Groovy. Batch. Prepared statement. Nice!

Scripting with Groovy is exciting thing. You get a feeling that the language was inspired by an Oracle who read your mind and then made it 10 times better. So imagine how I felt after finding out that Groovy can NOT batch prepared statements.

Batching sql updates, inserts or deletes is one of the top features that database scripts would need. Without prepared statements I had to resort to generating SQL with GString:

sql.withBatch { stmt ->
          mymap.each { k,v ->
              stmt.addBatch("""UPDATE some_table 
                                  SET some_column = '${v}' 
                                WHERE id = ${k} """)
          }
}

Besides SQL injection this presents the problem of escaping strings in SQL: big pain in some cases. By arguing that injection is not an issue for internal script (it's not in a wild on the web after all) you would leave yourself with the loop hole anyway. Don't forget about performance. The bottom line: I need support for prepared statements!

I would have to stop here joining ranks of complaints like this if not for Groovy 1.8.1. This latest stable version (as of today) addresses bunch of bugs and just couple of features. And one of two is batch support for prepared statements. Below is secure and reliable (as well as more readable) version with batch support for prepared statement in 1.8.1:

sql.withBatch(20, """UPDATE some_table 
                        SET some_column = ? 
                      WHERE id = ? """) { ps ->   
              
          mymap.each { k,v ->
              ps.addBatch(v, k)
          }
}

You can find more options on how use batching with prepared statements in Groovy 1.8.1 docs.

Java Anti-Pattern: Constructors and Template Method Pattern

My love for template method pattern hit the wall. The wall of Java constructors that is. Do not mix them together ever or at least without double checking.

Again, I am all for template method pattern when it operates on fully initialized objects. But, by definition, Java constructors do not operate on such objects: they are exactly in the business of initializing them.

Imagine that your template method calls on an object that is still under construction - not something proponents of template method had in mind. But this is exactly what happens with Java constructors. Let's go straight to the example.

Suppose we have an abstract class Vehicle:

public abstract class Vehicle {
    
    private boolean registered = false;

    public Vehicle() {
        registered = registerWithDMV(getMileage());
    }
    
    public abstract int getMileage();
    
    private boolean registerWithDMV(int mileage) {
        return (registered = (mileage > 0) ? true : false);
    }
    
    public boolean isRegistered() {
        return registered;
    }
}

Every vehicle registers with DMV when created. Its constructor (using template method pattern) calls concrete method registerWithDMV and abstract method getMileage. So concrete sub-classes of the Vehicle must provide mileage:

public class Car extends Vehicle { 
    
    private int mileage = 6;

    @Override
    public int getMileage() {
        return mileage;
    }
}

Our implementation for DMV registration is part of Vehicle for demonstration only. It is simple: give me non-zero mileage and you are good to go (drive that is). But alas, look at this test that promptly fails:

public class VehicleTests {
    
    @Test
    public void testNewCar() {
        Vehicle car = new Car();
        assertTrue(car.isRegistered());
    }
}

Unfortunately our template method (Vehicle default constructor) runs when Car object is not yet fully initialized: Java constructors run in order from higher in hierarchy (abstract) to lower (concrete) classes. That is why property mileage is still 0 and not 6 when constructor is run.

My first recommendation is not to use template method in constructors in Java at all. This is rather drastic but doable. Replace it with some init method that is called by concrete classes upon creation. If you don't like radical approaches then use lazy initialization and/or static variables in concrete classes:

public class Car extends Vehicle {
    
    private static int INITIAL_MILEAGE = 6;
    private int mileage;

    @Override
    public int getMileage() {
        if (mileage == 0) {
            mileage = INITIAL_MILEAGE;
        }
        return mileage;
    }
}

Test succeeds now. But lazy initialization may need more work: assuming mileage 0 is valid value you would end up introducing yet another property (flag) to indicate if mileage is initialized or not.

Again, my preference is avoiding this conflict all together by placing object initialization in the constructor of concrete class. Just think of Java constructors as non-polymorphic hierarchical artifacts.

Quick Start with Mercurial and Bitbucket Hosting

I started my project rather unprepared - no version control, no hosting. But eventually I did my homework. I am going to use Mercurial DVCS (distributed version control system) and Bitbucket to host Mercurial (Hg) repository. I am quite confident that if you choose Google code or another hosting for Hg repo you can adopt the steps below with minor changes.

Let's call our project demo. It sits in a folder demo on my local drive. All I want is to enable Mercurial and start syncing it with Bitbucket repository. To accomplish just that we need just few steps.

1. Install Mercurial. Make sure Mercurial command hg is in your classpath.
2. Go to your project folder demo and run from command line:
   hg init
   You initiated brand new Mercurial repository that will contain files from demo project in a couple of steps.
3. Create .hgignore file in demo folder to prevent Mercurial from adding files that don't belong to source control: build (or target with maven) folder, compiled classes, etc. For example see here.
4. From command line in demo folder run:
   hg add
   You added all your files to Mercurial repository with exception of those identified by .hgignore.
5. From command line in demo folder run:
   hg commit
   This is actual commit - without it those files that you added in the step before are just placeholders.

We are done with initializing new Mercurial repository. If you don't want any hosting then you are done - your project is a Mercurial repository now. I recommend to read this to get started with Mercurial.

6. Register on Bitbucket (Suppose you registered using name myusername).
7. Create new repository and call it demo. This is your hosted Mercurial repository that you can access with url https://bitbucket.org/myusername/demo. Check with Bitbucket for exact URL.
8. Update your remote repository (which is empty) with your existing demo files, from command line in demo folder run:
   hg push https://bitbucket.org/myusername/demo
9. Save a push URL so that you don't need to enter it each time when you use hg push or hg outgoing commands. Locate hgrc file in demo/.hg folder (create if it doesn't exist) and add:
   

   [paths]
   default-push = https://myusername@bitbucket.org/myusername/demo
   

You've done it! Your lonely project just became both hosted (source code) and backed by enterprise-strength DVCS.

Enhancing JUnit Suites with Categories to eliminate test-suite dependency

With JUnit 3 the extra step of creating and maintaining test suites on top of unit tests never felt right. With JUnit 4 test suites became just simple annotation boilerplate:

@RunWith(Suite.class)
@Suite.SuiteClasses({
  SomeTests.class,
  SomeOtherTests.class,
  SomethingElseTests.class
})
public class SomeTestSuite {
  // this class is just a place holder for test suite annotations above
}

Next logical step would be aggregation of tests based on the information contained in tests themselves. Instead of specifying concrete tests, test suites would contain qualifiers (using annotations) to match. Those tests having matching qualifiers are included in a suite, those without are not. For example I have a qualifier JMSTest that is obviously assigned to tests that use a JMS provider.

Thus, my test suite classes will become completely decoupled from the tests and vice verse. This is actually even better than it sounds: even though there is no language dependency from tests to test suites in JUnit, functional dependency does exist: tests will not run if they are not bound to one or more test suites.

With introduction of JUnit Categories we received a qualifier support that is almost what we need:

public interface JMSTest {}


@RunWith(Categories.class)
@Categories.IncludeCategory(JMSTest.class)
@Suite.SuiteClasses({
  SomeTestSuite.class,
})
public class JMSTestSuite {}


@Category(IntegrationTest.class)
public class SomeTests {
   ....
}


@Category(JMSTest.class) 
Public class SomeOtherTests{
   ....
}


@Category(DatabaseTest.class) 
Public class SomethingElseTests{
   ....
}

This setup will run SomeOtherTests marked with JMSTest category when running JMSTestSuite. But categories didn’t eliminate the dependency from suites to tests – we still depend on test suite SomeTestSuite that explicitly references our tests.

Now, imagine you can define suite AllProjectTests that always contains all tests from the project. Then you can define category-based test suites like JMSTestSuite above and never care about maintaining your test suites again. Fortunately, we can use open source project to do just that - ClasspathSuite:

import org.junit.extensions.cpsuite.ClasspathSuite;
import org.junit.runner.RunWith;
@RunWith(ClasspathSuite.class)
public class AllProjectTests {}


@RunWith(Categories.class)
@Categories.IncludeCategory(JMSTest.class)
@Suite.SuiteClasses({
  AllProjectTests.class,
})
public class JMSTestSuite {}

To summarize, if you want to define a test suite that runs all database-based tests:
1. Define AllProjectTests suite using ClasspathSuite.
2. Define JUnit category DatabaseTest.
3. Define corresponding suite DatabaseTestSuite to run all tests marked with category DatabaseTest.
4. More complex category-based suites are easy to construct with JUnit category support.

Extracting text from PDF files with Apache Tika 0.9 (and PDFBox under the hood)

Extracting and processing text from multiple sources (file formats) is the job Apache Tika does quite well. It abstracts you away from format internals and Tika's coverage (pdf, MS Office, graphics, audio, video, etc.) is superb. Tika doesn't implement actual parsers - instead it offers uniform API to access other parsers for supported document types (all you need is to implement SAX parser ContentHandler - see here). Indeed Tika utilizes PDFBox internally for pdf files. Nothing prevents you from concentrating on parsing extracted text and converting it to information. Except when little details begin to matter.

Sometimes extracting and processing text depends on order of lines in the document: for example headers carry over additional information for line items below them. By default, Tika parser (actually PDFBox parser that does it in case of pdf) will not keep the order when stripping the text out (see org.apache.pdfbox.util.PDFTextStripper and its property sortByPosition). Thus, some headers may occur after its line items are fed to a handler. Apache PDFBox explains that performance is better when order is not preserved. Tika as of version 0.9 doesn't let you control this behavior in PDFBox (they plan addressing it in 1.0 release - see TIKA-100 and TIKA-612).

Simple patch allows us to take advantage of sortByPosition property in PDFBox PDFTextStripper when using it with Tika: lines 6-10 below replaced setSortByPosition(true or false); and remember that PDF2XHTML (from Tika) extends PDFTextStripper (from PDFBox):

private PDF2XHTML(ContentHandler handler, Metadata metadata)
            throws IOException {
        this.handler = new XHTMLContentHandler(handler, metadata);
        setForceParsing(true);
        
        // CUSTOM CODE:
        String sortEnabled = metadata.get("org.apache.tika.parser.pdf.sortbyposition");
        if (sortEnabled != null) {
            setSortByPosition(sortEnabled.equalsIgnoreCase("true"));
        }
}

Now, you can control order in pdf parser like follows (there are no PDFBox classes in this code):

        
        Parser parser = new PDFParser();
        ContentHandler handler = new BodyContentHandler(myHandler);
        Metadata metadata = new Metadata();
        metadata.set("org.apache.tika.parser.pdf.sortbyposition", "false");
        ParseContext context = new ParseContext();

        try {
            parser.parse(instream, handler, metadata, context);
        } finally {
            instream.close();
        }

Of course, if you deal only with single file type (e.g. pdf) then it's easier to use dedicated library such as Apache PDFBox. Then my recommendation would be downloading Tika source code for real examples of PDFBox in action.

UPDATE
The issue is still present in Tika 1.0 with hard-coded setSortByPosition(false); in org.apache.tika.parser.pdf.PDF2XHTML.

The upcoming 1.1 release will add PDFParser.setSortByPosition method, so patch would be replaced with this:

      
        Parser parser = new PDFParser();
        parser.setSortByPosition(true); // or false
        ContentHandler handler = new BodyContentHandler(myHandler);
        Metadata metadata = new Metadata();
        ParseContext context = new ParseContext();

        try {
            parser.parse(instream, handler, metadata, context);
        } finally {
            instream.close();
        }

Persistence with JPA and Hibernate using Guice 3.0 and guice-persist

Why do I like Google frameworks? Because they are academic-like: elegant, concise, focused, open to extension so they evolve gradually and naturally. Why I don't like Google frameworks? Because they are academic-like: barely documented, lack in visual design and appearance.

Google Guice is no exception. It's been around probably as long as Spring. Anyone looking for DI framework must give it a shot. But even today you hear Spring not Guice when people talk DI.

Using JPA with Spring before I firmly decided to stick with Guice this time. I have simple back-end program: no web, no application server (PostgreSQL database and Hibernate as a JPA 1.0 provider).

I decided to take advantage of the latest JPA support in Guice: guice-persist. Guice 3.0 is required (upgrade from 2.0 if necessary) but Guice jar doesn't contain guice-persist: have both dependencies in your pom.xml when using Maven (updated):

<dependency>
  <groupid>com.google.inject</groupId>
  <artifactid>guice</artifactId>
  <version>3.0</version>
</dependency>
<dependency>
  <groupid>com.google.inject.extensions</groupId>
  <artifactid>guice-persist</artifactId>
  <version>3.0</version>
</dependency>

Without Maven simply follow your regular practices to add jars above to Java classpath.

Existing Guice configuration using module(s) need not change, but add guice-persist module when creating injector. Before:

Injector injector = Guice.createInjector(new MyAppModule());

After:

Injector injector = Guice.createInjector(new MyAppModule(), 
                          new JpaPersistModule("myapp-db"));

myapp-db is a persistence unit defined in persistence.xml placed on classpath (e.g. in Maven project it's in src/main/resources/META-INF directory):

    
        org.hibernate.ejb.HibernatePersistence
        com.example.domain.MyEntity
        true
        
            
            
            
            
            
            
            
        
    

This is JPA 1.0 persistence unit - you should be able to use 2.0 without any problems. I disabled scanning of classpath for entities using exclude-unlisted-classes: all JPA entities should be listed with class element now. The properties are specific to Hibernate and PostgreSQL.

guice-persist works via PersistService that has to be started (initialized). I do it immediately after initializing injector and using injector:

Injector injector = Guice.createInjector(new MyAppModule(), 
                          new JpaPersistModule("myapp-db"));
injector.getInstance(ApplicationInitializer.class);

and

public class ApplicationInitializer {
 @Inject ApplicationInitializer(PersistService service) {
  service.start(); 
  // At this point JPA is started and ready.

  // other application initializations if necessary
 }
}

Persistence unit defined transaction-type="RESOURCE_LOCAL" to have JPA EntityManager created and destroyed for each database transaction. Define both EntityManager and transactions in DAO class with the following Guice annotations:

public class MyAppDAO {
    @Inject
    private EntityManager em;

    @Transactional
    public MyEntity find(long id) {
        return em.find(MyEntity.class, id);
    }

    @Transactional
    public void save(MyEntity entity) {
        em.persist(entity);
    }
}

We injected EntityManager with @com.google.inject.Inject and declared transactions with @com.google.inject.persist.Transactional annotations. Now, each time find or save method called new transaction is started and committed in JPA entity manager. For more details on transaction scope (unit of work) and exception handling see this.

Wrapping it up: there is bare minimum of artifacts/configuration needed on top of standard JPA persistence.xml: new Guice persistence module (provided by guice-persist), persistence service initialization, standard Guice injection of EntityManager, and new @Transactional annotation. Of course, your mileage may vary depending on your transactional (unit of work) needs but it's hard to imagine less configuration and code when implementing data access with JPA and Hibernate.

For completeness I add a Guice module here (it needs no special configuration for guice-persist though, so I have DAO configuration here only):

public class MyAppModule extends AbstractModule {
 
    @Override
    protected void configure() {
        ...
        bind(ISomeDao.class).to(MyDao.class);
    }
}

References:
Using JPA with Guice Persist
Hibernate with JPA Annotations and Guice

Efficient Keyword Search with Relation Index Entities and Objectify for Google Datastore

Free text search with keywords on Google App Engine datastore made simple - in fact simple enough to fit into single blog entry.

I will use GAE/Java with Objectify for datastore API (also see my newer post with Python implementation). Assume we maintain a document library where each document has several textual attributes: name, title, subtitle, authors, publisher, reference number (similar to ISBN), tags, abstract, etc. While each attribute is semantically different, for a searcher they all present some value (or relevance). Thus, user may search for any of them with one or more keywords. For simplification, I consider only AND searches.

First, let’s model our entities (remember, we use Objectify that in turn uses standard JPA annotations wherever possible):

@Entity(name = "Document")
public class Document {

  @Id
  private Long id;

  private String title;
  private List<String> authors = = new ArrayList<String>();
  private String publisher;
  private List<String> tags = new ArrayList<String>();
  // more attributes as necessary...

  public Document() {
    super();
  }

  // standard getters/setters follow...
}

One thing to emphasize is the use of list properties such as authors and tags. Datastore treats them as multi-valued attributes so that condition like authors == ‘John Doe’ would return all documents that have John doe as one of authors. This list property feature is critical in the next (and last) entity we define:

@Entity(name = "DocumentKeywords")
public class DocumentKeywords {

  @Id Long id;
  @Parent Key<Document> document;
  List<String> keywords = new ArrayList<String>();

  private DocumentKeywords() {
    super();
  }

  public DocumentKeywords (Key<Document> parent) {
    this(parent, Collections.<string>emptyList());
  }

  public DocumentKeywords (Key<Document> parent, Collection<String> keywords) {
    super();

    this. document = parent;
    this.keywords.addAll(keywords);
  }

  // add single keyword
  public boolean add(String keyword) {
    return keywords.add(keyword);
  }

  // add collection of keywords
  public boolean add(Collection<String> keywords) {
    return this.keywords.addAll(keywords);
  }
}

There are several things worth noting about DocumentKeywords.

First, it’s a child entity to Document (see @Parent annotation in Objectify). Parent Document and child DocumentKeywords make an entity group in datastore. This is important for data integrity – entity group rows can participate in transactions in datastore. Data integrity is critical in this case (you'll see shortly). Indeed, we'll duplicate attribute values between Document and DocumentKeywords. For each Document entity we create corresponding child DocumentKeywords to consolidate all document attributes into property keywords.

Secondly, keywords is a list property. List property is limited to 5000 entries which is often sufficient. And if it’s not we could add more DocumentKeywords child rows for the same Document parent (not implemented here).

Finally, what is DocumentKeywords entity defined for? Why is its keywords attribute not part of Document entity? The answer is in this Google IO presentation (Spoiler: Keywords being list property in Document would produce serialization overhead on Document entity (at least doubling it since it's exact copy of the rest of Document attributes). Moving keywords to separate entity is called Relation Index Entity and it gives us best of both worlds: fully indexed attributes (via list property) and no serialization overhead for documents.)

We add new Document and index document attributes in child DocumentKeywords in one transaction:

// we use DI to initialize factory (application scoped)
private final ObjectifyFactory factory;

private Objectify tran = null;

public Document addDocument(Document document) throws {

  try {
    Objectify ofy = beginTransaction();

    Key<Document> key = ofy.put(document);

    // crate Relation Index Entity
    DocumentKeywords rie = new DocumentKeywords(key);
    rie.add(document.getTitle());
    rie.add(document.getAuthors());
    rie.add(document.getPublisher());
    rie.add(document.getTags());
    ofy.put(rie);

    commit();

    Document savedDocument = beginQuery().find(key);
    return savedDocument;

  }finally {
    rollbackIfActive();
  }
}

I left out as an exercise transactional methods above. Just note important datastore gotcha: rows added inside transaction are not available within this transaction if it’s still active. If you add row and then try to query it before committing then you won’t find it. Commit transaction first and then read its data.

Now we are ready to make actual keyword searches:

public Collection<document> findByKeywords(Collection<String> keywords) {

  Objectify ofy = beginQuery();

  Query<DocumentKeywords> query = ofy.query(DocumentKeywords.class);
  for (String keyword : keywords) {
    query = query.filter("keywords", keyword);
  }

  Set<Key<Document>> keys = query.<Document>fetchParentKeys();

  Collection<Document> documents = ofy.get(keys).values();

  return documents;
}

You can see that keyword search is a 3-step process: during first step we iteratively build search condition (AND only), in second step we query DocumentKeywrods toretrieve keys only – no overhead of serialization bulky keywords here. And lastly we convert retrieved DocumentKeywords keys into parent keys (documents) and use datastore batch get to return them. Objectify made all steps quite transparent and efficient.

This is all to it. Let me make few comments about this example. It is purposely contrived but it should map to real cases with no principal changes. Documents could be friends in a social network, products from online retail catalog, or blog entries in blogging web site. I intentionally left document content out of the list of attributes. Current limit of datastore doesn’t allow me to build elegant and concise solution beyond 5000 thousand keywords per document so it makes inclusion of document content risky. Even though simple enhancement trounces this limitation I didn’t want to overload code above.

Extending to free text search would mean support for such features as word normalization and stemming, case-sensitivity, logical operations, keyword proximity (e.g. same attribute or related attributes), extending beyond datastore 5000 list property limit.

References:
1. Building Scalable, Complex Apps on App Engine
2. Datastore List Property
3. Stemming
4. Objectify
5. RIE with Python