developer has to define ImageCache class their self and has to initialize ImageLoader object with RequestQueue and ImageCache objects. So now I am sure now you can be able to compare libraries. Choosing library is a bit difficult talk because it always depends on the requirement and type of projects. If the project is large then you should go for Picasso or Universal Image loader. If the project is small then you can consider to use Volley librar, because Volley isn’t an image loading library only but it tries to solve a more generic solution.). I suggest you to start with Picasso. If you want more control and customization, go for UIL. Read more: http://blog.bignerdranch.com/3177-solving-the-android-image-loading-problem-volley-vs-picasso/ http://stackoverflow.com/questions/19995007/local-image-caching-solution-for-android-square-picasso-vs-universal-image-load https://plus.google.com/103583939320326217147/posts/bfAFC5YZ3mq Hope you liked this part of “Lazy android developer: Be productive” series. Till the next part, keep exploring image loading libraries mentioned above and enjoy!

Usually if I concentrate I am able to understand most SQL statements. There are times though such as: When a set of tables is not familiar When I did not write the SQL statement When the SQL statement is long and involves many tables and joins When I want to discuss a statement with a colleague All of the above Having a visual representation of a SQL statement can be helpful in deciphering the statement. My visualisation tool of choice for SQL is an Open Source application called Reverse Snowflake Joins (REVJ). As the name implies, this tool shines when it comes to showing you how your tables are related. I have installed the tool on my workstation but when I am on the move I use the online version of the tool. Using the tool is straight forward, simply paste your SQL statement in the text area and generate the diagram, the online version generates an SVG image. I have at times found that the tool struggles with complex CASE statements. In such cases I remove the CASE statement and just include the fields used in the case statement. Below is a sample statement to show REVJ at work. SELECT a.prod_cat_name ,b.prod_name ,c.prod_owner_name ,p.promo_id ,pt.promo_type ,sum(s.units) as total_units ,sum(s.sale_price) as total_sale_price ,sum(prev_s.units) as prev_yr_total_units FROM product_category a JOIN product b ON a.product_cat_id = b.product_cat_id LEFT OUTER JOIN product_owner c ON a.product_cat_id = c.product_cat_id JOIN sales s ON b.product_id = s.product_id JOIN sales prev_s ON s.sale_year = prev_s.sale_year-1 LEFT OUTER JOIN promotion p ON s.promo_id = p.promo_id RIGHT OUTER JOIN promo_type pt ON p.promo_type_id = pt.promo_type_id WHERE pt.promo_type IN ('Email', 'TV') AND a.prod_cat_name = 'Electronics' AND s.sale_year >=2013 GROUP BY a.prod_cat_name ,b.prod_name ,c.prod_owner_name ,p.promo_id ,pt.promo_type HAVING sum(s.units)>100 The generated image shown below. Notice how the filters applied to each table are also shown, further simplifying the task of understanding the SQL statement. For more complex examples have a look at big samples page.

Performance tuning SELECT statements can be a time consuming task which in my opinion follows Pareto principle’s. 20% effort is likely give you an 80% performance improvement. To get another 20% performance improvement you probably need to spend 80% of the time. Unless you work on the planet Venus where each day on Venus is equal to 243 Earth days, delivery deadlines are likely to mean you will not have enough time to put into tuning your SQL queries. After years writing and running SQL statements I began to develop a mental check-list of things I looked at when trying to improve query performance. These are the things I check before moving on to query plans and reading the sometimes complicated documentation of the database I am working on. My check-list is by no means comprehensive or scientific, more like a back of the envelope calculation but can I can say that most of the time I do get performance improvements following these simple steps. The check-list follows. Check Indexes There should be indexes on all fields used in the WHERE and JOIN portions of the SQL statement. Take the 3-Minute SQL performance test. Regardless of your score be sure to read through the answers as they are informative. Limit Size of Your Working Data Set Examine the tables used in the SELECT statement to see if you can apply filters in the WHERE clause of your statement. A classic example is when a query initially worked well when there were only a few thousand rows in the table. As the application grew the query slowed down. The solution may be as simple as restricting the query to looking at the current month’s data. When you have queries that have sub-selects, look to apply filtering to the inner statement of the sub-selects as opposed to the outer statements. Only Select Fields You Need Extra fields often increase the grain of the data returned and thus result in more (detailed) data being returned to the SQL client. Additionally: When using reporting and analytical applications, sometimes the slow report performance is because the reporting tool has to do the aggregation as data is received in detailed form. Occasionally the query may run quickly enough but your problem could be a network related issue as large amounts of detailed data are sent to the reporting server across the network. When using a column-oriented DBMS only the columns you have selected will be read from disk, the less columns you include in your query the less IO overhead. Remove Unnecessary Tables The reasons for removing unnecessary tables are the same as the reasons for removing fields not needed in the select statement. Writing SQL statements is a process that usually takes a number of iterations as you write and test your SQL statements. During development it is possible that you add tables to the query that may not have any impact on the data returned by the SQL code. Once the SQL is correct I find many people do not review their script and remove tables that do not have any impact or use in the final data returned. By removing the JOINS to these unnecessary tables you reduce the amount of processing the database has to do. Sometimes, much like removing columns you may find your reduce the data bring brought back by the database. Remove OUTER JOINS This can easier said than done and depends on how much influence you have in changing table content. One solution is to remove OUTER JOINS by placing placeholder rows in both tables. Say you have the following tables with an OUTER JOIN defined to ensure all data is returned: customer_id customer_name 1 John Doe 2 Mary Jane 3 Peter Pan 4 Joe Soap customer_id sales_person NULL Newbee Smith 2 Oldie Jones 1 Another Oldie NULL Greenhorn The solution is to add a placeholder row in the customer table and update all NULL values in the sales table to the placeholder key. customer_id customer_name 0 NO CUSTOMER 1 John Doe 2 Mary Jane 3 Peter Pan 4 Joe Soap customer_id sales_person 0 Newbee Smith 2 Oldie Jones 1 Another Oldie 0 Greenhorn Not only have you removed the need for an OUTER JOIN you have also standardised how sales people with no customers are represented. Other developers will not have to write statements such as ISNULL(customer_id, “No customer yet”). Remove Calculated Fields in JOIN and WHERE Clauses This is another one of those that may at times be easier said than done depending on your permissions to make changes to the schema. This can be done by creating a field with the calculated values used in the join on the table. Given the following SQL statement: FROM sales a JOIN budget b ON ((year(a.sale_date)* 100) + month(a.sale_date)) = b.budget_year_month Performance can be improved by adding a column with the year and month in the sales table. The updated SQL statement would be as follows: SELECT * FROM PRODUCTSFROM sales a JOIN budget b ON a.sale_year_month = b.budget_year_month Conclusion The recommendations boil down to a few short pointers check for indexes work with the smallest data set required remove unnecessary fields and tables and remove calculations in your JOIN and WHERE clauses. If all these recommendations fail to improve your SQL query performance my last suggestion is you move to Venus. All you will need is a single day to tune your SQL.

In SQL Server management studio, using, View, Registered Servers (Ctrl+Alt+G) set up the servers that you want to execute the same query across all servers for, right click the group, select new query. Then when you execute the query, the results will come back with the first column showing you the database instance that that row came from.

over the last several months, i've been developing a rest api using spring boot . my client hired an outside company to develop a native ios app, and my development team was responsible for developing its api. our main task involved integrating with epic , a popular software system used in health care. we also developed a crowd -backed authentication system, based loosely on philip sorst's angular rest security . to document our api, we used spring mvc integration for swagger (a.k.a. swagger-springmvc). i briefly looked into swagger4spring-web , but gave up quickly when it didn't recognize spring's @restcontroller. we started with swagger-springmvc 0.6.5 and found it fairly easy to integrate. unfortunately, it didn't allow us to annotate our model objects and tell clients which fields were required. we were quite pleased when a new version (0.8.2) was released that supports swagger 1.3 and its @apimodelproperty. what is swagger? the goal of swagger is to define a standard, language-agnostic interface to rest apis which allows both humans and computers to discover and understand the capabilities of the service without access to source code, documentation, or through network traffic inspection. to demonstrate how swagger works, i integrated it into josh long's x-auth-security project. if you have a boot-powered project, you should be able to use the same steps. 1. add swagger-springmvc dependency to your project. com.mangofactory swagger-springmvc 0.8.2 note: on my client's project, we had to exclude "org.slf4j:slf4j-log4j12" and add "jackson-module-scala_2.10:2.3.1" as a dependency. i did not need to do either of these in this project. 2. add a swaggerconfig class to configure swagger. the swagger-springmvc documentation has an example of this with a bit more xml. package example.config; import com.mangofactory.swagger.configuration.jacksonscalasupport; import com.mangofactory.swagger.configuration.springswaggerconfig; import com.mangofactory.swagger.configuration.springswaggermodelconfig; import com.mangofactory.swagger.configuration.swaggerglobalsettings; import com.mangofactory.swagger.core.defaultswaggerpathprovider; import com.mangofactory.swagger.core.swaggerapiresourcelisting; import com.mangofactory.swagger.core.swaggerpathprovider; import com.mangofactory.swagger.scanners.apilistingreferencescanner; import com.wordnik.swagger.model.*; import org.springframework.beans.factory.annotation.autowired; import org.springframework.beans.factory.annotation.value; import org.springframework.context.annotation.bean; import org.springframework.context.annotation.componentscan; import org.springframework.context.annotation.configuration; import java.util.arraylist; import java.util.arrays; import java.util.list; import static com.google.common.collect.lists.newarraylist; @configuration @componentscan(basepackages = "com.mangofactory.swagger") public class swaggerconfig { public static final list default_include_patterns = arrays.aslist("/news/.*"); public static final string swagger_group = "mobile-api"; @value("${app.docs}") private string docslocation; @autowired private springswaggerconfig springswaggerconfig; @autowired private springswaggermodelconfig springswaggermodelconfig; /** * adds the jackson scala module to the mappingjackson2httpmessageconverter registered with spring * swagger core models are scala so we need to be able to convert to json * also registers some custom serializers needed to transform swagger models to swagger-ui required json format */ @bean public jacksonscalasupport jacksonscalasupport() { jacksonscalasupport jacksonscalasupport = new jacksonscalasupport(); //set to false to disable jacksonscalasupport.setregisterscalamodule(true); return jacksonscalasupport; } /** * global swagger settings */ @bean public swaggerglobalsettings swaggerglobalsettings() { swaggerglobalsettings swaggerglobalsettings = new swaggerglobalsettings(); swaggerglobalsettings.setglobalresponsemessages(springswaggerconfig.defaultresponsemessages()); swaggerglobalsettings.setignorableparametertypes(springswaggerconfig.defaultignorableparametertypes()); swaggerglobalsettings.setparameterdatatypes(springswaggermodelconfig.defaultparameterdatatypes()); return swaggerglobalsettings; } /** * api info as it appears on the swagger-ui page */ private apiinfo apiinfo() { apiinfo apiinfo = new apiinfo( "news api", "mobile applications and beyond!", "https://helloreverb.com/terms/", "[email protected]", "apache 2.0", "http://www.apache.org/licenses/license-2.0.html" ); return apiinfo; } /** * configure a swaggerapiresourcelisting for each swagger instance within your app. e.g. 1. private 2. external apis * required to be a spring bean as spring will call the postconstruct method to bootstrap swagger scanning. * * @return */ @bean public swaggerapiresourcelisting swaggerapiresourcelisting() { //the group name is important and should match the group set on apilistingreferencescanner //note that swaggercache() is by defaultswaggercontroller to serve the swagger json swaggerapiresourcelisting swaggerapiresourcelisting = new swaggerapiresourcelisting(springswaggerconfig.swaggercache(), swagger_group); //set the required swagger settings swaggerapiresourcelisting.setswaggerglobalsettings(swaggerglobalsettings()); //use a custom path provider or springswaggerconfig.defaultswaggerpathprovider() swaggerapiresourcelisting.setswaggerpathprovider(apipathprovider()); //supply the api info as it should appear on swagger-ui web page swaggerapiresourcelisting.setapiinfo(apiinfo()); //global authorization - see the swagger documentation swaggerapiresourcelisting.setauthorizationtypes(authorizationtypes()); //every swaggerapiresourcelisting needs an apilistingreferencescanner to scan the spring request mappings swaggerapiresourcelisting.setapilistingreferencescanner(apilistingreferencescanner()); return swaggerapiresourcelisting; } @bean /** * the apilistingreferencescanner does most of the work. * scans the appropriate spring requestmappinghandlermappings * applies the correct absolute paths to the generated swagger resources */ public apilistingreferencescanner apilistingreferencescanner() { apilistingreferencescanner apilistingreferencescanner = new apilistingreferencescanner(); //picks up all of the registered spring requestmappinghandlermappings for scanning apilistingreferencescanner.setrequestmappinghandlermapping(springswaggerconfig.swaggerrequestmappinghandlermappings()); //excludes any controllers with the supplied annotations apilistingreferencescanner.setexcludeannotations(springswaggerconfig.defaultexcludeannotations()); // apilistingreferencescanner.setresourcegroupingstrategy(springswaggerconfig.defaultresourcegroupingstrategy()); //path provider used to generate the appropriate uri's apilistingreferencescanner.setswaggerpathprovider(apipathprovider()); //must match the swagger group set on the swaggerapiresourcelisting apilistingreferencescanner.setswaggergroup(swagger_group); //only include paths that match the supplied regular expressions apilistingreferencescanner.setincludepatterns(default_include_patterns); return apilistingreferencescanner; } /** * example of a custom path provider */ @bean public apipathprovider apipathprovider() { apipathprovider apipathprovider = new apipathprovider(docslocation); apipathprovider.setdefaultswaggerpathprovider(springswaggerconfig.defaultswaggerpathprovider()); return apipathprovider; } private list authorizationtypes() { arraylist authorizationtypes = new arraylist<>(); list authorizationscopelist = newarraylist(); authorizationscopelist.add(new authorizationscope("global", "access all")); list granttypes = newarraylist(); loginendpoint loginendpoint = new loginendpoint(apipathprovider().getappbasepath() + "/user/authenticate"); granttypes.add(new implicitgrant(loginendpoint, "access_token")); return authorizationtypes; } @bean public swaggerpathprovider relativeswaggerpathprovider() { return new apirelativeswaggerpathprovider(); } private class apirelativeswaggerpathprovider extends defaultswaggerpathprovider { @override public string getappbasepath() { return "/"; } @override public string getswaggerdocumentationbasepath() { return "/api-docs"; } } } the apipathprovider class referenced above is as follows: package example.config; import com.mangofactory.swagger.core.swaggerpathprovider; import org.springframework.beans.factory.annotation.autowired; import org.springframework.web.util.uricomponentsbuilder; import javax.servlet.servletcontext; public class apipathprovider implements swaggerpathprovider { private swaggerpathprovider defaultswaggerpathprovider; @autowired private servletcontext servletcontext; private string docslocation; public apipathprovider(string docslocation) { this.docslocation = docslocation; } @override public string getapiresourceprefix() { return defaultswaggerpathprovider.getapiresourceprefix(); } public string getappbasepath() { return uricomponentsbuilder .fromhttpurl(docslocation) .path(servletcontext.getcontextpath()) .build() .tostring(); } @override public string getswaggerdocumentationbasepath() { return uricomponentsbuilder .fromhttpurl(getappbasepath()) .pathsegment("api-docs/") .build() .tostring(); } @override public string getrequestmappingendpoint(string requestmappingpattern) { return defaultswaggerpathprovider.getrequestmappingendpoint(requestmappingpattern); } public void setdefaultswaggerpathprovider(swaggerpathprovider defaultswaggerpathprovider) { this.defaultswaggerpathprovider = defaultswaggerpathprovider; } } in src/main/resources/application.properties , add an "app.docs" property. this will need to be changed as you move your application from local -> test -> staging -> production. spring boot's externalized configuration makes this fairly simple. app.docs=http://localhost:8080 3. verify swagger produces json. after completing the above steps, you should be able to see the json swagger generates for your api. open http://localhost:8080/api-docs in your browser or curl http://localhost:8080/api-docs . { "apiversion": "1", "swaggerversion": "1.2", "apis": [ { "path": "http://localhost:8080/api-docs/mobile-api/example_newscontroller", "description": "example.newscontroller" } ], "info": { "title": "news api", "description": "mobile applications and beyond!", "termsofserviceurl": "https://helloreverb.com/terms/", "contact": "[email protected]", "license": "apache 2.0", "licenseurl": "http://www.apache.org/licenses/license-2.0.html" } } 4. copy swagger ui into your project. swagger ui is a good-looking javascript client for swagger's json. i integrated it using the following steps: git clone https://github.com/wordnik/swagger-ui cp -r swagger-ui/dist ~/dev/x-auth-security/src/main/resources/public/docs i modified docs/index.html, deleting its header () element, as well as made its url dynamic. ... $(function () { var apiurl = window.location.protocol + "//" + window.location.host; if (window.location.pathname.indexof('/api') > 0) { apiurl += window.location.pathname.substring(0, window.location.pathname.indexof('/api')) } apiurl += "/api-docs"; log('api url: ' + apiurl); window.swaggerui = new swaggerui({ url: apiurl, dom_id: "swagger-ui-container", ... after making these changes, i was able to open fire up the app with "mvn spring-boot:run" and view http://localhost:8080/docs/index.html in my browser. 5. annotate your api. there are two services in x-auth-security: one for authentication and one for news. to provide more information to the "news" service's documentation, add @api and @apioperation annotations. these annotations aren't necessary to get a service to show up in swagger ui, but if you don't specify the @api("user"), you'll end up with an ugly-looking class name instead (e.g. example_xauth_userxauthtokencontroller). @restcontroller @api(value = "news", description = "news api") class newscontroller { map entries = new concurrenthashmap(); @requestmapping(value = "/news", method = requestmethod.get) @apioperation(value = "get news", notes = "returns news items") collection entries() { return this.entries.values(); } @requestmapping(value = "/news/{id}", method = requestmethod.delete) @apioperation(value = "delete news item", notes = "deletes news item by id") newsentry remove(@pathvariable long id) { return this.entries.remove(id); } @requestmapping(value = "/news/{id}", method = requestmethod.get) @apioperation(value = "get a news item", notes = "returns a news item") newsentry entry(@pathvariable long id) { return this.entries.get(id); } @requestmapping(value = "/news/{id}", method = requestmethod.post) @apioperation(value = "update news", notes = "updates a news item") newsentry update(@requestbody newsentry news) { this.entries.put(news.getid(), news); return news; } ... } you might notice the screenshot above only shows news. this is because swaggerconfig.default_include_patterns only specifies news. the following will include all apis. public static final list default_include_patterns = arrays.aslist("/.*"); after adding these annotations and modifying swaggerconfig , you should see all available services. in swagger-springmvc 0.8.x, the ability to use @apimodel and @apimodelproperty annotations was added. this means you can annotate newsentry to specify which fields are required. @apimodel("news entry") public static class newsentry { @apimodelproperty(value = "the id of the item", required = true) private long id; @apimodelproperty(value = "content", required = true) private string content; // getters and setters } this results in the model's documentation showing up in swagger ui. if "required" isn't specified, a property shows up as optional . parting thoughts the qa engineers and 3rd party ios developers have been very pleased with our api documentation. i believe this is largely due to swagger and its nice-looking ui. the swagger ui also provides an interface to test the endpoints by entering parameters (or json) into html forms and clicking buttons. this could benefit those qa folks that prefer using selenium to test html (vs. raw rest endpoints). i've been quite pleased with swagger-springmvc, so kudos to its developers. they've been very responsive in fixing issues i've reported . the only thing i'd like is support for recognizing jsr303 annotations (e.g. @notnull) as required fields. to see everything running locally, checkout my modified x-auth-security project on github and the associated commits for this article.

I've been hunting for good database tools to perform that class of tasks that we all need, but that we end up re-implementing over and over again. One such task is database migrations. I've been experimenting with Goose to provide general-purpose database migration support. What Is Goose? Goose is a general purpose database migration manager. The idea is simple: You provide SQL schema files that follow a particular naming convention You provide a simple dbconf.yml file that tells Goose how to connect to your various databases Goose provides you simple tools to upgrade (goose up), check on (goose status), and even revert (goose down) schema changes. Goose does this by adding one more table inside your database. This table tracks which schema changes it has made. Based on its history, it can tell which scheme updates need to be run and which have already been run. While Goose is written in Go (golang), it is agnostic about what language your app is written in. Getting Started I got Goose up and running in less than 30 minutes, and you can probably do it faster. I already have an empty Postgres database called foo. But it has no tables. I have an existing codebase, too (MyProject). Here is the process for configuring Goose to manage the database schema management. First, I create the db/ directory, which will house all of the Goose-specific files, including my schema. $ cd MyProject $ mkdir db $ cd db $ vim dbconf.yml # Open with the editor of your choice. The dbconf.yml file contains a list of databases along with the relevant information for connecting to each. Mine looks something like this: test: driver: postgres open: user=foo dbname=foo_test sslmode=disable development: driver: postgres open: user=foo dbname=foo_dev sslmode=disable (Important: use spaces, not tabs, in YAML.) Now I have two databases configured. One for testing and one for development. By default, Goose assumes the target database is development. The above is just configured to connect to the PostgreSQL instance locally running. If I need support for a remote host, I can add host=... password=... (and remove sslmode=disable). At this point, I can generate a new migration. $ cd .. # Back to MyProject/, not in db/ $ goose create NewSchema sql goose: created db/migrations/20140311133014_NewSchema.sql $ vim db/migrations/20140311133014_NewSchema.sql # Use whatever editor you like Notice that the goose create command will create a new SQL file that follows Goose's naming convention. (That trailing sql on the command is important. goose create can also generate go migration files) My new schema file has two sections: a section for goose up and a section to rollback with goose down: -- +goose Up CREATE TABLE foo ( -- ... ); -- +goose Down DROP TABLE foo; With that done, I can now very easily create by development database: $ goose up If I want to setup test instead, I use the -env flag: $ goose -env=test up And that's it! In subsequent schema files, I may ALTER existing tables or CREATE new ones, and so on. Just about anything that your SQL engine can execute can be passed through Goose. (Though there are some formatting annotations you need to use for things like stored procedures.) Goose Pros In addition to the general ease of use of Goose, here are some additional features that I really like: You do not need your entire codebase to execute Goose. Our deployment box, for example, only has the Goose db/ directory, not the rest of the code. It is largely language neutral if you're just migrating SQL. It works with PostgreSQL, MySQL, and SQLite. The history table that it creates is human-readable, which makes it easy for me to see what's been going on. It supports environment variable interpolation. Don't want your password inside the dbconf.yml file? Just do something like this: development: driver: postgres open: user=foo dbname=foo_dev sslmode=disable password=$MY_DB_PASSWORD This will cause Goose to check the environment for a variable named $MY_DB_PASSWORD. Goose Cons Honestly, I have very few. Right now, you need the Go runtime to install and build Goose. Of course, you can compile Goose once, and then use it wherever. While it has support for Go language migrations, it would be nice to be able to write migration scripts that are executed via the shell. That way, one could use Bash, Python, Perl, or whatever else to trigger migrations. But, hey... this is a pretty minor complaint. Overall, though, Goose is a fantastic tool for handling migrations with ease.

We all know we need to use connection pools where ever we connect to a database. All of the modern drivers using JDBC type 4 support it. In this post we will have look at an overview ofconnection pooling in spring applications and how to deal with same context in a non JEE enviorements (like tests). Most examples of connecting to database in spring is done using DriverManagerDataSource. If you don't read the documentation properly then you are going to miss a very important point. NOTE: This class is not an actual connection pool; it does not actually pool Connections. It just serves as simple replacement for a full-blown connection pool, implementing the same standard interface, but creating new Connections on every call. Useful for test or standalone environments outside of a J2EE container, either as a DataSource bean in a corresponding ApplicationContext or in conjunction with a simple JNDI environment. Pool-assuming Connection.close() calls will simply close the Connection, so any DataSource-aware persistence code should work. Yes, by default the spring applications does not use pooled connections. There are two ways to implement the connection pooling. Depending on who is managing the pool. If you are running in a JEE environment, then it is prefered use the container for it. In a non-JEE setup there are libraries which will help the application to manage the connection pools. Lets discuss them in bit detail below. 1. Server (Container) managed connection pool (Using JNDI) When the application connects to the database server, establishing the physical actual connection takes much more than the execution of the scripts. Connection pooling is a technique that was pioneered by database vendors to allow multiple clients to share a cached set of connection objects that provide access to a database resource. The JavaWorld article gives a good overview about this. In a J2EE container, it is recommended to use a JNDI DataSource provided by the container. Such a DataSource can be exposed as a DataSource bean in a Spring ApplicationContext via JndiObjectFactoryBean, for seamless switching to and from a local DataSource bean like this class. The below articles helped me in setting up the data source in JBoss AS. 1. DebaJava Post 2. JBoss Installation Guide 3. JBoss Wiki Next step is to use these connections created by the server from the application. As mentioned in the documentation you can use the JndiObjectFactoryBean for this. It is as simple as below If you want to write any tests using springs "SpringJUnit4ClassRunner" it can't load the context becuase the JNDI resource will not be available. For tests, you can then either set up a mock JNDI environment through Spring's SimpleNamingContextBuilder, or switch the bean definition to a local DataSource (which is simpler and thus recommended). As I was looking for a good solutions to this problem (I did not want a separate context for tests) this SO answer helped me. It sort of uses the various tips given in the Javadoc to good effect. The issue with the above solution is the repetition of code to create the JNDI connections. I have solved it using a customized runner SpringWithJNDIRunner. This class adds the JNDI capabilities to the SpringJUnit4ClassRunner. It reads the data source from "test-datasource.xml" file in the class path and binds it to the JNDI resource with name "java:/my-ds". After the execution of this code the JNDI resource is available for the spring container to consume. import javax.naming.NamingException; import org.junit.runners.model.InitializationError; import org.springframework.context.ApplicationContext; import org.springframework.context.support.ClassPathXmlApplicationContext; import org.springframework.mock.jndi.SimpleNamingContextBuilder; import org.springframework.test.context.junit4.SpringJUnit4ClassRunner; /** * This class adds the JNDI capabilities to the SpringJUnit4ClassRunner. * @author mkadicha * */ public class SpringWithJNDIRunner extends SpringJUnit4ClassRunner { public static boolean isJNDIactive; /** * JNDI is activated with this constructor. * * @param klass * @throws InitializationError * @throws NamingException * @throws IllegalStateException */ public SpringWithJNDIRunner(Class klass) throws InitializationError, IllegalStateException, NamingException { super(klass); synchronized (SpringWithJNDIRunner.class) { if (!isJNDIactive) { ApplicationContext applicationContext = new ClassPathXmlApplicationContext( "test-datasource.xml"); SimpleNamingContextBuilder builder = new SimpleNamingContextBuilder(); builder.bind("java:/my-ds", applicationContext.getBean("dataSource")); builder.activate(); isJNDIactive = true; } } } } To use this runner you just need to use the annotation @RunWith(SpringWithJNDIRunner.class) in your test. This class extends SpringJUnit4ClassRunner beacuse a there can only be one class in the @RunWith annotation. The JNDI is created only once is a test cycle. This class provides a clean solution to the problem. 2. Application managed connection pool If you need a "real" connection pool outside of a J2EE container, consider Apache's Jakarta Commons DBCP or C3P0. Commons DBCP's BasicDataSource and C3P0's ComboPooledDataSource are full connection pool beans, supporting the same basic properties as this class plus specific settings (such as minimal/maximal pool size etc). Below user guides can help you configure this. 1. Spring Docs 2. C3P0 Userguide 3. DBCP Userguide The below articles speaks about the general guidelines and best practices in configuring the connection pools. 1. SO question on Spring JDBC Connection pools 2. Connection pool max size in MS SQL Server 2008 3. How to decide the max number of connections 4. Monitoring the number of active connections in SQL Server 2008 Note:- All the text in italics are copied from the spring documentation of the DriverManagerDataSource.

Postgres and Oracle compatibility with Hibernate There are situations your JEE application needs to support Postgres and Oracle as a Database. Hibernate should do the job here, however, there are some specifics worth mentioning. While enabling Postgres for application already running Oracle I came across following tricky parts: BLOBs support, CLOBs support, Oracle not knowing Boolean type (using Integer) instead and DUAL table. These were the tricks I had to apply to make the @Entity classes running on both of these. Please note I’ve used Postgres 9.3 with Hibernate 4.2.1.SP1. BLOBs support The problem with Postgres is that it offers 2 types of BLOB storage: bytea - data stored in table oid - table holds just identifier to data stored elsewhere I guess in the most of the situations you can live with the bytea as well as I did. The other one as far as I’ve read is to be used for some huge data (in gigabytes) as it supports streams for IO operations. Well, it sounds nice there is such a support, however using Hibernate in this case can make things quite problematic (due to need to use the specific annotations), especially if you try to achieve compatibility with Oracle. To see the trouble here, see StackOverflow: proper hibernate annotation for byte[] All- the combinations are described there: annotation postgres oracle works on ------------------------------------------------------------- byte[] + @Lob oid blob oracle byte[] bytea raw(255) postgresql byte[] + @Type(PBA) oid blob oracle byte[] + @Type(BT) bytea blob postgresql where @Type(PBA) stands for: @Type(type="org.hibernate.type.PrimitiveByteArrayBlobType") and @Type(BT) stands for: @Type(type="org.hibernate.type.BinaryType"). These result in all sorts of Postgres errors, like: ERROR: column “foo” is of type oid but expression is of type bytea or ERROR: column “foo” is of type bytea but expression is of type oid Well, there seems to be a solution, still it includes patching of Hibernate library (something I see as the last option when playing with 3.rd party library). There is also a reference to official blog post from the Hibernate guys on the topic: PostgreSQL and BLOBs. Still solution described in blog post seems not working for me and based on the comments, seems to be invalid for more people. BLOBs solved OK, so now the optimistic part. After quite some debugging I ended up with the Entity definition like this : @Lob private byte[] foo; Oracle has no trouble with that, moreover I had to customize the Postgres dialect in a way: public class PostgreSQLDialectCustom extends PostgreSQL82Dialect { @Override public SqlTypeDescriptor remapSqlTypeDescriptor(SqlTypeDescriptor sqlTypeDescriptor) { if (sqlTypeDescriptor.getSqlType() == java.sql.Types.BLOB) { return BinaryTypeDescriptor.INSTANCE; } return super.remapSqlTypeDescriptor(sqlTypeDescriptor); } } That’s it! Quite simple right? That works for persisting to bytea typed columns in Postgres (as that fits my usecase). CLOBs support The errors in misconfiguration looked something like this: org.postgresql.util.PSQLException: Bad value for type long : ... So first I’ve found (on String LOBs on PostgreSQL with Hibernate 3.6) following solution: @Lob @Type(type = "org.hibernate.type.TextType") private String foo; Well, that works, but for Postgres only. Then there was a suggestion (on StackOverflow: Postgres UTF-8 clobs with JDBC) from to go for: @Lob @Type(type="org.hibernate.type.StringClobType") private String foo; That pointed me the right direction (the funny part was that it was just a comment to some answers). It was quite close, but didn’t work for me in all cases, still resulted in errors in my tests. CLOBs solved The important was @deprecation javadocs in the org.hibernate.type.StringClobType that brought me to working one: @Lob @Type(type="org.hibernate.type.MaterializedClobType") private String foo; That works for both Postgres and Oracle, without any further hacking (on Hibernate side) needed. Boolean type Oracle knows no Boolean type and the trouble is that Postgres does. As there was also some plain SQL present, I ended up In Postgres with error: ERROR: column “foo” is of type boolean but expression is of type integer I decided to enable cast from Integer to Boolean in Postgres rather than fixing all the plain SQL places (in a way found in Forum: Automatically Casting From Integer to Boolean): update pg_cast set castcontext = 'i' where oid in ( select c.oid from pg_cast c inner join pg_type src on src.oid = c.castsource inner join pg_type tgt on tgt.oid = c.casttarget where src.typname like 'int%' and tgt.typname like 'bool%'); Please note you should run the SQL update by user with provileges to update catalogs (probably not your postgres user used for DB connection from your application), as I’ve learned on Stackoverflow: Postgres - permission denied on updating pg_catalog.pg_cast. DUAL table There is one more specific in the Oracle I came across. If you have plain SQL, in Oracle there is DUAL table provied (see more info on Wikipedia on that) that might harm you in Postgres. Still the solution is simple. In Postgres create a view that would fill the similar purpose. It can be created like this: create or replace view dual as select 1; Conclusion Well that should be it. Enjoy your cross DB compatible JEE apps.

this is another experiment with longer posts. previously, i used the time series example as the bed on which to test some ideas regarding feature design, to explain how we work and in general work out the rough patches along the way. i should probably note that these posts are purely fiction at this point. we have no plans to include a time series feature in ravendb at this time. i am trying to work out some thoughts in the open and get your feedback. at any rate, yesterday we had a request for cassandra style counters at the mailing list. and as long as i am doing feature design series, i thought that i could talk about how i would go about implementing this. again, consider this fiction, i have no plans of implementing this at this time. the essence of what we want is to be able to… count stuff. efficiently, in a distributed manner, with optional support for cross data center replication. very roughly, the idea is to have “sub counters”, unique for every node in the system. whenever you increment the value, we log this to our own sub counter, and then replicate it out. whenever you read it, we just sum all the data we have from all the sub counters. let us outline the various parts of the solution in the same order as the one i used for time series. storage a counter is just a named 64 bits signed integer. a counter name can be any string up to 128 printable characters. the external interface of the storage would look like this: 1: public struct counterincrement 2: { 3: public string name; 4: public long change; 5: } 6: 7: public struct counter 8: { 9: public string name; 10: public string source; 11: public long value; 12: } 13: 14: public interface icounterstorage 15: { 16: void localincrementbatch(counterincrement[] batch); 17: 18: counter[] read(string name); 19: 20: void replicatedupdates(counter[] updates); 21: } as you can see, this gives us very simple interface for the storage. we can either change the data locally (which modify our own storage) or we can get an update from a replica about its changes. there really isn’t much more to it, to be fair. the localincrementbatch() increment a local value, and read() will return all the values for a counter. there is a little bit of trickery involved in how exactly one would store the counter values. for now, i think we’ll store each counter as two step values. we’ll have a tree of multi tree values that will carry each value from each source. that means that a counter will take roughly 4kb or so. this is easy to work with and nicely fit the model voron uses internally. note that we’ll outline additional requirement for storage (searching for counter by prefix, iterating over counters, addresses of other servers, stats, etc) below. i’m not showing them here because they aren’t the major issue yet. over the wire skipping out on any optimizations that might be required, we will expose the following endpoints: get /counters/read?id=users/1/visits&users/1/posts <—will return json response with all the relevant values (already summed up). { “users/1/visits”: 43, “users/1/posts”: 3 } get /counters/read?id=users/1/visits&users/1/1/posts&raw=true <—will return json response with all the relevant values, per source. { “users/1/visits”: {“rvn1”: 21, “rvn2”: 22 } , “users/1/posts”: { “rvn1”: 2, “rvn3”: 1 } } post /counters/increment <– allows to increment counters. the request is a json array of the counter name and the change. for a real system, you’ll probably need a lot more stuff, metrics, stats, etc. but this is the high level design, so this would be enough. note that we are skipping the high performance stream based writes we outlined for time series. we’ll probably won’t need them, so that doesn’t matter, but they are an option if we need them. system behavior this is where it is really not interesting, there is very little behavior here, actually. we only have to read the data from the storage, sum it up, and send it to the user. hardly what i’ll call business logic. client api the client api will probably look something like this: 1: counters.increment("users/1/posts"); 2: counters.increment("users/1/visits", 4); 3: 4: using(var batch = counters.batch()) 5: { 6: batch.increment("users/1/posts"); 7: batch.increment("users/1/visits",5); 8: batch.submit(); 9: } note that we’re offering both batch and single api. we’ll likely also want to offer a fire & forget style, which will be able to offer even better performance (because they could do batching across more than a single thread), but that is out of scope for now. for simplicity sake, we are going to have the client just a container for all of endpoints that it knows about. the container would be responsible for… updating the client visible topology, selecting the best server to use at any given point, etc. user interface there isn’t much to it. just show a list of counter values in a list. allow to search by prefix, allow to dive into a particular counter and read its raw values, but that is about it. oh, and allow to delete a counter. deleting data honestly, i really hate deletes. they are very expensive to handle properly the moment you have more than a single node. in this case, there is an inherent race condition between a delete going out and another node getting an increment. and then there is the issue of what happens if you had a node down when you did the delete, etc. this just sucks. deletion are handled normally, (with the race condition caveat, obviously), and i’ll discuss how we replicate them in a bit. high availability / scale out by definition, we actually don’t want to have storage replication here. either log shipping or consensus based. we actually do want to have different values, because we are going to be modifying things independently on many servers. that means that we need to do replication at the database level. and that leads to some interesting questions. again, the hard part here is the deletes. actually, the really hard part is what we are going to do with the new server problem. the new server problem dictates how we are going to bring a new server into the cluster. if we could fix the size of the cluster, that would make things a lot easier. however, we are actually interested in being able to dynamically grow the cluster size. therefor, there are only two real ways to do it: add a new empty node to the cluster, and have it be filled from all the other servers. add a new node by backing up an existing node, and restoring as a new node. ravendb, for example, follows the first option. but it means that in needs to track a lot more information. the second option is actually a lot simpler, because we don’t need to care about keeping around old data. however, this means that the process of bringing up a new server would now be: update all nodes in the cluster with the new node address (node isn’t up yet, replication to it will fail and be queued). backup an existing node and restore at the new node. start the new node. the order of steps is quite important. and it would be easy to get it wrong. also, on large systems, backup & restore can take a long time. operationally speaking, i would much rather just be able to do something like, bring a new node into the cluster in “silent” mode. that is, it would get information from all the other nodes, and i can “flip the switch” and make it visible to clients at any point in time. that is how you do it with ravendb, and it is an incredibly powerful system, when used properly. that means that for all intents and purposes, we don’t do real deletes. what we’ll actually do is replace the counter value with delete marker. this turns deletes into a much simple “just another write”. it has the sad implication of not free disk space on deletes, but deletes tend to be rare, and it is usually fine to add a “purge” admin option that can be run on as needed basis. but that brings us to an interesting issue, how do we actually handle replication. the topology map to simplify things, we are going to go with one way replication from a node to another. that allows complex topologies like master-master, cluster-cluster, replication chain, etc. but in the end, this is all about a single node replication to another. the first question to ask is, are we going to replicate just our local changes, or are we going to have to replicate external changes as well? the problem with replicating external changes is that you may have the following topology: now, server a got a value and sent it to server b. server b then forwarded it to server c. however, at that point, we also have a the value from server a replicated directly to server c. which value is it supposed to pick? and what about a scenario where you have more complex topology? in general, because in this type of system, we can have any node accept writes, and we actually desire this to be the case , we don’t want this behavior. we want to only replicate local data, not all the data. of course, that leads to an annoying question, what happens if we have a 3 node cluster, and one node fails catastrophically. we can bring a new node in, and the other two nodes will be able to fill in their values via replication, but what about the node that is down? the data isn’t gone, it is still right there in the other two nodes, but we need a way to pull it out. therefor, i think that the best option would be to say that nodes only replicate their local state, except in the case of a new node. a new node will be told the address of an existing node in the cluster, at which point it will: register itself in all the nodes in the cluster (discoverable from the existing node). this assumes a standard two way replication link between all servers, if this isn’t the case, the operators would have the responsibility to setup the actual replication semantics on their own. new node now starts getting updates from all the nodes in the cluster. it keeps them in a log for now, not doing anything yet. ask that node for a complete update of all of its current state. when it has all the complete state of the existing node, it replays all of the remembered logs that it didn’t have a chance to apply yet. then it announces that it is in a valid state to start accepting client connections. note that this process is likely to be very sensitive to high data volumes. that is why you’ll usually want to select a backup node to read from, and that decision is an ops decision. you’ll also want to be able to report extensively on the current status of the node, since this can take a while, and ops will be watching this very closely. server name a node requires a unique name. we can use guids, but those aren’t readable, so we can use machine name + port, but those can change. ideally, we can require the user to set us up with a unique name. that is important for readability and for being able to alter see all the values we have in all the nodes. it is important that names are never repeated, so we’ll probably have a guid there anyway, just to be on the safe side. actual replication semantics since we have the new server problem down to an automated process, we can choose the drastically simpler model of just having an internal queue per each replication destination. whenever we make a change, we also make a note of that in the queue for that destination, then we start an async replication process to that server, sending all of our updates there. it is always safe to overwrite data using replication, because we are overwriting our own data, never anyone else. and… that is about it, actually. there are probably a lot of details that i am missing / would discover if we were to actually implement this. but i think that this is a pretty good idea about what this feature is about.

In my previous post, you have seen how we can start a WebLogic admin and multiple managed servers. One downside with that instruction is that those processes will start in foreground and the STDOUT are printed on terminal. If you intended to run these severs as background services, you might want to try the WebLogic node manager wlscontrol.sh tool. I will show you how you can get Node Manager started here. The easiest way is still to create the domain directory with the admin server running temporary and then create all your servers through the /console application as described in last post. Once you have these created, then you may shut down all these processes and start it with Node Manager. 1. cd $WL_HOME/server/bin && startNodeManager.sh & 3. $WL_HOME/common/bin/wlscontrol.sh -d mydomain -r $HOME/domains/mydomain -c -f startWebLogic.sh -s myserver START 4. $WL_HOME/common/bin/wlscontrol.sh -d mydomain -r $HOME/domains/mydomain -c -f startManagedWebLogic.sh -s appserver1 START The first step above is to start and run your Node Manager. It is recommended you run this as full daemon service so even OS reboot can restart itself. But for this demo purpose, you can just run it and send to background. Using the Node Manager we can then start the admin in step 2, and then to start the managed server on step 3. The NodeManager can start not only just the WebLogic server for you, but it can also monitor them and automatically restart them if they were terminated for any reasons. If you want to shutdown the server manually, you may use this command using Node Manager as well: $WL_HOME/common/bin/wlscontrol.sh -d mydomain -s appserver1 KILL The Node Manager can also be used to start servers remotely through SSH on multiple machines. Using this tool effectively can help managing your servers across your network. You may read more details here: http://docs.oracle.com/cd/E23943_01/web.1111/e13740/toc.htm TIPS1: If there is problem when starting server, you may wnat to look into the log files. One log file is the/servers//logs/.out of the server you trying to start. Or you can look into the Node Manager log itself at $WL_HOME/common/nodemanager/nodemanager.log TIPS2: You add startup JVM arguments to each server starting with Node Manager. You need to create a file under /servers//data/nodemanager/startup.properties and add this key value pair:Arguments = -Dmyapp=/foo/bar TIPS3: If you want to explore Windows version of NodeManager, you may want to start NodeManager without native library to save yourself some trouble. Try adding NativeVersionEnabled=false to$WL_HOME/common/nodemanager/nodemanager.properties file.

In a previous post, I presented a method of loading initial data into a Django database by using a custom management command. An accompanying task is cleaning the database up. Here I want to discuss a few options for doing that. First, some general design notes on Django management commands. If you run manage.py help you’ll see a whole bunch of commands starting with sql. These all share a common idiom – print SQL statements to the standard output. Almost all DB engines have means to pipe commands from the standard input, so this plays great with the Unix philosophy of building pipes of single-task programs. Django even provides a convenient shortcut for us to access the actual DB that’s being used with a given project – the dbshell command. As an example, we have the sqlflush command, which returns a list of the SQL statements required to return all tables in the database to the state they were in just after they were installed. In a simple blog-like application with "post" and "tag" models, it may return something like: $ python manage.py sqlflush BEGIN; DELETE FROM "auth_permission"; DELETE FROM "auth_group"; DELETE FROM "django_content_type"; DELETE FROM "django_session"; DELETE FROM "blogapp_tag"; DELETE FROM "auth_user_groups"; DELETE FROM "auth_group_permissions"; DELETE FROM "auth_user_user_permissions"; DELETE FROM "blogapp_post"; DELETE FROM "blogapp_post_tags"; DELETE FROM "auth_user"; DELETE FROM "django_admin_log"; COMMIT; Note there’s a lot of tables here, because the project also installed the admin and auth applications from django.contrib. We can actually execute these SQL statements, and thus wipe out all the DB tables in our database, by running: $ python manage.py sqlflush | python manage.py dbshell For this particular sequence, since it’s so useful, Django has a special built-in command named flush. But there’s a problem with running flush that may or may not bother you, depending on what your goals are. It wipes out all tables, and this means authentication data as well. So if you’ve created a default admin user when jump-starting the application, you’ll have to re-create it now. Perhaps there’s a more gentle way to delete just your app’s data, without messing with the other apps? Yes. In fact, I’m going to show a number of ways. First, let’s see what other existing management commands have to offer. sqlclear will emit the commands needed to drop all tables in a given app. For example: $ python manage.py sqlclear blogapp BEGIN; DROP TABLE "blogapp_tag"; DROP TABLE "blogapp_post"; DROP TABLE "blogapp_post_tags"; COMMIT; So we can use it to target a specific app, rather than using the kill-all approach of flush. There’s a catch, though. While flush runs delete to wipe all data from the tables, sqlclear removes the actual tables. So in order to be able to work with the database, these tables have to be re-created. Worry not, there’s a command for that: $ python manage.py sql blogapp BEGIN; CREATE TABLE "blogapp_post_tags" ( "id" integer NOT NULL PRIMARY KEY AUTOINCREMENT, "post_id" integer NOT NULL REFERENCES "blogapp_post" ("id"), "tag_id" varchar(50) NOT NULL REFERENCES "blogapp_tag" ("name"), UNIQUE ("post_id", "tag_id") ) ; CREATE TABLE "blogapp_post" ( "id" integer NOT NULL PRIMARY KEY AUTOINCREMENT, <.......> ) ; CREATE TABLE "blogapp_tag" ( <.......> ) ; COMMIT; So here’s a first way to do a DB cleanup: pipe sqlclear appname into dbshell. Then pipe sql appname to dbshell. An alternative way, which I like less, is to take the subset of DELETE statements generated by sqlflush, save them in a text file, and pipe it through to dbshell when needed. For example, for the blog app discussed above, these statements should do it: BEGIN; DELETE FROM "blogapp_tag"; DELETE FROM "blogapp_post"; DELETE FROM "blogapp_post_tags"; DELETE COMMIT; The reason I don’t like it is that it forces you to have explicit table names stored somewhere, which is a duplication of the existing models. If you happen to change some of your foreign keys, for example, tables will need changing so this file will have to be regenerated. The approach I like best is more programmatic. Django’s model API is flexible and convenient, and we can just use it in a custom management command: from django.core.management.base import BaseCommand from blogapp.models import Post, Tag class Command(BaseCommand): def handle(self, *args, **options): Tag.objects.all().delete() Post.objects.all().delete() Save this code as blogapp/management/commands/clear_models.py, and now it can be invoked with: $ python manage.py clear_models

WSO2 DSS wraps Data Services Layer and provides us with a simple GUI to define a Data Service with zero Java code. With this, a change to the data source is just a simple click away and no other party needs to be aware of this. With this sample demonstration, we will see how to do a batch insert to a table. Batch insert is useful when you want to insert data in sequential manner. This also means that if at least one of the insertion query fails all the other queries ran so far in the batch will be rolled back as well. If one insertion in the batch fails means whole batch is failed. This can be used if you are running the same query to insert data many times. With batch insert all the data will be sent in one call. So this reduce the number calls you have to call, to get the data inserted. This comes with one condition that, The query should not be producing results back. (We will only be notified whether the query was successful or not.) Prerequisites: WSO2 Data Services Server - http://wso2.com/products/data-services-server/ (current latest 3.1.1) Mysql connector (JDBC) - https://www.mysql.com/products/connector/ If we already have a data service running which is not sending back a result set , then it's just a matters of adding following property in service declaration. enableBatchRequests="true" Anyway I will be demonstrating the creation of the service from the scratch. 1. Create a service as follows going through the wizard, 2. Create the data source 3. Create the query - (This is an insert query. Also note the input mapping we have add as relevant to the query. To know more about input mapping and using validation refer the documentation.) 4. Create the operation - Select the query to be executed once the operation is called. By enabling return request status, we will be notified whether the operation was a success or not. 5. Try it! - When we list the services we will see this new service now. In the right we will have an option to try it. Here we can see the option to try the service giving the input parameters. Here I have tried it two insertions in a batch. Now if we go to XML view of the service it will be similar to following, which is saved in server as a .dbs file. com.mysql.jdbc.Driver jdbc:mysql://localhost:3306/json_array root root 1 10 SELECT 1 insert into flights (flight_no, number_of_cases, created_by, description, trips) values (:flight_no,:number_of_cases,:created_by,:description,:trips) If we hit on the service name in the list of services, we will be directed to Service Dashboard where we can see several other options for the service. It provides the option to generate an Axis2 client for the service. Once we get the client then it's a matter of calling the methods in the stub as follows. private static BatchRequestSampleOldStub.AddFlight_type0 createFlight(int cases, String creator, String description, int trips) { BatchRequestSampleOldStub.AddFlight_type0 val = new BatchRequestSampleOldStub.AddFlight_type0(); val.setNumber_of_cases(cases); val.setCreated_by(creator); val.setDescription(description); val.setTrips(trips); printFlightInfo(cases, creator, description, trips); return val; } public static void main(String[] args) throws Exception { String epr = "http://localhost:9763" + "/services/BatchInsertSample"; BatchRequestSampleOldStub stub = new BatchRequestSampleOldStub(epr); BatchRequestSampleOldStub.AddFlight_batch_req vals1 = new BatchRequestSampleOldStub.AddFlight_batch_req(); vals1.addAddFlight(createFlight(1, "Pushpalanka", "test", 2)); vals1.addAddFlight(createFlight(2, "Jayawardhana", "test", 2)); vals1.addAddFlight(createFlight(3, "[email protected]", "test", 2)); try { System.out.println("Executing Add Flights.."); stub.addFlight_batch_req(vals1); } catch (Exception e) { System.out.println("Error in Add Flights!"); } Complete client code can be found here. Cheers! Ref: http://docs.wso2.org/display/DSS311/Batch+Processing+Sample

Sometimes we want to use Hibernate native SQL in our code. For example we might need to invoke a selectable stored procedure, we cannot invoke in another way. To invoke a native SQL query we use the method createSQLQuery() which is available from the Hibernate session object. In our Grails code we must then first get access to the current Hibernate session. Luckily we only have to inject the sessionFactory bean in our Grails service or controller. To get the current session we invoke the getCurrentSession() method and we are ready to execute a native SQL query. The query itself is defined as a String value and we can use placeholders for variables, just like with other Hibernate queries. In the following sample we create a new Grails service and use a Hibernate native SQL query to execute a selectable stored procedure with the nameorganisation_breadcrumbs. This stored procedure takes one argument startId and will return a list of results with an id, name and level column. // File: grails-app/services/com/mrhaki/grails/OrganisationService.groovy package com.mrhaki.grails import com.mrhaki.grails.Organisation class OrganisationService { // Auto inject SessionFactory we can use // to get the current Hibernate session. def sessionFactory List breadcrumbs(final Long startOrganisationId) { // Get the current Hiberante session. final session = sessionFactory.currentSession // Query string with :startId as parameter placeholder. final String query = 'select id, name, level from organisation_breadcrumbs(:startId) order by level desc' // Create native SQL query. final sqlQuery = session.createSQLQuery(query) // Use Groovy with() method to invoke multiple methods // on the sqlQuery object. final results = sqlQuery.with { // Set domain class as entity. // Properties in domain class id, name, level will // be automatically filled. addEntity(Organisation) // Set value for parameter startId. setLong('startId', startOrganisationId) // Get all results. list() } results } } In the sample code we use the addEntity() method to map the query results to the domain class Organisation. To transform the results from a query to other objects we can use the setResultTransformer() method. Hibernate (and therefore Grails if we use the Hibernate plugin) already has a set of transformers we can use. For example with the org.hibernate.transform.AliasToEntityMapResultTransformer each result row is transformed into a Map where the column aliases are the keys of the map. // File: grails-app/services/com/mrhaki/grails/OrganisationService.groovy package com.mrhaki.grails import org.hibernate.transform.AliasToEntityMapResultTransformer class OrganisationService { def sessionFactory List> breadcrumbs(final Long startOrganisationId) { final session = sessionFactory.currentSession final String query = 'select id, name, level from organisation_breadcrumbs(:startId) order by level desc' final sqlQuery = session.createSQLQuery(query) final results = sqlQuery.with { // Assign result transformer. // This transformer will map columns to keys in a map for each row. resultTransformer = AliasToEntityMapResultTransformer.INSTANCE setLong('startId', startOrganisationId) list() } results } } Finally we can execute a native SQL query and handle the raw results ourselves using the Groovy Collection API enhancements. The result of thelist() method is a List of Object[] objects. In the following sample we use Groovy syntax to handle the results: // File: grails-app/services/com/mrhaki/grails/OrganisationService.groovy package com.mrhaki.grails class OrganisationService { def sessionFactory List> breadcrumbs(final Long startOrganisationId) { final session = sessionFactory.currentSession final String query = 'select id, name, level from organisation_breadcrumbs(:startId) order by level desc' final sqlQuery = session.createSQLQuery(query) final queryResults = sqlQuery.with { setLong('startId', startOrganisationId) list() } // Transform resulting rows to a map with key organisationName. final results = queryResults.collect { resultRow -> [organisationName: resultRow[1]] } // Or to only get a list of names. //final List names = queryResults.collect { it[1] } results } } Code written with Grails 2.3.7.

[Article originally written by Barak Merimovich.] Automation the Linux Way In the Linux world SSH, secure shell, is the de facto standard for remote connectivity and automation for the purpose of logging into a remote machine to install tools and run commands. It's pretty much ubiquitous, runs across multiple Linux versions and distributions, and every Linux admin worth their salt knows SSH and how to configure it. What's more, it's even the default enabled port on most clouds - port 22. An important feature available with SSH is support for file transfer via its secure copy protocol - AKA SCP, and secure file transfer protocol - AKA SFTP. These are a built-in part of the tool or exist as add-ons to the protocol that are almost always available. Therefore, using SSH for file transfer and remote execution is basically a given with Linux, and there are even tools to support SSH clients available for virtually every major programming language and operating system. WinRM in a Linux World So what comes out-of-the-box with Linux, is less of a given with Windows. SSH, obviously, is not built in with Windows; over the years there have been different protocols attempting to achieve the same functionality, such as Secure Telnet and others, however to date, none have really caught on. From Windows Server 2003, a new tool called WinRM - windows remote management, was introduced. WinRM is a SOAP-based protocol built on web services that among other things, allows you to connect to a remote system, providing a shell, essentially offering similar functionality to SSH. WinRM is currently the Windows world alternative to SSH. The Pros The advantage with WinRM is that you can use a vanilla VM with nothing pre-configured on it, with the only prerequisite being that the WinRM service needs to be running. EC2, the largest cloud provider today, supports this out-of-the-box, so if you want to run a standard Amazon machine image (AMI) for Windows, WinRM is enabled by default. This makes it possible to quickly start working with a cloud, all that needs to be done is bring up a standard Windows VM, and then it's possible to remotely configure it - and start using it. This is very useful in cloud environments where you are sometimes unable to create a custom Windows image or are limited to a very small number of images and want to limit your resource usage. The Challenges Where SSH has become the de facto protocol with Linux, WinRM is far less known tool in the Windows world, although it does offer comparable features as far as security, as well as connecting and executing commands to a remote machine. The standard tool for using WinRM is usually PowerShell, the new Windows shell that is intended to supersede the standard command prompt. To date though, there are still relatively few programming languages with built-in support for WinRM, making automation and remote execution of tasks over WinRM much more complex. To achieve these tasks, Cloudify employs PowerShell itself, as an external process to act as a client library for accessing WinRM. The primary issue with this, however, is that the client-side also needs to be running Windows, as PowerShell cannot run on Linux. Another aspect where WinRM differs from SSH is that it does not really have built-in file transfer. There is no direct equivalent for secure copy in SSH for WinRM. That said, it is possible to implement file transfer through PowerShell scripts. There are currently several open source initiatives looking to build a WinRM client for Linux - or specifically for some programming languages, such as Java, however, these are in different levels of maturity, where none of them are fully featured yet. Hence, PowerShell remains the default tool for Cloudify, which essentially provides the same level of functionality you would expect for running remote commands on a Linux machine with Windows. WinRM & Security Another interesting point to consider about WinRM is its support for encryption. WinRM supports three types of transfer protocols, HTTP, HTTPS, and encrypted HTTP. With HTTP, inevitably your wire protocol is unencrypted. It is only a good idea to use HTTP inside your own data center in the event that you are completely convinced that no one can monitor anything going over the wire. HTTPS is commonly used instead of HTTP, however with WinRM there's a chicken and egg issue. If you want to work with HTTPS you are required to set up an SSL certificate on the remote machine. The challenge here is when you're starting with a vanilla Windows VM that will not have the certificate installed, there is a need to automate the insertion of that certificate, however this often cannot be done, as WinRM is not running. Encrypted HTTP, which is also the default in EC2, basically uses your login credentials as your encryption key and it works. From a security perspective this is the recommended secure transfer protocol to use. It is worth noting that most attempts to create a WinRM client library tend to encounter problems around the encrypted HTTP protocol, as implementing MS' encrypted HTTP system - credSSP - is challenging. However, there are various projects working on achieving this, so it will hopefully be solved in the near future. Where Cloudify Comes Into the Mix Where WinRM comes into play with Cloudify, is during the cloud bootstrapping process. By using WinRM Cloudify is able to remotely connect to a vanilla VM provided by the cloud, and set up the Cloudify manager or agent to run on the machine. In addition to traditional cloud environments, WinRM also works on non-cloud and non-virtualized environments, such as a standard data center with multiple Windows servers running. All that needs to be done is provide Cloudify with the credentials, and it will use WinRM to connect and set up the machine remotely. Since WinRM is pre-packaged with Windows, there is no need to install anything. The only thing requirement, as mentioned above, is to have the WinRM service running, as not all Windows images will have this service running. Conclusion In short WinRM is the Window's world alternative to SSHD that allows you to remotely login securely and execute commands on Windows machines. From a cloud automation perspective, it provides virtually all the necessary functionality requirements, and thus it is recommended to have WinRM running in your Windows environment.

So, after reaching the conclusion that replication is going to be hard, I went back to the office and discussed those challenges and was in general pretty annoyed by it. Then Michael made a really interesting suggestion. Why not put it on RAFT? And once he explained what he meant, I really couldn’t hold my excitement. We now have a major feature for 4.0. But before I get excited about that (we’ll only be able to actually start working on that in a few months, anyway), let us talk about what the actual suggestion was. Raft is a consensus algorithm. It allows a distributed set of computers to arrive into a mutually agreed upon set of sequential log records. Hm… I wonder where else we can find sequential log records, and yes, I am looking at you Voron.Journal. The basic idea is that we can take the concept of log shipping, but instead of having a single master/slave relationship, we change things so we can put Raft in the middle. When committing a transaction, we’ll hold off committing the transaction until we have a Raft consensus that it should be committed. The advantage here is that we won’t be constrained any longer by the master/slave issue. If there is a server down, we can still process requests (maybe need to elect a new cluster leader, but that is about it). That means that from an architectural standpoint, we’ll have the ability to process write requests for any quorum (N/2+1). That is a pretty standard requirement for distributed databases, so that is perfectly fine. That is a pretty awesome thing to have, to be honest, and more importantly, this is happening at the low level storage layer. That means that we can apply this behavior not just to a single database solution, but to many database solutions. I’m pretty excited about this.

IntelliJ IDEA 13 added the Terminal tool window to the IDE. We can open a terminal window with Tools | Open Terminal.... To change the font of the terminal we must open the preferences and select IDE Settings | Editor | Colors & Fonts | Console Font. Here we can choose a font and change the font size:

Spring Data modules provides various modules to work with various types of datasources like RDBMS, NOSQL stores etc in unified way. In my previous article SpringMVC4 + Spring Data JPA + SpringSecurity configuration using JavaConfig I have explained how to configure Spring Data JPA using JavaConfig. Now in this post let us see how we can use Spring Data JPA repositories and export JPA entities as REST endpoints using Spring Data REST. First let us configure spring-data-jpa and spring-data-rest-webmvc dependencies in our pom.xml. org.springframework.data spring-data-jpa 1.5.0.RELEASE org.springframework.data spring-data-rest-webmvc 2.0.0.RELEASE Make sure you have latest released versions configured correctly, otherwise you will encounter the following error: java.lang.ClassNotFoundException: org.springframework.data.mapping.SimplePropertyHandler Create JPA entities. @Entity @Table(name = "USERS") public class User implements Serializable { private static final long serialVersionUID = 1L; @Id @GeneratedValue(strategy = GenerationType.IDENTITY) @Column(name = "user_id") private Integer id; @Column(name = "username", nullable = false, unique = true, length = 50) private String userName; @Column(name = "password", nullable = false, length = 50) private String password; @Column(name = "firstname", nullable = false, length = 50) private String firstName; @Column(name = "lastname", length = 50) private String lastName; @Column(name = "email", nullable = false, unique = true, length = 50) private String email; @Temporal(TemporalType.DATE) private Date dob; private boolean enabled=true; @OneToMany(fetch=FetchType.EAGER, cascade=CascadeType.ALL) @JoinColumn(name="user_id") private Set roles = new HashSet<>(); @OneToMany(mappedBy = "user") private List contacts = new ArrayList<>(); //setters and getters } @Entity @Table(name = "ROLES") public class Role implements Serializable { private static final long serialVersionUID = 1L; @Id @GeneratedValue(strategy = GenerationType.IDENTITY) @Column(name = "role_id") private Integer id; @Column(name="role_name",nullable=false) private String roleName; //setters and getters } @Entity @Table(name = "CONTACTS") public class Contact implements Serializable { private static final long serialVersionUID = 1L; @Id @GeneratedValue(strategy = GenerationType.IDENTITY) @Column(name = "contact_id") private Integer id; @Column(name = "firstname", nullable = false, length = 50) private String firstName; @Column(name = "lastname", length = 50) private String lastName; @Column(name = "email", nullable = false, unique = true, length = 50) private String email; @Temporal(TemporalType.DATE) private Date dob; @ManyToOne @JoinColumn(name = "user_id") private User user; //setters and getters } Configure DispatcherServlet using AbstractAnnotationConfigDispatcherServletInitializer. Observe that we have added RepositoryRestMvcConfiguration.class to getServletConfigClasses() method. RepositoryRestMvcConfiguration is the one which does the heavy lifting of looking for Spring Data Repositories and exporting them as REST endpoints. package com.sivalabs.springdatarest.web.config; import javax.servlet.Filter; import org.springframework.data.rest.webmvc.config.RepositoryRestMvcConfiguration; import org.springframework.orm.jpa.support.OpenEntityManagerInViewFilter; import org.springframework.web.servlet.support.AbstractAnnotationConfigDispatcherServletInitializer; import com.sivalabs.springdatarest.config.AppConfig; public class SpringWebAppInitializer extends AbstractAnnotationConfigDispatcherServletInitializer { @Override protected Class[] getRootConfigClasses() { return new Class[] { AppConfig.class}; } @Override protected Class[] getServletConfigClasses() { return new Class[] { WebMvcConfig.class, RepositoryRestMvcConfiguration.class }; } @Override protected String[] getServletMappings() { return new String[] { "/rest/*" }; } @Override protected Filter[] getServletFilters() { return new Filter[]{ new OpenEntityManagerInViewFilter() }; } } Create Spring Data JPA repositories for JPA entities. public interface UserRepository extends JpaRepository { } public interface RoleRepository extends JpaRepository { } public interface ContactRepository extends JpaRepository { } That's it. Spring Data REST will take care of rest of the things. You can use spring Rest Shell https://github.com/spring-projects/rest-shell or Chrome's Postman Addon to test the exported REST services. D:\rest-shell-1.2.1.RELEASE\bin>rest-shell http://localhost:8080:> Now we can change the baseUri using baseUri command as follows: http://localhost:8080:>baseUri http://localhost:8080/spring-data-rest-demo/rest/ http://localhost:8080/spring-data-rest-demo/rest/> http://localhost:8080/spring-data-rest-demo/rest/>list rel href ====================================================================================== users http://localhost:8080/spring-data-rest-demo/rest/users{?page,size,sort} roles http://localhost:8080/spring-data-rest-demo/rest/roles{?page,size,sort} contacts http://localhost:8080/spring-data-rest-demo/rest/contacts{?page,size,sort} Note: It seems there is an issue with rest-shell when the DispatcherServlet url mapped to "/" and issue list command it responds with "No resources found". http://localhost:8080/spring-data-rest-demo/rest/>get users/ { "_links": { "self": { "href": "http://localhost:8080/spring-data-rest-demo/rest/users/{?page,size,sort}", "templated": true }, "search": { "href": "http://localhost:8080/spring-data-rest-demo/rest/users/search" } }, "_embedded": { "users": [ { "userName": "admin", "password": "admin", "firstName": "Administrator", "lastName": null, "email": "[email protected]", "dob": null, "enabled": true, "_links": { "self": { "href": "http://localhost:8080/spring-data-rest-demo/rest/users/1" }, "roles": { "href": "http://localhost:8080/spring-data-rest-demo/rest/users/1/roles" }, "contacts": { "href": "http://localhost:8080/spring-data-rest-demo/rest/users/1/contacts" } } }, { "userName": "siva", "password": "siva", "firstName": "Siva", "lastName": null, "email": "[email protected]", "dob": null, "enabled": true, "_links": { "self": { "href": "http://localhost:8080/spring-data-rest-demo/rest/users/2" }, "roles": { "href": "http://localhost:8080/spring-data-rest-demo/rest/users/2/roles" }, "contacts": { "href": "http://localhost:8080/spring-data-rest-demo/rest/users/2/contacts" } } } ] }, "page": { "size": 20, "totalElements": 2, "totalPages": 1, "number": 0 } } You can find the source code at https://github.com/sivaprasadreddy/sivalabs-blog-samples-code/tree/master/spring-data-rest-demo For more Info on Spring Rest Shell: https://github.com/spring-projects/rest-shell

In one of my previous posts I explained how we can convert json data to avro data and vice versa using avro tools command line option. Today I was trying to see what options we have for converting csv data to avro format, as of now we don't have any avro tool option to accomplish this . Now, we can either write our own java program (MapReduce program or a simple java program) or we can use various SerDe's available with Hive to do this quickly and without writing any code :) To convert csv data to Avro data using Hive we need to follow the steps below: Create a Hive table stored as textfile and specify your csv delimiter also. Load csv file to above table using "load data" command. Create another Hive table using AvroSerDe. Insert data from former table to new Avro Hive table using "insert overwrite" command. To demonstrate this I will use use the data below (student.csv): 0,38,91 0,65,28 0,78,16 1,34,96 1,78,14 1,11,43 Now execute below queries in Hive: --1. Create a Hive table stored as textfile USE test; CREATE TABLE csv_table ( student_id INT, subject_id INT, marks INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE; --2. Load csv_table with student.csv data LOAD DATA LOCAL INPATH "/path/to/student.csv" OVERWRITE INTO TABLE test.csv_table; --3. Create another Hive table using AvroSerDe CREATE TABLE avro_table ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.avro.AvroSerDe' STORED AS INPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat' OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat' TBLPROPERTIES ( 'avro.schema.literal'='{ "namespace": "com.rishav.avro", "name": "student_marks", "type": "record", "fields": [ { "name":"student_id","type":"int"}, { "name":"subject_id","type":"int"}, { "name":"marks","type":"int"}] }'); --4. Load avro_table with data from csv_table INSERT OVERWRITE TABLE avro_table SELECT student_id, subject_id, marks FROM csv_table; Now you can get data in Avro format from Hive warehouse folder. To dump this file to local file system use below command: hadoop fs -cat /path/to/warehouse/test.db/avro_table/* > student.avro If you want to get json data from this avro file you can use avro tools command: java -jar avro-tools-1.7.5.jar tojson student.avro > student.json So we can easily convert csv to avro and csv to json also by just writing 4 HQLs.

NoSQL has been a hot topic a pretty long time (well, it's not only a buzz anymore). However, when should we really use it instead of an RDBMS?

Last week Alistair and I were working on an internal application and we needed to make a HTTPS request directly to an AWS machine using a certificate signed to a different host. We use jersey-client so our code looked something like this: Client client = Client.create(); client.resource("https://some-aws-host.compute-1.amazonaws.com").post(); // and so on When we ran this we predictably ran into trouble: com.sun.jersey.api.client.ClientHandlerException: javax.net.ssl.SSLHandshakeException: java.security.cert.CertificateException: No subject alternative DNS name matching some-aws-host.compute-1.amazonaws.com found. at com.sun.jersey.client.urlconnection.URLConnectionClientHandler.handle(URLConnectionClientHandler.java:149) at com.sun.jersey.api.client.Client.handle(Client.java:648) at com.sun.jersey.api.client.WebResource.handle(WebResource.java:670) at com.sun.jersey.api.client.WebResource.post(WebResource.java:241) at com.neotechnology.testlab.manager.bootstrap.ManagerAdmin.takeBackup(ManagerAdmin.java:33) at com.neotechnology.testlab.manager.bootstrap.ManagerAdminTest.foo(ManagerAdminTest.java:11) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:45) at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:15) at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:42) at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:20) at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:263) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:68) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:47) at org.junit.runners.ParentRunner$3.run(ParentRunner.java:231) at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:60) at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:229) at org.junit.runners.ParentRunner.access$000(ParentRunner.java:50) at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:222) at org.junit.runners.ParentRunner.run(ParentRunner.java:300) at org.junit.runner.JUnitCore.run(JUnitCore.java:157) at com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs(JUnit4IdeaTestRunner.java:74) at com.intellij.rt.execution.junit.JUnitStarter.prepareStreamsAndStart(JUnitStarter.java:202) at com.intellij.rt.execution.junit.JUnitStarter.main(JUnitStarter.java:65) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) at com.intellij.rt.execution.application.AppMain.main(AppMain.java:120) Caused by: javax.net.ssl.SSLHandshakeException: java.security.cert.CertificateException: No subject alternative DNS name matching some-aws-host.compute-1.amazonaws.com found. at sun.security.ssl.Alerts.getSSLException(Alerts.java:192) at sun.security.ssl.SSLSocketImpl.fatal(SSLSocketImpl.java:1884) at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:276) at sun.security.ssl.Handshaker.fatalSE(Handshaker.java:270) at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1341) at sun.security.ssl.ClientHandshaker.processMessage(ClientHandshaker.java:153) at sun.security.ssl.Handshaker.processLoop(Handshaker.java:868) at sun.security.ssl.Handshaker.process_record(Handshaker.java:804) at sun.security.ssl.SSLSocketImpl.readRecord(SSLSocketImpl.java:1016) at sun.security.ssl.SSLSocketImpl.performInitialHandshake(SSLSocketImpl.java:1312) at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1339) at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:1323) at sun.net.www.protocol.https.HttpsClient.afterConnect(HttpsClient.java:563) at sun.net.www.protocol.https.AbstractDelegateHttpsURLConnection.connect(AbstractDelegateHttpsURLConnection.java:185) at sun.net.www.protocol.http.HttpURLConnection.getInputStream(HttpURLConnection.java:1300) at java.net.HttpURLConnection.getResponseCode(HttpURLConnection.java:468) at sun.net.www.protocol.https.HttpsURLConnectionImpl.getResponseCode(HttpsURLConnectionImpl.java:338) at com.sun.jersey.client.urlconnection.URLConnectionClientHandler._invoke(URLConnectionClientHandler.java:240) at com.sun.jersey.client.urlconnection.URLConnectionClientHandler.handle(URLConnectionClientHandler.java:147) ... 31 more Caused by: java.security.cert.CertificateException: No subject alternative DNS name matching some-aws-host.compute-1.amazonaws.com found. at sun.security.util.HostnameChecker.matchDNS(HostnameChecker.java:191) at sun.security.util.HostnameChecker.match(HostnameChecker.java:93) at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:347) at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:203) at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:126) at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1323) ... 45 more We figured that we needed to get our client to ignore the certificate and came across this Stack Overflow thread which had some suggestions on how to do this. None of the suggestions worked on their own but we ended up with a combination of a couple of the suggestions which did the trick: public Client hostIgnoringClient() { try { SSLContext sslcontext = SSLContext.getInstance( "TLS" ); sslcontext.init( null, null, null ); DefaultClientConfig config = new DefaultClientConfig(); Map properties = config.getProperties(); HTTPSProperties httpsProperties = new HTTPSProperties( new HostnameVerifier() { @Override public boolean verify( String s, SSLSession sslSession ) { return true; } }, sslcontext ); properties.put( HTTPSProperties.PROPERTY_HTTPS_PROPERTIES, httpsProperties ); config.getClasses().add( JacksonJsonProvider.class ); return Client.create( config ); } catch ( KeyManagementException | NoSuchAlgorithmException e ) { throw new RuntimeException( e ); } } You’re welcome Future Mark.