Gradle and Update of SNAPSHOT Dependencies

We use Gradle as the main build tool for our Java projects. Builds are scheduled by our Hudson CI server and artifacts are published with Gradle to our Artifactory server. Artifactory is also used for dependency resolution and referenced as a Maven repository within our Gradle setup.

The most annoying problem in this special constellation is that SNAPSHOT dependencies are not properly updated for depending projects. For example, if project B depends on project A and project A is currently in a SNAPSHOT state (i.e. the version entry in the build.gradle looks something like '2.1.3-SNAPSHOT'), then project B would not always get the latest artifact version of project A. Entries on the Gradle user mailing list, numerous blog posts, and some JIRA tickets show that other people are facing similar problems, but there seems not to be a common solution for this problem, but each solution depends on a very specific build environment and cannot simply be adopted in other constellations.

It is not clear to me whether this (in our constellation) is a Gradle problem, an general Ivy bug, a problem with the Ivy Maven adapter or a missconfiguration of our artifactory server. Recently, I tried several different solution approaches and found one in a comment of GRADLE-629 which is actually working for us. When configuring the project dependencies of a project, explicitly setting the changing property for all dependencies in SNAPSHOT state resolves our update problems. For example, project B from above would configure the project A dependency like

dependencies {
  compile ('my.fancy:project-a:2.1.3-SNAPSHOT') { changing=true }
}

Whenever a new version of project A is available in Artifactory, this latest version is downloaded when Gradle tasks are executed for project B.

Although this approach works, it still feels more like a workaround than a solution. If someone knows the real problem cause and a better solution, please feel free to comment.

MultipleOutputFormat and File Handle Limitations

Recently, we used Hadoop for a heavy batch processing job. The job itself was not very special, in fact, the very same job is run on a daily basis to process some sort of data incrementally. The job instances had run fine for several months. Now we wanted to process the data of some months at once and all of a sudden, the processing job died with nasty (and somehow missleading) exceptions. The reduce task logs were filled with lots of stacktraces like

java.io.EOFException
 at java.io.DataInputStream.readByte(DataInputStream.java:250)
 at org.apache.hadoop.io.WritableUtils.readVLong(WritableUtils.java:298)
 at org.apache.hadoop.io.WritableUtils.readVInt(WritableUtils.java:319)
 at org.apache.hadoop.io.Text.readString(Text.java:400)
 at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.createBlockOutputStream(DFSClient.java:2901)
 at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.nextBlockOutputStream(DFSClient.java:2826)
 at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.access$2000(DFSClient.java:2102)
 at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:2288)

This stacktrace alone did not provide a lot of information about the real exception cause. However, together with the corresponding name node logs, it became clear that the corresponding reduce processes could not open new files for output writing. After realizing that the job is using org.apache.hadoop.mapred.lib.MultipleOutputFormat for output writing, the reason for the failing jobs became clear: file handle limitations. The only question was: which ones? The Linux OS has these limitations and Hadoop's HDFS as well. To make a long story short, we had to increase both of them.

Linux and Open File Limits

Linux limits the number of parallel open files on a per process basis. A given user might only start a certain number of processes (type ulimit -u to see your own limit) in parallel and each of these processes is only allowed to write to a certain number of files in parallel (ulimit -n). The default value for open files is 1024 (at least in debian/ubuntu-flavored distributions). To get around our problem from above, we increased this limit for the user running our Hadoop cluster by editing the /etc/security/limits.conf. To make the machine recognize the new limits, it is necessary to log out and afterwards back in again. However, in our case we do not login as the user hadoop directly, but using the su command. Thus, no new login shell is started and the configuration option would not be recognized. In his blog post, Armin describes how to edit /etc/pamd.d/su in this scenario.

Hadoop and Open File Limits

Hadoop (we use version 0.20.2) has configuration parameters for almost everything. The one to specify the number of parallel files per datanode is named dfs.datanode.max.xcievers. Unfortunately, if not set otherwise, datanodes are at startup equipped with only 256 parallel file handles (see org.apache.hadoop.hdfs.server.datanode.DataXceiverServer class for details). The HBase documentation on the xcievers parameter recommends a value of 4096. As stated before, this parameter is evaluated during datanode startup time. Therefore, it is necessary to configure this parameter in the conf/hdfs-site.xml file on each datanode and restart the cluster afterwards.

Summary

We had to increase both the OS specific limits and the limits in the Hadoop configuration. None of them alone was sufficient. To accomplish the configuration changes, we had to update setting on each datanode machine and to restart the cluster. Afterwards, these exceptions from above were only to be seen on cluster nodes which were not properly updated.

Shutdown MiniDFSCluster and MiniMRCluster Takes Forever

Writing unit tests for Hadoop applications does not need to be more complicated than writing tests for any other Java application.
Usually, I use the following procedure for testing my Hadoop code:

1. Testing the classes as real units.
Each Mapper and each Reducer deserves to be tested in isolation. Spock with its awesome support for mocking and stubbing is a great tool for testing units in isolation. With few 3rd party dependencies it is even possible to mock final classes, bypass the existing constructors of these classes, and inject them into private fields of the units under test => isolation at its best (more on this in another post).
Furthermore, there might be other units like custom Writables, InputFormat and OutputFormat implementations and classes containing the business logic to convert data, and so on. I try to write a Specification (the Spock counterpart of a TestCase) for each non-trivial class.
This should reveal most of the nasty little bugs contained in the business logic of the application. Small side-note: I know, there is a mr-unit test framework provided by Cloudera. Personally, I found Spock to be more powerful and flexible, but as always, this is merely a matter of taste.
2. Next step is to execute complete job roundtrips using the local Hadoop mode, again with Spock. If the application features a command line interface, I set up a simple Specification which executes the main() method of the main driver class and compares some expected output files against the actual output files.
Being able to execute jobs in local mode is great, because it is a fast way to run blackbox test against the map reduce framework. Howerver, since the local mode is somewhat limited, it might be necessary to use a real cluster. For example, in local mode it is not possible to run more than one reducer. This limits the testing capabilities of custom partitioning and grouping code.
3. To execute m/r code on a real cluster, I often use the hadoop-test project (include it with testCompile 'org.apache.hadoop:hadoop-test:0.20.2' in the build.gradle file). This project features implementations of both a distributed filesystem (org.apache.hadoop.hdfs.MiniDFSCluster) and a m/r cluster (org.apache.hadoop.mapred.MiniMRCluster). The most annoying thing about these clusters is the very long startup time, but hey, they're distributed. The remainder of this post is about usage of these clusters.

In case there are several different Hadoop jobs to test or only a single job with different configurations, because of the long startup time, one should think about putting the cluster management code into the static setup methods of the test framework.

static MiniMRCluster mrCluster
static MiniDFSCluster dfsCluster

def setupSpec() {
	def conf = new JobConf()
	if (System.getProperty("hadoop.log.dir") == null) {
		System.setProperty("hadoop.log.dir", "/tmp");
	}
		
	dfsCluster = new MiniDFSCluster(conf, 2, true, null)
	mrCluster = new MiniMRCluster(2, dfsCluster.getFileSystem().getUri().toString(), 1)
		
	def hdfs = dfsCluster.getFileSystem()
	hdfs.delete(new Path('/main-testdata'), true)
	hdfs.delete(new Path('/user'), true)
	FileUtil.copy(inputData, hdfs, new Path('main-testdata'), false, conf)
}

Notes:

• setupSpec() is the Spock equivalent of JUnit 4's @BeforeClass
• If the system property (lines 6 and 7) is not set, the m/r cluster will not startup
• The clusters are configured to use 2 slave nodes each
• After successful filesystem startup, it is possible to perform the usual filesystem operations with it

Teardown and cleanup is similarly performed in the static context:

def cleanupSpec() {
	mrCluster?.shutdown()
	dfsCluster?.getFileSystem().delete(new Path('/main-testdata'), true)
	dfsCluster?.getFileSystem().delete(new Path('/user'), true)
	dfsCluster?.shutdown()
}

However, there is a really annoying problem with this code: it takes forever. I don't know why (presumably, I'm doing something wrong), but this shutdown procedure is blocked by several data integrity tests which take a long time themselves. Since the generated data is garbage and of zero relevance after the tests have completed, I'd like to get rid of these checks, but I really cannot figure out how. The logs get s-l-o-w-l-y filled with lines like

11/09/19 23:47:06 INFO datanode.DataBlockScanner: Verification succeeded for blk_3329401068442722923_1001
11/09/19 23:47:12 INFO datanode.DataBlockScanner: Verification succeeded for blk_654270692326292497_1008
11/09/19 23:47:39 INFO datanode.DataBlockScanner: Verification succeeded for blk_-3673127094860948561_1006

and the single test and thus the whole test suite as well takes several minutes to complete, fatal for each continuous integration system. However, there is a workaround which is neither obvious nor very nice, but it works: wrapping the shutdown procedure in another thread. Simply by modifying the code from above into

def cleanupSpec() {
	Thread.start { 
		mrCluster?.shutdown()
		dfsCluster?.getFileSystem().delete(new Path('/main-testdata'), true)
		dfsCluster?.getFileSystem().delete(new Path('/user'), true)
		dfsCluster?.shutdown()
	}.join(5000)
}

I could not spot any blocking behavior any longer. If someone has a better idea to avoid blocking on shutdown, please feel free to comment.

Hosting a Maven Repository on Github for Gradle Builds

Gradle is a great tool for organizing the build logic of software projects. Gradle is able to handle all the dependency management necessary even for very small projects. Under the hood, it relies on Ivy and can deal with Maven repositories.
The question is where to upload my own artifacts which should be accessible as dependencies for other projects. As always, there are several alternatives to choose from. A small excerpt:

1. Use one of the free Maven repository hosting services as provided by Sonatype for example
2. Set up an own server with Artifactory or Nexus or something similar (although http, ftp, ssh, ... would suffice, as well)
3. Create a new Git project and publish it to Github

One benefit of the Sonatype solution is the automated repository synchronization with the Maven Central Repository. However, since the projects have to fulfill a certain set of requirements and I do not need to find my pet projects at Maven Central, this is not my preferred solution.
The company I work for uses Artifactory set up on a dedicated server which is running 24/7 and plays nicely with the continuous integration system. Again, this seems a bit overkill for my private projects.
Since I'm using Git already and have a Github account featuring a very small number of projects, the third alternative seems very appealing to me. This solution requires three basic steps:

1. Set up and synchronize Git repositories
2. Upload project artifacts to local Git repository and synchronize it with Github
3. Configure the Github project as mavenRepo within Gradle build files for depending projects

Step 1: Create a Git Project and Synchronize with Github

For Github project creation I follow the Github Repository Creation Guide. At first I create a repository on Github using the web interface.

Afterwards, I set up a local repository und synchronize it with Github using the following steps:

$ mkdir maven-repository
$ cd maven-repository
$ git init
Initialized empty Git repository in /home/thevis/GIT/maven-repository/.git/
$ touch README.md
$ git add README.md
$ git commit -m 'first commit'
[master (root-commit) eeee5a8] first commit
 0 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 README.md
$ git remote add origin git@github.com:tthevis/maven-repository.git
$ git push -u origin master
Counting objects: 3, done.
Writing objects: 100% (3/3), 209 bytes, done.
Total 3 (delta 0), reused 0 (delta 0)
To git@github.com:tthevis/maven-repository.git
 * [new branch]      master -> master
Branch master set up to track remote branch master from origin.
$ 

A quick check at Github shows that README.md has actually been pushed to Github and that repository snchronization works fine.

Step 2: Upload Project Artifacts to Local Git Repository

My intention is to establish the following release workflow for my projects: Make and upload a release build using gradle clean build uploadArchives, manually check results in the local Git directory, add it to the Git repository, and finally push it to Github. Well, I know that software releases should not involve manual steps and should be performed on dedicated machines runing build server software and so on and so on...
However, since I'm the only contributor for my projects and the release cycles are somewhat unsettled, this procedure is fairly sufficient for my needs.
Therefore, I change to a project to be released and add following lines to the build.gradle file:

apply plugin: 'maven'

uploadArchives {
	repositories.mavenDeployer {
		repository(url: "file:///home/thevis/GIT/maven-repository/")
	}
}

Executing the release procedure described above yields the following result:

$ gradle clean build uploadArchives
[...suppressed a few output lines here...]
:build
:uploadArchives
Uploading: net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.jar to repository remote at file:///home/thevis/GIT/maven-repository/
Transferring 5239K from remote
Uploaded 5239K

BUILD SUCCESSFUL

Total time: 24.443 secs

Just to be sure, I check the result by hand:

$ find /home/thevis/GIT/maven-repository/ -name "*groovy-hadoop*"
/home/thevis/GIT/maven-repository/net/thevis/hadoop/groovy-hadoop
/home/thevis/GIT/maven-repository/net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.jar
/home/thevis/GIT/maven-repository/net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.jar.md5
/home/thevis/GIT/maven-repository/net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.jar.sha1
/home/thevis/GIT/maven-repository/net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.pom
/home/thevis/GIT/maven-repository/net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.pom.sha1
/home/thevis/GIT/maven-repository/net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.pom.md5

Looks good, so I add the files to Git and push them to Github:

$ cd /home/thevis/GIT/maven-repository/
$ git add .
$ git commit -m "released groovy-hadoop-0.2.0"
[master 5026f70] released groovy-hadoop-0.2.0
 9 files changed, 40 insertions(+), 0 deletions(-)
 create mode 100644 net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.jar
 create mode 100644 net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.jar.md5
 create mode 100644 net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.jar.sha1
 create mode 100644 net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.pom
 create mode 100644 net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.pom.md5
 create mode 100644 net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.pom.sha1
 create mode 100644 net/thevis/hadoop/groovy-hadoop/maven-metadata.xml
 create mode 100644 net/thevis/hadoop/groovy-hadoop/maven-metadata.xml.md5
 create mode 100644 net/thevis/hadoop/groovy-hadoop/maven-metadata.xml.sha1
$ git push origin master
Counting objects: 17, done.
Delta compression using up to 2 threads.
Compressing objects: 100% (7/7), done.
Writing objects: 100% (16/16), 5.12 MiB | 98 KiB/s, done.
Total 16 (delta 0), reused 0 (delta 0)
To git@github.com:tthevis/maven-repository.git
   eeee5a8..5026f70  master -> master

Step 3: Use Custom Repository for Depending Projects

There is only one potential pitfall here. The most difficult part is to determine the URL scheme for downloading files from Github. The file groovy-hadoop-0.2.0.jar page reveales the real artifact URL if one hovers the mous pointer over the raw link:
https://github.com/tthevis/maven-repository/raw/master/net/thevis/hadoop/groovy-hadoop/0.2.0/groovy-hadoop-0.2.0.jar. Thus, the maven repository URL to configure is not https://github.com/tthevis/maven-repository but rather https://github.com/tthevis/maven-repository/raw/master/.
With this finding in mind, all the rest is pretty straightforward. For testing purposes I set up the follwing build.gradle:

apply plugin: 'java'
apply plugin: 'eclipse'

version = '0.1.0'
group = 'net.thevis.hadoop'
sourceCompatibility = '1.6'

repositories { 
	mavenRepo urls: 'https://github.com/tthevis/maven-repository/raw/master/'
} 

dependencies {
	compile 'net.thevis.hadoop:groovy-hadoop:0.2.0'
}

And guess what? It works like a charm.

Spock in a Gradle-Powered Groovy Project

Spock. Is. Great.
One can do wonderful things with Spock, at leat when it comes to testing software. One of the fun things is that one can use Spock both for Groovy and for Java and obviously for mixed projects as well. I'll write about usecases and examples in another post. This one is about setting up Spock for a Gradle-powered Groovy project.

Basic Build

Spock relies heavily on Groovy itself, so the desired Spock version has to match the Groovy dependency for the project.
I tried it with Groovy-1.8.1 and Spock-0.5-groovy-1.8.
Excerpt from the build.gradle file:

apply plugin: 'groovy'
apply plugin: 'eclipse'

repositories { 
  mavenCentral()
} 

dependencies {
  groovy 'org.codehaus.groovy:groovy-all:1.8.1'
  testCompile 'org.spockframework:spock-core:0.5-groovy-1.8'
}

However, the gradle eclipse fails with an unresolved dependency:

:eclipseClasspath
:: problems summary ::
:::: WARNINGS
		module not found: org.codehaus.groovy#groovy-all;1.8.0-beta-3-SNAPSHOT
[...]
FAILURE: Build failed with an exception.

* Where:
Build file '/home/thevis/spock-test/build.gradle'

* What went wrong:
Execution failed for task ':eclipseClasspath'.
Cause: Could not resolve all dependencies for configuration 'detachedConfiguration1':
    - unresolved dependency: org.codehaus.groovy#groovy-all;1.8.0-beta-3-SNAPSHOT: not found


* Try:
Run with --stacktrace option to get the stack trace. Run with --info or --debug option to get more log output.

BUILD FAILED

Total time: 1.431 secs

What does this mean? gradle dependencies does not show any peculiarities. So I really don't know.
Fortunately, although kind of annoying, this is not really a problem for the project. Since the project is a Groovy project already, it is possible to exclude all the transitive Groovy dependency stuff introduced by Spock. One possibility is to change the Spock dependency in the build file to:

  testCompile ('org.spockframework:spock-core:0.5-groovy-1.8') {
    transitive = false
  }

Alternatively, one could also exclude just the single missing dependency explicitely:

  testCompile ('org.spockframework:spock-core:0.5-groovy-1.8') {
    exclude 'org.codehaus.groovy:groovy-all:1.8.0-beta-3-SNAPSHOT'
  }

Either way gradle eclipse will succeed.

Adding Optional Features

If you want to make use of Spock's mocking and stubbing support (and I'm sure you want) the basic configuration from above is kind of limited, since it allows only mocking of interfaces. Spock lets you also mock and stub classes and even bypass the standard object construction. For these purposes, Spock depends both on cglib-nodep and objenesis, but declares these dependencies as optional. Thus, we have to declare them ourselves:

  testCompile 'cglib:cglib-nodep:2.2'
  testCompile 'org.objenesis:objenesis:1.2'

Complete Build File

Finally, here is the build.gradle in its full glory providing full mock and stub support with Spock: Alternatively, one could also exclude just the single missing dependency explicitely:

apply plugin: 'groovy'
apply plugin: 'eclipse'

version = '0.1.0-SNAPSHOT'
sourceCompatibility = '1.6'

repositories { 
  mavenCentral()
} 

dependencies {
  groovy 'org.codehaus.groovy:groovy-all:1.8.1'

  testCompile ('org.spockframework:spock-core:0.5-groovy-1.8') {
    transitive = false
  }
  testCompile 'cglib:cglib-nodep:2.2'
  testCompile 'org.objenesis:objenesis:1.2'
  testCompile 'junit:junit:4.7'
}

Happy specifying!

Java + Clean Code = Groovy

Some months ago, I read Martin Fowler's Clean Code and discussed it every once a while with several professional Java developers working for different companies. Retrospectively, these discussions were twofold astonishing to me.

1. A great deal of these developers seemed to know Clean Code or at least some cachy claims out of it. No one disagreed with Fowler.
2. Most of them heard about Groovy, few of them know about Groovy and almost none of them does actually use Groovy.

Maybe I got it wrong, but in my understanding is the bottom line of Clean Code more or less: write code which is short, concise, self-documentary, and leaves no room for interpretation to the reader. You may guessed it by the title, but my understanding of Groovy is not too far away from that.
Recently, I had to provide a comma-separated list of IDs as a command line argument to an application. The IDs were a consecutive sequence of integer numbers. Too lazy to copy and paste the numbers I thought about the effort to generate the list. How would you do it in Java? In fact, you wouldn't, right? The time it takes to create a Java class with a main method, iterating integers, concatenate them with a StringBuilder, dealing with an redundant comma at the beginning or the end, and finally compiling this class only for a single execution won't pay off. Alternatives: copy and paste or learning bash (awk, python, perl, ... insert your scripting solution of choice).
Enter Groovy.

$ groovy -e 'println ((12345 .. 12355).join(","))'
12345,12346,12347,12348,12349,12350,12351,12352,12353,12354,12355

My assumption is, even if somebody had no idea what Groovy is all about, just by looking at the command, she'll guess the outcome of the command correctly. But what if the task was a bit more involved as only listing consecutive integers? What about filtering and transforming result entries? Suppose, out of curiosity you want to list all the numbers between 1 and 1000 dividable by 17 and containing a 9 as digit. The numbers should be listed line by line with proper line numbers (rather academic example, I know).
What about Java? The proper way to deal with collection filtering and decoration would be to use commons-collection or guava or something similar or providing own implementations of AbstractCollections with lots of anonymous inner classes even for this simple task. The not so clean Java solution could look like the following code.

class Dummy {
    public static void main (String[] args) {
        String result = "";
        int counter = 0;
        for (int i = 17; i <= 1000; i += 17) {
            if (("" + i).contains("9")) {
                result += (++counter) + ": " + i + "\n";
            }
        }
        System.out.print(result);
    }
}

It will work, but it is rather ugly. Moreover, one has to create a file (insert your editor of choice), compile it (javac), and run it (java) just for a single execution (and afterwards delete it). Many different tools and commands for a trival task. In contrast, the Groovy inline script solution is very short, concise, and self-documentary (and does not produce trash in the file system):

$ groovy -e 'counter = 0
> println ((1 .. 1000)                            /* iteration*/
>     .grep{it %17 == 0 && "${it}".contains("9")} /* filter */
>     .collect{"${++counter}: ${it}"}             /* decoration */
>     .join("\n"))'                               /* concatenation */
1: 119
2: 289
[...]
14: 969
15: 986

My biggest problem when working with Groovy is to become aware of lots and lots of wasted hours spent with developing clean code Java solutions which were given by Groovy as language features out of the box in a very concise manner. Don't get me wrong. I'm a big fan of the Java language and I like solutions with lovely design patterns. However, for some problems the full featured Java design patterns sledgehammer approach seems to be a little overkill, considering the opportunities Groovy might add to your default toolkit. Since integrating Groovy is merely a matter of adding jars to the classpath and tweak the IDE appropriately, I am astonished how few people do actually make use of Groovy's opportunities.
Bad marketing, new technology anxiety, other limitations I'm not aware of? I simply don't get it.