Data access, specifically SQL access from within Java, has never been nice. This is in large part due to the fact that the JDBC api has a lot of ceremony. Java 7 vastly improved things with ARM blocks by taking away a lot of the ceremony around managing database objects such as Statements and ResultSets but fundamentally the code flow is still the same. Java 8 Lambdas gives us a very nice tool for improving the flow of JDBC. Out first attempt at improving things here is very simply to make it easy to work with ajava.sql.ResultSet. Here we simply wrap the ResultSet iteration and then delegate it to Lambda function. This is very similar in concept to Spring's JDBCTemplate. NOTE: I've released All the code snippets you see here under an Apache 2.0 license on Github. First we create a functional interface called ResultSetProcessor as follows: @FunctionalInterface public interface ResultSetProcessor { public void process(ResultSet resultSet, long currentRow) throws SQLException; } Very straightforward. This interface takes the ResultSet and the current row of theResultSet as a parameter. Next we write a simple utility to which executes a query and then calls ourResultSetProcessor each time we iterate over the ResultSet: public static void select(Connection connection, String sql, ResultSetProcessor processor, Object... params) { try (PreparedStatement ps = connection.prepareStatement(sql)) { int cnt = 0; for (Object param : params) { ps.setObject(++cnt, param)); } try (ResultSet rs = ps.executeQuery()) { long rowCnt = 0; while (rs.next()) { processor.process(rs, rowCnt++); } } catch (SQLException e) { throw new DataAccessException(e); } } catch (SQLException e) { throw new DataAccessException(e); } } Note I've wrapped the SQLException in my own unchecked DataAccessException. Now when we write a query it's as simple as calling the select method with a connection and a query: select(connection, "select * from MY_TABLE",(rs, cnt)-> { System.out.println(rs.getInt(1)+" "+cnt) }); So that's great, but I think we can do more... One of the nifty Lambda additions in Java is the new Streams API. This would allow us to add very powerful functionality with which to process a ResultSet. Using the Streams API over a ResultSet however creates a bit more of a challenge than the simple select with Lambda in the previous example. The way I decided to go about this is create my own Tuple type which represents a single row from a ResultSet. My Tuple here is the relational version where a Tuple is a collection of elements where each element is identified by an attribute, basically a collection of key value pairs. In our case the Tuple is ordered in terms of the order of the columns in the ResultSet. The code for the Tuple ended up being quite a bit so if you want to take a look, see the GitHub project in the resources at the end of the post. Currently the Java 8 API provides the java.util.stream.StreamSupport object which provides a set of static methods for creating instances of java.util.stream.Stream. We can use this object to create an instance of a Stream. But in order to create a Stream it needs an instance ofjava.util.stream.Spliterator. This is a specialised type for iterating and partitioning a sequence of elements, the Stream needs for handling operations in parallel. Fortunately the Java 8 api also provides the java.util.stream.Spliterators class which can wrap existing Collection and enumeration types. One of those types being ajava.util.Iterator. Now we wrap a query and ResultSet in an Iterator: public class ResultSetIterator implements Iterator { private ResultSet rs; private PreparedStatement ps; private Connection connection; private String sql; public ResultSetIterator(Connection connection, String sql) { assert connection != null; assert sql != null; this.connection = connection; this.sql = sql; } public void init() { try { ps = connection.prepareStatement(sql); rs = ps.executeQuery(); } catch (SQLException e) { close(); throw new DataAccessException(e); } } @Override public boolean hasNext() { if (ps == null) { init(); } try { boolean hasMore = rs.next(); if (!hasMore) { close(); } return hasMore; } catch (SQLException e) { close(); throw new DataAccessException(e); } } private void close() { try { rs.close(); try { ps.close(); } catch (SQLException e) { //nothing we can do here } } catch (SQLException e) { //nothing we can do here } } @Override public Tuple next() { try { return SQL.rowAsTuple(sql, rs); } catch (DataAccessException e) { close(); throw e; } } } This class basically delegates the iterator methods to the underlying result set and then on the next() call transforms the current row in the ResultSet into my Tuple type. And that's the basics done (This class will need a little bit more work though). All that's left is to wire it all together to make a Stream object. Note that due to the nature of a ResultSet it's not a good idea to try process them in parallel, so our stream cannot process in parallel. public static Stream stream(final Connection connection, final String sql, final Object... parms) { return StreamSupport .stream(Spliterators.spliteratorUnknownSize( new ResultSetIterator(connection, sql), 0), false); } Now it's straightforward to stream a query. In the usage example below I've got a table TEST_TABLE with an integer column TEST_ID which basically filters out all the non even numbers and then runs a count: long result = stream(connection, "select TEST_ID from TEST_TABLE") .filter((t) -> t.asInt("TEST_ID") % 2 == 0) .limit(100) .count(); And that's it! We now have a very powerful way of working with a ResultSet. So all this code is available under an Apache 2.0 license on GitHub here. I've rather lamely dubbed the project "lambda tuples," and the purpose really is to experiment and see where you can take Java 8 and Relational DB access, so please download or feel free to contribute.

This is a code snippet to retrieve the path of the current running web application project in java public String getPath() throws UnsupportedEncodingException { String path = this.getClass().getClassLoader().getResource("").getPath(); String fullPath = URLDecoder.decode(path, "UTF-8"); String pathArr[] = fullPath.split("/WEB-INF/classes/"); System.out.println(fullPath); System.out.println(pathArr[0]); fullPath = pathArr[0]; String reponsePath = ""; // to read a file from webcontent reponsePath = new File(fullPath).getPath() + File.separatorChar + "newfile.txt"; return reponsePath; }

This post demonstrates a Groovy script for uncompressing files with the 7-Zip archive format. The two primary objectives of this post are to demonstrate uncompressing 7-Zip files with Groovy and the handy 7-Zip-JBindingand to call out and demonstrate some key characteristics of Groovy as a scripting language. The 7-Zip page describes 7-Zip as "a file archiver with a high compression ratio." The page further adds, "7-Zip is open source software. Most of the source code is under the GNU LGPL license." More license information in available on the site along with information on the 7z format ("LZMA is default and general compression method of 7z format"). The 7-Zip page describes it as "a Java wrapper for 7-Zip C++ library" that "allows extraction of many archive formats using a very fast native library directly from Java through JNI." The 7z format is based on "LZMA andLZMA2 compression." Although there is an LZMA SDK available, it is easier to use the open source (SourceForge)7-Zip-JBinding project when manipulating 7-Zip files with Java. A good example of using Java with 7-Zip-JBinding to uncompress 7z files is available in the StackOverflowthread Decompress files with .7z extension in java. Dark Knight's response indicates how to use Java with 7-Zip-JBinding to uncompress a 7z file. I adapt Dark Knight's Java code into a Groovy script in this post. To demonstrate the adapted Groovy code for uncompressing 7z files, I first need a 7z file that I can extract contents from. The next series of screen snapshots show me using Windows 7-Zip installed on my laptop to compress the six PDFs available under the Guava Downloads page into a single 7z file called Guava.7z. Six Guava PDFs Sitting in a Folder Contents Selected and Right-Click Menu to Compress to 7z Format Guava.7z Compressed Archive File Created With a 7z file in place, I now turn to the adapted Groovy script that will extract the contents of this Guava.7zfile. As mentioned previously, this Groovy script is an adaptation of the Java code provided by Dark Knight on aStackOverflow thread. unzip7z.groovy // // This Groovy script is adapted from Java code provided at // http://stackoverflow.com/a/19403933 import static java.lang.System.err as error import java.io.File import java.io.FileNotFoundException import java.io.FileOutputStream import java.io.IOException import java.io.RandomAccessFile import java.util.Arrays import net.sf.sevenzipjbinding.ExtractOperationResult import net.sf.sevenzipjbinding.ISequentialOutStream import net.sf.sevenzipjbinding.ISevenZipInArchive import net.sf.sevenzipjbinding.SevenZip import net.sf.sevenzipjbinding.SevenZipException import net.sf.sevenzipjbinding.impl.RandomAccessFileInStream import net.sf.sevenzipjbinding.simple.ISimpleInArchive import net.sf.sevenzipjbinding.simple.ISimpleInArchiveItem if (args.length < 1) { println "USAGE: unzip7z.groovy .7z\n" System.exit(-1) } def fileToUnzip = args[0] try { RandomAccessFile randomAccessFile = new RandomAccessFile(fileToUnzip, "r") ISevenZipInArchive inArchive = SevenZip.openInArchive(null, new RandomAccessFileInStream(randomAccessFile)) ISimpleInArchive simpleInArchive = inArchive.getSimpleInterface() println "${'Hash'.center(10)}|${'Size'.center(12)}|${'Filename'.center(10)}" println "${'-'.multiply(10)}+${'-'.multiply(12)}+${'-'.multiply(10)}" simpleInArchive.getArchiveItems().each { item -> final int[] hash = new int[1] if (!item.isFolder()) { final long[] sizeArray = new long[1] ExtractOperationResult result = item.extractSlow( new ISequentialOutStream() { public int write(byte[] data) throws SevenZipException { //Write to file try { File file = new File(item.getPath()) file.getParentFile()?.mkdirs() FileOutputStream fos = new FileOutputStream(file) fos.write(data) fos.close() } catch (Exception e) { printExceptionStackTrace("Unable to write file", e) } hash[0] ^= Arrays.hashCode(data) // Consume data sizeArray[0] += data.length return data.length // Return amount of consumed data } }) if (result == ExtractOperationResult.OK) { println(String.format("%9X | %10s | %s", hash[0], sizeArray[0], item.getPath())) } else { error.println("Error extracting item: " + result) } } } } catch (Exception e) { printExceptionStackTrace("Error occurs", e) System.exit(1) } finally { if (inArchive != null) { try { inArchive.close() } catch (SevenZipException e) { printExceptionStackTrace("Error closing archive", e) } } if (randomAccessFile != null) { try { randomAccessFile.close() } catch (IOException e) { printExceptionStackTrace("Error closing file", e) } } } /** * Prints the stack trace of the provided exception to standard error without * Groovy meta data trace elements. * * @param contextMessage String message to precede stack trace and provide context. * @param exceptionToBePrinted Exception whose Groovy-less stack trace should * be printed to standard error. * @return Exception derived from the provided Exception but without Groovy * meta data calls. */ def Exception printExceptionStackTrace( final String contextMessage, final Exception exceptionToBePrinted) { error.print "${contextMessage}: ${org.codehaus.groovy.runtime.StackTraceUtils.sanitize(exceptionToBePrinted).printStackTrace()}" } In my adaptation of the Java code into the Groovy script shown above, I left most of the exception handling in place. Although Groovy allows exceptions to be ignored whether they are checked or unchecked, I wanted to maintain this handling in this case to make sure resources are closed properly and that appropriate error messages are presented to users of the script. One thing I did change was to make all of the output that is error-related be printed to standard error rather than to standard output. This required a few changes. First, I used Groovy's capability to rename something that is statically imported (see my related post Groovier Static Imports) to reference "java.lang.System.err" as "error" so that I could simply use "error" as a handle in the script rather than needing to use "System.err" to access standard error for output. Because Throwable.printStackTrace() already writes to standard error rather than standard output, I just used it directly. However, I placed calls to it in a new method that would first runStackTraceUtils.sanitize(Throwable) to remove Groovy-specific calls associated with Groovy's runtime dynamic capabilities from the stack trace. There were some other minor changes to the script as part of making it Groovier. I used Groovy's iteration on the items in the archive file rather than the Java for loop, removed semicolons at the ends of statements, used Groovy's String GDK extension for more controlled output reporting [to automatically center titles and tomultiply a given character by the appropriate number of times it needs to exist], and took advantage ofGroovy's implicit inclusion of args to add a check to ensure file for extraction was provided to the script. With the file to be extracted in place and the Groovy script to do the extracting ready, it is time to extract the contents of the Guava.7z file I demonstrated generating earlier in this post. The following command will run the script and places the appropriate 7-Zip-JBinding JAR files on the classpath. groovy -classpath "C:/sevenzipjbinding/lib/sevenzipjbinding.jar;C:/sevenzipjbinding/lib/sevenzipjbinding-Windows-x86.jar" unzip7z.groovy C:\Users\Dustin\Downloads\Guava\Guava.7z Before showing the output of running the above script against the indicated Guava.7z file, it is important to note the error message that will occur if the native operating system specific 7-Zip-JBinding JAR (sevenzipjbinding-Windows-x86.jar in my laptop's case) is not included on the classpath of the script. As the last screen snapshot indicates, neglecting to include the native JAR on the classpath leads to the error message: "Error occurs: java.lang.RuntimeException: SevenZipJBinding couldn't be initialized automaticly using initialization from platform depended JAR and the default temporary directory. Please, make sure the correct 'sevenzipjbinding-.jar' file is in the class path or consider initializing SevenZipJBinding manualy using one of the offered initialization methods: 'net.sf.sevenzipjbinding.SevenZip.init*()'" Although I simply added C:/sevenzipjbinding/lib/sevenzipjbinding-Windows-x86.jar to my script's classpath to make it work on this laptop, a more robust script might detect the operating system and apply the appropriate JAR to the classpath for that operating system. The 7-Zip-JBinding Download pagefeatures multiple platform-specific downloads (including platform-specific JARs) such assevenzipjbinding-4.65-1.06-rc-extr-only-Windows-amd64.zip, sevenzipjbinding-4.65-1.06-rc-extr-only-Mac-x86_64.zip, sevenzipjbinding-4.65-1.06-rc-extr-only-Mac-i386.zip, and sevenzipjbinding-4.65-1.06-rc-extr-only-Linux-i386.zip. Once the native 7-Zip-JBinding JAR is included on the classpath along with the core sevenzipjbinding.jar JAR, the script runs beautifully as shown in the next screen snapshot. The script extracts the contents of the 7z file into the same working directory as the Groovy script. A further enhancement would be to modify the script to accept a directory to which to write the extracted files or might write them to the same directory as the 7z archive file by default instead. Use of Groovy's built-in CLIBuilder support could also improve the script. Groovy is my preferred language of choice when scripting something that makes use of the JVM and/or of Java libraries and frameworks. Writing the script that is the subject of this post has been another reminder of that.

This post is following up the experiment we ran exactly a year ago comparing the performance of different GC algorithms in real-life settings. We took the same experiment, expanded the tests to contain the G1 garbage collector and ran the tests on different platform. This year our tests were run with the following Garbage Collectors: -XX:+UseParallelOldGC -XX:+UseConcMarkSweepGC -XX:+UseG1GC Description of the environment The experiment was ran on out-of-the-box JIRA configuration. The motivation for the test run was loud and clear – Minecraft, Dalvik-based Angry Bird and Eclipse asides, JIRA should be one of the most popular Java applications out there. And opposed to the alternatives it is a more typical representative of what most of us are dealing with on the everyday business – after all Java is still by far most used in server side Java EE apps. What also affected our decision was – the engineers from Atlassian ship nicely packaged load tests along the JIRA download. So we had a benchmark to use for our configuration. We carefully unzipped our fresh JIRA 6.1 download and installed it on a Mac OS X Mavericks. And ran the bundled tests without changing anything in the default memory settings. The Atlassian team had been kind enough to set them for us: -Xms256m -Xmx768m -XX:MaxPermSize=256m The tests used JIRA functionality in different common ways – creating tasks, assigning tasks, resolving tasks, searching and discovering tasks, etc. Total runtime for the test was 30 minutes. We ran the test using three different garbage collection algorithms – Parallel, CMS and G1 were used in our case. Each test started with a fresh JVM boot, followed by prepopulating the storage to the exactly the same state. Only after the preparations we launched the load generation. Results During each run we have collected GC logs using -XX:+PrintGCTimeStamps -Xloggc:/tmp/gc.log -XX:+PrintGCDetails and analyzed this statistics with the help of GCViewer The results can be aggregated as follows. Note that all measurements are in milliseconds: Parallel CMS G1 Total GC pauses 20 930 18 870 62 000 Max GC pause 721 64 50 Interpretation and results First stop – Parallel GC (-XX:+UseParallelOldGC). Out of the 30 minutes the tests took to complete, we spent close to 21 seconds in GC pauses with the parallel collector. And the longest pause took 721 milliseconds. So let us take this as the baseline: GC cycles reduced the throughput by 1.1% of the total runtime. And the worst-case latency was 721ms. Next contestant: CMS (-XX:+UseConcMarkSweepGC). Again, 30 minutes of tests out of which we lost a bit less than 19 seconds to GC. Throughput-wise this is roughly in the same neighbourhood as the parallel mode. Latency on the other hand has been improved significantly - the worst-case latency is reduced more than 10 times! We are now facing just 64ms as the maximum pause time from the GC. Last experiment used the newest and shiniest GC algorithm available – G1 (-XX:+UseG1GC). The very same tests were run and throughput-wise we saw results suffering severely. This time our application spent more than a minute waiting for the GC to complete. Comparing this to the just 1% of the overhead with CMS, we are now facing close to 3.5% effect on the throughput. But if you really do not care about throughput and want to squeeze out the last bit from the latency then – we have improved around 20% comparing to the already-good CMS – using G1 saw the longest GC pause only taking 50ms. Conclusion As always, trying to summarize such an experiment into a single conclusion is dangerous. So if you have time and required skills – definitely go ahead and measure your own environment instead of adopting to one-size-fits-all solution. But if I would dare to make such a conclusion, I would say that CMS is still the best “default” option to go with. G1 throughput is still so much worse that the improved latency is usually not worth it. If you enjoyed the content, consider subscribe to either our RSS feed or Twitter stream – we continue to publish on different performance optimization topics.

Since Groovy 2.2 we can subtract a part of a String value using a regular expression pattern. The first match found is replaced with an empty String. In the following sample code we see how the first match of the pattern is removed from the String: // Define regex pattern to find words starting with gr (case-insensitive). def wordStartsWithGr = ~/(?i)\s+Gr\w+/ assert ('Hello Groovy world!' - wordStartsWithGr) == 'Hello world!' assert ('Hi Grails users' - wordStartsWithGr) == 'Hi users' // Remove first match of a word with 5 characters. assert ('Remove first match of 5 letter word' - ~/\b\w{5}\b/) == 'Remove match of 5 letter word' // Remove first found numbers followed by a whitespace character. assert ('Line contains 20 characters' - ~/\d+\s+/) == 'Line contains characters' Code written with Groovy 2.2.

How do you create a range from 1 to 10 in SQL? Have you ever thought about it? This is such an easy problem to solve in any imperative language, it’s ridiculous. Take Java (or C, whatever) for instance: for (int i = 1; i <= 10; i++) System.out.println(i); This was easy, right? Things even look more lean when using functional programming. Take Scala, for instance: (1 to 10) foreach { t => println(t) } We could fill about 25 pages about various ways to do the above in Scala, agreeing on how awesome Scala is (or what hipsters we are). But how to create a range in SQL? … And we’ll exclude using stored procedures, because that would be no fun. In SQL, the data source we’re operating on are tables. If we want a range from 1 to 10, we’d probably need a table containing exactly those ten values. Here are a couple of good, bad, and ugly options of doing precisely that in SQL. OK, they’re mostly bad and ugly. By creating a table The dumbest way to do this would be to create an actual temporary table just for that purpose: CREATE TABLE "1 to 10" AS SELECT 1 value FROM DUAL UNION ALL SELECT 2 FROM DUAL UNION ALL SELECT 3 FROM DUAL UNION ALL SELECT 4 FROM DUAL UNION ALL SELECT 5 FROM DUAL UNION ALL SELECT 6 FROM DUAL UNION ALL SELECT 7 FROM DUAL UNION ALL SELECT 8 FROM DUAL UNION ALL SELECT 9 FROM DUAL UNION ALL SELECT 10 FROM DUAL See also this SQLFiddle This table can then be used in any type of select. Now that’s pretty dumb but straightforward, right? I mean, how many actual records are you going to put in there? By using a VALUES() table constructor This solution isn’t that much better. You can create a derived table and manually add the values from 1 to 10 to that derived table using the VALUES() table constructor. In SQL Server, you could write: SELECT V FROM ( VALUES (1), (2), (3), (4), (5), (6), (7), (8), (9), (10) ) [1 to 10](V) See also this SQLFiddle By creating enough self-joins of a sufficent number of values Another “dumb”, yet a bit more generic solution would be to create only a certain amount of constant values in a table, view or CTE (e.g. two) and then self join that table enough times to reach the desired range length (e.g. four times). The following example will produce values from 1 to 10, “easily”: WITH T(V) AS ( SELECT 0 FROM DUAL UNION ALL SELECT 1 FROM DUAL ) SELECT V FROM ( SELECT 1 + T1.V + 2 * T2.V + 4 * T3.V + 8 * T4.V V FROM T T1, T T2, T T3, T T4 ) WHERE V <= 10 ORDER BY V See also this SQLFiddle By using grouping sets Another way to generate large tables is by using grouping sets, or more specifically by using the CUBE() function. This works much in a similar way as the previous example when self-joining a table with two records: SELECT ROWNUM FROM ( SELECT 1 FROM DUAL GROUP BY CUBE(1, 2, 3, 4) ) WHERE ROWNUM <= 10 See also this SQLFiddle By just taking random records from a “large enough” table In Oracle, you could probably use ALL_OBJECTs. If you’re only counting to 10, you’ll certainly get enough results from that table: SELECT ROWNUM FROM ALL_OBJECTS WHERE ROWNUM <= 10 See also this SQLFiddle What’s so “awesome” about this solution is that you can cross join that table several times to be sure to get enough values: SELECT ROWNUM FROM ALL_OBJECTS, ALL_OBJECTS, ALL_OBJECTS, ALL_OBJECTS WHERE ROWNUM <= 10 OK. Just kidding. Don’t actually do that. Or if you do, don’t blame me if your productive system runs low on memory. By using the awesome PostgreSQL GENERATE_SERIES() function Incredibly, this isn’t part of the SQL standard. Neither is it available in most databases but PostgreSQL, which has the GENERATE_SERIES() function. This is much like Scala’s range notation: (1 to 10) SELECT * FROM GENERATE_SERIES(1, 10) See also this SQLFiddle By using CONNECT BY If you’re using Oracle, then there’s a really easy way to create such a table using the CONNECT BY clause, which is almost as convenient as PostgreSQL’s GENERATE_SERIES() function: SELECT LEVEL FROM DUAL CONNECT BY LEVEL < 10 See also this SQLFiddle By using a recursive CTE Recursive common table expressions are cool, yet utterly unreadable. the equivalent of the above Oracle CONNECT BY clause when written using a recursive CTE would look like this: WITH "1 to 10"(V) AS ( SELECT 1 FROM DUAL UNION ALL SELECT V + 1 FROM "1 to 10" WHERE V < 10 ) SELECT * FROM "1 to 10" See also this SQLFiddle By using Oracle’s MODEL clause A decent “best of” comparison of how to do things in SQL wouldn’t be complete without at least one example using Oracle’s MODEL clause (see this awesome use-case for Oracle’s spreadsheet feature). Use this clause only to make your co workers really angry when maintaining your SQL code. Bow before this beauty! SELECT V FROM ( SELECT 1 V FROM DUAL ) T MODEL DIMENSION BY (ROWNUM R) MEASURES (V) RULES ITERATE (10) ( V[ITERATION_NUMBER] = CV(R) + 1 ) ORDER BY 1 See also this SQLFiddle Conclusion There aren’t actually many nice solutions to do such a simple thing in SQL. Clearly, PostgreSQL’s GENERATE_SERIES() table function is the most beautiful solution. Oracle’s CONNECT BY clause comes close. For all other databases, some trickery has to be applied in one way or another. Unfortunately.

The lack of Base64 encoding API in Java is, in my opinion, by far one of the most annoying holes in the libraries. Finally Java 8 includes a decent API for it in the java.util package. Here is a short introduction of this new API (apparently it has a little more than the regular encode/decode API). The java.util.Base64 Utility Class The entry point to Java 8's Base64 support is the java.util.Base64 class. It provides a set of static factory methods used to obtain one of the three Base64 encodes and decoders: Basic URL MIME Basic Encoding The standard encoding we all think about when we deal with Base64: no line feeds are added to the output and the output is mapped to characters in the Base64 Alphabet: A-Za-z0-9+/ (we see in a minute why is it important). Here is a sample usage: // Encode String asB64 = Base64.getEncoder().encodeToString("some string".getBytes("utf-8")); System.out.println(asB64); // Output will be: c29tZSBzdHJpbmc= // Decode byte[] asBytes = Base64.getDecoder().decode("c29tZSBzdHJpbmc="); System.out.println(new String(asBytes, "utf-8")); // And the output is: some string It can't get simpler than that and, unlike in earlier versions of Java, there is any need for external dependencies (commons-codec), Sun classes (sun.misc.BASE64Decoder) or JAXB's DatatypeConverter (Java 6 and above). However it gets even better. URL Encoding Most of us are used to get annoyed when we have to encode something to be later included in a URL or as a filename - the problem is that the Base64 Alphabet contains meaningful characters in both URIs and filesystems (most specifically the forward slash (/)). The second type of encoder uses the alternative "URL and Filename safe" Alphabet which includes -_ (minus and underline) instead of +/. See the following example: String basicEncoded = Base64.getEncoder().encodeToString("subjects?abcd".getBytes("utf-8")); System.out.println("Using Basic Alphabet: " + basicEncoded); String urlEncoded = Base64.getUrlEncoder().encodeToString("subjects?abcd".getBytes("utf-8")); System.out.println("Using URL Alphabet: " + urlEncoded); The output will be: Using Basic Alphabet: c3ViamVjdHM/YWJjZA== Using URL Alphabet: c3ViamVjdHM_YWJjZA== The sample above illustrates some content which if encoded using a basic encoder will result in a string containing a forward slash while when using a URL safe encoder the output will include an underscore instead (URL Safe encoding is described in clause 5 of the RFC) MIME Encoding The MIME encoder generates a Base64 encoded output using the Basic Alphabet but in a MIME friendly format: each line of the output is no longer than 76 characters and ends with a carriage return followed by a linefeed (\r\n). The following example generates a block of text (this is needed just to make sure we have enough 'body' to encode into more than 76 characters) and encodes it using the MIME encoder: StringBuilder sb = new StringBuilder(); for (int t = 0; t < 10; ++t) { sb.append(UUID.randomUUID().toString()); } byte[] toEncode = sb.toString().getBytes("utf-8"); String mimeEncoded = Base64.getMimeEncoder().encodeToString(toEncode); System.out.println(mimeEncoded); The output: NDU5ZTFkNDEtMDVlNy00MDFiLTk3YjgtMWRlMmRkMWEzMzc5YTJkZmEzY2YtM2Y2My00Y2Q4LTk5 ZmYtMTU1NzY0MWM5Zjk4ODA5ZjVjOGUtOGMxNi00ZmVjLTgyZjctNmVjYTU5MTAxZWUyNjQ1MjJj NDMtYzA0MC00MjExLTk0NWMtYmFiZGRlNDk5OTZhMDMxZGE5ZTYtZWVhYS00OGFmLTlhMjgtMDM1 ZjAyY2QxNDUyOWZiMjI3NDctNmI3OC00YjgyLThiZGQtM2MyY2E3ZGNjYmIxOTQ1MDVkOGQtMzIz Yi00MDg0LWE0ZmItYzkwMGEzNDUxZTIwOTllZTJiYjctMWI3MS00YmQzLTgyYjUtZGRmYmYxNDA4 Mjg3YTMxZjMxZmMtYTdmYy00YzMyLTkyNzktZTc2ZDc5ZWU4N2M5ZDU1NmQ4NWYtMDkwOC00YjIy LWIwYWItMzJiYmZmM2M0OTBm Wrapping All encoders and decoders created by the java.util.Base64 class support streams wrapping - this is a very elegant construct - both coding and efficiency wise (since no redundant buffering are needed) - to stream in and out buffers via encoders and decoders. The sample bellow illustrates how a FileOutputStream can be wrapped with an encoder and a FileInputStream is wrapped with a decoder - in both cases there is no need to buffer the content: public void wrapping() throws IOException { String src = "This is the content of any resource read from somewhere" + " into a stream. This can be text, image, video or any other stream."; // An encoder wraps an OutputStream. The content of /tmp/buff-base64.txt will be the // Base64 encoded form of src. try (OutputStream os = Base64.getEncoder().wrap(new FileOutputStream("/tmp/buff-base64.txt"))) { os.write(src.getBytes("utf-8")); } // The section bellow illustrates a wrapping of an InputStream and decoding it as the stream // is being consumed. There is no need to buffer the content of the file just for decoding it. try (InputStream is = Base64.getDecoder().wrap(new FileInputStream("/tmp/buff-base64.txt"))) { int len; byte[] bytes = new byte[100]; while ((len = is.read(bytes)) != -1) { System.out.print(new String(bytes, 0, len, "utf-8")); } } }

In the last post I wrote about Nathan and my attempts at the Kaggle Titanic Problem, I mentioned that our next step was to try out scikit-learn, so I thought I should summarize where we’ve got up to. We needed to write a classification algorithm to work out whether a person onboard the Titanic survived, and luckily, scikit-learn has extensive documentation on each of the algorithms. Unfortunately almost all those examples use numpy data structures and we’d loaded the data using pandas and didn’t particularly want to switch back! Luckily it was really easy to get the data into numpy format by calling ‘values’ on the pandas data structure, something we learnt from a reply on Stack Overflow. For example if we were to wire up an ExtraTreesClassifier which worked out survival rate based on the ‘Fare’ and ‘Pclass’ attributes we could write the following code: import pandas as pd from sklearn.ensemble import ExtraTreesClassifier from sklearn.cross_validation import cross_val_score train_df = pd.read_csv("train.csv") et = ExtraTreesClassifier(n_estimators=100, max_depth=None, min_samples_split=1, random_state=0) columns = ["Fare", "Pclass"] labels = train_df["Survived"].values features = train_df[list(columns)].values et_score = cross_val_score(et, features, labels, n_jobs=-1).mean() print("{0} -> ET: {1})".format(columns, et_score)) To start with with read in the CSV file which looks like this: $ head -n5 train.csv PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S Next we create our classifier which “fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting.“ i.e. a better version of a random forest. On the next line we describe the features we want the classifier to use, then we convert the labels and features into numpy format so we can pass the to the classifier. Finally we call the cross_val_score function which splits our training data set into training and test components and trains the classifier against the former and checks its accuracy using the latter. If we run this code we’ll get roughly the following output: $ python et.py ['Fare', 'Pclass'] -> ET: 0.687991021324) This is actually a worse accuracy than we’d get by saying that females survived and males didn’t. We can introduce ‘Sex’ into the classifier by adding it to the list of columns: columns = ["Fare", "Pclass", "Sex"] If we re-run the code we’ll get the following error: $ python et.py An unexpected error occurred while tokenizing input The following traceback may be corrupted or invalid The error message is: ('EOF in multi-line statement', (514, 0)) ... Traceback (most recent call last): File "et.py", line 14, in et_score = cross_val_score(et, features, labels, n_jobs=-1).mean() File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/cross_validation.py", line 1152, in cross_val_score for train, test in cv) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/externals/joblib/parallel.py", line 519, in __call__ self.retrieve() File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/externals/joblib/parallel.py", line 450, in retrieve raise exception_type(report) sklearn.externals.joblib.my_exceptions.JoblibValueError/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/externals/joblib/my_exceptions.py:26: DeprecationWarning: BaseException.message has been deprecated as of Python 2.6 self.message, : JoblibValueError ___________________________________________________________________________ Multiprocessing exception: ... ValueError: could not convert string to float: male ___________________________________________________________________________ This is a slightly verbose way of telling us that we can’t pass non numeric features to the classifier – in this case ‘Sex’ has the values ‘female’ and ‘male’. We’ll need to write a function to replace those values with numeric equivalents. train_df["Sex"] = train_df["Sex"].apply(lambda sex: 0 if sex == "male" else 1) Now if we re-run the classifier we’ll get a slightly more accurate prediction: $ python et.py ['Fare', 'Pclass', 'Sex'] -> ET: 0.813692480359) The next step is to use the classifier against the test data set, so let’s load the data and run the prediction: test_df = pd.read_csv("test.csv") et.fit(features, labels) et.predict(test_df[columns].values) Now if we run that: $ python et.py ['Fare', 'Pclass', 'Sex'] -> ET: 0.813692480359) Traceback (most recent call last): File "et.py", line 22, in et.predict(test_df[columns].values) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 444, in predict proba = self.predict_proba(X) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 479, in predict_proba X = array2d(X, dtype=DTYPE) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/utils/validation.py", line 91, in array2d X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order) File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/numeric.py", line 235, in asarray return array(a, dtype, copy=False, order=order) ValueError: could not convert string to float: male which is the same problem we had earlier! We need to replace the ‘male’ and ‘female’ values in the test set too so we’ll pull out a function to do that now. def replace_non_numeric(df): df["Sex"] = df["Sex"].apply(lambda sex: 0 if sex == "male" else 1) return df Now we’ll call that function with our training and test data frames: train_df = replace_non_numeric(pd.read_csv("train.csv")) ... test_df = replace_non_numeric(pd.read_csv("test.csv")) If we run the program again: $ python et.py ['Fare', 'Pclass', 'Sex'] -> ET: 0.813692480359) Traceback (most recent call last): File "et.py", line 26, in et.predict(test_df[columns].values) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 444, in predict proba = self.predict_proba(X) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/ensemble/forest.py", line 479, in predict_proba X = array2d(X, dtype=DTYPE) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/utils/validation.py", line 93, in array2d _assert_all_finite(X_2d) File "/Library/Python/2.7/site-packages/scikit_learn-0.14.1-py2.7-macosx-10.8-intel.egg/sklearn/utils/validation.py", line 27, in _assert_all_finite raise ValueError("Array contains NaN or infinity.") ValueError: Array contains NaN or infinity. There are missing values in the test set so we’ll replace those with average values from our training set using an Imputer: from sklearn.preprocessing import Imputer imp = Imputer(missing_values='NaN', strategy='mean', axis=0) imp.fit(features) test_df = replace_non_numeric(pd.read_csv("test.csv")) et.fit(features, labels) print et.predict(imp.transform(test_df[columns].values)) If we run that it completes successfully: $ python et.py ['Fare', 'Pclass', 'Sex'] -> ET: 0.813692480359) [0 1 0 0 1 0 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0] The final step is to add these values to our test data frame and then write that to a file so we can submit it to Kaggle. The type of those values is ‘numpy.ndarray’ which we can convert to a pandas Series quite easily: predictions = et.predict(imp.transform(test_df[columns].values)) test_df["Survived"] = pd.Series(predictions) We can then write the ‘PassengerId’ and ‘Survived’ columns to a file: test_df.to_csv("foo.csv", cols=['PassengerId', 'Survived'], index=False) Then output file looks like this: $ head -n5 foo.csv PassengerId,Survived 892,0 893,1 894,0 The code we’ve written is on github in case it’s useful to anyone.

I came across a debate about whether naming a class [Something]Manager is proper. Mainly, I came across this article, but also found other references here and here. I already had my own thoughts on this topic and decided I would share them with you. I started thinking on this a while ago when working in a Swing-powered desktop app. We decided we would make use of the Listeners model that Swing establishes for its components. To register a listener in a Swing component you say: // Some-Type = {Action, Key, Mouse, Focus, ...} component.add[Some-Type]Listener(listener); That's not a problem until you need to dynamically remove those listeners, or override them (remove and add a new listener that listens to the same event but fires a different action), or do any other 'unnatural' thing with them. This turns out to be a little difficult if you use the interface Swing provides out-of-the-box. For instance, to remove a listener, you need to pass the whole instance you want to remove; you would have to hold the reference to the listener if you intend to remove it some time later. So I decided I would take a step further and try to abstract the mess by creating a Manager class. What you could do with this class was something like this: EventsListenersManager.registerListener( "some unique name", component, listener); EventsListenersManager.overrideListener("some unique name", newListener); EventsListenerManager.disableListener("some unique name"); EventsListenerManager.enableListener("some unique name"); EventsListenerManager.unregisterListener("some unique name"); What we gained here??? We got rid of the explicit specification of [Some-Type] and have a single function for every type of listener; and we defined a unique name for the binding, which will be used as an id to remove, enable/disable, and override listeners; a.k.a. no need to hold references. Obviously, it is now convenient to always use the manager’s interface, since it reduces the need to trick our code and gives us a simple and more readable interface to achieve many things with listeners. But that’s not the only advantage of this manager class. You see, manager classes in general have one big advantage: they create an abstraction of a service and work as a centralized point to add logic of any nature, like security logic or performance logic. In the case of our EventsListenersManager, we put some extra logic to enchance the way we can use listeners by providing an interface that simplifies their use: it is easier to make a switch between two listeners now than it was before. Another thing we could have done was to restrict the number of listeners registered in one component for performance sake (Hint: listeners execution don’t run in parallel). So manager classes seem to be a good thing, but we can’t make a manager for every object we want to control in an application. This would consume time and would make us start thinking in a manager class even when we don’t need it. But we can discover a pattern for the need of defining a manager. I’ve seen them used when the resources they control are expensive, like database connections, media resources, hardware resources, etc; and as I see it, expensiveness can come in many flavors: Listeners are expensive because they may be difficult to play with and can be a bottleneck in performance (we must keep them short and fast). Connections are expensive to construct. Media resources are expensive to load. Hardware resources are expensive because they are scarce. So we create different managers for them: We create a manager for listeners to ease and control their use. We create a connections pool in order to limit and control the number of connections to a database. Games developers create a resources manager to have a single point to access resources and reduce the possibility to load them multiple times. We all know there is a driver for every piece of hardware. That’s their manager. Here we can see multiple variations of manager classes implementations, but they all attempt to solve a single thing: reducing the cost we may incur in when using expensive resources directly. I think people should have this line of thought: make a manager class for every expensive resource you detect in your application. You will be more than thankful when you want to add extra logic and you only have to go to one place. Also, enforce the use of manager objects instead of accessing the resources directly. The next time you are planning to make a manager class, ask yourself: is the 'thing' I want to control expensive in any way???

This blog is more of a tutorial where we describe the development of a simple data access module, more for fun and learning than anything else. All code can be found here for those who don’t want to type along: https://github.com/ricston-git/tododb As a heads-up, we will be covering the following: Using Groovy in a Maven project within Eclipse Using Groovy to interact with our database Testing our code using the Spock framework We include Spring in our tests with ContextConfiguration A good place to start is to write a pom file as shown here. The only dependencies we want packaged with this artifact are groovy-all and commons-lang. The others are either going to be provided by Tomcat or are only used during testing (hence the scope tags in the pom). For example, we would put the jar with PostgreSQL driver in Tomcat’s lib, and tomcat-jdbc and tomcat-dbcp are already there. (Note: regarding the postgre jar, we would also have to do some minor configuration in Tomcat to define a DataSource which we can get in our app through JNDI – but that’s beyond the scope of this blog. See here for more info). Testing-wise, I’m depending on spring-test, spock-core, and spock-spring (the latter is to get spock to work with spring-test). Another significant addition in the pom is the maven-compiler-plugin. I have tried to get gmaven to work with Groovy in Eclipse, but I have found the maven-compiler-plugin to be a lot easier to work with. With your pom in an empty directory, go ahead and mkdir -p src/main/groovy src/main/java src/test/groovy src/test/java src/main/resources src/test/resources. This gives us a directory structure according to the Maven convention. Now you can go ahead and import the project as a Maven project in Eclipse (install the m2e plugin if you don’t already have it). It is important that you do not mvn eclipse:eclipse in your project. The .classpath it generates will conflict with your m2e plugin and (at least in my case), when you update your pom.xml the plugin will not update your dependencies inside Eclipse. So just import as a maven project once you have your pom.xml and directory structure set up. Okay, so our tests are going to be integration tests, actually using a PostgreSQL database. Since that’s the case, lets set up our database with some data. First go ahead and create a tododbtest database which will only be used for testing purposes. Next, put the following files in your src/test/resources: Note, fill in your username/password: DROP TABLE IF EXISTS todouser CASCADE; CREATE TABLE todouser ( id SERIAL, email varchar(80) UNIQUE NOT NULL, password varchar(80), registered boolean DEFAULT FALSE, confirmationCode varchar(280), CONSTRAINT todouser_pkey PRIMARY KEY (id) ); insert into todouser (email, password, registered, confirmationCode) values ('[email protected]', 'abc123', FALSE, 'abcdefg') insert into todouser (email, password, registered, confirmationCode) values ('[email protected]', 'pass1516', FALSE, '123456') insert into todouser (email, password, registered, confirmationCode) values ('[email protected]', 'anon', FALSE, 'codeA') insert into todouser (email, password, registered, confirmationCode) values ('[email protected]', 'anon2', FALSE, 'codeB') Basically, testContext.xml is what we’ll be configuring our test’s context with. The sub-division into datasource.xml and initdb.xml may be a little too much for this example… but changes are usually easier that way. The gist is that we configure our data source in datasource.xml (this is what we will be injecting in our tests), and the initdb.xml will run the schema.sql and test-data.sql to create our table and populate it with data. So lets create our test, or should I say, our specification. Spock is specification framework that allows us to write more descriptive tests. In general, it makes our tests easier to read and understand, and since we’ll be using Groovy, we might as well make use of the extra readability Spock gives us. package com.ricston.blog.sample.model.spec; import javax.sql.DataSource import org.springframework.beans.factory.annotation.Autowired import org.springframework.test.annotation.DirtiesContext import org.springframework.test.annotation.DirtiesContext.ClassMode import org.springframework.test.context.ContextConfiguration import spock.lang.Specification import com.ricston.blog.sample.model.data.TodoUser import com.ricston.blog.sample.model.dao.postgre.PostgreTodoUserDAO // because it supplies a new application context after each test, the initialize-database in initdb.xml is // executed for each test/specification @DirtiesContext(classMode=ClassMode.AFTER_EACH_TEST_METHOD) @ContextConfiguration('classpath:testContext.xml') class PostgreTodoUserDAOSpec extends Specification { @Autowired DataSource dataSource PostgreTodoUserDAO postgreTodoUserDAO def setup() { postgreTodoUserDAO = new PostgreTodoUserDAO(dataSource) } def "findTodoUserByEmail when user exists in db"() { given: "a db populated with a TodoUser with email [email protected] and the password given below" String email = '[email protected]' String password = 'anon' when: "searching for a TodoUser with that email" TodoUser user = postgreTodoUserDAO.findTodoUserByEmail email then: "the row is found such that the user returned by findTodoUserByEmail has the correct password" user.password == password } } One specification is enough for now, just to make sure that all the moving parts are working nicely together. The specification itself is easy enough to understand. We’re just exercising the findTodoUserByEmail method of PostgreTodoUserDAO – which we will be writing soon. Using the ContextConfiguration from Spring Test we are able to inject beans defined in our context (the dataSource in our case) through the use of annotations. This keeps our tests short and makes them easier to modify later on. Additionally, note the use of DirtiesContext. Basically, after each specification is executed, we cannot rely on the state of the database remaining intact. I am using DirtiesContext to get a new Spring context for each specification run. That way, the table creation and test data insertions happen all over again for each specification we run. Before we can run our specification, we need to create at least the following two classes used in the spec: TodoUser and PostgreTodoUserDAO package com.sample.data import org.apache.commons.lang.builder.ToStringBuilder class TodoUser { long id; String email; String password; String confirmationCode; boolean registered; @Override public String toString() { ToStringBuilder.reflectionToString(this); } } package com.ricston.blog.sample.model.dao.postgre import groovy.sql.Sql import javax.sql.DataSource import com.ricston.blog.sample.model.dao.TodoUserDAO import com.ricston.blog.sample.model.data.TodoUser class PostgreTodoUserDAO implements TodoUserDAO { private Sql sql public PostgreTodoUserDAO(DataSource dataSource) { sql = new Sql(dataSource) } /** * * @param email * @return the TodoUser with the given email */ public TodoUser findTodoUserByEmail(String email) { sql.firstRow """SELECT * FROM todouser WHERE email = $email""" } } package com.ricston.blog.sample.model.dao; import com.ricston.blog.sample.model.data.TodoUser; public interface TodoUserDAO { /** * * @param email * @return the TodoUser with the given email */ public TodoUser findTodoUserByEmail(String email); } We’re just creating a POGO in TodoUser, implementing its toString using common’s ToStringBuilder. In PostgreTodoUserDAO we’re using Groovy’s SQL to access the database, for now, only implementing the findTodoUserByEmail method. PostgreTodoUserDAO implements TodoUserDAO, an interface which specifies the required methods a TodoUserDAO must have. Okay, so now we have all we need to run our specification. Go ahead and run it as a JUnit test from Eclipse. You should get back the following error message: org.codehaus.groovy.runtime.typehandling.GroovyCastException: Cannot cast object '{id=3, [email protected], password=anon, registered=false, confirmationcode=codeA}' with class 'groovy.sql.GroovyRowResult' to class 'com.ricston.blog.sample.model.data.TodoUser' due to: org.codehaus.groovy.runtime.metaclass.MissingPropertyExceptionNoStack: No such property: confirmationcode for class: com.ricston.blog.sample.model.data.TodoUser Possible solutions: confirmationCode at com.ricston.blog.sample.model.dao.postgre.PostgreTodoUserDAO.findTodoUserByEmail(PostgreTodoUserDAO.groovy:23) at com.ricston.blog.sample.model.spec.PostgreTodoUserDAOSpec.findTodoUserByEmail when user exists in db(PostgreTodoUserDAOSpec.groovy:37) Go ahead and connect to your tododbtest database and select * from todouser; As you can see, our confirmationCode varchar(280), ended up as the column confirmationcode with a lower case ‘c’. In PostgreTodoUserDAO’s findTodoUserByEmail, we are getting back GroovyRowResult from our firstRow invocation. GroovyRowResult implements Map and Groovy is able to create a POGO (in our case TodoUser) from a Map. However, in order for Groovy to be able to automatically coerce the GroovyRowResult into a TodoUser, the keys in the Map (or GroovyRowResult) must match the property names in our POGO. We are using confirmationCode in our TodoUser, and we would like to stick to the camel case convention. What can we do to get around this? Well, first of all, lets change our schema to use confirmation_code. That’s a little more readable. Of course, we still have the same problem as before since confirmation_code will not map to confirmationCode by itself. (Note: remember to change the insert statements in test-data.sql too). One way to get around this is to use Groovy’s propertyMissing methods as show below: def propertyMissing(String name, value) { if(isConfirmationCode(name)) { this.confirmationCode = value } else { unknownProperty(name) } } def propertyMissing(String name) { if(isConfirmationCode(name)) { return confirmationCode } else { unknownProperty(name) } } private boolean isConfirmationCode(String name) { 'confirmation_code'.equals(name) } def unknownProperty(String name) { throw new MissingPropertyException(name, this.class) } By adding this to our TodoUser.groovy we are effectively tapping in on how Groovy resolves property access. When we do something like user.confirmationCode, Groovy automatically calls getConfirmationCode(), a method which we got for free when declared the property confirmationCode in our TodoUser. Now, when user.confirmation_code is invoked, Groovy doesn’t find any getters to invoke since we never declared the property confirmation_code, however, since we have now implemented the propertyMissing methods, before throwing any exceptions it will use those methods as a last resort when resolving properties. In our case we are effectively checking whether a get or set on confirmation_code is being made and mapping the respective operations to our confirmationCode property. It’s as simple as that. Now we can keep the auto coercion in our data access object and the property name we choose to have in our TodoUser. Assuming you’ve made the changes to the schema and test-data.sql to use confirmation_code, go ahead and run the spec file and this time it should pass. That’s it for this tutorial. In conclusion, I would like to discuss some finer points which someone who’s never used Groovy’s SQL before might not know. As you can see in PostgreTodoUserDAO.groovy, our database interaction is pretty much a one-liner. What about resource handling (e.g. properly closing the connection when we’re done), error logging, and prepared statements? Resource handling and error logging are done automatically, you just have to worry about writing your SQL. When you do write your SQL, try to stick to using triple quotes as used in the PostgreTodoUserDAO.groovy example. This produces prepared statements, therefore protecting against SQL injection and avoids us having to put ‘?’ all over the place and properly lining up the arguments to pass in to the SQL statement. Note that transaction management is something which the code using our artifact will have to take care of. Finally, note that a bunch of other operations (apart from findTodoUserByEmail) are implemented in the project on GitHub: https://github.com/ricston-git/tododb. Additionally, there is also a specification test for TodoUser, making sure that the property mapping works correctly. Also, in the pom.xml, there is some maven-surefire-plugin configuration in order to get the surefire-plugin to pick up our Spock specifications as well as any JUnit tests which we might have in our project. This allows us to run our specifications when we, for example, mvn clean package. After implementing all the operations you require in PostgreTodoUserDAO.groovy, you can go ahead and compile the jar or include in a Maven multi-module project to get a data access module you can use in other applications.

What is Functional Programming? Functional programming is a specific way to look at problems and model their solutions. Pragmatically, functional programming is a coding style that exhibits the following characteristics: Power and flexibility. We can solve many general real-world problems using functional constructs Simplicity. Most functional programs exhibit a small set of keywords and concise syntax for expressing concepts Suitable for parallel processing. Via immutable values and operators, functional programs lend themselves to asynchronous and parallel processing. In functional programming, programs are executed by evaluating expressions, in contrast with imperative programming where programs are composed of statements which change global state when executed. Functional programming typically avoids using mutable state. Everything is a mathematical function. Functional programming languages can have objects, but generally those objects are immutable -- either arguments or return values to functions. There are no for/next loops, as those imply state changes. Instead, that type of looping is performed with recursion and by passing functions as arguments. Functional Programming vs. Imperative Programming: We can think of imperative programming as writing code that describes in exacting detail the steps the software must take to execute a given computation. These steps are generally expressed as a combination of statement executions, state changes, loops and conditional branches. Many programming languages support programming in both functional and imperative style but the syntax and facilities of a language are typically optimised for only one of these styles, and social factors like coding conventions and libraries often force the programmer towards one of the styles. Therefore, programming languages may be categorized into functional and imperative ones. Why Functional Programming? While you can develop concurrent, scalable and asynchronous software without embracing functional programming, it's simpler, safer, and easier to use the right tool for the job. Functional programming enables you to take advantage of multi-core systems, develop robust concurrent algorithms, parallelize compute-intensive algorithms, and to readily leverage the growing number of cloud computing platforms. Imagine you've implemented a large program in a purely functional way. All the data is properly threaded in and out of functions, and there are no truly destructive updates to speak of. Now pick the two lowest-level and most isolated functions in the entire codebase. They're used all over the place, but are never called from the same modules. Now make these dependent on each other: function A behaves differently depending on the number of times function B has been called and vice-versa. In addition, if you've not already explored non-imperative programming, it's a great way to expand your problem solving skills and your horizons. The new concepts that you'll learn will help you to look at many problems from a different perspective and will help you to become a better and more insightful OO programmer. I encourage everyone who wants to be a better programmer: consider learning a functional language. Haskell and OCaml are both great choices, and F# and Erlang are pretty good as well. It won’t be easy, but that is probably a good sign. Try and identify the difficult concepts you encounter and see if other people are leveraging them; frequently you can break through a mental roadblock by finding out what the intent of an unfamiliar abstraction really is. While you’re learning, do be careful not to take it too seriously. Like anything that requires time and effort, there is a danger of becoming over-invested in FP. Falling into this cognitive trap will ruin your investment. It’s easy to forget how many models of computation there are out there, and even easier to forget how much beautiful software has been written with any of them. It’s a narrow path to walk down, but on the other side, you emerge with more core concepts and models to leverage in your everyday programming. You will almost certainly become more comfortable with denser code, and will certainly gain new insights into how to be a better software engineer.

Since git hooks can be any executable script with an appropriate #! line, Python is more than suitable for writing your git hooks. Simply stated, git hooks are scripts which are called at different points of time in the life cycle of working with your git repository. Let’s start by creating a new git repository: ~/work> git init git-hooks-exp Initialized empty Git repository in /home/gene/work/git-hooks-exp/.git/ ~/work> cd git-hooks-exp/ ~/work/git-hooks-exp (master)> tree -al .git/ .git/ ├── branches ├── config ├── description ├── HEAD ├── hooks │ ├── applypatch-msg.sample │ ├── commit-msg.sample │ ├── post-update.sample │ ├── pre-applypatch.sample │ ├── pre-commit.sample │ ├── prepare-commit-msg.sample │ ├── pre-rebase.sample │ └── update.sample ├── info │ └── exclude ├── objects │ ├── info │ └── pack └── refs ├── heads └── tags 9 directories, 12 files Inside the .git are a number of directories and files, one of them being hooks/ which is where the hooks live. By default, you will have a number of hooks with the file names ending in .sample. They may be useful as starting points for your own scripts. However, since they all have an extension .sample, none of the hooks are actually activated. For a hook to be activated, it must have the right file name and it should be executable. Let’s see how we can write a hook using Python. We will write a post-commit hook. This hook is called immediately after you have made a commit. We are going to do something fairly useless, but quite interesting in this hook. We will take the commit SHA1 of this commit, and print how it may look like in a more human form. I do the latter using the humanhash module. You will need to have it installed. Here is how the hook looks like: #!/usr/bin/python import subprocess import humanhash # get the last commit SHA and print it after humanizing it # https://github.com/zacharyvoase/humanhash print humanhash.humanize( subprocess.check_output( ['git','rev-parse','HEAD'])) I use the subprocess.check_output() function to execute the command git rev-parse HEAD so that I can get the commit SHA1 and then call the humanhash.humanize() function with it. Save the hook as a file, post-commit in your hooks/ directory and make it executable using chmod +x .git/hooks/post-commit. Let’s see the hook in action: ~/work/git-hooks-exp (master)> touch file ~/work/git-hooks-exp (master)> git add file ~/work/git-hooks-exp (master)> git commit -m "Added a file" carbon-network-connecticut-equal [master (root-commit) 2d7880b] Added a file 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 file The commit SHA1 for the commit turned out to be 2d7880be746a1c1e75844fc1aa161e2b8d955427. Let’s check it with the humanize function and check if we get the same message as above: >>> humanhash.humanize('2d7880be746a1c1e75844fc1aa161e2b8d955427') 'carbon-network-connecticut-equal' And you can see the same message above as well. For some of the hooks, you will see that they are called with some parameters. In Python you can access them using the sys.argv attribute from the sys module, with the first member being the name of the hook of course and the others will be the parameters that the hook is called with.

I just spent the better part of an hour trying to effectively install a version of python that doesn’t affect the rest of my system. This is usually pretty trivial effort according to most online articles. There are a few subtleties you might want to be aware of before you go and and well you know. I am writing this mostly for myself so pardon the casual nature, and/or grammatical errors. So you have a clean install of Mavericks, the world is beautiful. You have a new beautiful new background and you have sudden inspiration to get some work done. So many guides will tell you to do simply use system python which comes with easy_install and install pip with sudo. Don’t do this, For many reasons. Homebrew always has the most recent version (currently 2.7.5). Apple has made significant changes to its bundled Python, potentially resulting in hidden bugs. Homebrew’s Python includes the latest Python package management tools: pip and Setuptools First thing you do is install homebrew. Then do a little something like this. brew install python Once that is done, you will want to update your PATH variable to include the following directories in PATH /usr/local: /usr/local/bin: You want to put local on PATH because it contains other important directories like lib and sbin. What we are doing here is modifying the PATH variable which your system looks for commands, it will traverse the paths until it finds a match and use that. We want it to use copy of python in /usr/local You can put these modifications in your .zshrc or .bashrc whichever floats your boat. Now to test to see if you are using the correct python on your system (you have multiple copies now after the brew install) you want to you use: which python If you get /usr/bin/python you either didn’t set the PATH properly or didn’t reload either .zshrc or .bashrc (here is a hint: restart your terminal) If you get a usr/local/bin/python you are winning. You will also want to make sure you are using the correct copy of pip on your system as well, use the same steps as above. Never install anything with sudo. Don’t listen to random READMEs online. You make the decision, my son. Now you install things you want system wide without having to worry about breaking your system’s copy of Python. Swaggin'.

Over the years I have been a panelist in many of the interviews for Java Developers. I have previously written a post titled Top 7 tips for succeeding in a technical interview for software engineers which covers few of the general guidelines. In this post I will share a mind map containing general topics covered in a Java developer interview. I have prepared this as a general reference for myself to remember the pointers and to keep a common standard across the multiple interviews. XMind gives a nice listing of the map. You can find the map here. Here is Image which you can download and use. Finally here is a old fashioned tabbed content list which is easier to copy paste. Java-Topics OOPs Encapsulation Abstraction Inheritance Interface - Abstract Class Casting IS-A vs HAS-A Relationships Aggregation vs Composition Plymorphism Method overloading vs Method Overloading Compile time vs Runtime Threads Creating threads Multitasking Synchronization Thread Transitions Marker Interface Serialization Clonnable Shallow copy vs Deep Copy Collections Map, List and Set Equals - Hashcode Legacy - Synchronized Classes JVM Stack vs Heap Memory Garbage Collection JRE, JVM, JDK Class loaders Exception Checked Vs Unchecked Exceptions Exception handling best practices try, catch, finally, throw, throws APIs Files String - StringBuffer - String Builder Java IO XML SAX Based & DOM Based JAXB - Java API for XML Binding Access specifier Access modifier public protected deafult private final static synchronized abstract transient volatile Inner/Nested Classes JavaEE Basics Packaging the Applications WAR EAR Basics MVC Servlets Listeners Lifecycle JSPs APIs JPA JAX-WS SOAP, WSDL Webservices basics Contract first vs JAX-RS RESTful and its advantages JSF This is a work in progress and I hope to refine it further. Let me know if you have any comments. - See more at: http://jyops.blogspot.ie/2013/10/a-mindmap-for-java-developer-interviews.html#sthash.K0A5wDAz.dpuf

In this seventh post of my series on addressing the issue of too many parameters in a Java method or constructor, I look at using state to reduce the need to pass parameters. One of the reasons I have waited until the 7th post of this series to address this is that it is one of my least favorite approaches for reducing parameters passed to methods and constructors. That stated, there are multiple flavors of this approach and I definitely prefer some flavors over others. Perhaps the best known and most widely scorned approach in all of software development for using state to reduce parameter methods is the use global variables. Although it may be semantically accurate to say thatJava does not have global variables, the reality is that for good or for bad the equivalent of global variables is achieved in Java via public static constructs. A particularly popular way to achieve this in Java is via the Stateful Singleton. In Patterns of Enterprise Application Architecture, Martin Fowler wrote that "any global data is always guilty until proven innocent." Global variables and "global-like" constructs in Java are considered bad form for several reasons. They can make it difficult for developers maintaining and reading code to know where the values are defined or last changed or even come from. By their very nature and intent, global data violates the principles of encapsulation and data hiding. Miško Hevery has written the following regarding the problems of static globals in an object-oriented language: Accessing global state statically doesn’t clarify those shared dependencies to readers of the constructors and methods that use the Global State. Global State and Singletons make APIs lie about their true dependencies. ... The root problem with global state is that it is globally accessible. In an ideal world, an object should be able to interact only with other objects which were directly passed into it (through a constructor, or method call). Having state available globally reduces the need for parameters because there is no need for one object to pass data to another object if both objects already have direct access to that data. However, as Hevery put it, that's completely orthogonal to the intent of object-oriented design. Mutable state is also an increasing problem as concurrent applications become more common. In his JavaOne 2012 presentation on Scala, Scala creator Martin Odersky stated that "every piece of mutable state you have is a liability" in a highly concurrent world and added that the problem is "non-determinism caused by concurrent threads accessing shared mutable state." Although there are reasons to avoid mutable state, it still remains a generally popular approach in software development. I think there are several reasons for this including that it's superfically easy to write mutable state sharing code and mutable shared code does provide ease of access. Some types of mutable data are popular because those types of mutable data have been taught and learned as effective for years. Finally, three are times when mutable state may be the most appropriate solution. For that last reason and to be complete, I now look at how the use of mutable state can reduce the number of parameters a method must expect. Stateful Singleton and Static Variables A Java implementation of Singleton and other public Java static fields are generally available to any Java code within the same Java Virtual Machine (JVM) and loaded with the same classloader [for more details, see When is a Singleton not a Singleton?]. Any data stored universally (at least from JVM/classloader perspective) is already available to client code in the same JVM and loaded with the same class loader. Because of this, there is no need to pass that data between clients and methods or constructors in that same JVM/classloader combination. Instance State While "statics" are considered "globally available," narrower instance-level state can also be used in a similar fashion to reduce the need to pass parameters between methods of the same class. An advantage of this over global variables is that the accessibility is limited to instances of the class (private fields) or instances of the class's children (private fields). Of course, if the fields are public, accessibility is pretty wide open, but the same data is not automatically available to other code in the same JVM/classloader. The next code listing demonstrates how state data can and sometimes is used to reduce the need for parameters between two methods internal to a given class. Example of Instance State Used to Avoid Passing Parameters /** * Simple example of using instance variable so that there is no need to * pass parameters to other methods defined in the same class. */ public void doSomethingGoodWithInstanceVariables() { this.person = Person.createInstanceWithNameAndAddressOnly( new FullName.FullNameBuilder(new Name("Flintstone"), new Name("Fred")).createFullName(), new Address.AddressBuilder(new City("Bedrock"), State.UN).createAddress()); printPerson(); } /** * Prints instance of Person without requiring it to be passed in because it * is an instance variable. */ public void printPerson() { out.println(this.person); } The above example is somewhat contrived and simplified, but does illustrate the point: the instance variableperson can be accessed by other instance methods defined in the same class, so that instance does not need to be passed between those instance methods. This does reduce the signature of potentially (public accessibility means it may be used by external methods) internal methods, but also introduces state and now means that the invoked method impacts the state of that same object. In other words, the benefit of not having to pass the parameter comes at the cost of another piece of mutable state. The other side of the trade-off, needing to pass the instance of Person because it is not an instance variable, is shown in the next code listing for comparison. Example of Passing Parameter Rather than Using Instance Variable /** * Simple example of passing a parameter rather than using an instance variable. */ public void doSomethingGoodWithoutInstanceVariables() { final Person person = Person.createInstanceWithNameAndAddressOnly( new FullName.FullNameBuilder(new Name("Flintstone"), new Name("Fred")).createFullName(), new Address.AddressBuilder(new City("Bedrock"), State.UN).createAddress()); printPerson(person); } /** * Prints instance of Person that is passed in as a parameter. * * @param person Instance of Person to be printed. */ public void printPerson(final Person person) { out.println(person); } The previous two code listings illustrate that parameter passing can be reduced by using instance state. I generally prefer to not use instance state solely to avoid parameter passing. If instance state is needed for other reasons, than the reduction of parameters to be passed is a nice side benefit, but I don't like introducing unnecessary instance state simply to remove or reduce the number of parameters. Although there was a time when the readability of reduced parameters might have justified instance state in a large single-threaded environment, I feel that the slight readability gain from reduced parameters is not worth the cost of classes that are not thread-safe in an increasingly multi-threaded world. I still don't like to pass a whole lot of parameters between methods of the same class, but I can use the parameters object (perhaps with a package-private scope class) to reduce the number of these parameters and pass that parameters object around instead of the large number of parameters. JavaBean Style Construction The JavaBeans convention/style has become extremely popular in the Java development community. Many frameworks such as Spring Framework and Hibernate rely on classes adhering to the JavaBeans conventions and some of the standards like Java Persistence API also are built around the JavaBeans conventions. There are multiple reasons for the popularity of the JavaBeans style including its ease-of-use and the ability to usereflection against this code adhering to this convention to avoid additional configuration. The general idea behind the JavaBean style is to instantiate an object with a no-argument constructor and then set its fields via single-argument "set" methods and access it fields via no-argument "get" methods. This is demonstrated in the next code listings. The first listing shows a simple example of a PersonBean class with no-arguments constructor and getter and setter methods. That code listing also includes some of the JavaBeans-style classes it uses. That code listing is followed by code using that JavaBean style class. Examples of JavaBeans Style Class public class PersonBean { private FullNameBean name; private AddressBean address; private Gender gender; private EmploymentStatus employment; private HomeownerStatus homeOwnerStatus; /** No-arguments constructor. */ public PersonBean() {} public FullNameBean getName() { return this.name; } public void setName(final FullNameBean newName) { this.name = newName; } public AddressBean getAddress() { return this.address; } public void setAddress(final AddressBean newAddress) { this.address = newAddress; } public Gender getGender() { return this.gender; } public void setGender(final Gender newGender) { this.gender = newGender; } public EmploymentStatus getEmployment() { return this.employment; } public void setEmployment(final EmploymentStatus newEmployment) { this.employment = newEmployment; } public HomeownerStatus getHomeOwnerStatus() { return this.homeOwnerStatus; } public void setHomeOwnerStatus(final HomeownerStatus newHomeOwnerStatus) { this.homeOwnerStatus = newHomeOwnerStatus; } } /** * Full name of a person in JavaBean style. * * @author Dustin */ public final class FullNameBean { private Name lastName; private Name firstName; private Name middleName; private Salutation salutation; private Suffix suffix; /** No-args constructor for JavaBean style instantiation. */ private FullNameBean() {} public Name getFirstName() { return this.firstName; } public void setFirstName(final Name newFirstName) { this.firstName = newFirstName; } public Name getLastName() { return this.lastName; } public void setLastName(final Name newLastName) { this.lastName = newLastName; } public Name getMiddleName() { return this.middleName; } public void setMiddleName(final Name newMiddleName) { this.middleName = newMiddleName; } public Salutation getSalutation() { return this.salutation; } public void setSalutation(final Salutation newSalutation) { this.salutation = newSalutation; } public Suffix getSuffix() { return this.suffix; } public void setSuffix(final Suffix newSuffix) { this.suffix = newSuffix; } @Override public String toString() { return this.salutation + " " + this.firstName + " " + this.middleName + this.lastName + ", " + this.suffix; } } package dustin.examples; /** * Representation of a United States address (JavaBeans style). * * @author Dustin */ public final class AddressBean { private StreetAddress streetAddress; private City city; private State state; /** No-arguments constructor for JavaBeans-style instantiation. */ private AddressBean() {} public StreetAddress getStreetAddress() { return this.streetAddress; } public void setStreetAddress(final StreetAddress newStreetAddress) { this.streetAddress = newStreetAddress; } public City getCity() { return this.city; } public void setCity(final City newCity) { this.city = newCity; } public State getState() { return this.state; } public void setState(final State newState) { this.state = newState; } @Override public String toString() { return this.streetAddress + ", " + this.city + ", " + this.state; } } Example of JavaBeans Style Instantiation and Population public PersonBean createPerson() { final PersonBean person = new PersonBean(); final FullNameBean personName = new FullNameBean(); personName.setFirstName(new Name("Fred")); personName.setLastName(new Name("Flintstone")); person.setName(personName); final AddressBean address = new AddressBean(); address.setStreetAddress(new StreetAddress("345 Cave Stone Road")); address.setCity(new City("Bedrock")); person.setAddress(address); return person; } The examples just shown demonstrate how the JavaBeans style approach can be used. This approach makes some concessions to reduce the need to pass a large number of parameters to a class's constructor. Instead, no parameters are passed to the constructor and each individual attribute that is needed must be set. One of the advantages of the JavaBeans style approach is that readability is enhanced as compared to a constructor with a large number of parameters because each of the "set" methods is hopefully named in a readable way. The JavaBeans approach is simple to understand and definitely achieves the goal of reducing lengthy parameters in the case of constructors. However, there are some disadvantages to this approach as well. One advantage is a lot of tedious client code for instantiating the object and setting its attributes one-at-a-time. It is easy with this approach to neglect to set a required attribute because there is no way for the compiler to enforce all required parameters be set without leaving the JavaBeans convention. Perhaps most damaging, there are several objects instantiated in this last code listing and these objects exist in different incomplete states from the time they are instantiated until the time the final "set" method is called. During that time, the objects are in what is really an "undefined" or "incomplete" state. The existence of "set" methods necessarily means that the class's attributes cannot be final, rendering the entire object highly mutable. Regarding the prevalent use of the JavaBeans pattern in Java, several credible authors have called into questionits value. Allen Holub's controversial article Why getter and setter methods are evil starts off with no holds barred: Though getter/setter methods are commonplace in Java, they are not particularly object oriented (OO). In fact, they can damage your code's maintainability. Moreover, the presence of numerous getter and setter methods is a red flag that the program isn't necessarily well designed from an OO perspective. Josh Bloch, in his less forceful and more gently persuasive tone, says of the JavaBeans getter/setter style: "The JavaBeans pattern has serious disadvantages of its own" (Effective Java, Second Edition, Item #2). It is in this context that Bloch recommends the builder pattern instead for object construction. I'm not against using the JavaBeans get/set style when the framework I've selected for other reasons requires it and the reasons for using that framework justify it. There are also areas where the JavaBeans style class is particularly well suited such as interacting with a data store and holding data from the data store for use by the application. However, I am not a fan of using the JavaBeans style for instantiating a question simply to avoid the need to pass parameters. I prefer one of the other approaches such as builder for that purpose. Benefits and Advantages I've covered different approaches to reducing the number of arguments to a method or constructor in this post, but they also share the same trade-off: exposing mutable state to reduce or eliminate the number of parameters that must be passed to a method or to a constructor. The advantages of these approaches are simplicity, generally readable (though "globals" can be difficult to read), and ease of first writing and use. Of course, their biggest advantage from this post's perspective is that they generally eliminate the need for any parameter passing. Costs and Disadvantages The trait that all approaches covered in this post share is the exposure of mutable state. This can lead to an extremely high cost if the code is used in a highly concurrent environment. There is a certain degree of unpredictability when object state is exposed for anyone to tinker with it as they like. It can be difficult to know which code made the wrong change or failed to make a necessary change (such as failing to call a "set" method when populating a newly instantiated object). Conclusion Some of the approaches covered in this post are highly popular despite their drawbacks. This may be for a variety of reasons including prevalence of use in popular frameworks (forcing users of the framework to use that style and also providing examples to others for their own code development). Other reasons for these approaches' popularity is the relative ease of initial development and the seemingly (deceptively) relatively little thought that needs to go into design with these approaches. In general, I prefer to spend a little more design and implementation effort to use builders and less mutable approaches when practical. However, there are cases where these mutable approaches work well in reducing the number of parameters passed around and introduce no more risk than was already present. My feeling is that Java developers should carefully consider use of any mutable Java classes and ensure that the mutability is either desired or is a cost that is justified by the reasons for using a mutable state approach.

JavaScript’s prototype-based inheritance is interesting and has its uses, but sometimes one just wants to express classical inheritance, familiar from C++ and Java. This need has been recognized by the ECMAScript committee and classes are being discussed for inclusion in the next version of the standard. It was surprisingly hard for me to find a good and simple code sample that shows how to cleanly and correctly express inheritance with ES5 (a lot of links discuss how to implement the pre-ES5 tools required for that) and explains why the thing works. Mozilla’s Object.Create reference came close, but not quite there because it still left some open questions. Hence this short post. Without further ado, the following code defines a parent class named Shape with a constructor and a method, and a derived class named Circle that has its own method: // Shape - superclass // x,y: location of shape's bounding rectangle function Shape(x, y) { this.x = x; this.y = y; } // Superclass method Shape.prototype.move = function(x, y) { this.x += x; this.y += y; } // Circle - subclass function Circle(x, y, r) { // Call constructor of superclass to initialize superclass-derived members. Shape.call(this, x, y); // Initialize subclass's own members this.r = r; } // Circle derives from Shape Circle.prototype = Object.create(Shape.prototype); Circle.prototype.constructor = Circle; // Subclass methods. Add them after Circle.prototype is created with // Object.create Circle.prototype.area = function() { return this.r * 2 * Math.PI; } The most interesting part here, the one that actually performs the feat of inheritance is these two lines, so I’ll explain them a bit: Circle.prototype = Object.create(Shape.prototype); Circle.prototype.constructor = Circle; The first line is the magic – it sets up the prototype chain. To understand it, you must first understand that "the prototype of an object" and "the .prototype property of an object" are different things. If you don’t, go read on that a bit. The first line, interpreted very technically, says: the prototype of new objects created with the Circle constructor is an object whose prototype is the prototype of objects created by Shape constructor. Yeah, that’s a handful. But it can be simplified as: each Circle has a Shape as its prototype. What about the second line? While not strictly necessary, it’s there to preserve some useful invariants, as we’ll see below. Since the assignment to Circle.prototype kills the existing Circle.prototype.constructor (which was set to Circle when the Circle constructor was created), we restore it. Let’s whip up a JavaScript console and load that code inside, to quickly try some stuff: > var shp = new Shape(1, 2) undefined > [shp.x, shp.y] [1, 2] > shp.move(1, 1) undefined > [shp.x, shp.y] [2, 3] … but we’re here for the circles: > var cir = new Circle(5, 6, 2) undefined > [cir.x, cir.y, cir.r] [5, 6, 2] > cir.move(1, 1) undefined > [cir.x, cir.y, cir.r] [6, 7, 2] > cir.area() 12.566370614359172 So far so good, a Circle initialized itself correctly using the Shape constructor; it responds to the methods inherited from Shape, and to its own area method too. Let’s check that the prototype shenanigans worked as expected: > var shape_proto = Object.getPrototypeOf(shp) undefined > var circle_proto = Object.getPrototypeOf(cir) undefined > Object.getPrototypeOf(circle_proto) === shape_proto true Great. Now let’s see what instanceof has to say: > cir instanceof Shape true > cir instanceof Circle true > shp instanceof Shape true > shp instanceof Circle false Finally, here are some things we can do with the constructor property that wouldn’t have been possible had we not preserved it: > cir.constructor === Circle true // Create a new Circle object based on an existing Circle instance > var new_cir = new cir.constructor(3, 4, 1.5) undefined > new_cir Circle {x: 3, y: 4, r: 1.5, constructor: function, area: function} A lot of existing code (and programmers) expect the constructor property of objects to point back to the constructor function used to create them with new. In addition, it is sometimes useful to be able to create a new object of the same class as an existing object, and here as well the constructor property is useful. So that is how we express classical inheritance in JavaScript. It is very explicit, and hence on the long-ish side. Hopefully the future ES standards will provide nice sugar for succinct class definitions.

I wanted to know: how would Java EE compare to Node.js in this particular case?

This article will be useful if you want to support both PostgreSQL and SQLite using JDBC. It will be especially useful if you: Are already accessing values from your (PostgreSQL) database using the regular JDBC ResultSet interface, like: Date d = rs.getDate("date_field"); BigDecimal bd = rs.getBigDecimal("bigdecimal_field"); And it is creating trouble when doing the same for SQLite, but you don't want to change that code. Are already retrieving autogenerated keys in PostgreSQL with a RETURNING clause, but this won't work in SQLite. You want a unified solution that works for both databases. Thought foreign keys are enforced in SQLite by default (like in PostgreSQL) and crashed with a wall. SQLite is allowing you to delete entries from your tables even when they are referenced in another table and you have explicitly told SQLite about it with a REFERENCES table_name(field_name) clause. Are having trouble with the differences between PostgreSQL and SQLite dialects (mostly concerning data types), for example, when making query filters with boolean values. Had your own way to manage exceptions for PostgreSQL and it is not working for SQLite (obviously). You want SQLite to fit into the model you already have. Other stuff might appear if you keep up... A few months ago I wanted to migrate an app to use SQLite as a data backend. In fact, I wanted it to work with both PostgreSQL and SQLite indistinctly (but not at the same time). I wanted to switch between these two databases easily without changing any code. I did it, but along the way I had to solve some problems that might be interesting to many other people. Many solutions I found were spread across the web, but there was no single place that explained how to completely achieve what I wanted. So, the aim of this post is to try to condense my learning into one article that may be of help to others as a (semi) complete guide. This guide might be useful not only to those creating their own frameworks, but for anyone who doesn't use any and are willing to try some quirks and tricks to make their app work. THE BEGINNING There are many cross-database incompatibilities between PostgreSQL and SQLite, most notably on data types. If you want to have the same code to work for both databases, you better use a framework that manages this for you. But here's the thing: the framework I use is created by myself, and didn't (completely) take these differences into account, since I mainly use PostgreSQL as database; that's how and why my problems arose. My framework conveys many things, but I focus here in the data access part. It uses some JDBC driver to connect to the databases, but it provides more abstract ways to do it; that's pretty much the data access part of the framework. A basic DAO class for my framework would look like this: public class MyDAO extends BaseDAO { public MyDAO() { super("context_alias", new DefaultDataMappingStrategy() { @Override public Object createResultObject(ResultSet rs) throws SQLException { MyModel model = (MyModel)ObjectsFactory.getObject("my_model_alias"); model.setStringField(rs.getString("string_field")); model.setIntegerField(rs.getInt("integer_field")); model.setBigDecimalField(rs.getBigDecimal("bigdecimal_field")); model.setDateField(rs.getDate("date_field")); model.setBooleanField(rs.getBoolean("boolean_field")); return model; } }); } @Override public String getTableName() { return "table_name"; } @Override public String getKeyFields() { return "string_field|integer_field"; } @Override protected Map getInsertionMap(Object obj) { Map map = new HashMap(); MyModel model = (MyModel) obj; map.put("string_field", model.getStringField()); map.put("integer_field", model.getIntegerField()); map.put("bigdecimal_field", model.getBigDecimalField()); map.put("date_field", model.getDateField()); map.put("boolean_field", model.getBooleanField()); return map; } @Override protected Map getUpdateMap(Object obj) { Map map = new HashMap(); MyModel model = (MyModel) obj; map.put("bigdecimal_field", model.getBigDecimalField()); map.put("date_field", model.getDateField()); map.put("boolean_field", model.getBooleanField()); return map; } @Override public String getFindAllStatement() { return "SELECT * FROM :@ "; } So, that I wanted to switch between databases without changing code means that I wanted to switch without changing my DAO classes. For SQLite, I used the xerial-jdbc-sqlite driver. I talk about drivers because there are some things that might be driver-specific when solving some problems; so when I say 'SQlite does it this way', I generally mean 'xerial-jdbc-sqlite driver does it this way'. Now, let's start. WARNING: Some of the solutions I give here fit into my framework, but might not directly fit into your code. It's up to you to imagine how to adapt what I provide here. DATA TYPES Since there are some differences between PostgreSQL and SQLite regarding data types, and I wanted to continue to access database values through the regular ResultSet interface, I had to have some mechanism to intercept the call to, for instance, resultset.getDate("date_field"). So I created a ResultSetWrapper class that would redefine the methods I was interested in, like this: public class ResultSetWrapper implements ResultSet { // The wrappped ResultSet ResultSet wrapped; /* I will use this DateFormat to format dates. I'm assuming an SQLite style pattern. I should not */ SimpleDateFormat df = new SimpleDateFormat("yyyy-mm-dd"); public ResultSetWrapper(ResultSet wrapped) { this.wrapped = wrapped; } /* Lots of ResultSet methods implementations go here, but this is an example of redefining a method I'm interested in changing its behavior: */ public Date getDate(String columnLabel) throws SQLException { Object value = this.wrapped.getObject(columnLabel); return (Date)TypesInferreer.inferDate(value); } } The getDate() method in ResultSetWrapper relies on TypesInferreer to convert the value retrieved to a Date value. All data types convertions would be encapsulated inside TypesInferreer, which would have methods to convert from different data types as needed. For instance, it would have a method like this one: public static Object inferDate(Object value) { java.util.Date date; // Do convertions here (convert value and asign to date) return date; } Which tries to convert any value to a Date (I'll show the actual implementation further). Now, instead of using the original resultset retrieved from saying preparedStatement.executeQuery(), you use new ResultSetWrapper(preparedStatement.executeQuery()). That's what my framework does: it passes this new resultset to DAO objects. Now let's see some type conversions. Mixing PostgreSQL Date and SQLite Long/String You could store Date values as text in a SQLite database (eg. '2013-10-09'); this you can do manually when creating the database, but when SQLite stores a Date object, by default it converts it to a Long value. There is no problem with this when saving the value to the SQLite database, but if you try to retrieve it using resultset.getDate("date_field"), then things get messy; It simply won't work (CastException). How do you access Date values, then? You create this method in TypesInfereer, which covers both String and Long variations: public static Object inferDate(Object value) { java.util.Date date = null; if(value == null) return null; if(value instanceof String) { try { date = df.parse((String)value); } catch (ParseException ex) { // Deal with ex } } else if(value instanceof Long) { date = new java.util.Date((Long)value); } else { date = (Date)value; } return new Date(date.getTime()); } And as you saw, the getDate() function in ResultSetWrapper is redefined like this: @Override public Date getDate(String columnLabel) throws SQLException { Object value = this.wrapped.getObject(columnLabel); return (Date)TypesInferreer.inferDate(value); } Now all DAOs can retrieve Date values from both databases indistinctly, using resultset.getDate("date_field"). Mixing PostgreSQL Numeric and SQLite Integer/Double/... My SQLite driver didn't implement the getBigDecimal() function. It complained like this when I called it: java.sql.SQLException: not implemented by SQLite JDBC driver. So I had to come up with a solution that was valid for both PostgreSQL and SQlite. This is what I did in ResultSetWrapper: @Override public BigDecimal getBigDecimal(String columnLabel) throws SQLException { Object value = this.wrapped.getObject(columnLabel); return (BigDecimal)TypesInferreer.inferBigDecimal(value); } But value would get different types depending on the actual value stored in the database; it could be an Integer, or a Double, or perhaps something else. I solved all the cases by doing this in TypesInfereer: public static Object inferBigDecimal(Object value) { if(value == null) return null; if(value instanceof BigDecimal == false) { return new BigDecimal(String.valueOf(value)); } return value; } Anyway, the String constructor of BigDecimal is the recommended one, so everything's fine with this. Now you can retrieve BigDecimal values using resultset.getBigDecimal("bigdecimal_field") from both databases. Mixing PostgreSQL Boolean and SQLite Integer SQLite doesn't have boolean values. Instead, it interprets any other value as boolean by following some rules. When SQLite saves a Boolean value to the database, it saves it as 0 or 1 for false or true respectively. Also, because drivers can interpret any value as boolean, you can use resultset.getBoolean("boolean_field") and it will work as expected by the rules. But the problem I faced was when creating filters. If a value for true is stored as 1 in the SQLite database, you can't expect the clause WHERE boolean_field = true to work. You will never find a match. Instead, you should have said WHERE boolean_field = 1. In my app, I created filters like this: dao.addFilter(new FilterSimple("boolean_field", true)); Now I needed FilterSimple to infer that, for SQLite, I meant 1 instead of true. So I created what I called a DatasourceVariation. These are objects that are specific for each type of database and are used accross all data accesses, by DAOs, Filters, and other objects. These objects would take care of managing all my cross-database incompatibilities, including: The way to reference a database object: in PostgreSQL you must prepend the schema name to every database object you refer in your queries. In SQLite you don't. The way to manage exeptions: explained further in this post. The way to backup and restore data: explained further in this post. Expressing BETWEEN clauses: Explained further in this post. And also, infering boolean values. For VariationSQLite, I did this: @Override public Object getReplaceValue(Object value) { if(value instanceof Boolean) { if((Boolean)value == true) return new Integer(1); else return new Integer(0); } return value; } Now we can say dao.addFilter(new FilterSimple("boolean_field", true)) for both databases, assuming that FilterSimple uses the variation to adapt the value before constructing the clause. RETRIEVING AUTOGENERATED KEYS When you have autonumeric fields (eg. Serial), in PostgreSQL you can specify a RETURNING clause at the end of an INSERT statement to automatically retrieve the values of autogenerated fields by doing this: PreparedStatement pstm = conn.prepareStatement(queryWithReturningClause); // ex. select * from table_x returning field_x ResultSet rs = statement.executeQuery(); if(rs.next()) { // Get autogenerated fields from rs } But that won't work with SQLite. In SQLite, retrieving autogenerated fields conveys a process that goes from creating the statement, executing the query and explicitly asking for the generated values. Like this: PreparedStatement pstm = conn.prepareStatement(queryWITHOUTreturningClause, Statement.RETURN_GENERATED_KEYS); pstm.executeUpdate(); ResultSet rs = pstm.getGeneratedKeys(); if (rs != null && rs.next()) { // Get autogenerated fields from rs } The good news is that this code works both for PostgreSQL and SQLite, so I replaced my previous code for this, and didn't have to make any distinction between databases. ENFORCING FOREIGN KEYS You'd think that using a REFERENCES table_name(field_name) clause when creating a SQLite database table makes foreign keys to be checked when deleting, updating, etc. You're wrong! Foreign keys are not enforced in SQLite by default. You have to explicitly say it, and it's done when creating the connection (WARNING: This is very driver-specific): SQLiteConfig config = new SQLiteConfig(); config.enforceForeignKeys(true); Connection conn = DriverManager.getConnection("jdbc:sqlite:" + dataSourcePath, config.toProperties()); For PostgreSQL it's different, so you better have a connection pool for each type of database, and decide which one to use at runtime. My framework does exactly that. NOTE: If you are capable of getting the connection depending on the database type, then you can enforce foreign keys transparently for both databases (for PostgreSQL it happens naturally without extra code). For instance, you could have an abstract getConnection() method, and each database's connection pool would return the connection in its own way. MANAGING EXCEPTIONS I had defined some different types of database exceptions in my framework: ExceptionDBDuplicateEntry, ExceptionDBEntryReferencedElsewhere, etc, which would be thrown and raised to upper layers in my architecture. For PostgreSQL, these exceptions directly mapped to some constant codes (which normally are vendor/driver specific): UNIQUE_VIOLATION = "23505", FOREIGN_KEY_VIOLATION = "23503", etc. So, for PostgreSQL, I managed database exceptions something like this: @Override public void manageException(SQLException ex) throws ExceptionDBDuplicateEntry, ExceptionDBEntryReferencedElsewhere { if (ex.getSQLState() == null) { ex = (SQLException) ex.getCause(); } if (ex.getSQLState().equals(UNIQUE_VIOLATION)) { throw new ExceptionDBDuplicateEntry(); } else if(ex.getSQLState().equals(FOREIGN_KEY_VIOLATION)) { throw new ExceptionDBEntryReferencedElsewhere(); } else { DAOPackage.log(ex); throw new ExceptionDBUnknownError(ex); } } That won't work for SQLite, obviously! So, what I did was move the database exceptions management to the DataSourceVariation. The VariationPostgresql class would have a method similar to the one above. For VarialtionSQLite, I did sort of a hack, but it's something that has worked until now (maybe until I change my driver). @Override public void manageException(SQLException ex) throws ExceptionDBDuplicateEntry, ExceptionDBEntryReferencedElsewhere { // This is a hack (is it???) String message = ex.getMessage().toLowerCase(); if(message.contains("sqlite_constraint")) { if(message.contains("is not unique")) throw new ExceptionDBDuplicateEntry(); else if(message.contains("foreign key constraint failed")) throw new ExceptionDBEntryReferencedElsewhere(); else { DAOPackage.log(ex); throw new ExceptionDBUnknownError(ex); } } else { DAOPackage.log(ex); throw new ExceptionDBUnknownError(ex); } } Update: This technique might have some flaws. But hey, can you find a better approach right away? FIXING BETWEEN CLAUSE The problem with the BETWEEN clause appeared while using a filter like this: dao.addFilter(new FilterBetween("date_field", date1, date2)); // date1 and date2 are java.util.Date objects FilterBetween would create a BETWEEN clause by formatting Dates as Strings, normally with the format 'yyyy-MM-dd' (although this should be configurable). Since dates in SQLite are long values, we can't create a clause like date_field BETWEEN '2013-01-01' AND '2013-02-01'. It had to be something like date_field >=1357016400000 AND date_field <= 1359694800000. So, I moved the creation of BETWEEN clauses to.... that's right, to DataSourceVariation. VariationSQLite does it like this: @Override public String getBetweenExpression(String fieldName, Object d1, Object d2) { String filter = ""; try { Date dd1 = null; Date dd2 = null; SimpleDateFormat df = new SimpleDateFormat("yyyy-mm-dd"); // Remember, this should be configurable if(d1 instanceof String) dd1 = df.parse((String)d1); else dd1 = (Date)d1; if(d2 instanceof String)dd2 = df.parse((String)d2); else dd2 = (Date)d2; filter = fieldName + " >= " + dd1.getTime() + " AND " + fieldName + " <= " + dd2.getTime(); } catch (ParseException ex) { DAOPackage.log(ex); throw new ExceptionDBUnknownError(ex); } return filter; } CONCLUSIONS As you can see, there are many intricacies when making an app support multiple database types. All I did here was only to support PostgreSQL and SQLite, but who knows what is needed to support other databases at the same time too. You can't expect JDBC alone will do all the work, so be prepared to solve some problems (and another problem, and another, ...) to make a database migration. And please, share your journey.

Introduction One of the nice recent features of Spring (2.x era) is support for custom XML. This is the way that Spring itself has added all kinds of new tags such as and . The way this works is pretty elegant, to the point that it makes an interesting alternative for configuring Java using XML, particularly if the application already uses Spring. I’ve written an example application to try to give an easily-copied example of how it’s done. The example uses Spring and a custom XML parser to build dynamic Swing menus. It makes a nice comparison to doing dynamic Swing menus using the Digester version I posted a while back. Of course, this is not a good way to make Java menus in general! In most applications, this would be an example of Soft Coding. This would really only make sense in an application where it was really important to be able to add or remove menus without changing Java code. So treat it as a nice example, but please don’t start making your GUIs this way. Spring Custom XML Custom XML works in a Spring configuration file because Spring can dynamically validate and parse XML. To do this, Spring first has to be able to validate the XML it parses against a schema. It does this by looking for all files on the classpath called META-INF/spring.schemas. These files provide a location on the classpath for the XML schema that goes with a given namespace. For example, the “core” XML for Spring is defined in the beansnamespace. The META-INF/spring.schemas file in the spring-beans JAR has entries like this one: http\://www.springframework.org/schema/beans/spring-beans-3.0.xsd=org/springframework/beans/factory/xml/spring-beans-3.0.xsd So when we use the beans schema in our Spring XML, it knows where on the classpath to hunt down the schema so it can validate that XML. Once the schema is validated, Spring needs to find a “handler” that knows how to make Spring beans based on the XML. Spring finds handlers by looking through all the files on the classpath called META-INF/spring.handlers. Thespring.handlers file in the spring-beans JAR has entries like this one: http\://www.springframework.org/schema/p=org.springframework.beans.factory.xml.SimplePropertyNamespaceHandler It’s really the job of the handler to make bean definitions, not the regular Java objects that will live as beans in the Spring application context. This is because Spring still has to manage things like beans depending on other beans, which means Spring has to parse all the XML to figure out the dependency graph before any objects can be instantiated. Example Application Our example application has several parts: The spring.schemas and spring.handlers files in META-INF. An XML schema defining what is valid in our custom namespace. MenuNamespaceHandler, the entry class that allows us to register what XML elements go with what parser classes. MenuDefinitionParser, the actual XML parser for our custom XML namespace. A regular Spring XML configuration file that also includes our custom XML. A main class to get the whole thing kicked off. There’s also a Java class called MenuItem that we use to store the ID, the title, and any children of the menu item. It doesn’t know anything about Spring or XML; it’s just a POJO. Defining the custom XML The spring.schemas file is pretty simple. Note that it’s matching to a file on the classpath; Spring is not going to be looking out on the Internet for your XML schema at runtime. http\://anvard.org/springxml/menu.xsd=org/anvard/springxml/menu.xsd The spring.handlers file is also pretty simple. It just points to the right handler class: http\://anvard.org/springxml/menu=org.anvard.springxml.MenuNamespaceHandler The XML schema is omitted here; it’s an XML schema and not much need be said. Of note is that it allows for arbitrary nesting of elements inside other elements. One more piece of boilerplate; the namespace handler. Since our namespace is really simple and only contains one top-level element (menu), it’s a one-liner: public void init() { registerBeanDefinitionParser("menu", new MenuDefinitionParser()); } The parser is where it gets interesting. The parser will get called while Spring is reading the XML file, whenever it comes across an element that belongs to the matching namespace. However, it will only be called for the top-level element; it’s up to us to handle any nested elements as required. protected AbstractBeanDefinition parseInternal(Element element, ParserContext context) { BeanDefinitionBuilder builder = parseItem(element); List childElements = DomUtils.getChildElementsByTagName( element, "menu"); if (null != childElements && childElements.size() > 0) { ManagedList children = new ManagedList<>( childElements.size()); for (Element child : childElements) { children.add(parseInternal(child, context)); } builder.addPropertyValue("children", children); } return builder.getBeanDefinition(); } private BeanDefinitionBuilder parseItem(Element element) { BeanDefinitionBuilder builder = BeanDefinitionBuilder .rootBeanDefinition(MenuItem.class); String id = element.getAttribute("id"); if (StringUtils.hasText(id)) { builder.addPropertyValue("id", id); } String title = element.getAttribute("title"); if (StringUtils.hasText(title)) { builder.addPropertyValue("title", title); } String listener = element.getAttribute("listener"); if (StringUtils.hasText(listener)) { builder.addPropertyReference("listener", listener); } return builder; } In this case, because we allowed for the idea that a menu could contain child menus, we have to handle that here with some recursion. Note that for every element at whatever level, we are creating a separate Spring bean definition (that’s one purpose of the rootBeanDefinition() static method call). The really important thing to notice is that as we build the bean definition, we are not creating a MenuItem object directly, nor are we setting any properties directly. In fact, in the case of the children property, we are not even building a list of the correct type, as the MenuItem class expects to receive a list of MenuItem children, but we are building a list ofAbstractBeanDefinition. Spring handles all of the necessary wiring when it actually instantiates our MenuList objects, including looking up each of the references in the list and populating a new list with the real objects. One other thing that’s slightly confusing is that a reference to a single other Spring bean uses addPropertyReference(), while a managed list of Spring bean definitions uses addPropertyValue(). Using the custom XML Now that these items are in place, we can use the custom XML just the same as any other XML in a Spring configuration file. For example: Note that we can make our custom XML the default namespace so we don’t have to prefix our XML elements; we can also make the bean namespace the default as is more typical in a Spring XML configuration file. We can mix our custom XML freely with standard Spring XML. Also note that our custom XML can make references back to ordinary Spring beans as long as we do the right thing in our parser to make this work. We use a list called toplevel as a handy way of finding the outermost menu items for our menu bar. Once the XML is parsed, the beans are all loaded into the Spring application context and the structure of the XML no longer really applies. Using this file from our main class looks just the same as any Spring code: ClassPathXmlApplicationContext ctx = new ClassPathXmlApplicationContext("/menuDefinition.xml"); All of our menu items are available in the Spring application context, so we could do ctx.getBean("menu9") and get back the menu item with the title “Child 5”. Conclusion Even though many Spring users are shifting toward annotation-driven configuration, there are still things that are easier to do in XML, like creating many instances of a class with different properties. A custom XML namespace is a way to make Spring XML configuration more compact and more readable.

These days, no community member writes or speaks about using DataTables and DataSets for data operations. But, there are a number of real projects built using them, and many developers still feel happy when they use them in their projects. Sometimes it is not easy to completely replace DataTables with typed generic lists, particularly in bulky projects. But now is the right time to move, as future developers may not even learn about DataTables :). Generic collections have a number of advantages over DataTables. One cannot imagine a day without generic collections once he/she gets to know how beneficial they are. The following is a list of the reasons to move from DataTables to collections that I could think of now: DataTable stores boxed objects, and one needs to unbox values when needed. This adds overhead on the runtime environment. However, values in generic collections are strongly typed, so no boxing involved. Unboxing happens at runtime, as does the type checking. If there is a mismatch between types of source and target, it leads to a runtime exception. This may lead to a number of issues while using DataTables. In case of collections, as the types are checked at the compile time, such type mismatches are caught during compilation. .NET languages got very nice support for creating collections, like object initializer and collection initializer. We don’t have such features for DataTables. LINQ queries can be used on both DataTables and collections. But the experience of writing the queries on generic collections is better because of IntelliSense support provided by Visual Studio. DataTables are framework specific; we often see issues with serializing and de-serializing them in web services. Generic collections are easier to serialize and de-serialize, so they can be easily used in any service and consumed from a client written in any language. ORMs are becoming increasingly popular, and they use generic collections for all data operations. Mocking DataTables in unit tests is a pain, as it involves creating the structure of the table wherever needed. But a generic collection needs a class defined just once. These are my opinions on preferring collections over DataTables. Any feedback is welcome. Happy coding!