TokuMX 1.5 is around the corner. The big feature will be something we discussed briefly when talking about replication changes in 1.4: partitioned collections. Before introducing the feature, I wanted to mention the following. Although TokuMX 1.5 is not available as of this writing, we would love to hear feedback on partitioned collections, which we think are wonderful for time-series data, as I describe below. If you are interested in trying out the feature, email [email protected] for a pre-release version of TokuMX 1.5. What is a partitioned collection? A partitioned collection is analogous to a partitioned table in relational databases. Oracle, MySQL, SQL Server, and Postgres all support partitioned tables. We are happy to bring this functionality to TokuMX. So, if the remainder of this blog is unclear, and you have friends in the office who are familiar with relational databases, you may want to ask them for more information :). Nevertheless, a partitioned collection is a collection that underneath the covers is broken into (or partitioned into) several individual collections, based on ranges of a “partition key”. From the application developer’s point of view, the collection is just another collection. Queries, inserts, updates, and deletes just work with no syntactical changes. Secondary indexes and replication work as well. But underneath the covers, the data will be broken into several collections, with each collection responsible for all data for a range of the partition key. If you are running TokuMX 1.4, a simple example is the oplog, which is a partitioned collection. Any normal query works just fine on the oplog. However, if you look in your data directory, you will see several .tokumx files named “local_oplog_rs_p…”. These files are the individual partitions that break up the data. Each partition stores a range of _id fields in the oplog. Why should I bother using a partitioned collection? This will be its own post with longer examples, but here is a summary. Partitioned collections have two big advantages: Large chunks of data can be deleted very efficiently by dropping partitions. The cost is that of performing an “rm” on some files in the filesystem. This is really fast and efficient. Queries that include the partition key may be isolated to individual partitions, and therefore run faster. This is similar to “query isolation” for shard keys. So, one scenario you may want a partitioned collection for is where the oldest data gets dropped periodically, and many queries benefit from a time based key. That will be a good fit. In short: time series data. If you have a time-series application where you want to keep a rolling period of data (e.g. the last 6 months worth), then using a partitioned collection will be great, and is preferable to using a TTL index or a capped collection. In a future blog post, I will expand on this. How do I use a partitioned collection in TokuMX 1.5? Basically, just like a normal collection, except with some commands added to create a partitioned collection, add partitions, and drop partitions. Below, I explain the shell commands added for this functionality. Our documentation contains the full commands so that they may be called by any driver’s runCommand method. Ok, so how do I create a partitioned collection? The first thing to consider is what your partition key should be. That is, what key do you want to use ranges of to partition your data? This key has similarities with a shard key. It should be a key that can be used to isolate partitions, the way a shard key is used to isolate shards (as explained here). Also, it should be a key that contains a range of data you would like to delete all at once. With time series data, that key will likely be a timestamp. In TokuMX, the partition key is always the primary key. To create a partitioned collection, “foo”, with a timestamp field, “ts”, used for the partitioning run the following: > db.createCollection("foo", { partitioned : 1 , primaryKey : { ts : 1 , _id : 1 } }) { "ok" : 1 } Note that in TokuMX, the primary key must have the _id field appended to it to ensure uniqueness. As a side note, we do not support hash based partitioning, only range based partitioning. Adding partitions? In TokuMX, partitions can only be appended to the end. Individual partitions cannot be split. So, say we have a collection that partitions on the _id field, where all _id’s happen to be integers. Suppose we have three partitions with the following ranges: _id <= 0 0 < _id <= 1000 _id > 1000 With this collection we cannot create a partition with the range 500 < _id <= 1000, because that would split the second partition. All we can do is add a new partition to the end, and “cap” the current last partition with a new maximum value. This new maximum value must be greater than or equal to the primary key (or in this case, _id) of the last partition’s last document. So, if the last partition’s last document has an _id of 2500, we can only partitions that create a range whose maximum is at least 2500. There are two ways to add a partition. The first method peeks at the last document in the current last partition, caps the partition with the primary key of that last document, and creates a new partition. To do so, one does: > db.foo.addPartition() { "ok" : 1 } In the above example, the partitioned collection would now have partitions with the following ranges: _id <= 0 0 < _id <= 1000 1000 < _id <= 2500 _id > 2500 Alternatively, we can specify what the new maximum of the existing last partition may be, provided it is greater than the last document in last partition (which in this example is 2500). To do so, we simply pass in the new maximum as a parameter: > db.foo.addPartition({ _id : 3000 }); { "ok" : 1 } This would make the collection have partitions with the following ranges: _id <= 0 0 < _id <= 1000 1000 < _id <= 3000 _id > 3000 Dropping partitions? Dropping partitions is simple. First, see what the partitions are with the following shell command: > db.foo.getPartitionInfo() { "numPartitions" : NumberLong(4), "partitions" : [ { "_id" : NumberLong(0), "max" : { "_id" : 0 }, "createTime" : ISODate("2014-05-29T01:50:15.839Z") }, { "_id" : NumberLong(1), "max" : { "_id" : 1000 }, "createTime" : ISODate("2014-05-29T01:50:27.049Z") }, { "_id" : NumberLong(2), "max" : { "_id" : 2500 }, "createTime" : ISODate("2014-05-29T01:50:30.549Z") }, { "_id" : NumberLong(3), "max" : { "_id" : { "$maxKey" : 1 } }, "createTime" : ISODate("2014-05-29T01:50:35.903Z") } ], "ok" : 1 } This lists each partition, what the maximum value that each partition may hold (thus defining the range of the partition), and the id of the partition (in the _id field). So, in the example we used for adding partitions, we have four partitions with _ids 0 through 3. To drop a partition, we run the following command and pass the _id of the partition we want to drop. To drop partition 0, we run: > db.foo.dropPartition(0) { "ok" : 1 } Looking at the list of partitions after this operation, we see the partition is dropped: > db.foo.getPartitionInfo() { "numPartitions" : NumberLong(3), "partitions" : [ { "_id" : NumberLong(1), "max" : { "_id" : 1000 }, "createTime" : ISODate("2014-05-29T01:50:27.049Z") }, { "_id" : NumberLong(2), "max" : { "_id" : 2500 }, "createTime" : ISODate("2014-05-29T01:50:30.549Z") }, { "_id" : NumberLong(3), "max" : { "_id" : { "$maxKey" : 1 } }, "createTime" : ISODate("2014-05-29T01:50:35.903Z") } ], "ok" : 1 } This covers how to use partitioned collections. We hope users in the MongoDB ecosystem find this feature as useful as relational database users do. In the comments section below, feel free to leave questions and/or feedback.

A common tactic to help speed up your website is to use a technique called lazy loading which means that instead of loading everything your page needs at the start it will only load resources as and when it needs them. For example you can lazy load images so you can start the page off only with the images you need to view the page correctly, then other images that are out of view you won't need to load straight away. As the user scrolls down the page you can then search to see if the images are about to come into view and lazy load the images when they are needed. You can do the same with other resources such as JavaScript or CSS files, you can make sure you only load in the script as and when they need them to be used. An example of this I have used in the past is loading Disqus comments on the click event of a button, this jQuery code will then load in the Disqus Javascript file and initialise the Disqus code on the selected div. $j=jQuery.noConflict(); $j(document).ready(function() { $j('.showDisqus').on('click', function(){ // click event of the show comments button var disqus_shortname = 'enter_your_disqus_user_name'; // Enter your disqus user name // ajax request to load the disqus javascript $j.ajax({ type: "GET", url: "http://" + disqus_shortname + ".disqus.com/embed.js", dataType: "script", cache: true }); $j(this).fadeOut(); // remove the show comments button }); }); Ajax Method As you can see from the code above we have a click event of the .showDisqus button, inside this using the jQuery method .ajax() which is making a GET request for the script of embedding Disqus into your application. The ajax method is normally used to make basic http requests to a server side script and return the output of the script. On this occasion we are making a GET request and setting the dataType to be a script. This tells jQuery to treat the return as if we are including a new JavaScript file, this will disable browser caching on the script by adding a timestamp parameter to the end of the script. If you would like to enable caching of the script then you need to make sure you include a cache: true parameter. $.ajax({ type: "GET", url: "http://test_script.js", dataType: "script", cache: true }); Get Script Method Another option to get the script via GET ajax is to use the method getScript() this is simply a wrapper for the above ajax method. $.ajax({ url: url, dataType: "script", success: success }); This allows you to reduce the amount of code you are writing by simply using this method. $.getScript( "http://test_script.js" ) .done(function( script, textStatus ) { alert('Successfully loaded script'); }) .fail(function( jqxhr, settings, exception ) { alert('Failed to load script'); }); The problem with using getScript() is that it will never cache the script as it will always add the timestamp querystring to the end of the JavaScript file. As the ajax() method allows you to choose if you cache the script or not it is better to use this method when loading in a script that will not change.

When I am trying to install a third party apk using the ADB tool, I have faced "Failure [INSTALL_PARSE_FAILED_NO_CERTIFICATES]" error. To resolve the issue, I have followed these few steps. Open command prompt; Go to your debug.keystore location. For eg: You can find the debug.keystore file in the following location C:\Documents and Settings\User\.android 1. Using Zip align copied apk. zipalign -v 4 D:\Test.apk D:\Testc.apk 2. keytool -genkey -v -keystore debug.keystore -alias sampleName -keyalg RSA -keysize 2048 -validity 20000 Now a prompt will ask for Password First and lastname Name of Organization unit Name of Organization City State Country After entering these fields we get our Certificate 3. jarsigner -verbose -keystore debug.keystore D:\Testc.apk sampleName In some cases we need add -sigalg SHA1withRSA -digestalg SHA1 arguments to work out the step 3 jarsigner -verbose -sigalg SHA1withRSA -digestalg SHA1 -keystore debug.keystore D:\Testc.apk sampleName Now it will ask for the password and then it will replace the apk with the signed one. To check whether it is working or not, you can check using the following command. jarsigner -verify D:\Testc.apk Then I have installed apk using ADB. Adb install D:\Testc.apk Thanks for reading :) Origin: Vardhan Blog - "install parse failed no certificates"

Explore different message brokers, and discover how these important web technologies impact a customer's backlog of messages, and cluster/data performance.

A new Java based DSL has now been introduced for Spring Integration which makes it possible to define the Spring Integration message flows using pure java based configuration instead of using the Spring XML based configuration. I tried the DSL for a sample Integration flow that I have - I call it the Rube Goldberg flow, for it follows a convoluted path in trying to capitalize a string passed in as input. The flow looks like this and does some crazy things to perform a simple task: It takes in a message of this type - "hello from spring integ" splits it up into individual words(hello, from, spring, integ) sends each word to a ActiveMQ queue from the queue the word fragments are picked up by a enricher to capitalize each word placing the response back into a response queue It is picked up, resequenced based on the original sequence of the words aggregated back into a sentence("HELLO FROM SPRING INTEG") and returned back to the application. To start with Spring Integration Java DSL, a simple Xml based configuration to capitalize a String would look like this: There is nothing much going on here, a messaging gateway takes in the message passed in from the application, capitalizes it in a transformer and this is returned back to the application. Expressing this in Spring Integration Java DSL: @Configuration @EnableIntegration @IntegrationComponentScan @ComponentScan public class EchoFlow { @Bean public DirectChannel requestChannel() { return new DirectChannel(); } @Bean public IntegrationFlow simpleEchoFlow() { return IntegrationFlows.from(requestChannel()) .transform((String s) -> s.toUpperCase()) .get(); } } @MessagingGateway public interface EchoGateway { @Gateway(requestChannel = "requestChannel") String echo(String message); } Do note that @MessagingGateway annotation is not a part of Spring Integration Java DSL, it is an existing component in Spring Integration and serves the same purpose as the gateway component in XML based configuration. I like the fact that the transformation can be expressed using typesafe Java 8 lambda expressions rather than the Spring-EL expression. Note that the transformation expression could have coded in quite few alternate ways: ??.transform((String s) -> s.toUpperCase()) Or: ??.transform(s -> s.toUpperCase()) Or using method references: ??.transform(String::toUpperCase) Moving onto the more complicated Rube Goldberg flow to accomplish the same task, again starting with XML based configuration. There are two configurations to express this flow: rube-1.xml: This configuration takes care of steps 1, 2, 3, 6, 7, 8 : It takes in a message of this type - "hello from spring integ" splits it up into individual words(hello, from, spring, integ) sends each word to a ActiveMQ queue from the queue the word fragments are picked up by a enricher to capitalize each word placing the response back into a response queue It is picked up, resequenced based on the original sequence of the words aggregated back into a sentence("HELLO FROM SPRING INTEG") and returned back to the application. and rube-2.xml for steps 4, 5: It takes in a message of this type - "hello from spring integ" splits it up into individual words(hello, from, spring, integ) sends each word to a ActiveMQ queue from the queue the word fragments are picked up by a enricher to capitalize each word placing the response back into a response queue It is picked up, resequenced based on the original sequence of the words aggregated back into a sentence("HELLO FROM SPRING INTEG") and returned back to the application. Now, expressing this Rube Goldberg flow using Spring Integration Java DSL, the configuration looks like this, again in two parts: EchoFlowOutbound.java: @Bean public DirectChannel sequenceChannel() { return new DirectChannel(); } @Bean public DirectChannel requestChannel() { return new DirectChannel(); } @Bean public IntegrationFlow toOutboundQueueFlow() { return IntegrationFlows.from(requestChannel()) .split(s -> s.applySequence(true).get().getT2().setDelimiters("\\s")) .handle(jmsOutboundGateway()) .get(); } @Bean public IntegrationFlow flowOnReturnOfMessage() { return IntegrationFlows.from(sequenceChannel()) .resequence() .aggregate(aggregate -> aggregate.outputProcessor(g -> Joiner.on(" ").join(g.getMessages() .stream() .map(m -> (String) m.getPayload()).collect(toList()))) , null) .get(); } and EchoFlowInbound.java: @Bean public JmsMessageDrivenEndpoint jmsInbound() { return new JmsMessageDrivenEndpoint(listenerContainer(), messageListener()); } @Bean public IntegrationFlow inboundFlow() { return IntegrationFlows.from(enhanceMessageChannel()) .transform((String s) -> s.toUpperCase()) .get(); } Again here the code is completely typesafe and is checked for any errors at development time rather than at runtime as with the XML based configuration. Again I like the fact that transformation, aggregation statements can be expressed concisely using Java 8 lamda expressions as opposed to Spring-EL expressions. What I have not displayed here is some of the support code, to set up the activemq test infrastructure, this configuration continues to remain as xml and I have included this code in a sample github project. All in all, I am very excited to see this new way of expressing the Spring Integration messaging flow using pure Java and I am looking forward to seeing its continuing evolution and may be even try and participate in its evolution in small ways. Here is the entire working code in a github repo: https://github.com/bijukunjummen/rg-si References and Acknowledgement: Spring Integration Java DSL introduction blog article by Artem Bilan: https://spring.io/blog/2014/05/08/spring-integration-java-dsl-milestone-1-released Spring Integration Java DSL website and wiki: https://github.com/spring-projects/spring-integration-extensions/wiki/Spring-Integration-Java-DSL-Reference. A lot of code has been shamelessly copied over from this wiki by me :-). Also, a big thanks to Artem for guidance on a question that I had Webinar by Gary Russell on Spring Integration 4.0 in which Spring Integration Java DSL is covered in great detail.

The producer/consumer design pattern is a pre-designed solution to separate the two main components by placing a queue in the middle.

With Apache CXF 3.0 just being released a couple of weeks ago, the project makes yet another important step to fulfill the JAX-RS 2.0 specification requirements: integration with CDI 1.1. In this blog post we are going to look on a couple of examples of how Apache CXF 3.0 and Apache CXF 3.0 work together. Starting from version 3.0, Apache CXF includes a new module, named cxf-integration-cdi which could be added easily to your Apache Maven POM file: org.apache.cxf cxf-integration-cdi 3.0.0 This new module brings just two components (in fact, a bit more but those are the key ones): CXFCdiServlet: the servlet to bootstrap Apache CXF application, serving the same purpose asCXFServlet and CXFNonSpringJaxrsServlet, ... JAXRSCdiResourceExtension: portable CDI 1.1 extension where all the magic happens When run in CDI 1.1-enabled environment, the portable extensions are discovered by CDI 1.1 container and initialized using life-cycle events. And that is literally all what you need! Let us see the real application in action. We are going to build a very simple JAX-RS 2.0 application to manage people using Apache CXF 3.0 andJBoss Weld 2.1, the CDI 1.1 reference implementation. The Person class we are going to use for a person representation is just a simple Java bean: package com.example.model; public class Person{ private String email; private String firstName; private String lastName; public Person(){ } public Person(final String email, final String firstName, final String lastName){ this.email = email; this.firstName = firstName; this.lastName = lastName; } //getters and setters are ommited //... As it is quite common now, we are going to run our application inside embedded Jetty 9.1 container and ourStarter class does exactly that: package com.example; import org.apache.cxf.cdi.CXFCdiServlet; import org.eclipse.jetty.server.Server; import org.eclipse.jetty.servlet.ServletContextHandler; import org.eclipse.jetty.servlet.ServletHolder; import org.jboss.weld.environment.servlet.BeanManagerResourceBindingListener; import org.jboss.weld.environment.servlet.Listener; public class Starter { public static void main( final String[] args ) throws Exception { final Server server = new Server( 8080 ); // Register and map the dispatcher servlet final ServletHolder servletHolder = new ServletHolder( new CXFCdiServlet() ); final ServletContextHandler context = new ServletContextHandler(); context.setContextPath( "/" ); context.addEventListener( new Listener() ); context.addEventListener( new BeanManagerResourceBindingListener() ); context.addServlet( servletHolder, "/rest/*" ); server.setHandler( context ); server.start(); server.join(); } } Please notice the presence of CXFCdiServlet and two mandatory listeners which were added to the context: org.jboss.weld.environment.servlet.Listener is responsible for CDI injections org.jboss.weld.environment.servlet.BeanManagerResourceBindingListener binds the reference to the BeanManager to JNDI location java:comp/env/BeanManager to make it accessible anywhere from the application With that, the full power of CDI 1.1 is at your disposal. Let us introduce the PeopleService class annotated with @Named annotation and with an initialization method declared and annotated with @PostConstruct just to create one person. @Named public class PeopleService { private final ConcurrentMap< String, Person > persons = new ConcurrentHashMap< String, Person >(); @PostConstruct public void init() { persons.put( "[email protected]", new Person( "[email protected]", "Tom", "Bombadilt" ) ); } // Additional methods // ... } Up to now we have said nothing about configuring JAX-RS 2.0 applications and resources in CDI 1.1enviroment. The reason for that is very simple: depending on the application, you may go with zero-effort configuration or fully customizable one. Let us go through both approaches. With zero-effort configuration, you may define an empty JAX-RS 2.0 application and any number of JAX-RS 2.0 resources: Apache CXF 3.0 implicitly will wire them together by associating each resource class with this application. Here is an example of JAX-RS 2.0 application: package com.example.rs; import javax.ws.rs.ApplicationPath; import javax.ws.rs.core.Application; @ApplicationPath( "api" ) public class JaxRsApiApplication extends Application { } And here is a JAX-RS 2.0 resource PeopleRestService which injects the PeopleService managed bean: package com.example.rs; import java.util.Collection; import javax.inject.Inject; import javax.ws.rs.DELETE; import javax.ws.rs.DefaultValue; import javax.ws.rs.FormParam; import javax.ws.rs.GET; import javax.ws.rs.POST; import javax.ws.rs.PUT; import javax.ws.rs.Path; import javax.ws.rs.PathParam; import javax.ws.rs.Produces; import javax.ws.rs.QueryParam; import javax.ws.rs.core.Context; import javax.ws.rs.core.MediaType; import javax.ws.rs.core.Response; import javax.ws.rs.core.UriInfo; import com.example.model.Person; import com.example.services.PeopleService; @Path( "/people" ) public class PeopleRestService { @Inject private PeopleService peopleService; @Produces( { MediaType.APPLICATION_JSON } ) @GET public Collection< Person > getPeople( @QueryParam( "page") @DefaultValue( "1" ) final int page ) { // ... } @Produces( { MediaType.APPLICATION_JSON } ) @Path( "/{email}" ) @GET public Person getPerson( @PathParam( "email" ) final String email ) { // ... } @Produces( { MediaType.APPLICATION_JSON } ) @POST public Response addPerson( @Context final UriInfo uriInfo, @FormParam( "email" ) final String email, @FormParam( "firstName" ) final String firstName, @FormParam( "lastName" ) final String lastName ) { // ... } // More HTTP methods here // ... } Nothing else is required: Apache CXF 3.0 application could be run like that and be fully functional. The complete source code of the sample project is available on GitHub. Please keep in mind that if you are following this style, only single empty JAX-RS 2.0 application should be declared. With customizable approach more options are available but a bit more work have to be done. Each JAX-RS 2.0 application should provide non-empty getClasses() or/and getSingletons() collections implementation. However, JAX-RS 2.0 resource classes stay unchanged. Here is an example (which basically leads to the same application configuration we have seen before): package com.example.rs; import java.util.Arrays; import java.util.HashSet; import java.util.Set; import javax.enterprise.inject.Produces; import javax.inject.Inject; import javax.ws.rs.ApplicationPath; import javax.ws.rs.core.Application; import com.fasterxml.jackson.jaxrs.json.JacksonJsonProvider; @ApplicationPath( "api" ) public class JaxRsApiApplication extends Application { @Inject private PeopleRestService peopleRestService; @Produces private JacksonJsonProvider jacksonJsonProvider = new JacksonJsonProvider(); @Override public Set< Object > getSingletons() { return new HashSet<>( Arrays.asList( peopleRestService, jacksonJsonProvider ) ); } } Please notice, that JAXRSCdiResourceExtension portable CDI 1.1 extension automatically creates managed beans for each JAX-RS 2.0 applications (the ones extending Application) and resources (annotated with@Path). As such, those are immediately available for injection (as for example PeopleRestService in the snippet above). The class JacksonJsonProvider is annotated with @Provider annotation and as such will be treated as JAX-RS 2.0 provider. There are no limit on JAX-RS 2.0 applications which could be defined in this way. The complete source code of the sample project using this appoarch is available on GitHub No matter which approach you have chosen, our sample application is going to work the same. Let us build it and run: > mvn clean package > java -jar target/jax-rs-2.0-cdi-0.0.1-SNAPSHOT.jar Calling the couple of implemented REST APIs confirms that application is functioning and configured properly. Let us issue the GET command to ensure that the method of PeopleService annotated with @PostConstructhas been called upon managed bean creation. > curl http://localhost:8080/rest/api/people HTTP/1.1 200 OK Content-Type: application/json Date: Thu, 29 May 2014 22:39:35 GMT Transfer-Encoding: chunked Server: Jetty(9.1.z-SNAPSHOT) [{"email":"[email protected]","firstName":"Tom","lastName":"Bombadilt"}] And here is the example of POST command: > curl -i http://localhost:8080/rest/api/people -X POST -d "[email protected]&firstName=Tom&lastName=Knocker" HTTP/1.1 201 Created Content-Type: application/json Date: Thu, 29 May 2014 22:40:08 GMT Location: http://localhost:8080/rest/api/people/[email protected] Transfer-Encoding: chunked Server: Jetty(9.1.z-SNAPSHOT) {"email":"[email protected]","firstName":"Tom","lastName":"Knocker"} In this blog post we have just scratched the surface of what is possible now with Apache CXF and CDI 1.1integration. Just to mention that embedded Apache Tomcat 7.x / 8.x as well as WAR-based deployments ofApache CXF with CDI 1.1 are possible on most JEE application servers and servlet containers. Please take a look on official documentation and give it a try! The complete source code is available on GitHub.

Originally written by Lim Han Recently, an application that my team was working on encountered problems with a MySQL deadlock situation and it took us some time to figure out the reasons behind it. This application that we deployed was running on a 2-node cluster and they both are connected to an AWS MySQL database. The MySQL db tables are mostly based on InnoDB which supports transaction (meaning all the usual commit and rollback semantics) as well as row-level locking that MyISAM engine does not provide. So the problem arose when our users, due to some poorly designed user interface, was able to execute the same long running operation twice on the database. As it turned out, due to the fact that we have a dual node cluster, each of the user operation originated from a different web application (which in turn meant 2 different transaction running the same queries). The deadlock query happened to be a “INSERT INTO T… SELECT FROM S WHERE” query that introduced shared locks on the records that were used in the SELECT query. It didn’t help that both T and S in this case happened to be the same table. In effect, both the shared locks and exclusive locks were applied on the same table. An attempt to explain the possible cause of the deadlock on the queries could be explained by the following table. This is based on the assumption that we are using a default REPEATABLE_READ transaction isolation level (I will explain the concept of transaction isolation later) Assuming that we have a table as such RowId Value 1 Collection 1 2 Collection 2 … Collection N 450000 Collection 450000 The following is a sample sequence that could possibly cause a deadlock based on the 2 transactions running an SQL query like “INSERT INTO T SELECT FROM T WHERE … “ : Time Transaction 1 Transaction 2 Comment T1 Statement executed Statement executed. A shared lock is applied to records that are read by selection T2 Read lock s1 on Row 10-20 The lock on the index across a range. InnoDB has a concept of gap locks. T3 Statement executed Transaction 2 statement executed. Similar shared lock to s1 applied by selection T4 Read lock s2 on Row 10-20 Shared read locks allow both transaction to read the records only T5 Insert lock x1 into Row 13 in index wanted Transaction 1 attempts to get exclusive lock on Row 13 for insertion but Transaction 2 is holding a shared lock T6 Insert lock x2 into Row 13 in index wanted Transaction 2 attempts to get exclusive lock on Row 13 for insertion but Transaction 1 is holding a shared lock T7 Deadlock! The above scenario occurs only when we use REPEATABLE_READ (which introduces shared read locks). If we were to lower the transaction isolation level to READ_COMMITTED, we would reduce the chances of a deadlock happening. Of course, this would mean relaxing the consistency of the database records. In the case of our data requirements, we do not have such strict requirements for strong consistency. Thus, it is acceptable for one transaction to read records that are committed by other transactions. So, to delve deeper into the idea of Transaction Isolation, this concept has been defined by ANSI/ISO SQL as the following from highest isolation levels to lowest Serializable This is the highest isolation level and usually requires the use of shared read locks and exclusive write locks (as in the case of MySQL). What this means in essence that any query made will require access to a shared read lock on the records which prevents another transaction’s query to modify these records. Every update statement will require access to an exclusive write lock Also, range-locks must be acquired when a select statement with a WHERE condition is used. This is implemented as a gap lock in MySQL. Repeatable Reads This is the default level used in MySQL. This is mainly similar to Serializable beside the fact that a range lock is not used. However, the way that MySQL implements this level seemed to me a little different. Based on Wikipedia’s article on Transaction Isolation, a range lock is not implemented and so phantom reads can still occur. Phantom reads refer to a possibility that select queries will have additional records when the same query is made within a transaction. However, what I understand from MySQL’s document is that range locks are still used and the same select queries made in the same transaction will always return the same records. Maybe I’m mistaken in my understanding and if there’s any mistakes in my intepretations, I stand ready to be corrected. Read Committed This is an isolation level that will maintain a write lock until the end of the transaction but read locks will be released at the end of the SELECT statement. It does not promise that a SELECT statement will find the same data if it is re-run again in the same transaction. It will, however, guarantee that the data that is read are not “dirty” and has been committed. Read Uncommitted This is an isolation level that I doubt would be useful for most use cases. Basically, it allows a transaction to see all data that has been modified, including “dirty” or uncommitted data. This is the lowest isolation level Having gone through the different transaction isolation levels, we could see how the selection of the Transaction Isolation level determines the kind of database locking mechanism. From a practical standpoint, the default MySQL isolation level (REPEATABLE_READ) might not always be a good choice when you are dealing with a scenario like ours where there is really no need for such strong consistency in the data reads. I believe that by lowering the isolation level, it is likely to reduce chances that your database queries meet with a deadlock. Also, it might even allow a higher concurrent access to your database which improve the performance level of your queries. Of course, this comes with the caveat that you need to understand how important consistent reads are for your application. If you are dealing with data where precision is paramount (e.g. your bank accounts), then it is definitely necessary to impose as much isolation as possible so that you would not read inconsistent information within your transaction.

About 6 months ago, Jacqui Read created a github issue explaining how she wanted to round a float value to a number of decimal places but was unable to do so due to the round function not taking the appropriate parameter. I found myself wanting to do the same thing last week where I initially had the following value: RETURN toFloat("12.336666") AS value I wanted to round that to 2 decimal places and Wes suggested multiplying the value before using ROUND and then dividing afterwards to achieve that. For two decimal places we need to multiply and divide by 100: WITH toFloat("12.336666") AS value RETURN round(100 * value) / 100 AS value 12.34 Michael suggested abstracting the number of decimal places like so: WITH 2 as precision WITH toFloat("12.336666") AS value, 10^precision AS factor RETURN round(factor * value)/factor AS value If we want to round to 4 decimal places we can easily change that: WITH 4 as precision WITH toFloat("12.336666") AS value, 10^precision AS factor RETURN round(factor * value)/factor AS value 12.3367 The code is available as a graph gist too if you want to play around with it. And you may as well check out the other gists while you’re at it – enjoy!

Let's take a simple example of a thread that periodically does some clean up, but in between sleeps most of the time.

parquet is a new columnar storage format that come out of a collaboration between twitter and cloudera. parquet’s generating a lot of excitement in the community for good reason - it’s shaping up to be the next big thing for data storage in hadoop for a number of reasons: it’s a sophisticated columnar file format, which means that it’s well-suited to olap workloads, or really any workload where projection is a normal part of working with the data. it has a high level of integration with hadoop and the ecosystem - you can work with parquet in mapreduce, pig, hive and impala. it supports avro, thrift and protocol buffers. the last item raises a question - how does parquet work with avro and friends? to understand this you’ll need to understand three concepts: storage formats , which are binary representations of data. for parquet this is contained within the parquet-format github project. object model converters , whose job it is to map between an external object model and parquet’s internal data types. these converters exist in the parquet-mr github project. object models , which are in-memory representations of data. avro , thrift , protocol buffers , hive and pig are all examples of object models. parquet does actually supply an example object model (with mapreduce support ) , but the intention is that you’d use one of the other richer object models such as avro. the figure below shows a visual representation of these concepts ( view a larger image ). avro, thrift and protocol buffers all have have their own storage formats, but parquet doesn’t utilize them in any way. instead their objects are mapped to the parquet data model. parquet data is always serialized using its own file format. this is why parquet can’t read files serialized using avro’s storage format, and vice-versa. let’s examine what happens when you write an avro object to parquet: the avro converter stores within the parquet file’s metadata the schema for the objects being written. you can see this by using a parquet cli to dumps out the parquet metadata contained within a parquet file. $ export hadoop_classpath=parquet-avro-1.4.3.jar:parquet-column-1.4.3.jar:parquet-common-1.4.3.jar:parquet-encoding-1.4.3.jar:parquet-format-2.0.0.jar:parquet-generator-1.4.3.jar:parquet-hadoop-1.4.3.jar:parquet-hive-bundle-1.4.3.jar:parquet-jackson-1.4.3.jar:parquet-tools-1.4.3.jar $ hadoop parquet.tools.main meta stocks.parquet creator: parquet-mr (build 3f25ad97f209e7653e9f816508252f850abd635f) extra: avro.schema = {"type":"record","name":"stock","namespace" [more]... file schema: hip.ch5.avro.gen.stock -------------------------------------------------------------------------------- symbol: required binary o:utf8 r:0 d:0 date: required binary o:utf8 r:0 d:0 open: required double r:0 d:0 high: required double r:0 d:0 low: required double r:0 d:0 close: required double r:0 d:0 volume: required int32 r:0 d:0 adjclose: required double r:0 d:0 row group 1: rc:45 ts:2376 -------------------------------------------------------------------------------- symbol: binary uncompressed do:0 fpo:4 sz:84/84/1.00 vc:45 enc:b [more]... date: binary uncompressed do:0 fpo:88 sz:198/198/1.00 vc:45 en [more]... open: double uncompressed do:0 fpo:286 sz:379/379/1.00 vc:45 e [more]... high: double uncompressed do:0 fpo:665 sz:379/379/1.00 vc:45 e [more]... low: double uncompressed do:0 fpo:1044 sz:379/379/1.00 vc:45 [more]... close: double uncompressed do:0 fpo:1423 sz:379/379/1.00 vc:45 [more]... volume: int32 uncompressed do:0 fpo:1802 sz:199/199/1.00 vc:45 e [more]... adjclose: double uncompressed do:0 fpo:2001 sz:379/379/1.00 vc:45 [more]... the “avro.schema” is where the avro schema information is stored. this allows the avro parquet reader the ability to marshall avro objects without the client having to supply the schema. you can also use the “schema” command to view the parquet schema. $ hadoop parquet.tools.main schema stocks.parquet message hip.ch4.avro.gen.stock { required binary symbol (utf8); required binary date (utf8); required double open; required double high; required double low; required double close; required int32 volume; required double adjclose; } this tool is useful when loading a parquet file into hive, as you’ll need to use the field names defined in the parquet schema when defining the hive table (note that the syntax below only works with hive 0.13 and newer). hive> create external table parquet_stocks( symbol string, date string, open double, high double, low double, close double, volume int, adjclose double ) stored as parquet location '...';

Deprecated methods have to be treated different. At least in my opinion. The question I did not discuss in that article is if we have to unit test deprecated methods or not. For the impatient here is my statement: Deprecated methods have to be unit tested the same way as other methods. Probably this is not a question when there is already a unit test for the method. In that case you just leave it there and keep it running each time the CI server fires. The question may come up in your mind when you inherit some legacy code and you, yourself deprecate some methods or just find it deprecated with no appropriate unit test. Why bother to invest time writing unit tests when the method will no longer be in use? The answer to this why lays where the difference is between a deprecated and a deleted method. The deprecated method is still in use. It may happen that no one uses the method but that is not guaranteed. If it were you could just delete the method. Deprecated method is still in the published API with a slight comment: you better do not use it. Clear? What if there is no time to write the unit tests? If there is no time (treat this precondition as a hypothetic and not questionable: that is another topic for what to have time) then there is no question. Unit test are not writing themselves during the night, while you sleep. What if you have some time but kind of short. In that case, if nothing else prevails, you can linger the tests for the deprecated methods to the end of the task list. If nothing else prevails. Being deprecated does not necessarily mean: not important. Many may still use it. It means: deprecated.

ken fogel is the program coordinator and chairperson of the computer science technology program at dawson college in montreal, canada. he is also a program consultant to and part-time instructor in the computer institute of concordia university 's school of extended learning. he blogs at omniprogrammer.com and tweets @omniprof . his regular columns about netbeans in education are listed here . i am old enough to remember commercial applications that had a console interface. my students have no experience with the console. when java was introduced the era of such applications was over. instead you used awt, then swing and now javafx. for the student starting out in programming these gui frameworks add complexity they may not be ready for. therefore i start my classes using console input and output. input can be divided into two categories. the first is values. the primitive types such as int and double are value types. as i explain it, a value is a something you can perform both mathematical and logical operations on. the second is strings. a string does not have a value in the mathematical sense. instead it has a state. therefore it cannot be used in a mathematical expression. state can be compared and so strings can be used is logical expressions such as determining if two strings are the same. if you are ordering strings then you can also determine if one string is greater or less than the other. java does not have any console input routines for values. to java everything you type is a string. this has meant that for many years every java textbook author provided a console input routine that could convert strings to values. java 1.5 made all these routines redundant with the introduction of the scanner class. i could now teach input without needing to go into detail about wrapper classes or the numberformatexception in a standard approach. /** * ask the user for their weight */ public void inputweight() { scanner sc = new scanner(system.in); system.out.print("please enter your weight: "); int weight = sc.nextint(); system.out.println("weight = " + weight); } there is a problem with this code. if the user enters a string that cannot be converted we get the numberformatexception we want to avoid. scanner provides a solution with its ‘hasnext. . .’ methods. these methods determine if the user input can be converted to the target value. you can reject input and avoid the exception. /** * ask the user for their weight */ public void inputweight() { scanner sc = new scanner(system.in); system.out.print("please enter your weight: "); int weight; // accept input and test to see if it’s an integer if (sc.hasnextint()) { // success, it was an integer so get it weight = sc.nextint(); system.out.println("weight = " + weight); } else { // failure so retrieve the input as a string string bad = sc.next(); // inform the user what went wrong system.out.println(bad + " cannot be converted to an integer"); } } now when you run the program you get the following for a good input. this is what you get for a bad input. i will return to numeric input in another article as you do not want a program to end when the user makes one mistake. character input to make my assignments more interesting i have the students create a menu from which they must choose an action. i like to use letters as the menu choice. the problem is that there is no nextchar() method in scanner. instead you need to use next() which will accept anything that you enter. public void displaymenu() { scanner sc = new scanner(system.in); system.out.println("select an account"); system.out.println("a: savings"); system.out.println("b: checking"); system.out.println("c: exit"); system.out.print("please choose a, b or c: "); string choice = sc.next(); system.out.println("you chose: " + choice); } which could result in the following: the first improvement is to convert the string into a character. to do this the string must only be one character long. rather than checking the length you can just extract the first character in the string using the string class charat method. string choice = sc.next(); char letter = choice.charat(0); system.out.println("you chose: " + letter); this will work and if you enter “a” you will get ‘a’. the problem now is that the case of the character you entered should not matter. this is easy to solve by adding one more method from string into the mix. char letter = choice.touppercase().charat(0); almost perfect but since we are using string input the user can enter any string they want. so “bacon” will result in ‘b’. we also need some way to control the allowable letters. in this menu the choices are a, b and c. the solution is to use the ‘hasnext’ method for ‘next’. not the normal, no parameter version, but the version that takes a pattern. pattern is a polite way of saying regular expression. from https://xkcd.com/208/ just as we did with integer input we first test the string that the user has entered. the regular expression will be the pattern that will be used to determine if the string is correct. this regular expression is probably the simplest one you can write. we want a single character, either upper or lower case and is one of the three allowable characters. here it is: [abcabc] the square brackets indicate that this is a character class expression that can match only a single character. what goes inside the brackets are the allowable characters, in this case a, b, c, a, b or c. since these characters are adjacent in the ascii/unicode table they can also be written as a range. [a-ca-c] putting this all together we get a routine that will only accept a single letter. public void displaymenu() { scanner sc = new scanner(system.in); system.out.println("select an account"); system.out.println("a: savings"); system.out.println("b: checking"); system.out.println("c: exit"); system.out.print("please choose a, b or c: "); char letter; if (sc.hasnext("[a-ca-c]")) { string choice = sc.next(); letter = choice.touppercase().charat(0); system.out.println("you chose: " + letter); } else { string choice = sc.next(); system.out.println(choice + " is not valid input"); } } now if you enter ‘a’: but if you enter ‘aardvark’: in my next article i will look at console input a bit more and i will introduce you to the beethoven test.

In this post, I look at the basics of connecting to a Cassandra database from a Java client. I will use the DataStax Java Client JAR in order to do so.

For most typical Spring/Hibernate enterprise applications, the application performance depends almost entirely on the performance of it's persistence layer. This post will go over how to confirm that we are in presence of a 'database-bound' application, and then walk through 7 frequently used 'quick-win' tips that can help improve application performance. How to confirm that an application is 'database-bound' To confirm that an application is 'database-bound', start by doing a typical run in some development environment, using VisualVM for monitoring. VisualVM is a Java profiler shipped with the JDK and launchable via the command line by calling jvisualvm. After launching Visual VM, try the following steps: double click on your running application Select Sampler click on Settings checkbox Choose Profile only packages, and type in the following packages: your.application.packages.* org.hibernate.* org.springframework.* your.database.driver.package, for example oracle.* Click Sample CPU The CPU profiling of a typical 'database-bound' application should look something like this: We can see that the client Java process spends 56% of it's time waiting for the database to return results over the network. This is a good sign that the queries on the database are what's keeping the application slow. The 32.7% in Hibernate reflection calls is normal and nothing much can be done about it. First step for tuning - obtaining a baseline run The first step to do tuning is to define a baseline run for the program. We need to identify a set of functionally valid input data that makes the program go through a typical execution similar to the production run. The main difference is that the baseline run should run in a much shorter period of time, as a guideline an execution time of around 5 to 10 minutes is a good target. What makes a good baseline? A good baseline should have the following characteristics: it's functionally correct the input data is similar to production in it's variety it completes in a short amount of time optimizations in the baseline run can be extrapolated to a full run Getting a good baseline is solving half of the problem. What makes a bad baseline? For example, in a batch run for processing call data records in a telecommunications system, taking the first 10 000 records could be the wrong approach. The reason being, the first 10 000 might be mostly voice calls, but the unknown performance problem is in the processing of SMS traffic. Taking the first records of a large run would lead us to a bad baseline, from which wrong conclusions would be taken. Collecting SQL logs and query timings The SQL queries executed with their execution time can be collected using for example log4jdbc. See this blog post for how to collect SQL queries using log4jdbc - Spring/Hibernate improved SQL logging with log4jdbc. The query execution time is measured from the Java client side, and it includes the network round-trip to the database. The SQL query logs look like this: 16 avr. 2014 11:13:48 | SQL_QUERY /* insert your.package.YourEntity */ insert into YOUR_TABLE (...) values (...) {executed in 13 msec} The prepared statements themselves are also a good source of information - they allow to easily identify frequent query types. They can be logged by following this blog post - Why and where is Hibernate doing this SQL query? What metrics can be extracted from SQL logs The SQL logs can give the answer these questions: What are slowest queries being executed? What are the most frequent queries? What is the amount of time spent generating primary keys? Is there some data that could benefit from caching ? How to parse the SQL logs Probably the only viable option for large log volumes is to use command line tools. This approach has the advantage of being very flexible. At the expense of writing a small script or command, we can extract mostly any metric needed. Any command line tool will work as long as you are comfortable with it. If you are used to the Unix command line, bash might be a good option. Bash can be used also in Windows workstations, using for example Cygwin, or Git that includes a bash command line. Frequently applied Quick-Wins The quick-wins bellow identify common performance problems in Spring/Hibernate applications, and their corresponding solutions. Quick-win Tip 1 - Reduce primary key generation overhead In processes that are 'insert-intensive', the choice of a primary key generation strategy can matter a lot. One common way to generate id's is to use database sequences, usually one per table to avoid contention between inserts on different tables. The problem is that if 50 records are inserted, we want to avoid that 50 network round-trips are made to the database in order to obtain 50 id's, leaving the Java process hanging most of the time. How does Hibernate usually handle this? Hibernate provides new optimized ID generators that avoid this problem. Namely for sequences, a HiLo id generator is used by default. This is how the HiLo sequence generator it works: call a sequence once and get 1000 (the High value) calculate 50 id's like this: 1000 * 50 + 0 = 50000 1000 * 50 + 1 = 50001 ... 1000 * 50 + 49 = 50049, Low value (50) reached call sequence for new High value 1001 ... etc ... So from a single sequence call, 50 keys where generated, reducing the overhead caused my inumerous network round-trips. These new optimized key generators are on by default in Hibernate 4, and can even be turned off if needed by setting hibernate.id.new_generator_mappings to false. Why can primary key generation still be a problem? The problem is, if you declared the key generation strategy as AUTO, the optimized generators are still off, and your application will end up with a huge amount of sequence calls. In order to make sure the new optimized generators are on, make sure to use the SEQUENCE strategy instead of AUTO: @Id @GeneratedValue(strategy = GenerationType.SEQUENCE, generator = "your_key_generator") private Long id; With this simple change, an improvement in the range of 10%-20% can be measured in 'insert-intensive' applications, with basically no code changes. Quick-win Tip 2 - Use JDBC batch inserts/updates For batch programs, JDBC drivers usually provide an optimization for reducing network round-trips named 'JDBC batch inserts/updates'. When these are used, inserts/updates are queued at the driver level before being sent to the database. When a threshold is reached, then the whole batch of queued statements is sent to the database in one go. This prevents the driver from sending the statements one by one, which would waist multiple network round-trips. This is the entity manager factory configuration needed to active batch inserts/updates: 100 true true Setting only the JDBC batch size won't work. This is because the JDBC driver will batch the inserts only when receiving insert/updates for the exact same table. If an insert to a new table is received, then the JDBC driver will first flush the batched statements on the previous table, before starting to batch statements on the new table. A similar functionality is implicitly used if using Spring Batch. This optimization can easily buy you 30% to 40% to 'insert intensive' programs, without changing a single line of code. Quick-win Tip 3 - Periodically flush and clear the Hibernate session When adding/modifying data in the database, Hibernate keeps in the session a version of the entities already persisted, just in case they are modified again before the session is closed. But many times we can safely discard entities once the corresponding inserts where done in the database. This releases memory in the Java client process, preventing performance problems caused by long running Hibernate sessions. Such long-running sessions should be avoided as much as possible, but if by some reason they are needed, this is how to contain memory consumption: entityManager.flush(); entityManager.clear(); The flush will trigger the inserts from new entities to be sent to the database. The clear releases the new entities from the session. Quick-win Tip 4 - Reduce Hibernate dirty-checking overhead Hibernate uses internally a mechanism to keep track of modified entities called dirty-checking. This mechanism is not based on the equals and hashcode methods of the entity classes. Hibernate does it's most to keep the performance cost of dirty-checking to a minimum, and to dirty-check only when it needs to, but the mechanism does have a cost, which is more noticeable in tables with a large number of columns. Before applying any optimization, the most important is to measure the cost of dirty-checking using VisualVM. How to avoid dirty-checking? In Spring business methods that we know are read-only, dirty-checking can be turned off like this: @Transactional(readOnly=true) public void someBusinessMethod() { .... } An alternative to avoid dirty-checking is to use the Hibernate Stateless Session, which is detailed in the documentation. Quick-win Tip 5 - Search for 'bad' query plans Check the queries in the slowest queries list to see if they have good query plans. The most usual 'bad' query plans are: Full table scans: they happen when the table is being fully scanned due to usually a missing index or outdated table statistics. Full cartesian joins: This means that the full cartesian product of several tables is being computed. Check for missing join conditions, or if this can be avoided by splitting a step into several. Quick-win Tip 6 - check for wrong commit intervals If you are doing batch processing, the commit interval can make a large difference in the performance results, as in 10 to 100 times faster. Confirm that the commit interval is the one expected (usually around 100-1000 for Spring Batch jobs). It happens often that this parameter is not correctly configured. Quick-win Tip 7 - Use the second-level and query caches If some data is identified as being eligible for caching, then have a look at this blog post for how to setup the Hibernate caching: Pitfalls of the Hibernate Second-Level / Query Caches Conclusions To solve application performance problems, the most important action to take is to collect some metrics that allow to find what the current bottleneck is. Without some metrics it is often not possible to guess in useful time what the correct problem cause is. Also, many but not all of the typical performance pitfalls of a 'database-driven' application can be avoided in the first place by using the Spring Batch framework.

After attending Sam Newman’s microservice talks at Geecon last week I started to think more about what is most likely an essential feature of service-oriented/microservice platforms for monitoring, reporting and diagnostics: correlation ids. Correlation ids allow distributed tracing within complex service oriented platforms, where a single request into the application can often be dealt with by multiple downstream service. Without the ability to correlate downstream service requests it can be very difficult to understand how requests are being handled within your platform. I’ve seen the benefit of correlation ids in several recent SOA projects I have worked on, but as Sam mentioned in his talks, it’s often very easy to think this type of tracing won’t be needed when building the initial version of the application, but then very difficult to retrofit into the application when you do realise the benefits (and the need for!). I’ve not yet found the perfect way to implement correlation ids within a Java/Spring-based application, but after chatting to Sam via email he made several suggestions which I have now turned into a simple project using Spring Boot to demonstrate how this could be implemented. Why? During both of Sam’s Geecon talks he mentioned that in his experience correlation ids were very useful for diagnostic purposes. Correlation ids are essentially an id that is generated and associated with a single (typically user-driven) request into the application that is passed down through the stack and onto dependent services. In SOA or microservice platforms this type of id is very useful, as requests into the application typically are ‘fanned out’ or handled by multiple downstream services, and a correlation id allows all of the downstream requests (from the initial point of request) to be correlated or grouped based on the id. So called ‘distributed tracing’ can then be performed using the correlation ids by combining all the downstream service logs and matching the required id to see the trace of the request throughout your entire application stack (which is very easy if you are using a centralised logging framework such as logstash) The big players in the service-oriented field have been talking about the need for distributed tracing and correlating requests for quite some time, and as such Twitter have created their open source Zipkin framework (which often plugs into their RPC framework Finagle), and Netflix has open-sourced their Karyon web/microservice framework, both of which provide distributed tracing. There are of course commercial offering in this area, one such product being AppDynamics, which is very cool, but has a rather hefty price tag. Creating a proof-of-concept in Spring Boot As great as Zipkin and Karyon are, they are both relatively invasive, in that you have to build your services on top of the (often opinionated) frameworks. This might be fine for some use cases, but no so much for others, especially when you are building microservices. I’ve been enjoying experimenting with Spring Boot of late, and this framework builds on the much known and loved (at least by me :-) ) Spring framework by providing lots of preconfigured sensible defaults. This allows you to build microservices (especially ones that communicate via RESTful interfaces) very rapidly. The remainder of this blog pos explains how I implemented a (hopefully) non-invasive way of implementing correlation ids. Goals Allow a correlation id to be generated for a initial request into the application Enable the correlation id to be passed to downstream services, using as method that is as non-invasive into the code as possible Implementation I have created two projects on GitHub, one containing an implementation where all requests are being handled in a synchronous style (i.e. the traditional Spring approach of handling all request processing on a single thread), and also one for when an asynchronous (non-blocking) style of communication is being used (i.e., using the Servlet 3 asynchronous support combined with Spring’s DeferredResult and Java’s Futures/Callables). The majority of this article describes the asynchronous implementation, as this is more interesting: Spring Boot asynchronous (DeferredResult + Futures) communication correlation id Github repo The main work in both code bases is undertaken by the CorrelationHeaderFilter, which is a standard Java EE Filter that inspects the HttpServletRequest header for the presence of a correlationId. If one is found then we set a ThreadLocal variable in the RequestCorrelation Class (discussed later). If a correlation id is not found then one is generated and added to the RequestCorrelation Class: public class CorrelationHeaderFilter implements Filter { //... @Override public void doFilter(ServletRequest servletRequest, ServletResponse servletResponse, FilterChain filterChain) throws IOException, ServletException { final HttpServletRequest httpServletRequest = (HttpServletRequest) servletRequest; String currentCorrId = httpServletRequest.getHeader(RequestCorrelation.CORRELATION_ID_HEADER); if (!currentRequestIsAsyncDispatcher(httpServletRequest)) { if (currentCorrId == null) { currentCorrId = UUID.randomUUID().toString(); LOGGER.info("No correlationId found in Header. Generated : " + currentCorrId); } else { LOGGER.info("Found correlationId in Header : " + currentCorrId); } RequestCorrelation.setId(currentCorrId); } filterChain.doFilter(httpServletRequest, servletResponse); } //... private boolean currentRequestIsAsyncDispatcher(HttpServletRequest httpServletRequest) { return httpServletRequest.getDispatcherType().equals(DispatcherType.ASYNC); } The only thing is this code that may not instantly be obvious is the conditional checkcurrentRequestIsAsyncDispatcher(httpServletRequest), but this is here to guard against the correlation id code being executed when the Async Dispatcher thread is running to return the results (this is interesting to note, as I initially didn’t expect the Async Dispatcher to trigger the execution of the filter again?) Here is the RequestCorrelation Class, which contains a simple ThreadLocal static variable to hold the correlation id for the current Thread of execution (set via the CorrelationHeaderFilter above) public class RequestCorrelation { public static final String CORRELATION_ID = "correlationId"; private static final ThreadLocal id = new ThreadLocal(); public static String getId() { return id.get(); } public static void setId(String correlationId) { id.set(correlationId); } } Once the correlation id is stored in the RequestCorrelation Class it can be retrieved and added to downstream service requests (or data store access etc) as required by calling the static getId() method within RequestCorrelation. It is probably a good idea to encapsulate this behaviour away from your application services, and you can see an example of how to do this in a RestClient Class I have created, which composes Spring’s RestTemplate and handles the setting of the correlation id within the header transparently from the calling Class. @Component public class CorrelatingRestClient implements RestClient { private RestTemplate restTemplate = new RestTemplate(); @Override public String getForString(String uri) { String correlationId = RequestCorrelation.getId(); HttpHeaders httpHeaders = new HttpHeaders(); httpHeaders.set(RequestCorrelation.CORRELATION_ID, correlationId); LOGGER.info("start REST request to {} with correlationId {}", uri, correlationId); //TODO: error-handling and fault-tolerance in production ResponseEntity response = restTemplate.exchange(uri, HttpMethod.GET, new HttpEntity(httpHeaders), String.class); LOGGER.info("completed REST request to {} with correlationId {}", uri, correlationId); return response.getBody(); } } //... calling Class public String exampleMethod() { RestClient restClient = new CorrelatingRestClient(); return restClient.getForString(URI_LOCATION); //correlation id handling completely abstracted to RestClient impl } Making this work for asynchronous requests… The code included above works fine when you are handling all of your requests synchronously, but it is often a good idea in a SOA/microservice platform to handle requests in a non-blocking asynchronous manner. In Spring this can be achieved by using the DeferredResult Class in combination with the Servlet 3 asynchronous support. The problem with using ThreadLocal variables within the asynchronous approach is that the Thread that initially handles the request (and creates the DeferredResult/Future) will not be the Thread doing the actual processing. Accordingly, a bit of glue code is needed to ensure that the correlation id is propagated across the Threads. This can be achieved by extending Callable with the required functionality: (don’t worry if example Calling Class code doesn’t look intuitive – this adaption between DeferredResults and Futures is a necessary evil within Spring, and the full code including the boilerplate ListenableFutureAdapter is in my GitHub repo): public class CorrelationCallable implements Callable { private String correlationId; private Callable callable; public CorrelationCallable(Callable targetCallable) { correlationId = RequestCorrelation.getId(); callable = targetCallable; } @Override public V call() throws Exception { RequestCorrelation.setId(correlationId); return callable.call(); } } //... Calling Class @RequestMapping("externalNews") public DeferredResult externalNews() { return new ListenableFutureAdapter<>(service.submit(new CorrelationCallable<>(externalNewsService::getNews))); } And there we have it – the propagation of correlation id regardless of the synchronous/asynchronous nature of processing! You can clone the Github report containing my asynchronous example, and execute the application by running mvn spring-boot:run at the command line. If you access http://localhost:8080/externalNewsin your browser (or via curl) you will see something similar to the following in your Spring Boot console, which clearly demonstrates a correlation id being generated on the initial request, and then this being propagated through to a simulated external call (have a look in the ExternalNewsServiceRest Class to see how this has been implemented): [nio-8080-exec-1] u.c.t.e.c.w.f.CorrelationHeaderFilter : No correlationId found in Header. Generated : d205991b-c613-4acd-97b8-97112b2b2ad0 [pool-1-thread-1] u.c.t.e.c.w.c.CorrelatingRestClient : start REST request to http://localhost:8080/news with correlationId d205991b-c613-4acd-97b8-97112b2b2ad0 [nio-8080-exec-2] u.c.t.e.c.w.f.CorrelationHeaderFilter : Found correlationId in Header : d205991b-c613-4acd-97b8-97112b2b2ad0 [pool-1-thread-1] u.c.t.e.c.w.c.CorrelatingRestClient : completed REST request to http://localhost:8080/news with correlationId d205991b-c613-4acd-97b8-97112b2b2ad0 Conclusion I’m quite happy with this simple prototype, and it does meet the two goals I listed above. Future work will include writing some tests for this code (shame on me for not TDDing!), and also extend this functionality to a more realistic example. I would like to say a massive thanks to Sam, not only for sharing his knowledge at the great talks at Geecon, but also for taking time to respond to my emails. If you’re interested in microservices and related work I can highly recommend Sam’s Microservice book which is available in Early Access at O’Reilly. I’ve enjoyed reading the currently available chapters, and having implemented quite a few SOA projects recently I can relate to a lot of the good advice contained within. I’ll be following the development of this book with keen interest! If you have any comments or thoughts then please do share them via the comment below, or feel free to get in touch via the usual mechanisms! References I used Tomasz Nurkiewicz’s excellent blog several times for learning how best to wire up all of the DeferredResult/Future code in Spring: http://www.nurkiewicz.com/2013/03/deferredresult-asynchronous-processing.html

What is CXF? Apache CXF is an open source services framework. CXF helps you build and develop services using frontend programming APIs, like JAX-WS and JAX-RS. These services can speak a variety of protocols such as SOAP, XML/HTTP, RESTful HTTP, or CORBA and work over a variety of transports such as HTTP, JMS etc. How CXF Works? As you can see here and here, how cxf service calls are processed,most of the functionality in the Apache CXF runtime is implemented by interceptors. Every endpoint created by the Apache CXF runtime has potential interceptor chains for processing messages. The interceptors in the these chains are responsible for transforming messages between the raw data transported across the wire and the Java objects handled by the endpoint’s implementation code. Interceptors in CXF When a CXF client invokes a CXF server, there is an outgoing interceptor chain for the client and an incoming chain for the server. When the server sends the response back to the client, there is an outgoing chain for the server and an incoming one for the client. Additionally, in the case of SOAPFaults, a CXF web service will create a separate outbound error handling chain and the client will create an inbound error handling chain. The interceptors are organized into phases to ensure that processing happens on the proper order.Various phases involved during the Interceptor chains are listed in CXF documentation here. Adding your custom Interceptor involves extending one of the Abstract Intereceptor classes that CXF provides, and providing a phase when that interceptor should be invoked. AbstractPhaseInterceptor class - This abstract class provides implementations for the phase management methods of the PhaseInterceptor interface. The AbstractPhaseInterceptor class also provides a default implementation of the handleFault() method. Developers need to provide an implementation of the handleMessage() method. They can also provide a different implementation for the handleFault() method. The developer-provided implementations can manipulate the message data using the methods provided by the generic org.apache.cxf.message.Message interface. For applications that work with SOAP messages, Apache CXF provides an AbstractSoapInterceptor class. Extending this class provides the handleMessage() method and the handleFault() method with access to the message data as an org.apache.cxf.binding.soap.SoapMessage object. SoapMessage objects have methods for retrieving the SOAP headers, the SOAP envelope, and other SOAP metadata from the message. Below piece of code will show, how we can add a Custom Object as Header to an outgoing request – Spring Configuration - Interceptor :- public class SoapHeaderInterceptor extends AbstractSoapInterceptor { public SoapHeaderInterceptor() { super(Phase.POST_LOGICAL); } @Override public void handleMessage(SoapMessage message) throws Fault { List headers = message.getHeaders(); TestHeader testHeader = new TestHeader(); JAXBElement testHeaders = new ObjectFactory() .createTestHeader(testHeader); try { Header header = new Header(testHeaders.getName(), testHeader, new JAXBDataBinding(TestHeader.class)); headers.add(header); message.put(Header.HEADER_LIST, headers); } catch (JAXBException e) { e.printStackTrace(); } }

neo4j is quite awesome for pretty much everything . here is an extract i found from this blog whenever someone gives you a problem, think graphs . they are the most fundamental and flexible way of representing any kind of a relationship, so it’s about a 50-50 shot that any interesting design problem has a graph involved in it. make absolutely sure you can’t think of a way to solve it using graphs before moving on to other solution types. as a baby step, let us try to visualize an xml as a graph. we can start with a simple xml here, belgian waffles $5.95 two of our famous belgian waffles with plenty of real maple syrup 650 it is a simple ‘breakfast menu’ with just one item, ‘ belgian waffles ‘. neo4j has a neat interface which lets us visualize the graph we created. the graph for the above xml looks slick.. the above xml has 6 tags and we have 6 nodes in our graph. the nested tags/nodes has a ‘child-of’ relationship with the parent tag/node. in the graph above, node numbered 0 is the root node – corresponds to the tag node numbered 1 is the immediate child of the root – corresponds to the tag nodes numbered from 2-4 are the tags which come under tag, viz , , and transforming xml.. let us parse the xml into a data structure which can be easily persisted using neo4j . personally, i would prefer sax parser as there are serious memory constraints when creating a dom object (really painful if you talking big data as xml). additionally sax parsing gives you unlimited freedom to do whatever you want to do with the xml. this is how i represent a node, using the object xmlelement . each xmlelement is identified by an id. this is basically an integer or long. only thing to make sure is that the number has to be unique across all xmlelements. publicclassxmlelement { privatestring tagname; privatestring tagvalue; privatemap attributes = newhashmap(); privatehierarchyidentifier hierarchyidentifier; privateintparentid; privatebooleanpersisted; //setters and getters for the members publicstring getatrributestring(){ objectmapper jsonmapper = newobjectmapper(); try{ returnjsonmapper.writevalueasstring(this.attributes); } catch(jsonprocessingexception e) { logger.severe(e.getmessage()); e.printstacktrace(); } returnnull; } } the xml tag name and value are stored as string and the attributes are stored as a map. also, there is another member object, hierarchyidentifier publicclasshierarchyidentifier { privateintdepth; privateintwidth; privateintid; //getters and setters for member element } the hierarchyidentifier class contains the id class which is used to identify an xmlelement , which translates to identifying an xml tag. each xmlelement has the id of it’s parent stored. and after the parse, the xml should be represented as a map of and each xmlelement identified by the corresponding integer id. you can see the sax parser here .. so we pass the map to the graphwriter object. we create a node with the following properties value – the value of the xml tag id – the id of the current node parent – the id of the parent tag attributes – the string representation of the tag attributes also, the node has the following labels node – for all nodes parent – for the seed ( highest parent ) node xml tag name – say, the node for the tag will have label food currently, there is only a single type of relationship, ‘child_of’ , between immediate nested tags. see the below the example for a bigger xml belgian waffles $5.95 two of our famous belgian waffles with plenty of real maple syrup 650 masala dosa $10.95 south india's famous slim pancake with mashed potatoes 650 25 i have added a second item to the ‘breakfast_menu’.. the legendary masala dosa . also, the second food item has a new child tag, . and the resultant graph looks prettier the sets of tags,relationships and properties can be seen in the neo4j local server interface. the entire codebase can be found here in github .. visualizing as a graphgist an easy representation can be done in graphgist. i have created the graph of this xml. here is the link to the gist . also, the gist script can be found in the github gist here ...

In this post will give introduction to Markov models and Hidden Markov models as mathematical abstractions, with some examples. In probability theory, a Markov model is a stochastic model that assumes the Markov property. A stochastic model models a process where the state depends on previous states in a non-deterministic way. A stochastic process has the Markov property if the conditional probability distribution of future states of the process. System state is fully observable System state is partially observable System is autonomous Markov chain Hidden Markov model System is controlled Markov decision process Partially observable Markov decision process Markov chain A Markov chain named by Andrey Markov, is a mathematical system that representing transitions from one state to another on a state space. The state is directly visible to the observer. It is a random process usually characterized as memoryless: the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of "memorylessness" is called the Markov property. Let's talk about the weather. we have three types of weather sunny, rainy and cloudy. Let's assume for the moment that the weather lasts all day and it does not change from rainy to sunny in the middle of the day. Weather prediction is try to guess what the weather will be like tomorrow based on a history of observations of weather simplified model of weather prediction Wewill collect statistics on what the weather was like today based on what the weather was like yesterday the day before and so forth. We want to collect the following probabilities. Using above expression, we can give probabilities of types of weather for tomorrow and the next day using n days of history. The larger n will be problem in here. The more statistics we must collect Suppose that n=5 then we must collect statistics for 35 = 243 past histories Therefore we will make a simplifying assumption called the "Markov Assumption". This is called a "first-order Markov assumption" since we say that the probability of an observation at time n only depends on the observation at time n-1. A second-order Markov assumption would have the observation at time n depend on n-1 and n-2. We can the express the joint probability using the “Markov assumption”. So this now has a profound affect on the number of histories that we have to find statistics for, we now only need 32 = 9 numbers to characterize the probabilities of all of the sequences. (This assumption may or may not be a valid assumption depending on the situation.) Arbitrarily pick some numbers for P (wtomorrow | wtoday). Tabel2: Probabilities of Tomorrow's weather based on Today's Weather “What is w0?” In general, one can think of w as the START word so P(w1w2) is the probability that w1 can start a sentence. For first-order Markov models we can use these probabilities to draw a probabilistic finite state automaton. eg: 1. Today is sunny what's the probability that tomorrow is sunny and the day after is rainy First we translates into P(w2= sunny,w3=rainy|w1=sunny) P(w2= sunny,w3=rainy|w1=sunny) = P(w2=sunny|w1=sunny) * P(w3=rainy|w2=sunny,w3sunny) = P(w2=sunny|w1=sunny) * P(w3=rainy|w2=sunny) = 0.8 * 0.05 =0.04 Hidden Markov model A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. A HMM can be considered the simplest dynamic Bayesian network. Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics. Example Well suppose you were locked in a room for several days and you were asked about the weather outside The only piece of evidence you have is whether the person who comes into the room carrying your daily meal is carrying an umbrella or not. Table 3: Probabilities of Seeing an Umbrella The equation for the weather Markov process before you were locked in the room. Now we have to factor in the fact that the actual weather is hidden from you We do that by using Bayes Rule. Where ui is true if your caretaker brought an umbrella on day i and false if the caretaker did not. The probability P(w1,..,wn) is the same as the Markov model from the last section and the probability P(u1,..,un) is the prior probability of seeing a particular sequence of umbrella events. The probability P(w1,..,wn|u1,..,un) can be estimated as, Assume that for all i given wi, ui is independent of all uj and wj. I and J not equal Next post will explain about “Markov decision process” and “Partially observable Markov decision process”.

Utility Method for Spring REST public static HttpServletRequest getCurrentRequest() { RequestAttributes requestAttributes = RequestContextHolder.getRequestAttributes(); Assert.state(requestAttributes != null, "Could not find current request via RequestContextHolder"); Assert.isInstanceOf(ServletRequestAttributes.class, requestAttributes); HttpServletRequest servletRequest = ((ServletRequestAttributes) requestAttributes).getRequest(); Assert.state(servletRequest != null, "Could not find current HttpServletRequest"); return servletRequest; } Sometimes it’s easier to get the underlying Servlet request to get some headers or variables. final String userIpAddress = getCurrentRequest().getRemoteAddr(); final String userAgent = getCurrentRequest().getHeader("user-agent"); This is used in the simple REST service using HTTP Post verb @ the awesome CloudFoundry: (Source) Tool for Creating Your Test JSON. Spring Boot Documentation