As I've shown it in my previous post JBoss Drools are a very useful rules engine. The only problem is that creating the rules in the Rule language might be pretty complicated for a non-technical person. That's why one can provide an easy way for creating business rules - decision tables created in a spreadsheet! In the following example I will show you a really complicated business rule example converted to a decision table in a spreadsheet. As a backend we will have Drools, Camel and Spring. To begin with let us take a look at our imaginary business problem. Let us assume that we are running a business that focuses on selling products (either Medical or Electronic). We are shipping our products to several countries (PL, USA, GER, SWE, UK, ESP) and depending on the country there are different law regulations concerning the buyer's age. In some countries you can buy products when you are younger than in others. What is more depending on the country from which the buyer and the product comes from and on the quantity of products, the buyer might get a discount. As you can see there is a substantial number of conditions needed to be fullfield in this scenario (imagine the number of ifs needed to program this :P ). Another problem would be the business side (as usual). Anybody who has been working on a project knows how fast the requirements are changing. If one entered all the rules in the code he would have to redeploy the software each time the requirements changed. That's why it is a good practice to divide the business logic from the code itself. Anyway, let's go back to our example. To begin with let us take a look at the spreadsheets (before that it is worth taking a look at the JBoss website with precise description of how the decision table should look like): The point of entry of our program is the first spreadsheet that checks if the given user should be granted with the possibility of buying a product (it will be better if you download the spreadsheets and play with them from Too Much Coding's repository at Bitbucket: user_table.xls and product_table.xls, or Github user_table.xls and product_table.xls): user_table.xls (tables worksheet) Once the user has been approved he might get a discount: product_table.xls (tables worksheet) product_table.xls (lists worksheet) As you can see in the images the business problem is quite complex. Each row represents a rule, and each column represents a condition. Do you remember the rules syntax from my recent post? So you would understand the hidden part of the spreadsheet that is right above the first visible row: The rows from 2 to 6 represent some fixed configuration values such as rule set, imports ( you've already seen that in my recent post) and functions. Next in row number 7 you can find the name of the RuleTable. Then in row number 8 you have in our scenario either a CONDITION or an ACTION - so in other words either the LHS or rhe RHS respectively. Row number 9 is both representation of types presented in the condition and the binding to a variable. In row number 10 we have the exact LHS condition. Row number 11 shows the label of columns. From row number 12 we have the rules one by one. You can find the spreadsheets in the sources. Now let's take a look at the code. Let's start with taking a look at the schemas defining the Product and the User. Person.xsd User.xsd Due to the fact that we are using maven we may use a plugin that will convert the XSD into Java classes. part of the pom.xml org.apache.maven.plugins maven-compiler-plugin 2.5.1 org.codehaus.mojo jaxb2-maven-plugin 1.5 xjc xjc pl.grzejszczak.marcin.drools.decisiontable.model ${project.basedir}/src/main/resources/xsd Thanks to this plugin we have our generated by JAXB classes in the pl.grzejszczak.marcin.decisiontable.model package. Now off to the drools-context.xml file where we've defined all the necessary beans as far as Drools are concerned: As you can see in comparison to the application context from the recent post there are some differences. First instead of passing the DRL file as the resource inside the knowledge base we are providing the Decision table (DTABLE). I've decided to pass in two seperate files but you can provide one file with several worksheets and access those worksheets (through the decisiontable-conf element). Also there is an additional element called node. We have to choose an implementation of the Node interface (Execution, Grid...) for the Camel route to work properly as you will see in a couple of seconds in the Spring application context file. applicationContext.xml As you can see in order to access the Drools Camel Component we have to provide the node through which we will access the proper knowledge session. We have defined two routes - the first one ends at the Drools component that accesses the users knowledge session and the other the products knowledge session. We have a ProductService interface implementation called ProductServiceImpl that given an input User and Product objects pass them through the Camel's Producer Template to two Camel routes each ending at the Drools components. The concept behind this product service is that we are first processing the User if he can even buy the software and then we are checking what kind of a discount he would receive. From the service's point of view in fact we are just sending the object out and waiting for the response. Finally having reveived the response we are passing the User and the Product to the Financial Service implementation that will bill the user for the products that he has bought or reject his offer if needed. ProductServiceImpl.java package pl.grzejszczak.marcin.drools.decisiontable.service; import org.apache.camel.CamelContext; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Component; import pl.grzejszczak.marcin.drools.decisiontable.model.Product; import pl.grzejszczak.marcin.drools.decisiontable.model.User; import static com.google.common.collect.Lists.newArrayList; /** * Created with IntelliJ IDEA. * User: mgrzejszczak * Date: 14.01.13 */ @Component("productServiceImpl") public class ProductServiceImpl implements ProductService { private static final Logger LOGGER = LoggerFactory.getLogger(ProductServiceImpl.class); @Autowired CamelContext camelContext; @Autowired FinancialService financialService; @Override public void runProductLogic(User user, Product product) { LOGGER.debug("Running product logic - first acceptance Route, then discount Route"); camelContext.createProducerTemplate().sendBody("direct:acceptanceRoute", newArrayList(user, product)); camelContext.createProducerTemplate().sendBody("direct:discountRoute", newArrayList(user, product)); financialService.processOrder(user, product); } } Another crucial thing to remember about is that the Camel Drools Component requires the Command object as the input. As you can see, in the body we are sending a list of objects (and these are not Command objects). I did it on purpose since in my opinion it is better not to bind our code to a concrete solution. What if we find out that there is a better solution than Drools? Will we change all the code that we have created or just change the Camel route to point at our new solution? That's why Camel has the TypeConverters. We have our own here as well. First of all let's take a look at the implementation. ProductTypeConverter.java package pl.grzejszczak.marcin.drools.decisiontable.converter; import org.apache.camel.Converter; import org.drools.command.Command; import org.drools.command.CommandFactory; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import pl.grzejszczak.marcin.drools.decisiontable.model.Product; import java.util.List; /** * Created with IntelliJ IDEA. * User: mgrzejszczak * Date: 30.01.13 * Time: 21:42 */ @Converter public class ProductTypeConverter { private static final Logger LOGGER = LoggerFactory.getLogger(ProductTypeConverter.class); @Converter public static Command toCommandFromList(List inputList) { LOGGER.debug("Executing ProductTypeConverter's toCommandFromList method"); return CommandFactory.newInsertElements(inputList); } @Converter public static Command toCommand(Product product) { LOGGER.debug("Executing ProductTypeConverter's toCommand method"); return CommandFactory.newInsert(product); } } There is a good tutorial on TypeConverters on the Camel website - if you needed some more indepth info about it. Anyway, we are annotating our class and the functions used to convert different types into one another. What is important here is that we are showing Camel how to convert a list and a single product to Commands. Due to type erasure this will work regardless of the provided type that is why even though we are giving a list of Product and User, the toCommandFromList function will get executed. In addition to this in order for the type converter to work we have to provide the fully quallified name of our class (FQN) in the /META-INF/services/org/apache/camel/TypeConverter file. TypeConverter pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter In order to properly test our functionality one should write quite a few tests that would verify the rules. A pretty good way would be to have input files stored in the test resources folders that are passed to the rule engine and then the result would be compared against the verified output (unfortunately it is rather impossible to make the business side develop such a reference set of outputs). Anyway let's take a look at the unit test that verifies only a few of the rules and the logs that are produced from running those rules: ProductServiceImplTest.java package pl.grzejszczak.marcin.drools.decisiontable.service.drools; import org.apache.commons.lang.builder.ReflectionToStringBuilder; import org.apache.commons.lang.builder.ToStringStyle; import org.junit.Test; import org.junit.runner.RunWith; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.test.context.ContextConfiguration; import org.springframework.test.context.junit4.SpringJUnit4ClassRunner; import pl.grzejszczak.marcin.drools.decisiontable.model.*; import pl.grzejszczak.marcin.drools.decisiontable.service.ProductService; import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertTrue; /** * Created with IntelliJ IDEA. * User: mgrzejszczak * Date: 03.02.13 * Time: 16:06 */ @RunWith(SpringJUnit4ClassRunner.class) @ContextConfiguration("classpath:applicationContext.xml") public class ProductServiceImplTest { private static final Logger LOGGER = LoggerFactory.getLogger(ProductServiceImplTest.class); @Autowired ProductService objectUnderTest; @Test public void testRunProductLogicUserPlUnderageElectronicCountryPL() throws Exception { int initialPrice = 1000; int userAge = 6; int quantity = 10; User user = createUser("Smith", CountryType.PL, userAge); Product product = createProduct("Electronic", initialPrice, CountryType.PL, ProductType.ELECTRONIC, quantity); printInputs(user, product); objectUnderTest.runProductLogic(user, product); printInputs(user, product); assertTrue(product.getPrice() == initialPrice); assertEquals(DecisionType.REJECTED, user.getDecision()); } @Test public void testRunProductLogicUserPlHighAgeElectronicCountryPLLowQuantity() throws Exception { int initialPrice = 1000; int userAge = 19; int quantity = 1; User user = createUser("Smith", CountryType.PL, userAge); Product product = createProduct("Electronic", initialPrice, CountryType.PL, ProductType.ELECTRONIC, quantity); printInputs(user, product); objectUnderTest.runProductLogic(user, product); printInputs(user, product); assertTrue(product.getPrice() == initialPrice); assertEquals(DecisionType.ACCEPTED, user.getDecision()); } @Test public void testRunProductLogicUserPlHighAgeElectronicCountryPLHighQuantity() throws Exception { int initialPrice = 1000; int userAge = 19; int quantity = 8; User user = createUser("Smith", CountryType.PL, userAge); Product product = createProduct("Electronic", initialPrice, CountryType.PL, ProductType.ELECTRONIC, quantity); printInputs(user, product); objectUnderTest.runProductLogic(user, product); printInputs(user, product); double expectedDiscount = 0.1; assertTrue(product.getPrice() == initialPrice * (1 - expectedDiscount)); assertEquals(DecisionType.ACCEPTED, user.getDecision()); } @Test public void testRunProductLogicUserUsaLowAgeElectronicCountryPLHighQuantity() throws Exception { int initialPrice = 1000; int userAge = 19; int quantity = 8; User user = createUser("Smith", CountryType.USA, userAge); Product product = createProduct("Electronic", initialPrice, CountryType.PL, ProductType.ELECTRONIC, quantity); printInputs(user, product); objectUnderTest.runProductLogic(user, product); printInputs(user, product); assertTrue(product.getPrice() == initialPrice); assertEquals(DecisionType.REJECTED, user.getDecision()); } @Test public void testRunProductLogicUserUsaHighAgeMedicalCountrySWELowQuantity() throws Exception { int initialPrice = 1000; int userAge = 22; int quantity = 4; User user = createUser("Smith", CountryType.USA, userAge); Product product = createProduct("Some name", initialPrice, CountryType.SWE, ProductType.MEDICAL, quantity); printInputs(user, product); objectUnderTest.runProductLogic(user, product); printInputs(user, product); assertTrue(product.getPrice() == initialPrice); assertEquals(DecisionType.ACCEPTED, user.getDecision()); } @Test public void testRunProductLogicUserUsaHighAgeMedicalCountrySWEHighQuantity() throws Exception { int initialPrice = 1000; int userAge = 22; int quantity = 8; User user = createUser("Smith", CountryType.USA, userAge); Product product = createProduct("Some name", initialPrice, CountryType.SWE, ProductType.MEDICAL, quantity); printInputs(user, product); objectUnderTest.runProductLogic(user, product); printInputs(user, product); double expectedDiscount = 0.25; assertTrue(product.getPrice() == initialPrice * (1 - expectedDiscount)); assertEquals(DecisionType.ACCEPTED, user.getDecision()); } private void printInputs(User user, Product product) { LOGGER.debug(ReflectionToStringBuilder.reflectionToString(user, ToStringStyle.MULTI_LINE_STYLE)); LOGGER.debug(ReflectionToStringBuilder.reflectionToString(product, ToStringStyle.MULTI_LINE_STYLE)); } private User createUser(String name, CountryType countryType, int userAge){ User user = new User(); user.setUserName(name); user.setUserCountry(countryType); user.setUserAge(userAge); return user; } private Product createProduct(String name, double price, CountryType countryOfOrigin, ProductType productType, int quantity){ Product product = new Product(); product.setPrice(price); product.setCountryOfOrigin(countryOfOrigin); product.setName(name); product.setType(productType); product.setQuantity(quantity); return product; } } Of course the log.debugs in the tests are totally redundant but I wanted you to quickly see that the rules are operational :) Sorry for the length of the logs but I wrote a few tests to show different combinations of rules (in fact it's better too have too many logs than the other way round :) ) pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@1d48043[ userName=Smith userAge=6 userCountry=PL decision= decisionDescription= ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@1e8f2a0[ name=Electronic type=ELECTRONIC price=1000.0 countryOfOrigin=PL additionalInfo= quantity=10 ] pl.grzejszczak.marcin.drools.decisiontable.service.ProductServiceImpl:31 Running product logic - first acceptance Route, then discount Route pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Sorry, according to your age (< 18) and country (PL) you can't buy this product pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.FinancialServiceImpl:29 Sorry, user has been rejected... pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@1d48043[ userName=Smith userAge=6 userCountry=PL decision=REJECTED decisionDescription=Sorry, according to your age (< 18) and country (PL) you can't buy this product ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@1e8f2a0[ name=Electronic type=ELECTRONIC price=1000.0 countryOfOrigin=PL additionalInfo= quantity=10 ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@b28f30[ userName=Smith userAge=19 userCountry=PL decision= decisionDescription= ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@d6a0e0[ name=Electronic type=ELECTRONIC price=1000.0 countryOfOrigin=PL additionalInfo= quantity=1 ] pl.grzejszczak.marcin.drools.decisiontable.service.ProductServiceImpl:31 Running product logic - first acceptance Route, then discount Route pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Congratulations, you have successfully bought the product pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Sorry, no discount will be granted. pl.grzejszczak.marcin.drools.decisiontable.service.FinancialServiceImpl:25 User has been approved - processing the order... pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@b28f30[ userName=Smith userAge=19 userCountry=PL decision=ACCEPTED decisionDescription=Congratulations, you have successfully bought the product ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@d6a0e0[ name=Electronic type=ELECTRONIC price=1000.0 countryOfOrigin=PL additionalInfo=Sorry, no discount will be granted. quantity=1 ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@14510ac[ userName=Smith userAge=19 userCountry=PL decision= decisionDescription= ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@1499616[ name=Electronic type=ELECTRONIC price=1000.0 countryOfOrigin=PL additionalInfo= quantity=8 ] pl.grzejszczak.marcin.drools.decisiontable.service.ProductServiceImpl:31 Running product logic - first acceptance Route, then discount Route pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Congratulations, you have successfully bought the product pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Congratulations - you've been granted a 10% discount! pl.grzejszczak.marcin.drools.decisiontable.service.FinancialServiceImpl:25 User has been approved - processing the order... pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@14510ac[ userName=Smith userAge=19 userCountry=PL decision=ACCEPTED decisionDescription=Congratulations, you have successfully bought the product ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@1499616[ name=Electronic type=ELECTRONIC price=900.0 countryOfOrigin=PL additionalInfo=Congratulations - you've been granted a 10% discount! quantity=8 ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@17667bd[ userName=Smith userAge=19 userCountry=USA decision= decisionDescription= ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@ad9f5d[ name=Electronic type=ELECTRONIC price=1000.0 countryOfOrigin=PL additionalInfo= quantity=8 ] pl.grzejszczak.marcin.drools.decisiontable.service.ProductServiceImpl:31 Running product logic - first acceptance Route, then discount Route pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Sorry, according to your age (< 18) and country (USA) you can't buy this product pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.FinancialServiceImpl:29 Sorry, user has been rejected... pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@17667bd[ userName=Smith userAge=19 userCountry=USA decision=REJECTED decisionDescription=Sorry, according to your age (< 18) and country (USA) you can't buy this product ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@ad9f5d[ name=Electronic type=ELECTRONIC price=1000.0 countryOfOrigin=PL additionalInfo= quantity=8 ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@9ff588[ userName=Smith userAge=22 userCountry=USA decision= decisionDescription= ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@1b0d2d0[ name=Some name type=MEDICAL price=1000.0 countryOfOrigin=SWE additionalInfo= quantity=4 ] pl.grzejszczak.marcin.drools.decisiontable.service.ProductServiceImpl:31 Running product logic - first acceptance Route, then discount Route pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Congratulations, you have successfully bought the product pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.FinancialServiceImpl:25 User has been approved - processing the order... pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@9ff588[ userName=Smith userAge=22 userCountry=USA decision=ACCEPTED decisionDescription=Congratulations, you have successfully bought the product ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@1b0d2d0[ name=Some name type=MEDICAL price=1000.0 countryOfOrigin=SWE additionalInfo= quantity=4 ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@1b27882[ userName=Smith userAge=22 userCountry=USA decision= decisionDescription= ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@5b84b[ name=Some name type=MEDICAL price=1000.0 countryOfOrigin=SWE additionalInfo= quantity=8 ] pl.grzejszczak.marcin.drools.decisiontable.service.ProductServiceImpl:31 Running product logic - first acceptance Route, then discount Route pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Congratulations, you have successfully bought the product pl.grzejszczak.marcin.drools.decisiontable.converter.ProductTypeConverter:25 Executing ProductTypeConverter's toCommandFromList method pl.grzejszczak.marcin.drools.decisiontable.service.ProductService:8 Congratulations, you are granted a discount pl.grzejszczak.marcin.drools.decisiontable.service.FinancialServiceImpl:25 User has been approved - processing the order... pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:150 pl.grzejszczak.marcin.drools.decisiontable.model.User@1b27882[ userName=Smith userAge=22 userCountry=USA decision=ACCEPTED decisionDescription=Congratulations, you have successfully bought the product ] pl.grzejszczak.marcin.drools.decisiontable.service.drools.ProductServiceImplTest:151 pl.grzejszczak.marcin.drools.decisiontable.model.Product@5b84b[ name=Some name type=MEDICAL price=750.0 countryOfOrigin=SWE additionalInfo=Congratulations, you are granted a discount quantity=8 ] In this post I've presented how you can push some of your developing work to your BA by giving him a tool which he can be able to work woth - the Decision Tables in a spreadsheet. What is more now you will now how to integrate Drools with Camel. Hopefully you will see how you can simplify (thus minimize the cost of implementing and supporting) the implementation of business rules bearing in mind how prone to changes they are. I hope that this example will even better illustrate how difficult it would be to implement all the business rules in Java than in the previous post about Drools. If you have any experience with Drools in terms of decision tables, integration with Spring and Camel please feel free to leave a comment here or on my blog - let's have a discussion on that :) All the code is available at Too Much Coding repository at Bitbucket and GitHub.

Testing and debugging multi threaded programs is hard. Now take the same programs and massively distribute them across multiple JVMs deployed on a cluster of machines and the complexity goes off the roof. One way to overcome this complexity is to do testing in isolation and catch as many bugs as possible locally. MRUnit is a testing framework that lets you test and debug Map Reduce jobs in isolation without spinning up a Hadoop cluster. In this blog post we will cover various features of MRUnit by walking through a simple MapReduce job. Lets say we want to take the input below and create an inverted index using MapReduce. Input www.kohls.com,clothes,shoes,beauty,toys www.amazon.com,books,music,toys,ebooks,movies,computers www.ebay.com,auctions,cars,computers,books,antiques www.macys.com,shoes,clothes,toys,jeans,sweaters www.kroger.com,groceries Expected output antiques www.ebay.com auctions www.ebay.com beauty www.kohls.com books www.ebay.com,www.amazon.com cars www.ebay.com clothes www.kohls.com,www.macys.com computers www.amazon.com,www.ebay.com ebooks www.amazon.com jeans www.macys.com movies www.amazon.com music www.amazon.com shoes www.kohls.com,www.macys.com sweaters www.macys.com toys www.macys.com,www.amazon.com,www.kohls.com groceries www.kroger.com below are the Mapper and Reducer that do the transformation public class InvertedIndexMapper extends MapReduceBase implements Mapper { public static final int RETAIlER_INDEX = 0; @Override public void map(LongWritable longWritable, Text text, OutputCollector outputCollector, Reporter reporter) throws IOException { final String[] record = StringUtils.split(text.toString(), ","); final String retailer = record[RETAIlER_INDEX]; for (int i = 1; i < record.length; i++) { final String keyword = record[i]; outputCollector.collect(new Text(keyword), new Text(retailer)); } } } public class InvertedIndexReducer extends MapReduceBase implements Reducer { @Override public void reduce(Text text, Iterator textIterator, OutputCollector outputCollector, Reporter reporter) throws IOException { final String retailers = StringUtils.join(textIterator, ','); outputCollector.collect(text, new Text(retailers)); } } Implementation details are not really important but basically Mapper gets a line at a time, splits the line and emits key value pairs where Key is a category of product and value is the website which is selling the product. For example line retailer,category1,category2 will be emitted as (category1,retailer) and (category2,retailer). Reducer gets a key and a list of values, transforms the list of values to a comma delimited String and emits the key and value out. Now lets use MRUnit to write various tests for this Job. Three key classes in MRUnits are MapDriver for Mapper Testing, ReduceDriver for Reducer Testing and MapReduceDriver for end to end MapReduce Job testing. This is how we will setup the Test Class. public class InvertedIndexJobTest { private MapDriver mapDriver; private ReduceDriver reduceDriver; private MapReduceDriver mapReduceDriver; @Before public void setUp() throws Exception { final InvertedIndexMapper mapper = new InvertedIndexMapper(); final InvertedIndexReducer reducer = new InvertedIndexReducer(); mapDriver = MapDriver.newMapDriver(mapper); reduceDriver = ReduceDriver.newReduceDriver(reducer); mapReduceDriver = MapReduceDriver.newMapReduceDriver(mapper, reducer); } } MRUnit supports two style of testings. First style is to tell the framework both input and output values and let the framework do the assertions, second is the more traditional approach where you do the assertion yourself. Lets write a test using the first approach. @Test public void testMapperWithSingleKeyAndValue() throws Exception { final LongWritable inputKey = new LongWritable(0); final Text inputValue = new Text("www.kroger.com,groceries"); final Text outputKey = new Text("groceries"); final Text outputValue = new Text("www.kroger.com"); mapDriver.withInput(inputKey, inputValue); mapDriver.withOutput(outputKey, outputValue); mapDriver.runTest(); } In the test above we tell the framework both input and output Key and Value pairs and the framework does the assertion for us. This test can be written in a more traditional way as follow @Test public void testMapperWithSingleKeyAndValueWithAssertion() throws Exception { final LongWritable inputKey = new LongWritable(0); final Text inputValue = new Text("www.kroger.com,groceries"); final Text outputKey = new Text("groceries"); final Text outputValue = new Text("www.kroger.com"); mapDriver.withInput(inputKey, inputValue); final List> result = mapDriver.run(); assertThat(result) .isNotNull() .hasSize(1) .containsExactly(new Pair(outputKey, outputValue)); } Sometimes Mapper emits multiple Key Value pairs for a single input. MRUnit provides a fluent API to support this use case. Here is an example @Test public void testMapperWithSingleInputAndMultipleOutput() throws Exception { final LongWritable key = new LongWritable(0); mapDriver.withInput(key, new Text("www.amazon.com,books,music,toys,ebooks,movies,computers")); final List> result = mapDriver.run(); final Pair books = new Pair(new Text("books"), new Text("www.amazon.com")); final Pair toys = new Pair(new Text("toys"), new Text("www.amazon.com")); assertThat(result) .isNotNull() .hasSize(6) .contains(books, toys); } You write the test for the reduce exactly the same way. @Test public void testReducer() throws Exception { final Text inputKey = new Text("books"); final ImmutableList inputValue = ImmutableList.of(new Text("www.amazon.com"), new Text("www.ebay.com")); reduceDriver.withInput(inputKey,inputValue); final List> result = reduceDriver.run(); final Pair pair2 = new Pair(inputKey, new Text("www.amazon.com,www.ebay.com")); assertThat(result) .isNotNull() .hasSize(1) .containsExactly(pair2); } Finally you can use MapReduceDriver to test your Mapper, Combiner and Reducer together as a single job. You can also pass multiple key value pairs as input to your job. Test below demonstrate MapReduceDriver in action @Test public void testMapReduce() throws Exception { mapReduceDriver.withInput(new LongWritable(0), new Text("www.kohls.com,clothes,shoes,beauty,toys")); mapReduceDriver.withInput(new LongWritable(1), new Text("www.macys.com,shoes,clothes,toys,jeans,sweaters")); final List> result = mapReduceDriver.run(); final Pair clothes = new Pair(new Text("clothes"), new Text("www.kohls.com,www.macys.com")); final Pair jeans = new Pair(new Text("jeans"), new Text("www.macys.com")); assertThat(result) .isNotNull() .hasSize(6) .contains(clothes, jeans); }

PHPUnit is the standard testing framework for PHP code: always available through Pear or Composer, following xUnit conventions on tests and providing many features from grouping to code coverage to logging of results. There's even an extension for running Selenium tests (that I maintain), which allows you to run browser-based tests. Parallelism What PHPUnit lacks is parallelism: tests are run one after the other, usually in the same process. This means that when you have more available resources, such as a multicore CPU, some of the computational power is not used as the PHPUnit process may reach 100% utilization while the other cores are not working at all. This is not surprising. PHP does not have multithreading capabilities, but it can start new processes at the OS level. So many developers have came up with the same idea: starting multiple PHPUnit processes, each working on a different subset of tests, and aggregate the results. This could theoretically give you a N times speedup when working with N different cores, for example passing from 10 minutes on a single core to 2'30'' on a quad core CPU. Caveats Of course the cost of coordinating different processes is always going to be present, so we will never reach the theoretical speedup. I'll report later in this article some simulations. The most important constraints come from the design of our test suites. I can only think of two categories of tests as easily parallelizable: unit tests, which only use memory and CPU as resources and not disk or other external infrastructure. Selenium tests, which run against a live HTTP server that must be able to serve multiple requests without race conditions if your application is going to work. By design, these two kinds of tests are always capable to run in parallel. However, other intensive and long-running tests such as end-to-end tests and integration ones usually conflict with each other: public function setUp() { $this->pdo = new PDO(...); $this->pdo->query('DELETE FROM users'); } public function testUsersCanBeAddedWithAllDetails() { $this->request->post('/users', ...); $this->assertEquals(1, $this->request->get('/users')); } public function testUsersCanBeDeletedByAnAdmin() { $this->insertAnUser(); $this->assertEquals(1, $this->request->get('/users')); $this->request->delete('/users', ...); $this->assertEquals(0, $this->request->get('/users')); } These API-based tests are never going to run in parallel (on the same machine) when written in this way, due to the race condition on the users table. If you have a slow suite that you want to speed up, chances are that it contains many end-to-end tests like these. Some of these tests can be isolated with RDBMS transactions, but it's difficult for black-box tests to intervene on the transaction isolation inside the application. The tools PHPUnit is due to support parallelism since 2007, but it has never come up in the package and pull requests for the feature have never been accepted. So we have to resort to external tools. Probably the most complete tool working on top of PHPUnit is Paratest , which has two peculiarities: It uses reflection to compose a list of all of your tests instead of grepping *Test.php files. It reads PHPUnit JUnit-format logs to aggregate results from different tests, which makes it difficult to break than tools that parse the output of the command itself. The only limitations of it are that it poses some stronger constraints on your tests, for example they have to follow the PSR-0 convention. However, it delegates much to PHPUnit and lets you use many of the same command line switches such as --configuration and --bootstrap. Experiments To experiment with Paratest, I created a simulated unit test suite that only works with the CPU. I have 10 test of the form : public function testExample() { for ($i = 0; $i < 1024*1024; $i++) { $this->assertTrue(true); } } I then tried to run this suite on a dual core CPU, on a physical (not virtual) home machine. I have tried different options, too: vanilla PHPUnit, serial execution Paratest, single process execution (to find out if it has an high overhead). Paratest with 2 parallel processes. These are the results: [21:13:25][giorgio@Desmond:~/paratestexample]$ ./compare.sh PHPUnit 3.7.13-5-g6937c46 by Sebastian Bergmann. .......... Time: 03:04, Memory: 3.25Mb OK (10 tests, 20971520 assertions) Running phpunit in 1 process with /home/giorgio/paratestexample/vendor/bin/phpunit .......... Time: 03:01, Memory: 3.75Mb OK (10 tests, 20971520 assertions) Running phpunit in 2 processes with /home/giorgio/paratestexample/vendor/bin/phpunit .......... Time: 02:15, Memory: 3.75Mb OK (10 tests, 20971520 assertions) The difference is a 25% decrease in total time, which is really worth investigating further. Conclusions I'm going to experiment more with Paratest to see if it's possible to speed up also batteries of end-to-end tests, for example making different processes using different databases or offloading the PHPUnit commands to different machines.

Curator's Note: This article was co-authored by Andrzej Jarzyna. At 3scale we find Amazon to be a fantastic platform for running APIs due to the complete control you have on the application stack. For people new to AWS the learning curve is quite steep. So we put together our best practices into this short tutorial. Besides Amazon EC2 we will use the Ruby Grape gem to create the API interface and an Nginx proxy to handle access control. Best of all everything in this tutorial is completely FREE! For the purpose of this tutorial you will need a running API based on Ruby and Thin server. If you don’t have one you can simply clone an example repo as described below (in the “Deploying the Application” section). If you are interested in the background of this example (Sentiment API), you can see a couple of previous guides which 3scale has published. Here we use version_1 of the API(‘API up and running in 10 minutes‘) with some extra sentiment analysis functionality (this part is covered in the second tutorial of the Sentiment API tutorial). Now we will start the creation and configuration of the Amazon EC2 instance. If you already have an EC2 instance (micro or not), you can jump to the next step -> Preparing Instance for Deployment. Creating and configuring EC2 Instance Let’s start by signing up for the Amazon Elastic Compute Cloud (Amazon EC2). For our needs the free tier http://aws.amazon.com/free/ is enough, covering all the basic needs. Once the account is created go to the EC2 dashboard under your AWS Management Console and click on the Launch Instance button. That will transfer you to a popup window where you will continue the process: Choose the classic wizard Choose an AMI (Ubuntu Server 12.04.1 LTS 32bit, T1micro instance) leaving all the other settings for Instance Details as default Create a keypair and download it – this will be the key which you will use to make an ssh connection to the server, it’s VERY IMPORTANT! Add inbound rules for the firewall with source always 0.0.0.0/0 (HTTP, HTTPS, ALL ICMP, TCP port 3000 used by the Ruby thin server) Preparing Instance for Deployment Now, as we have the instance created and running, we can directly connect there from our console (Windows users from PuTTY). Right click on your instance, connect and choose Connect with a standalone SSH Client. Follow the steps and change the username to ubuntu (instead of root) in the given example. After executing this step you are connected to your instance. We will have to install new packages. Some of them require root credentials, so you will have to set a new root password: sudo passwd root. Then login as root: su root. Now with root credentials execute: sudo apt-get update and switch back to your normal user with exit command and install all the required packages: install some libraries which will be required by rvm, ruby and git: sudo apt-get install build-essential git zlib1g-dev libssl-dev libreadline-gplv2-dev imagemagick libxml2-dev libxslt1-dev openssl libreadline6 libreadline6-dev zlib1g libyaml-dev libxslt-dev autoconf libc6-dev ncurses-dev automake libtool bison libpq-dev libpq5 libeditline-dev install git (on Linux rather than from Source): http://www.git-scm.com/book/en/Getting-Started-Installing-Git install rvm: https://rvm.io/rvm/install/ install ruby rvm install 1.9.3 rvm use 1.9.3 --default Deploying the Application Our sample Sentiment API is located on Github. Try cloning the repository: git clone [email protected]:jerzyn/api-demo.git you can once again review the code and tutorial on creating and deploying this app here: http://www.3scale.net/2012/06/the-10-minute-api-up-running-3scale-grape-heroku-api-10-minutes/ and here http://www.3scale.net/2012/07/how-to-out-of-the-box-api-analytics/ note the changes (we are using only v1, as authentication will go through the proxy). Now you can deploy the app by issuing: bundle install. Now you can start the thin server: thin start. To access the API directly (i.e. without any security or access control) access: your-public-dns:3000/v1/words/awesome.json (you can find your-public-dns in the AWS EC2 Dashboard->Instances in the details window of your instance) For the Nginx integration you will have to create an elastic IP address. Inside the AWS EC2 dashboard create an elastic IP in the same region as your instance and associate that IP to it (you won’t have to pay anything for the elastic IP as long as it is associated with your instance in the same region). OPTIONAL: If you want to assign a custom domain to your amazon instance you will have to do one thing: add an A record to the DNS record of your domain mapping the domain to the elastic IP address you have previously created. Your domain provider should either give you some way to set the A record (the IPv4 address), or it will give you a way to edit the nameservers of your domain. If they do not allow you to set the A record directly, find a DNS management service, register your domain as a zone there and the service will give you the nameservers to enter in the admin panel of your domain provider. You can then add the A record for the domain. Some possible DNS management services include ZoneEdit (basic, free), Amazon route 53, etc. At this point you API is open to the world. This is good and bad – great that you are sharing, but bad in the sense that without rate limits a few apps could kill the resources of your server, and you have no insight into who is using your API and how it is being used. The solution is to add some management for your API… Enabling API Management with 3scale Rather than reinvent the wheel and implement rate limits, access controls and analytics from scratch we will leverage the handy 3scale API Management service. Get your free 3scale account, activate and log-in to the new instance through the provided links. The first time you log-in you can choose the option for some sample data to be created, so you will have some API keys to use later. Next you would probably like to go through the tour to get a glimpse on the system functionality (optional) and then start with the implementation. To get some instant results we will start with the sandbox proxy which can be used while in development. Then we will also configure an Nginx proxy which can scale up for full production deployments. There is some documentation on the configuration of the API proxy at 3scale: https://support.3scale.net/howtos/api-configuration/nginx-proxy and for more advanced configuration options here: https://support.3scale.net/howtos/api-configuration/nginx-proxy-advanced Once you sign into your 3scale account, Launch your API on the main Dashboard screen or Go to API->Select the service (API)->Integration in the sidebar->Proxy Set the address of of your API backend – this has to be the Elastic IP address unless the custom domain has been set, including http protocol and port 3000. Now you can save and turn on the sandbox proxy to test your API by hitting the sandbox endpoint (after creating some app credentials in 3scale): http://sandbox-endpoint/v1/words/awesome.json?app_id=APP_ID&app_key=APP_KEY where, APP_ID and APP_KEY are id and key of one of the sample applications which you created when you first logged into your 3scale account (if you missed that step just create a developer account and an application within that account). Try it without app credentials, next with incorrect credentials, and then once authenticated within and over any rate limits that you have defined. Only once it is working to your satisfaction do you need to download the config files for Nginx. Note: any time you have errors check whether you can access the API directly: your-public-dns:3000/v1/words/awesome.json. If that is not available, then you need to check if the AWS instance is running and if the Thin Server is running on the instance. Implement an Nginx Proxy for Access Control In order to streamline this step we recommend that you install the fantastic OpenResty web application that is basically a bundle of the standard Nginx core with almost all the necessary 3rd party Nginx modules built-in. Install dependencies: sudo apt-get install libreadline-dev libncurses5-dev libpcre3-dev perl Compile and install Nginx: cd ~ sudo wget http://agentzh.org/misc/nginx/ngx_openresty-1.2.3.8.tar.gz sudo tar -zxvf ngx_openresty-1.2.3.8.tar.gz cd ngx_openresty-1.2.3.8/ ./configure --prefix=/opt/openresty --with-luajit --with-http_iconv_module -j2 make sudo make install In the config file make the following changes: edit the .conf file from nginx download in line 28, which is preceded by info to change your server name put the correct domain (of your Elastic IP or custom domain name) in line 78 change the path to the .lua file, downloaded together with the .conf file. We are almost finished! Our last step is to start the NGINX proxy and put some traffic through it. If it is not running yet (remember, that thin server has to be started first), please go to your EC2 instance terminal (the one you were connecting through ssh before) and start it now: sudo /opt/openresty/nginx/sbin/nginx -p /opt/openresty/nginx/ -c /opt/openresty/nginx/conf/YOUR-CONFIG-FILE.conf The last step will be verifying that the traffic goes through with a proper authorization. To do that, access: http://your-public-dns/v1/words/awesome.json?app_id=APP_ID&app_key=APP_KEY where, APP_ID and APP_KEY are key and id of the application you want to access through the API call. Once everything is confirmed as working correctly, you will want to block public access to the API backend on port 3000, which bypasses any access controls. If encounter some problems with the Nginx configuration or need a more detailed guide, I encourage you to check the 3scale guide on configuring Nginx proxy: https://support.3scale.net/howtos/api-configuration/nginx-proxy. You can go completely wild with customization of your API gateway. If you want to dive more into the 3scale system configuration (like usage and monitoring of your API traffic) feel encouraged to browse our Quickstart guides and HowTo’s.

Curator's Note: Here's a post from back in 2011 that offers a general look at the difference in performance of graph databases and relational databases. A few weeks ago, Emil Eifrem, CEO of Neo Technology gave a webinar introduction to graph databases. I watched it as a lead up to my own presentation on graph databases and Neo4j. Right around the 54 minute mark, Emil talks about a very interesting experiment showing the performance difference between a relational database and a graph database for a certain type of problem called "arbitrary path query", specifically, given 1,000 users with an average of 50 "friend" relationships each, determine if one person is connected to another in 4 or fewer hops. Against a popular open-source relational database, the query took around 2,000 ms. For a graph database, the same determination took 2 ms. So the graph database was 1,000 times faster for this particular use case. Not satisfied with that, they then decided to run the same experiment with 1,000,000 users. The graph database took 2 ms. They stopped the relational database after several days of waiting for results. I showed this clip at my presentation to a few people who stuck around afterwards, and we tried to figure out why the graph database had the same performance with 1,000 times the data, while the relational database became unusably slow. The answer has to do with the way in which each type of database searches information. Relational databases search all of the data looking for anything that meets the search criteria. The larger the set of data, the longer it takes to find matches, because the database has to examine everything in the collection. Here's an example: let's assume there is a table with "friend" relationships: > SELECT * FROM friends; +-------------+--------------+ | user_id | friend_id | +-------------+--------------+ | 1 | 2 | | 1 | 3 | | 1 | 4 | | 2 | 5 | | 2 | 6 | | 2 | 7 | | 3 | 8 | | 3 | 9 | | 3 | 10 | +-------------+--------------+ In order to see if user "9" is connected to user "2", the database has to find all the friends of user "9", and see if user "2" is in that list. If not, find all of their friends, and then see if user "2" is in that list. The database has to scan the entire table each time. This means that if you double the number of rows in the table, you've doubled the amount of data to search, and thus doubled the amount of time it takes to find what you are looking for. (Even with indexing, it still has to find the values in the index tree, which involves traversing that tree. The index tree grows larger with each new record, meaning the time it takes to traverse grows larger as well. And for each search, you always start at the root of the tree.) Each new batch of friends to look at requires an entirely new scan of the table/index. So more records leads to more search time. Conversely, a graph database looks only at records that are directly connected to other records. If it is given a limit on how many "hops" it is allowed to make, it can ignore everything more than that number of hops away. Only the blue records are ever seen during the search. And since a graph traversal "remembers" where it is at any time, it never has to start from the beginning, only from its last known position. You could add another ring of records around the outside, or add a thousand more rings of records outside, but the search would never need to look at them because they are more steps away than the limit. The only way to increase the number of records searched (and thereby decrease performance) is to add more records within the 2-step limit, or add more relationships between the existing records. So the reason why having 1,000 vs. 1,000,000 records causes such a stark difference between a relational and a graph database is that relational database performance decreases in relation to the number of records in the table, while graph database performance decreases in relation to the number of connections between the records.

the repository pattern has been discussed a lot lately. especially about it’s usefulness since the introduction of or/m libraries. this post (which is the third in a series about the data layer) aims to explain why it’s still a great choice. let’s start with the definition : a repository mediates between the domain and data mapping layers, acting like an in-memory domain object collection. client objects construct query specifications declaratively and submit them to repository for satisfaction. objects can be added to and removed from the repository, as they can from a simple collection of objects, and the mapping code encapsulated by the repository will carry out the appropriate operations behind the scenes the repository pattern is used to create an abstraction between your domain and data layer. that is, when you use the repository you should not have to have any knowledge about the underlying data source or the data layer (i.e. entity framework, nhibernate or similar). why do we need it? read the abstractions part of my data layer article. it explains the basics to why we should use repositories or similar abstractions. but let’s also examine some simple business logic: var brokentrucks = _session.query().where(x => x.state == 1); foreach (var truck in brokentrucks) { if (truck.calculatereponsetime().totaldays > 30) sendemailtomanager(truck); } what does that give us? broken trucks? well. no. the statement was copied from another place in the code and the developer had forgot to update the query. any unit tests would likely just check that some trucks are returned and that they are emailed to the manager. so we basically have two problems here: a) most developers will likely just check the name of the variable and not on the query. b) any unit tests are against the business logic and not the query. both those problems would have been fixed with repositories. since if we create repositories we also have unit tests which targets the data layer only. implementations here are some different implementations with descriptions. base classes these classes can be reused for all different implementations. unitofwork the unit of work represents a transaction when used in data layers. typically the unit of work will roll back the transaction if savechanges() has not been invoked before being disposed. public interface iunitofwork : idisposable { void savechanges(); } paging we also need to have page results. public class pagedresult { ienumerable _items; int _totalcount; public pagedresult(ienumerable items, int totalcount) { _items = items; _totalcount = totalcount; } public ienumerable items { get { return _items; } } public int totalcount { get { return _totalcount; } } } we can with the help of that create methods like: public class userrepository { public pagedresult find(int pagenumber, int pagesize) { } } sorting finally we prefer to do sorting and page items, right? var constraints = new queryconstraints() .sortby("firstname") .page(1, 20); var page = repository.find("jon", constraints); do note that i used the property name, but i could also have written constraints.sortby(x => x.firstname) . however, that is a bit hard to write in web applications where we get the sort property as a string. the class is a bit big, but you can find it at github . in our repository we can apply the constraints as (if it supports linq): public class userrepository { public pagedresult find(string text, queryconstraints constraints) { var query = _dbcontext.users.where(x => x.firstname.startswith(text) || x.lastname.startswith(text)); var count = query.count(); //easy var items = constraints.applyto(query).tolist(); return new pagedresult(items, count); } } the extension methods are also available at github . basic contract i usually start use a small definition for the repository, since it makes my other contracts less verbose. do note that some of my repository contracts do not implement this interface (for instance if any of the methods do not apply). public interface irepository where tentity : class { tentity getbyid(tkey id); void create(tentity entity); void update(tentity entity); void delete(tentity entity); } i then specialize it per domain model: public interface itruckrepository : irepository { ienumerable findbrokentrucks(); ienumerable find(string text); } that specialization is important. it keeps the contract simple. only create methods that you know that you need. entity framework do note that the repository pattern is only useful if you have pocos which are mapped using code first. otherwise you’ll just break the abstraction using the entities. the repository pattern isn’t very useful then. what i mean is that if you use the model designer you’ll always get a perfect representation of the database (but as classes). the problem is that those classes might not be a perfect representation of your domain model. hence you got to cut corners in the domain model to be able to use your generated db classes. if you on the other hand uses code first you can modify the models to be a perfect representation of your domain model (if the db is reasonable similar to it). you don’t have to worry about your changes being overwritten as they would have been by the model designer. you can follow this article if you want to get a foundation generated for you. base class public class entityframeworkrepository where tentity : class { private readonly dbcontext _dbcontext; public entityframeworkrepository(dbcontext dbcontext) { if (dbcontext == null) throw new argumentnullexception("dbcontext"); _dbcontext = dbcontext; } protected dbcontext dbcontext { get { return _dbcontext; } } public void create(tentity entity) { if (entity == null) throw new argumentnullexception("entity"); dbcontext.set().add(entity); } public tentity getbyid(tkey id) { return _dbcontext.set().find(id); } public void delete(tentity entity) { if (entity == null) throw new argumentnullexception("entity"); dbcontext.set().attach(entity); dbcontext.set().remove(entity); } public void update(tentity entity) { if (entity == null) throw new argumentnullexception("entity"); dbcontext.set().attach(entity); dbcontext.entry(entity).state = entitystate.modified; } } then i go about and do the implementation: public class truckrepository : entityframeworkrepository, itruckrepository { private readonly truckerdbcontext _dbcontext; public truckrepository(truckerdbcontext dbcontext) { _dbcontext = dbcontext; } public ienumerable findbrokentrucks() { //compare having this statement in a business class compared //to invoking the repository methods. which says more? return _dbcontext.trucks.where(x => x.state == 3).tolist(); } public ienumerable find(string text) { return _dbcontext.trucks.where(x => x.modelname.startswith(text)).tolist(); } } unit of work the unit of work implementation is simple for entity framework: public class entityframeworkunitofwork : iunitofwork { private readonly dbcontext _context; public entityframeworkunitofwork(dbcontext context) { _context = context; } public void dispose() { } public void savechanges() { _context.savechanges(); } } nhibernate i usually use fluent nhibernate to map my entities. imho it got a much nicer syntax than the built in code mappings. you can use nhibernate mapping generator to get a foundation created for you. but you do most often have to clean up the generated files a bit. base class public class nhibernaterepository where tentity : class { isession _session; public nhibernaterepository(isession session) { _session = session; } protected isession session { get { return _session; } } public tentity getbyid(string id) { return _session.get(id); } public void create(tentity entity) { _session.saveorupdate(entity); } public void update(tentity entity) { _session.saveorupdate(entity); } public void delete(tentity entity) { _session.delete(entity); } } implementation public class truckrepository : nhibernaterepository, itruckrepository { public truckrepository(isession session) : base(session) { } public ienumerable findbrokentrucks() { return _session.query().where(x => x.state == 3).tolist(); } public ienumerable find(string text) { return _session.query().where(x => x.modelname.startswith(text)).tolist(); } } unit of work public class nhibernateunitofwork : iunitofwork { private readonly isession _session; private itransaction _transaction; public nhibernateunitofwork(isession session) { _session = session; _transaction = _session.begintransaction(); } public void dispose() { if (_transaction != null) _transaction.rollback(); } public void savechanges() { if (_transaction == null) throw new invalidoperationexception("unitofwork have already been saved."); _transaction.commit(); _transaction = null; } } typical mistakes here are some mistakes which can be stumbled upon when using or/ms. do not expose linq methods let’s get it straight. there are no complete linq to sql implementations. they all are either missing features or implement things like eager/lazy loading in their own way. that means that they all are leaky abstractions. so if you expose linq outside your repository you get a leaky abstraction. you could really stop using the repository pattern then and use the or/m directly. public interface irepository { iqueryable query(); // [...] } those repositories really do not serve any purpose. they are just lipstick on a pig (yay, my favorite) those who use them probably don’t want to face the truth: or are just not reading very good: learn about lazy loading lazy loading can be great. but it’s a curse for all which are not aware of it. if you don’t know what it is, google . if you are not careful you could get 101 executed queries instead of 1 if you traverse a list of 100 items. invoke tolist() before returning the query is not executed in the database until you invoke tolist() , firstordefault() etc. so if you want to be able to keep all data related exceptions in the repositories you have to invoke those methods. get is not the same as search there are to types of reads which are made in the database. the first one is to search after items. i.e. the user want to identify the items that he/she like to work with. the second one is when the user has identified the item and want to work with it. those queries are different. in the first one, the user only want’s to get the most relevant information. in the second one, the user likely want’s to get all information. hence in the former one you should probably return userlistitem or similar while the other case returns user . that also helps you to avoid the lazy loading problems. i usually let search methods start with findxxxx() while those getting the entire item starts with getxxxx() . also don’t be afraid of creating specialized pocos for the searches. two searches doesn’t necessarily have to return the same kind of entity information. summary don’t be lazy and try to make too generic repositories. it gives you no upsides compared to using the or/m directly. if you want to use the repository pattern, make sure that you do it properly.

By default, Spring beans are scoped singleton, meaning there’s only one instance for the whole application context. For most applications, this is a sensible default; then sometimes, not so much. This may be the case when using a custom scope, which is the case, on the product I’m currently working on. I’m not at liberty to discuss the details further: suffice to say that it is very painful to configure each and every needed bean with this custom scope. Since being lazy in a smart way is at the core of developer work, I decided to search for a way to ease my burden and found it in the BeanFactoryPostProcessor class. It only has a single method - postProcessBeanFactory(), but it gives access to the bean factory itself (which is at the root of the various application context classes). From this point on, the code is trivial even with no prior experience of the API: public class PrototypeScopedBeanFactoryPostProcessor implements BeanFactoryPostProcessor { @Override public void postProcessBeanFactory(ConfigurableListableBeanFactory factory) throws BeansException { for (String beanName : factory.getBeanDefinitionNames()) { BeanDefinition beanDef = factory.getBeanDefinition(beanName); String explicitScope = beanDef.getScope(); if ("".equals(explicitScope)) { beanDef.setScope("prototype"); } } } } The final touch is to register the post-processor in the context . This is achieved by treating it as a simple anonymous bean: Now, every bean which scope is not explicitly set will be scoped prototype. Sources for this article can be found attached in Eclipse/Maven format.

Groovy 2.0 brought us extension modules. An extension module is a JAR file with classes that provide extra methods to existing other classes like in the JDK or third-party libraries. Groovy uses this mechanism to add for example extra methods to the File class. We can implement our own extension module to add new extension methods to existing classes. Once we have written the module we can add it to the classpath of our code or application and all the new methods are immediately available. We define the new extension methods in helper classes, which are part of the module. We can create instance and static extension methods, but we need a separate helper class for each type of extension method. We cannot mix static and instance extension methods in one helper class. First we create a very simple class with an extension method for the String class. The first argument of the extension method defines the type or class we want the method to be added to. The following code shows the method likeAPirate. The extension method needs to be public and static even though we are creating an instance extension method. // File: src/main/groovy/com/mrhaki/groovy/PirateExtension.groovy package com.mrhaki.groovy class PirateExtension { static String likeAPirate(final String self) { // List of pirate language translations. def translations = [ ["hello", "ahoy"], ["Hi", "Yo-ho-ho"], ['are', 'be'], ['am', 'be'], ['is', 'be'], ['the', "th'"], ['you', 'ye'], ['your', 'yer'], ['of', "o'"] ] // Translate the original String to a // pirate language String. String result = self translations.each { translate -> result = result.replaceAll(translate[0], translate[1]) } result } } Next we need to create an extension module descriptor file. In this file we define the name of the helper class, so Groovy will know how to use it. The descriptor file needs to be placed in the META-INF/services directory of our module archive or classpath. The name of the file is org.codehaus.groovy.runtime.ExtensionModule. In the file we define the name of our module, version and the name of the helper class. The name of the helper class is defined with the property extensionClasses: # File: src/main/resources/META-INF/services/org.codehaus.groovy.runtime.ExtensionModule moduleName = pirate-module moduleVersion = 1.0 extensionClasses = com.mrhaki.groovy.PirateExtension Now are extension module is ready. The easiest way to distribute the module is by packaging the code and descriptor file in a JAR file and put it in a artifact repository manager. Other developers can then use build tools like Gradle or Maven to include the extension module in their projects and applications. If we use Gradle to create a JAR file we only needs this small build script: / File: build.gradle apply plugin: 'groovy' repositories.mavenCentral() dependencies { // Since Gradle 1.4 we don't use the groovy configuration // to define dependencies. We can simply use the // compile and testCompile configurations. compile 'org.codehaus.groovy:groovy-all:2.0.6' } Now we can invoke $ gradle build and we got ourselves an extension module. Let's add a test for our new extension method. Because we use Gradle the test classpath already will contain our extension module helper class and descriptor file. In our test we can simply invoke the method and test the results. We are going to use Spock to write a simple specification: // File: src/test/groovy/com/mrhaki/groovy/PirateExtensionSpec.groovy package com.mrhaki.groovy import spock.lang.Specification class PirateExtensionSpec extends Specification { def "likeAPirate method should work as instance method on a String value"() { given: final String originalText = "Hi, Groovy is the greatest language of the JVM." expect: originalText.likeAPirate() == "Yo-ho-ho, Groovy be th' greatest language o' th' JVM." } } We add the dependency to Spock in our Gradle build file: / File: build.gradle apply plugin: 'groovy' repositories.mavenCentral() dependencies { // Since Gradle 1.4 we don't use the groovy configuration // to define dependencies. We can simply use the // compile and testCompile configurations. compile 'org.codehaus.groovy:groovy-all:2.0.6' testCompile 'org.spockframework:spock-core:0.7-groovy-2.0' } We can run $ gradle test to run the Spock specification and test our new extension method. To add a static method to an existing class we need to add an extra helper class to our extension module and an extra property to our descriptor file to register the helper class. The first argument of the extension method define the type we want to add a static method to. In the following helper class we add the extension method talkLikeAPirate() to the String class. / File: src/main/groovy/com/mrhaki/groovy/PirateStaticExtension.groovy package com.mrhaki.groovy class PirateStaticExtension { static String talkLikeAPirate(final String type) { "Arr, me hearty," } We change the descriptor file and add the staticExtensionClasses property: # File: src/main/resources/META-INF/services/org.codehaus.groovy.runtime.ExtensionModule moduleName = pirate-module moduleVersion = 1.0 extensionClasses = com.mrhaki.groovy.PirateExtension staticExtensionClasses = com.mrhaki.groovy.PirateStaticExtension In our Spock specification we add an extra test for our new static method talkLikeAPirate() on the String class: // File: src/test/groovy/com/mrhaki/groovy/PirateExtensionSpec.groovy package com.mrhaki.groovy import spock.lang.Specification class PirateExtensionSpec extends Specification { def "likeAPirate method should work as instance method on a String value"() { given: final String originalText = "Hi, Groovy is the greatest language of the JVM." expect: originalText.likeAPirate() == "Yo-ho-ho, Groovy be th' greatest language o' th' JVM." } def "talkLikeAPirate method should work as static method on String class"() { expect: "Arr, me hearty, Groovy rocks!" == String.talkLikeAPirate() + " Groovy rocks!" } } Written with Groovy 2.1

Recently I had to tackle a task where I needed to send keystrokes to another application, that are initiated from a .NET Windows app.

I am working on a Spring MVC app that demonstrates all of the different MongoDB Java APIs. Some Links http://code.google.com/p/morphia/wiki/QuickStart http://www.mongodb.org/display/DOCS/Java+Tutorial http://jongo.org/#updating https://github.com/hibernate/hibernate-ogm https://openshift.redhat.com/community/blogs/configuring-hibernateogm-for-your-jboss-app-using-mongodb-on-openshift-paas http://www.hibernate.org/subprojects/ogm.html https://github.com/impetus-opensource/Kundera/wiki https://github.com/impetus-opensource/Kundera/wiki/Getting-Started-in-5-minutes http://blog.fisharefriends.us/morphia-vs-spring-data-mongodb/ http://www.ibm.com/developerworks/java/library/j-morphia/index.html Maven POM Settings For Various Drivers morphia Morphia http://morphia.googlecode.com/svn/mavenrepo/ default sonatype-nexus Kundera Public Repository https://oss.sonatype.org/content/repositories/releases true false kundera-missing Kundera Public Missing Resources Repository http://kundera.googlecode.com/svn/maven2/maven-missing-resources true true com.google.code.morphia morphia 0.99 org.hibernate.ogm hibernate-ogm-core 4.0.0-SNAPSHOT provided org.mongodb mongo-java-driver 2.10.1 org.springframework.data spring-data-mongodb 1.0.4.RELEASE Hibernate OGM for MongoDB https://community.jboss.org/wiki/PortingSeamHotelBookingExampleToOGM https://github.com/ajf8/seam-booking-ogm https://openshift.redhat.com/community/blogs/configuring-hibernateogm-for-your-jboss-app-using-mongodb-on-openshift-paas https://github.com/openshift/openshift-ogm-quickstart Kundera (JPA for MongoDB) https://github.com/impetus-opensource/Kundera-Examples/wiki/Using-Kundera-with-Spring https://github.com/impetus-opensource/Kundera https://github.com/impetus-opensource/kundera-mongo-performance https://github.com/impetus-opensource/Kundera-Examples https://github.com/impetus-opensource/Kundera/wiki/Sample-Codes-and-Examples https://github.com/impetus-opensource/Kundera-Examples/wiki/Twitter https://dzone.com/articles/sqlifying-nosql-–-are-orm https://github.com/xamry/twitample https://github.com/impetus-opensource/Kundera-Examples/wiki/Cross-datastore-persistence-using-Kundera http://prabhubuzz.wordpress.com/2012/05/25/mongodb-cassandra-jpa-service-using-kundera/ http://gora.apache.org/ http://xamry.wordpress.com/2011/05/02/working-with-mongodb-using-kundera/ https://github.com/impetus-opensource/Kundera/wiki/Getting-Started-in-5-minutes https://github.com/impetus-opensource/Kundera/wiki/Concepts

It has been few months since SOA Patterns was published and so far the book sold somewhere between 2K-3K copies which I guess is not bad for an unknown author – so first off, thanks to all of you who bought a copy (by the way, if you found the book useful I’d be grateful if you could also rate it on Amazon so that others would know about it too) I know at least a few of you actually read the book as from time to time I get questions about it :). Not all the questions are interesting to “the general public” but some are. One interesting question I got is about the so called “Canonical schema pattern“. I have a post in the making (for too long now,sorry about that Bill) that explains why I don’t consider it a pattern and why I think it verges on being an anti-pattern. Another question I got more recently, which is also the subject of this post, was about the Saga pattern. Here is (most of) the email I got from Ashic : “Garcia-Molina’s paper focuses on failure management and compensation so as to prevent partial success. It discusses a variety of approaches – with an SEC, with application code outside of the database, backward-forward and even forward-only (the latter having no “compensate” step per activity, rather a forward flow that takes care of the partial success). Nowadays, I see two viewpoints regarding sagas: 1. People calling process managers sagas, which is obviously incorrect. [e.g. NServiceBus "sagas".] 2. People focusing very strongly on a “context” of work, whereby the context gets passed around from activity to activity. For linear up front workflows, routing slips are an easy solution. An example of this can be found at Clemens’s post here: http://vasters.com/clemensv/2012/09/01/Sagas.aspx . For more complicated workflows, graph-like slips may be used. After discussing with some enthusiasts, they seem very keen to suggest that the context has to move along. They seem to reject the notion of a saga where a central coordinator controls the process. In other words, even if a process manager takes care of only routing messages, and that routing includes compensations to alleviate partial successes, they are unwilling (sometimes vehemently) to call that a saga. They acknowledge it can be useful, but say that is not a saga. I find this to be confusing. In this case the process manager acts as the SEC would in a Garcia-Molina saga capable database. This approach still allows interleaved transactions (or steps) without a global lock. Why would this not be a saga? In your book, I did see you mentioned orchestration as a way of implementing sagas. However, when this was brought up, the proponents of point 2 suggest that that is not what you really mean. To me it seems quite clear, and it aligns with Hector’s paper. I just want to make sure I have this right. I’d love your thoughts on this.” Let’s start with the answer to the question: When I think about the Saga pattern I see it as the application of the notions in the Garcia-Molina paper (which talked about databases) to SOA. In other words, I see sagas as the notion of getting distributed agreement of a process with reduced guarantees (vs. distributed transactions that propose ACID guarantees across systems). – So,basically, a Saga is loose transaction-like flow where, in case of failures, involved services perform compensation steps (which may be nothing, a complete undo or something else entirely). The Saga pattern can augment this process with temporary promises (which I call reservations). Under this definition both centrally managed processes and a “choreographed” processes are Sagas – as long as the semantics and intent mentioned above are kept. The centrally managed orchestration provides visibility of processes, ease of management etc; The cooperative event based, context shared sagas provide flexibility and allow serendipity of new processes; Both have merit and both have a place, at least in my opinion :) The main reason both of these, very different, approaches are valid designs and implementations for the Saga pattern is that the Saga pattern (like others in the book) is an Architectural pattern and not a Design pattern. Which brings us to the second reason for this post, the difference between “Architecture” and “Design”. In a nutshell, architecture is a type of design where the focus is quality attributes and wide(er) scope whereas design focuses on functional requirements and more localized concerns. The Saga pattern is an architectural pattern that focused on the integrity reliability quality attributes and it pertains to the communication patterns between services. When it comes to design the implementation of the pattern. you need to decide how to implement the concerns and roles defined in the pattern -e.g. controlling the flow and the status of the saga. One decision can be to implement it centrally and use orchestration another decision can be to decentralize it and use context… Design decision can be very meaningful sometimes it can be hard to find what’s left of the architecture – consider for example the whole idea behind blogging and RSS feeds. The architectural notion is a publish/subscribe system where the blog writer publish an “event” (a new post) and subscribers get a copy. When it came to design and implementation, considering it was implemented on top of HTTP and REST where there is no publish/subscribe capability it was actually designed as a pull system where the publisher provides a list of recent changes (the feed) and subscribers sample it and check if anything changed since the last time. So architecturally pub/sub, design pull a centralized server that exposes latest changes – a really big difference Does it matter at all? I think yes. Architecture lets us think about the system at a higher level of abstraction and thus tackle more complex systems. When we design and focus on more local issues we can tackle the nitty gritty details and make sure things actually work. we need to check the effects of design on architecture and vice versa to make sure the whole thing sticks together and actually does what we want/need. Note that architecture and design are not the complete story – another variable is the technology (e.g. HTTP in the example above) which affects the design decision and thus also the architecture (you can read a little more about it in my posts on SAF)

This morning Dave Richeson posted a humorous fake proof that depends on the famous approximation 22/7 for pi. It occurred to me that nearly everyone knows a decent rational approximation to pi. Some people may know more. But hardly anyone, myself included, knows a decent rational approximation for e. Another approximation for pi is 355/113. I like this approximation because it’s easy to remember: take the sequence 113355, split it in the middle, and make it into a fraction. It’s accurate to six decimal places, which is sufficient for most practical applications. The approximations 22/7 and 355/113 are part of the sequence of approximations coming from the continued fraction approximation for pi. So to come up with rational approximations for e, I turned to its continued fraction representation. The best analog of the approximation 22/7 for pi may be the approximation 19/7 for e. Obviously the denominators are the same, and the accuracy of the two approximations is roughly comparable. Here’s how you can make your own rational approximations for e. Find the coefficients in the continued fraction for e, for example here. You can turn this into a sequence of approximations by using the following Python code: from __future__ import division from math import e e_frac = [2,1,2,1,1,4,1,1,6,1,1,8] def display(n, d, exact): print n, d, n/d, n/d - exact def approx(a, exact): # initialize the recurrence n0 = a[0] d0 = 1 n1 = a[0]*a[1] + 1 d1 = a[1] display(n0, d0, exact) display(n1, d1, exact) for x in a[2:]: n = x*n1 + n0 # numerator d = x*d1 + d0 # denominator display(n, d, exact) n1, n0 = n, n1 d1, d0 = d, d1 approx(e_frac, e) This will print the numerator, denominator, value, and error for each approximation. You could include more terms in the continued fraction for e if you’d like. Here are some of the results: 19/7, 87/32, 106/39, etc. Unfortunately it doesn’t look like there are any approximations as memorable as 355/113 for pi. You could also use the code to create rational approximations to other numbers if you’d like. For example, you can find the continued fraction expansion for pi here and use the code above to find rational approximations for pi.

Like the Single Responsibility Principle, the Open/Closed Principle is pretty easy to apply to object-relational design in PostgreSQL, very much unlike the Liskov Substitution Principle which will be the subject of next week's post. However, the Open/Closed principle applies only in a weaker way to relational design and hence object-relational design for much the same reason that the Liskov Substitution Principle applies in completely different ways. This instalment thus begins the beginning of what is likely to be a full rotation going from similarity to difference and back to similarity again. As is always the case, relations can be thought of as specialized "fact classes" which are then manipulated and used to create usable information. Because they model facts, and not behavior, design concerns often apply differently to them. Additionally this is the first real taste of complexity in how object and relational paradigms often combine in an object-relational setup. The Open/Closed Principle in Application Design In application design and development the Open/Closed Principle facilitates code stability. The basic principle is that one should be able to extend a module without modifying the source code. In the LedgerSMB 1.4 codebase, for example, there are places where we prepare a screen for entering report criteria but wrap the functions which do so in other functions which set up report-specific input information. The function then is open to extension without modification and so the complexity of the function is limited to the most common aspects of generating report filter screens. Similarly customizations may simply inherit existing code interfaces and add new routines. This allows modified routines to exist without possibly interrupting other callers where needed. In addition to code stability (meaning lack of rate of changes to code due to changing requirements), there are two other frequently-overlooked benefits to respecting the open-closed principle. The first is that software tends to be quite complex and state management is a major source of bugs in virtually all software programs. When a frequently used subroutine is changed, it may introduce unexpected changes which are still compliant with the interface specification and test cases, but nonetheless introduce or uncover bugs elsewhere in the application. In essence the consequences of changing code are not always easily foreseen. The major problem here is that state often changes when interfaces are called, and consequently changing code behind a currently used interface means that there may be subtle state changes that are not adequately thought through. As the complexity of an interface grows, so too this problem grows. Instead if interfaces are built for flexibility, either via accepting additional data which can be passed on to the next stage, or via inheritance, then state is more easily assured, bugs are less likely to surface, and the application can more quickly be adapted to changing business rules. Problems Defining "Extension" and "Modification" in a db schema Inside the database, state changes are well defined and follow very specific rules. Consequently it is not sufficient to define "modification" as a "change in source code." If it were, an "alter table add column... " would always qualify. But is this the case? or is it merely an extension? In general merely adding an optional field can't possibly pose the state problems seen in the application environments, but it does possibly pose some problems. Defining modification and extension is thus a problem. In general some sorts of modification are more dangerous than others. For this reason we may look at these both in a strict view, where ideally tables are left alone and we add new joining tables to extend, and a more relaxed view where extension includes adding columns which do not change internal data constraints (i.e. where all previous insert and update statements would remain valid, and no constraints are relaxed). In general, what makes a database nice in terms of relational design, is that one can prove of disprove whether a schema change is backwards compatible using math. If it is not backwards-compatible, then it is always modification. If it is backwards compatible, it is always extension when using the relaxed view. Another point that must be born in mind is that relational math is quite capable of creating new synthetic relations based on the fact that relations are structurally transparent and encapsulation is typically weak, while state management issues are managed through a very well-developed framework of transaction management, locking, and, frequently, snapshot views. When you combine these techniques with declarative constraints on values, one has a very robust state engine which is based on a very different approach than object-oriented applications managing application behavior. Problems with modifying table schemas In a case where software may be managed via overlapping deployment cycles, certain problems can occur when extending tables by adding columns. This is because the knowledge of the deployment cycle typically only goes one way--- the extension team has knowledge of the base team's past cycles while the base team typically has no knowledge of the extending team's work. This is typical in cases where software is deployed and then customized. Typically adding fields to tables makes extension easy, but the cost is that major version upgrades of the base package may overwrite or clobber extensions or may fail. In essence a relaxed standard takes on risk that upgrades of the base package may not go so smoothly. On the other hand, if the software is deployed via a single deployment cycle, as is typical of purely inhouse applications and commercial software, these problems are avoided, and extension by adding fields does not break anything. The obvious problem here is that these categories are not mutually exclusive, however much they appear to be. The commercial software may be extended by a second team on-site, and therefore a single deployment cycle on one entity does not guarantee a single deployment cycle. Object/Relational Interfaces and the OCP Because object-relational interfaces can encapsulate data, and often can be made to run in certain security contexts (which can be made to cascade), the open/closed principle has a number of applications in ensuring testable database interfaces where security barriers are involved. For example, we might have an automation system where computers connect to the database in various roles to pull job information. Rather than having separate accounts for each computer, we can assign them the same login role, but filter out the data they can see based on the client IP address. This would increase the level of containment in the event of a system compromise. So we might create an interface of something like my_client() which instantiates the client information from the client IP address, and use that in various functions to filter. Consequently we might just run: select * from get_jobs(); The problem of course with such a system is that of testability. We can't readily test the output because it depends on client IP address. So we might instead create a more flexible interface, available only to superusers, which accepts a client object which we can instantiate by name, integer id, or the like. In that case we may have a query that can be called by dba's and test suites like this, where 123 is the internal id of the client: SELECT * FROM get_jobs(client(123)); The get_jobs() function for the production clients would now look like this: CREATE OR REPLACE FUNCTION get_jobs() RETURNS SETOF jobs LANGUAGE SQL SECURITY DEFINER AS $$ SELECT * FROM get_jobs(my_client()); $$; We have essentially built an API which is open to extension for security controls but closed to modification. This means we can run test cases on the underlying database cases even on production (since these can be in transactions that roll back), and push tests of the my_client() interface to the clients themselves, to verify their proper setup. A Functional Example: Arbitrary data type support in pg_message_queue There are a few cases, however, where the open-closed principle has more direct applicability. In pg_message_queue 0.1, only text, bytea, and xml queues were supported. Since virtually anything can be put in a text field, this was deemed to be sufficient at the time. However in 0.2, I wanted to be able to support JSON queues but in a way that would not preclude the extension from running on PostgreSQL 9.1. The solution was to return to the open/closed principle and build a system which could be extended easily for arbitrary types. The result was much more powerful than initially hoped for (and in fact now I am using queues with integer and ip address payloads). In 0.1, the code looked like this: CREATE TABLE pg_mq_base ( msg_id bigserial not null, sent_at timestamp not null default now(), sent_by name not null default session_user, delivered_at timestamp ); CREATE TABLE pg_mq_xml ( payload xml not null, primary key (msg_id) ) inherits (pg_mq_base); CREATE TABLE pg_mq_text ( payload text not null, primary key (msg_id) ) inherits (pg_mq_base); CREATE TABLE pg_mq_bytea ( payload bytea not null, primary key (msg_id) ) inherits (pg_mq_base); The approach here was to use table inheritance so that new queue types could be easily added. When queues are added a table is created like one of the other tables, including all indexes etc. The relevant portion of the pg_mq_create_queue function is: EXECUTE 'CREATE TABLE ' || quote_ident(t_table_name) || '( like ' || quote_ident('pg_mq_' || in_payload_type ) || ' INCLUDING ALL )'; The problem here is that while it was possible to extend this, one couldn't do so very easily without modifying the source code of the functions. In 0.2, we reduced the latter part to: -- these are types for return values only. they are not for storage. -- using tables because types don't inherit CREATE TABLE pg_mq_text (payload text) inherits (pg_mq_base); CREATE TABLE pg_mq_bin (payload bytea) inherits (pg_mq_base); But the real change that the payload type for the queue. The table creation portion of pg_mq_create_queue is now: EXECUTE 'CREATE TABLE ' || quote_ident(t_table_name) || '( like pg_mq_base INCLUDING ALL, payload ' || in_payload_type || ' NOT NULL )'; This has the advantage of allowing payloads of any type known to PostgreSQL. We can have queues for mac addresses, internet addresses, GIS data, and even complex types if we want to do more object-relational processing on output. This approach will become more important after 0.3 is out and we begin working on object-relational interfaces on pg_message_queue. Conclusions The Open/Closed principle is where we start to see a mismatch between object-oriented application programming and object-relational database design. It's not that the basic principle doesn't apply, but just that it does so in often strange and counter-intuitive ways.

Groovy 2.1 adds the method directorySize() to File objects. If the File object is a directory then the total size of all files is calculated. Notice this method will go recursively through all subdirectories so it might take some time before the method returns. If we invoke the method on a File object that is not a directory an IllegalArgumentException is thrown. def sampleDir = new File('sample') def sampleDirSize = sampleDir.directorySize() println sampleDirSize // Outputs size in bytes, eg. 130615981 Using Groovy from the command-line we can easily get the directory size like this: $ groovy -e "println new File('.').directorySize()" Written with Groovy 2.1

What PaaS performance metrics are you using to measure the success of your Cloud platform initiative? Adopting a few PaaS performance metrics can help you avoid the high-expectations, low-benefit trap that befalls many IT transformation initiatives. Rather than measuring for measurement sake, PaaS performance metrics can help you benchmark benefits, encourage adoption across the organization, and justify continued investment. PaaS performance metrics can be divided into three groups that correlate with maturity categories: foundation, optimize, and transform. Foundational PaaS performance metrics focus on time to market. Key metrics include: Time and effort to create new application environment Time to redeploy application Time to promote application into a new lifecycle phase Optimization PaaS performance metrics focus on portfolio efficiency. Key metrics include Ability to dynamically right-size infrastructure and elastic scalability Ability to re-use existing platform services and business services from resource pool instead of re-building solution stack Transformational PaaS performance metrics focus on productivity. Key metrics include: Time and effort required integrating business process, event processor – creating a complex app. Time and effort required to apply policy across tenant(s) Cost to operate application per user or transaction measured against the value provided by the application or transaction. Create a PaaS performance metric dashboard to measure progress towards your goals, and highlight how your team is meeting expectations!

In this post I'll document the simple configuration needed by ActiveMQ to configure the JDBC persistence adapter and setting up MySQL as the persistent storage. With this type of configuration you can also configure a Master/Slave broker setup by having more than one broker connect to the same database instance. List of Binaries used for this Example mysql-5.5.20-osx10.6-x86_64 mysql-connector-java-5.1.18-bin.jar apache-activemq-5.5.1-fuse-01-13 Configuring MySQL Download and install MySQL. Note for OS X users: The dmg provides a simple install that contains a Startup Item package which will configure MySQL to start automatically after each reboot as well as a Preference Pane plugin which will get added to the Settings panel to allow you to start/stop and configure autostart of MySQL. Once you have you have MySQL installed and properly configured, you will need to start the MySQL Monitor to create a user and database. macbookpro-251a:bin jsherman$ ./mysql -u root Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 29 Server version: 5.5.20 MySQL Community Server (GPL) Copyright (c) 2000, 2011, Oracle and/or its affiliates. All rights reserved. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. mysql> Then create the database for ActiveMQ mysql> CREATE DATABASE activemq; Then create a user and grant them privileges for the database mysql> CREATE USER 'activemq'@'%'localhost' IDENTIFIED BY 'activemq'; mysql> GRANT ALL ON activemq.* TO 'activemq'@'localhost'; mysql> exit Now log back into the MySQL Monitor and access the activemq database with the activemq user to make sure everything is okay macbookpro-251a:bin jsherman$ ./mysql -u activemq -p activemq Enter password: Reading table information for completion of table and column names You can turn off this feature to get a quicker startup with -A Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 28 Server version: 5.5.20 MySQL Community Server (GPL) Copyright (c) 2000, 2011, Oracle and/or its affiliates. All rights reserved. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. mysql>exit ActiveMQ Broker Configuration Download the latest FuseSource distribution of ActiveMQ. In the broker's configuration file, activemq.xml, add the following persistence adapter to configure a JDBC connection to MySQL. Then, just after the ending broker element () add the following bean Copy the MySQL diver to the ActiveMQ lib directory, mysql-connector-java-5.1.18-bin.jar was used in this example. Now start your broker, you should see the following output if running from the console INFO | Using Persistence Adapter: JDBCPersistenceAdapter(org.apache.commons.dbcp.BasicDataSource@303bc1a1) INFO | Database adapter driver override recognized for : [mysql-ab_jdbc_driver] - adapter: class org.apache.activemq.store.jdbc.adapter.MySqlJDBCAdapter INFO | Database lock driver override not found for : [mysql-ab_jdbc_driver]. Will use default implementation. INFO | Attempting to acquire the exclusive lock to become the Master broker INFO | Becoming the master on dataSource: org.apache.commons.dbcp.BasicDataSource@303bc1a1 INFO | ActiveMQ 5.5.1-fuse-01-13 JMS Message Broker (jdbcBroker1) is starting Now you can check your database in MySQL and see that ActiveMQ has created the required tables. mysql> USE activemq; SHOW TABLES; +--------------------+ | Tables_in_activemq | +--------------------+ | ACTIVEMQ_ACKS | | ACTIVEMQ_LOCK | | activemq_msgs | +--------------------+ 3 rows in set (0.00 sec) mysql> If you configure multiple brokers to use this same database instance in the jdbcPersistenceAdapter element then these brokers will attempt to acquire a lock, if they are unable to get a database lock the will wait until the lock becomes available. This can be seen by starting a second broker using the above JDBC persistence configuration. INFO | PListStore:activemq-data/jdbcBroker/tmp_storage started INFO | Using Persistence Adapter: JDBCPersistenceAdapter(org.apache.commons.dbcp.BasicDataSource@78979f67) INFO | Database adapter driver override recognized for : [mysql-ab_jdbc_driver] - adapter: class org.apache.activemq.store.jdbc.adapter.MySqlJDBCAdapter As you can see the second broker did not fully initialize as it is waiting to acquire the database lock. If the master broker is killed, then you see the slave will acquire the database lock and becomes the new master. INFO | Database lock driver override not found for : [mysql-ab_jdbc_driver]. Will use default implementation. INFO | Attempting to acquire the exclusive lock to become the Master broker INFO | Becoming the master on dataSource: org.apache.commons.dbcp.BasicDataSource@2e19fc25 INFO | ActiveMQ 5.5.1-fuse-01-13 JMS Message Broker (jdbcBroker2) is starting INFO | For help or more information please see: http://activemq.apache.org/ INFO | Listening for connections at: tcp://macbookpro-251a.home:61617 INFO | Connector openwire Started INFO | ActiveMQ JMS Message Broker (jdbcBroker2, ID:macbookpro-251a.home-53193-1328656157052-0:1) started Summary As you can see, it is fairly simple and straight forward to configure a robust highly-available messaging system using ActiveMQ with database persistence.

In this post I am going to discuss about how to get distinct object using property of it from collection. Here I am going to show three different way to achieve it easily. In this post I am going to discuss about extension method that can do task more than the current Distinct method available in .Net framework. Distinct method of Linq works as following right now. public class Product { public string Name { get; set; } public int Code { get; set; } } Consider that we have product Class which is having Code and Name as property in it. Now Requirement is I have to find out the all product with distinct Code values. Product[] products = { new Product { Name = "apple", Code = 9 }, new Product { Name = "orange", Code = 4 }, new Product { Name = "apple", Code = 10 }, new Product { Name = "lemon", Code = 9 } }; var lstDistProduct = products.Distinct(); foreach (Product p in list1) { Console.WriteLine(p.Code + " : " + p.Name); } Output It returns all the product event though two product have same Code value. So this doesn't meet requirement of getting object with distinct Code value. Way 1 : Make use of MoreLinq Library First way to achieve the requirement is make use of MoreLinq Library, which support function called DistinctBy in which you can specify the property on which you want to find Distinct objects. Below code is shows the use of the function. var list1 = products.DistinctBy(x=> x.Code); foreach (Product p in list1) { Console.WriteLine(p.Code + " : " + p.Name); } Output As you can see in output there is only two object get return which actually I want. i.e. distinct value by Code or product. If you want to pass more than on property than you can just do like this var list1 = products.DistinctBy(a => new { a.Name, a.Code }); You can read about the MoreLinq and Download this DLL from here : http://code.google.com/p/morelinq/ one more thing about this library also contains number of other function that you can check. Way 2: Implement Comparable Second way to achieve the same functionality is make use of overload Distinct function which support to have comparator as argument. here is MSDN documentation on this : Enumerable.Distinct Method (IEnumerable, IEqualityComparer) So for that I implemented IEqualityComparer and created new ProductComparare which you can see in below code. class ProductComparare : IEqualityComparer { private Func _funcDistinct; public ProductComparare(Func funcDistinct) { this._funcDistinct = funcDistinct; } public bool Equals(Product x, Product y) { return _funcDistinct(x).Equals(_funcDistinct(y)); } public int GetHashCode(Product obj) { return this._funcDistinct(obj).GetHashCode(); } } So In ProductComparare constructor I am passing function as argument, so when I create any object of it I have to pass my project function as argument. In Equal method I am comparing object which are returned by my projection function. Now following is the way how I used this Comparare implementation to satisfy my requirement. var list2 = products.Distinct(new ProductComparare( a => a.Code )); foreach (Product p in list2) { Console.WriteLine(p.Code + " : " + p.Name); Output So this approach also satisfy my requirement easily. I not looked in code of MoreLinq library but I think its also doing like this only. If you want to pass more than on property than you can just do like this var list1 = products.Distinct(a => new { a.Name, a.Code });. Way 3: Easy GroupBy wa The third and most eaisest way to avoide this I did in above like using MoreLine and Comparare implementation is just make use of GroupBy like as below List list = products .GroupBy(a => a.Code ) .Select(g => g.First()) .ToList(); foreach (Product p in list) { Console.WriteLine(p.Code + " : " + p.Name); } In above code I am doing grouping object on basis of property and than in Select function just selecting fist one of the each group will doing work for me. Output So this approach also satisfy my requirement easily and output is similar to above two approach. If you want to pass more than on property than you can just do like this .GroupBy(a => new { a.Name, a.Code }). So this one is very easy trick to achieve the functionality that I want without using any thing extra in my code. Conclusion So Above is the way you can achieve Distinct of collection easily by property of object. Leave comment if you have any query or if you like it.

One of the most popular posts on my blog is the short tutorial about the JDBC Security Realm and form based Authentication on GlassFish with Primefaces. After I received some comments about it that it isn't any longer working with latest GlassFish 3.1.2.2 I thought it might be time to revisit it and present an updated version. Here we go: Preparation As in the original tutorial I am going to rely on some stuff. Make sure to have a recent NetBeans 7.3 beta2 (which includes GlassFish 3.1.2.2) and the MySQL Community Server (5.5.x) installed. You should have verified that everything is up an running and that you can start GlassFish and the MySQL Server also is started. Some Basics A GlassFish authentication realm, also called a security policy domain or security domain, is a scope over which the GlassFish Server defines and enforces a common security policy. GlassFish Server is preconfigured with the file, certificate, and administration realms. In addition, you can set up LDAP, JDBC, digest, Oracle Solaris, or custom realms. An application can specify which realm to use in its deployment descriptor. If you want to store the user credentials for your application in a database your first choice is the JDBC realm. Prepare the Database Fire up NetBeans and switch to the Services tab. Right click the "Databases" node and select "Register MySQL Server". Fill in the details of your installation and click "ok". Right click the new MySQL node and select "connect". Now you see all the already available databases. Right click again and select "Create Database". Enter "jdbcrealm" as the new database name. Remark: We're not going to do all that with a separate database user. This is something that is highly recommended but I am using the root user in this examle. If you have a user you can also grant full access to it here. Click "ok". You get automatically connected to the newly created database. Expand the bold node and right click on "Tables". Select "Execute Command" or enter the table details via the wizard. CREATE TABLE USERS ( `USERID` VARCHAR(255) NOT NULL, `PASSWORD` VARCHAR(255) NOT NULL, PRIMARY KEY (`USERID`) ); CREATE TABLE USERS_GROUPS ( `GROUPID` VARCHAR(20) NOT NULL, `USERID` VARCHAR(255) NOT NULL, PRIMARY KEY (`GROUPID`) ); That is all for now with the database. Move on to the next paragraph. Let GlassFish know about MySQL First thing to do is to get the latest and greatest MySQL Connector/J from the MySQL website which is 5.1.22 at the time of writing this. Extract the mysql-connector-java-5.1.22-bin.jar file and drop it into your domain folder (e.g. glassfish\domains\domain1\lib). Done. Now it is finally time to create a project. Basic Project Setup Start a new maven based web application project. Choose "New Project" > "Maven" > Web Application and hit next. Now enter a name (e.g. secureapp) and all the needed maven cordinates and hit next. Choose your configured GlassFish 3+ Server. Select Java EE 6 Web as your EE version and hit "Finish". Now we need to add some more configuration to our GlassFish domain.Right click on the newly created project and select "New > Other > GlassFish > JDBC Connection Pool". Enter a name for the new connection pool (e.g. SecurityConnectionPool) and underneath the checkbox "Extract from Existing Connection:" select your registered MySQL connection. Click next. review the connection pool properties and click finish. The newly created Server Resources folder now shows your sun-resources.xml file. Follow the steps and create a "New > Other > GlassFish > JDBC Resource" pointing the the created SecurityConnectionPool (e.g. jdbc/securityDatasource).You will find the configured things under "Other Sources / setup" in a file called glassfish-resources.xml. It gets deployed to your server together with your application. So you don't have to care about configuring everything with the GlassFish admin console.Additionally we still need Primefaces. Right click on your project, select "Properties" change to "Frameworks" category and add "JavaServer Faces". Switch to the Components tab and select "PrimeFaces". Finish by clicking "OK". You can validate if that worked by opening the pom.xml and checking for the Primefaces dependency. 3.4 should be there. Feel free to change the version to latest 3.4.2. Final GlassFish Configuration Now it is time to fire up GlassFish and do the realm configuration. In NetBeans switch to the "Services" tab again and right click on the "GlassFish 3+" node. Select "Start" and watch the Output window for a successful start. Right click again and select "View Domain Admin Console", which should open your default browser pointing you to http://localhost:4848/. Select "Configurations > server-config > Security > Realms" and click "New..." on top of the table. Enter a name (e.g. JDBCRealm) and select the com.sun.enterprise.security.auth.realm.jdbc.JDBCRealm from the drop down. Fill in the following values into the textfields: JAAS jdbcRealm JNDI jdbc/securityDatasource User Table users User Name Column username Password Column password Group Table groups Group Name Column groupname Leave all the other defaults/blanks and select "OK" in the upper right corner. You are presented with a fancy JavaScript warning window which tells you to _not_ leave the Digest Algorithm Field empty. I field a bug about it. It defaults to SHA-256. Which is different to GlassFish versions prior to 3.1 which used MD5 here. The older version of this tutorial didn't use a digest algorithm at all ("none"). This was meant to make things easier but isn't considered good practice at all. So, let's stick to SHA-256 even for development, please. Secure your application Done with configuring your environment. Now we have to actually secure the application. First part is to think about the resources to protect. Jump to your Web Pages folder and create two more folders. One named "admin" and another called "users". The idea behind this is, to have two separate folders which could be accessed by users belonging to the appropriate groups. Now we have to create some pages. Open the Web Pages/index.xhtml and replace everything between the h:body tags with the following: Select where you want to go: Now add a new index.xhtml to both users and admin folders. Make them do something like this: Hello Admin|User On to the login.xhtml. Create it with the following content in the root of your Web Pages folder. Username: Password: As you can see, whe have the basic Primefaces p:panel component which has a simple html form which points to the predefined action j_security_check. This is, where all the magic is happening. You also have to include two input fields for username and password with the predefined names j_username and j_password. Now we are going to create the loginerror.xhtml which is displayed, if the user did not enter the right credentials. (use the same DOCTYPE and header as seen in the above example). Sorry, you made an Error. Please try again: Login The only magic here is the href link of the Login anchor. We need to get the correct request context and this could be done by accessing the faces context. If a user without the appropriate rights tries to access a folder he is presented a 403 access denied error page. If you like to customize it, you need to add it and add the following lines to your web.xml: 403 /faces/403.xhtml That snippet defines, that all requests that are not authorized should go to the 403 page. If you have the web.xml open already, let's start securing your application. We need to add a security constraint for any protected resource. Security Constraints are least understood by web developers, even though they are critical for the security of Java EE Web applications. Specifying a combination of URL patterns, HTTP methods, roles and transport constraints can be daunting to a programmer or administrator. It is important to realize that any combination that was intended to be secure but was not specified via security constraints, will mean that the web container will allow those requests. Security Constraints consist of Web Resource Collections (URL patterns, HTTP methods), Authorization Constraint (role names) and User Data Constraints (whether the web request needs to be received over a protected transport such as TLS). Admin Pages Protected Admin Area /faces/admin/* GET POST HEAD PUT OPTIONS TRACE DELETE admin NONE All Access None Protected User Area /faces/users/* GET POST HEAD PUT OPTIONS TRACE DELETE NONE If the constraints are in place you have to define, how the container should challenge the user. A web container can authenticate a web client/user using either HTTP BASIC, HTTP DIGEST, HTTPS CLIENT or FORM based authentication schemes. In this case we are using FORM based authentication and define the JDBCRealm FORM JDBCRealm /faces/login.xhtml /faces/loginerror.xhtml The realm name has to be the name that you assigned the security realm before. Close the web.xml and open the sun-web.xml to do a mapping from the application role-names to the actual groups that are in the database. This abstraction feels weird, but it has some reasons. It was introduced to have the option of mapping application roles to different group names in enterprises. I have never seen this used extensively but the feature is there and you have to configure it. Other appservers do make the assumption that if no mapping is present, role names and group names do match. GlassFish doesn't think so. Therefore you have to put the following into the glassfish-web.xml. You can create it via a right click on your project's WEB-INF folder, selecting "New > Other > GlassFish > GlassFish Descriptor" admin admin hat was it _basically_ ... everything you need is in place. The only thing that is missing are the users in the database. It is still empty ...We need to add a test user: Adding a Test-User to the Database And again we start by right clicking on the jdbcrealm database on the "Services" tab in NetBeans. Select "Execute Command" and insert the following: INSERT INTO USERS VALUES ("admin", "8c6976e5b5410415bde908bd4dee15dfb167a9c873fc4bb8a81f6f2ab448a918"); INSERT INTO USERS_GROUPS VALUES ("admin", "admin"); You can login with user: admin and password: admin and access the secured area. Sample code to generate the hash could look like this: try { MessageDigest md = MessageDigest.getInstance("SHA-256"); String text = "admin"; md.update(text.getBytes("UTF-8")); // Change this to "UTF-16" if needed byte[] digest = md.digest(); BigInteger bigInt = new BigInteger(1, digest); String output = bigInt.toString(16); System.out.println(output); } catch (NoSuchAlgorithmException | UnsupportedEncodingException ex) { Logger.getLogger(PasswordTest.class.getName()).log(Level.SEVERE, null, ex); } Have fun securing your apps and keep the questions coming! In case you need it, the complete source code is on https://github.com/myfear/JDBCRealmExample

This is a followup to my previous post on configuring multiple instances of the ActiveMQ web console into a single instance of Jetty. In this post I'll show how the pair of brokers were configured to enable them to be highly available in the event that one of the brokers should fail. The master/slave configuration used in this example is referred to as the Pure Master Slave. There several different types of master/slave configurations including Shared File System Master Slave and JDBC Master Slave, the latter of which I will look at in a later post. Pure Master/Slave Configuration There is not much to configuring a master/slave pair with ActiveMQ, in fact the configuration needed is a single attribute added to the slave broker. In the slave broker's configuration file (activemq.xml) you will need to add the masterConnectorURI attribute to the broker element as follows: Essentially, all you need is the one attribute above. However, if you need to provide credentials for the connection, then you can use the following alternative configuration to connect to the master: The URI specified should be the connection URI for the master broker. This allows the slave to make a connection to master as shown below: INFO | ActiveMQ 5.5.1-fuse-01-13 JMS Message Broker (amq1S) is starting INFO | For help or more information please see: http://activemq.apache.org/ INFO | Connector vm://amq1S Started INFO | Starting a slave connection between vm://amq1S#0 and tcp://localhost:61616 INFO | Slave connection between vm://amq1S#0 and tcp://localhost/127.0.0.1:61616 has been established. INFO | ActiveMQ JMS Message Broker (amq1S, ID:macbookpro-251a.home-53545-1328657220277-1:1) started From the output you can see the slave is aware of the master broker and has made a connection. This connection allows the slave broker to stay in sync with the master by replicating the master broker's data store. Once the slave detects the master has failed it will complete it's start up process by starting all it's connectors: ERROR | Network connection between vm://amq1S#0 and tcp://localhost/127.0.0.1:61616 shutdown: null java.io.EOFException at java.io.DataInputStream.readInt(DataInputStream.java:375) at org.apache.activemq.openwire.OpenWireFormat.unmarshal(OpenWireFormat.java:275) at org.apache.activemq.transport.tcp.TcpTransport.readCommand(TcpTransport.java:228) at org.apache.activemq.transport.tcp.TcpTransport.doRun(TcpTransport.java:220) at org.apache.activemq.transport.tcp.TcpTransport.run(TcpTransport.java:203) at java.lang.Thread.run(Thread.java:680) WARN | Master Failed - starting all connectors INFO | Listening for connections at: tcp://macbookpro-251a.home:62616 INFO | Connector openwire Started At this point, the slave is knowledgable about all events processed by the master and additional processing can continue without interruption. The documentation on configuring a pure master slave ActiveMQ instance also contains some optional parameters that you may find useful if you plan to implement this type of master/slave configuration that will ensure the master and slave broker's data stores stay in sync. Hit the link to see these additional parameters, and how they are used. Download Want to give this a try? Download the latest FuseSource distribution of ActiveMQ. Summary Configuring a Master/Slave ActiveMQ instance is easy. With one simple attribute you can create a highly available ActiveMQ messaging platform.

Quite sometime back I had written about Using JAXB to generate XML from the Java, XSD. And now I am writing how to do the reverse of it i.e generating Java objects from the XML document. There was one comment mentioning that JAXB reference implementation and hence in this article I am making use of the reference implementation that is shipped with the JDK. Firstly the XML which I am using to generate the java objects are: Sanaulla Seagate External HDD August 24, 2010 6776.5 Benq HD Monitor August 24, 2012 15000 And the XSD to which it conforms to is: The XSD has already been explained and one can find out more by reading this article. I create a class: XmlToJavaObjects which will drive the unmarshalling operation and before I generate the JAXB Classes from the XSD, the directory structure is: I go ahead and use the xjc.exe to generate JAXB classes for the given XSD: $> xjc expense.xsd and I now refresh the directory structure to see these generated classes as shown below: With the JAXB Classes generated and the XML data available, we can go ahead with the Unmarshalling process. Unmarshalling the XML: To unmarshall: We need to create JAXContext instance. Use JAXBContext instance to create the Unmarshaller. Use the Unmarshaller to unmarshal the XML document to get an instance of JAXBElement. Get the instance of the required JAXB Root Class from the JAXBElement. Once we get the instance of the required JAXB Root class, we can use it to get the complete XML data in Java objects. The code to unmarshal the XML data is given below: package problem; import generated.ExpenseT; import generated.ItemListT; import generated.ItemT; import generated.ObjectFactory; import generated.UserT; import javax.xml.bind.JAXBContext; import javax.xml.bind.JAXBElement; import javax.xml.bind.JAXBException; import javax.xml.bind.Unmarshaller; public class XmlToJavaObjects { /** * @param args * @throws JAXBException */ public static void main(String[] args) throws JAXBException { //1. We need to create JAXContext instance JAXBContext jaxbContext = JAXBContext.newInstance(ObjectFactory.class); //2. Use JAXBContext instance to create the Unmarshaller. Unmarshaller unmarshaller = jaxbContext.createUnmarshaller(); //3. Use the Unmarshaller to unmarshal the XML document to get an instance of JAXBElement. JAXBElement unmarshalledObject = (JAXBElement)unmarshaller.unmarshal( ClassLoader.getSystemResourceAsStream("problem/expense.xml")); //4. Get the instance of the required JAXB Root Class from the JAXBElement. ExpenseT expenseObj = unmarshalledObject.getValue(); UserT user = expenseObj.getUser(); ItemListT items = expenseObj.getItems(); //Obtaining all the required data from the JAXB Root class instance. System.out.println("Printing the Expense for: "+user.getUserName()); for ( ItemT item : items.getItem()){ System.out.println("Name: "+item.getItemName()); System.out.println("Value: "+item.getAmount()); System.out.println("Date of Purchase: "+item.getPurchasedOn()); } } } And the output would be: Do drop in your queries/feedback as comments and I will try to address them at the earliest.