Develop Reference

Java Watch Service - Wait until modifications are finished

I have a Watcher that updates my data structures when a change is heard. However, if the change is not instantaneous (i.e. if a large file is being copied from another file system, or a big part of the file is modified), the data-structure tries to update too early and throws an error.
How can I modify my code so that updateData() is called after only the last ENTRY_MODIFY is called, rather than after every single ENTRY_MODIFY.
private static boolean processWatcherEvents () {
WatchKey key;
try {
key = watcher.poll( 10, TimeUnit.MILLISECONDS );
} catch ( InterruptedException e ) {
return false;
}
Path directory = keys.get( key );
if ( directory == null ) {
return false;
}
for ( WatchEvent <?> event : key.pollEvents() ) {
WatchEvent.Kind eventKind = event.kind();
WatchEvent <Path> watchEvent = (WatchEvent<Path>)event;
Path child = directory.resolve( watchEvent.context() );
if ( eventKind == StandardWatchEventKinds.ENTRY_MODIFY ) {
//TODO: Wait until modifications are "finished" before taking these actions.
if ( Files.isDirectory( child ) ) {
updateData( child );
}
}
boolean valid = key.reset();
if ( !valid ) {
keys.remove( key );
}
}
return true;
}

As #TT suggested, you can do it pretty easily with file locks.
When you get an event, use a blocking method lock() on read and write access. Hence the operation is blocking, the code automatically waits until the write operation is finished.
FileChannel channel = new RandomAccessFile(file, "rw").getChannel();
try (channel) { // auto closable, uses channel.close() in finally block
channel.lock(); // wait until file modifications are finished
channel.read(...); // now you can safely read the file
}
However, this won't work between different JVM processes, because they don't share the same lock.

Is your problem can be solved by using timestamp.
Create a map for storing the timestamp to the map.
Map<Path, Long> fileTimeStamps;
For process event check last modified timestamp.
long oldFileModifiedTimeStamp = fileTimeStamps.get(filePath);
long newFileModifiedTimeStamp = filePath.toFile().lastModified();
if (newFileModifiedTimeStamp > oldFileModifiedTimeStamp)
{
fileTimeStamps.remove(filePath);
onEventOccurred();
fileTimeStamps.put(filePath, filePath.toFile().lastModified());
}

I ended up writing a thread that keeps a list of things I want updated and delays actually updating them until 80 milliseconds have passed. Whenever an ENTRY_MODIFY event happens, it resets the counter. I think this is a good solution, but there may be better?
#SuppressWarnings({ "rawtypes", "unchecked" })
private static boolean processWatcherEvents () {
WatchKey key;
try {
key = watcher.poll( 10, TimeUnit.MILLISECONDS );
} catch ( InterruptedException e ) {
return false;
}
Path directory = keys.get( key );
if ( directory == null ) {
return false;
}
for ( WatchEvent <?> event : key.pollEvents() ) {
WatchEvent.Kind eventKind = event.kind();
WatchEvent <Path> watchEvent = (WatchEvent<Path>)event;
Path child = directory.resolve( watchEvent.context() );
if ( eventKind == StandardWatchEventKinds.ENTRY_CREATE ) {
if ( Files.isDirectory( child ) ) {
loadMe.add( child );
} else {
loadMe.add( child.getParent() );
}
} else if ( eventKind == StandardWatchEventKinds.ENTRY_DELETE ) {
//Handled by removeMissingFiles(), can ignore.
} else if ( eventKind == StandardWatchEventKinds.ENTRY_MODIFY ) {
System.out.println( "Modified: " + child.toString() ); //TODO: DD
if ( Files.isDirectory( child ) ) {
modifiedFileDelayedUpdater.addUpdateItem( child );
} else {
modifiedFileDelayedUpdater.addUpdateItem( child );
}
} else if ( eventKind == StandardWatchEventKinds.OVERFLOW ) {
for ( Path path : musicSourcePaths ) {
updateMe.add( path );
}
}
boolean valid = key.reset();
if ( !valid ) {
keys.remove( key );
}
}
return true;
}
...
class UpdaterThread extends Thread {
public static final int DELAY_LENGTH_MS = 80;
public int counter = DELAY_LENGTH_MS;
Vector <Path> updateItems = new Vector <Path> ();
public void run() {
while ( true ) {
long sleepTime = 0;
try {
long startSleepTime = System.currentTimeMillis();
Thread.sleep ( 20 );
sleepTime = System.currentTimeMillis() - startSleepTime;
} catch ( InterruptedException e ) {} //TODO: Is this OK to do? Feels like a bad idea.
if ( counter > 0 ) {
counter -= sleepTime;
} else if ( updateItems.size() > 0 ) {
Vector <Path> copyUpdateItems = new Vector<Path> ( updateItems );
for ( Path path : copyUpdateItems ) {
Library.requestUpdate ( path );
updateItems.remove( path );
}
}
}
}
public void addUpdateItem ( Path path ) {
counter = DELAY_LENGTH_MS;
if ( !updateItems.contains( path ) ) {
updateItems.add ( path );
}
}
};

how can i refactor a method with retry policy. without using a 3rd party

I wonder how can i refactor this method with retry lambda [not using a 3rd party retry engine].
(somthing like a lambda that gets number of attempts that will passed as parameter into this method and done this same thing in more functional style
public static Connection createDbConnection( JobConfiguration jobConfiguration, ArrayList<String> hostsIps ) {
Connection retConnection = null;
Properties connectionProperties = new Properties();
connectionProperties.put( "user", jobConfiguration.getDbConfiguration().getUserName() );
connectionProperties.put( "password", jobConfiguration.getDbConfiguration().getPassword() );
connectionProperties.put( "ConnectionLoadBalance", 1 );
int attempts = 0;
while ( ++attempts <= hostsIps.size() ) {
try {
String connectionString = String.format( "jdbc:vertica://%s:%s/%s",
hostsIps.get( attempts - 1),
jobConfiguration.getDbConfiguration().getPort(),
jobConfiguration.getDbConfiguration().getDbName());
retConnection = DriverManager.getConnection
(
connectionString,
connectionProperties
);
retConnection.setAutoCommit( false );
break;
} catch ( Exception ex ) {
LOGGER.error( "Failed to connect to node - {}" + hostsIps.get( attempts - 1 ) + ex );
}
}
return retConnection;
}
can you help?
thanks

The main thing that will simplify your code is to extract all the code inside your while loop and put it in a separate method, e.g. "tryConnect". Then you could use a lambda, if desired, as follows:
Optional<Connection> retConnection = hostIps.stream().sequential()
.map(ip -> tryConnect(ip, jobConfiguration, connectionProperties))
.filter(Objects::nonNull).findFirst();
return retConnection.orElse(null);

Troubleshooting a delay in my function

I have a delay happening where my pushGreentText does not show up in my EditText until after the last brace of firstrollSP leaves scope. pushGreenText is a custom function on the UI thread I wrote that adds a text to my EditText in XML that overlays a SurfaceView. I would like to see the green text show up right away instead of waiting for the entire moveWholeAITurn sequence. Is there a way to accomplish that? I tried threading firstrollSP but there is still a delay and I thought about maybe threading pushGreenText but I'm not sure that is the answer yet.
Thank you...
static synchronized public void firstRollSP( ) throws InterruptedException
{
//Roll for Player and announce
mGame.mDice.setDie1( mGame.mDiceFirstRoll.getDie1( ) );
mGame.mDice.setRolled( true );
MainActivity.activity.pushTextGreen( Strings.get_First_roll_X_Die1( ) );
//Roll for two and announce
Thread one = new Thread( )
{
public void run( )
{
try
{
Thread.sleep( 2000 );
mGame.mDice.setDie2( mGame.mDiceFirstRoll.getDie2( ) );
while( mGame.mDice.getDie1( ) == mGame.mDice.getDie2( ) )
mGame.mDice.setDie2( (mRng.nextInt( 6 ) + 1) );
}
catch( InterruptedException e )
{
Log.d( "ACtionUP", "Interupted e" );
}
}
};
one.start( );
one.join( );
MainActivity.activity.pushTextGreen( "Android first roll is " + Integer.toString( mGame.mDice.getDie2() ) );
if( H.initWonFirstRoll( ) )
{
MainActivity.activity.pushTextGreen("Player won first roll.");
Thread tInitWon = new Thread( )
{
public void run( )
{
try
{
Thread.sleep( 2000 );
mGame.isFirstRoll = false;
mGame.isTurn = true;
mGameAI.isFirstRoll = false;
mGameAI.isTurn = false;
mGame.mDice.sort( );
mGame.mDice.setRolled( true );
mGame.mDice.setDiceAnimationComplete( true );
mGame.mOppDice.init( );
mGame.mPossibleIndexes.calcPossibleTriangles( );
}
catch( Exception e )
{
Log.d( "ACtionUP", "Interupted e" );
}
}
};
tInitWon.start( );
tInitWon.join( );
}
else
{
MainActivity.activity.pushTextGreen("Android won first roll.");
Thread tDroidWon = new Thread( )
{
public void run( )
{
try
{
Thread.sleep( 2000 );
mGame.isFirstRoll = false;
mGame.isTurn = false;
mGameAI.isFirstRoll = false;
mGameAI.isTurn = true;
mGameAI.mDice.init( );
mGame.mOppDice.init( );
mGame.mDice.sort( );
mGameAI.mDice.setDie1( mGame.mDice.getDie1() );
mGameAI.mDice.setDie2( mGame.mDice.getDie2() );
mGame.mOppDice.setDie1( mGame.mDice.getDie1() );
mGame.mOppDice.setDie2( mGame.mDice.getDie2() );
Thread.sleep( 2000 );
mGameAI.mPossibleIndexes.calcPossibleTrianglesAI( );
}
catch( InterruptedException e )
{
Log.d( "ACtionUP", "Interupted e" );
}
}
};
tDroidWon.start( );
tDroidWon.join( );
if( mGameAI.mPossibleIndexes.anyPossibles( ) )
{
moveWholeTurnAI( );
}
else
{
H.endTurnAI( );
}
}
}

Because the UI thread is an event loop. It will not update the screen until it returns to the main looper and handles the draw event- until whatever function of yours it calls exits.
You should never call thread.join on the main thread. Your app will freeze if you do, it may crash if it trips a watchdog timer. You need to refactor it to not require joins on the UI thread.
In addition, I see a lot of MainActivity.activity. That's a huge code smell- if you're holding the activity in a static variable you will have memory leaks and you are almost certain to have problems if your app is relaunched. Its something you should never do.

Scala actors inefficiency issue

Let me start out by saying that I'm new to Scala; however, I find the Actor based concurrency model interesting, and I tried to give it a shot for a relatively simple application. The issue that I'm running into is that, although I'm able to get the application to work, the result is far less efficient (in terms of real time, CPU time, and memory usage) than an equivalent Java based solution that uses threads that pull messages off an ArrayBlockingQueue. I'd like to understand why. I suspect that it's likely my lack of Scala knowledge, and that I'm causing all the inefficiency, but after several attempts to rework the application without success, I decided to reach out to the community for help.
My problem is this:
I have a gzipped file with many lines in the format of:
SomeID comma_separated_list_of_values
For example:
1234 12,45,82
I'd like to parse each line and get an overall count of the number of occurrences of each value in the comma separated list.
This file may be pretty large (several GB compressed), but the number of unique values per file is pretty small (at most 500). I figured this would be a pretty good opportunity to try to write an Actor-based concurrent Scala application. My solution involves a main driver that creates a pool of parser Actors. The main driver then reads lines from stdin, passes the line off to an Actor that parses the line and keeps a local count of the values. When the main driver has read the last line, it passes a message to each actor indicating that all lines have been read. When the actor receive the 'done' message, they pass their counts to an aggregator that sums the counts from all actors. Once the counts from all parsers have been aggregated, the main driver prints out the statistics.
The problem:
The main issue that I'm encountering is the incredible amount of inefficiency of this application. It uses far more CPU and far more memory than an "equivalent" Java application that uses threads and an ArrayBlockingQueue. To put this in perspective, here are some stats that I gathered for a 10 million line test input file:
Scala 1 Actor (parser):
real 9m22.297s
user 235m31.070s
sys 21m51.420s
Java 1 Thread (parser):
real 1m48.275s
user 1m58.630s
sys 0m33.540s
Scala 5 Actors:
real 2m25.267s
user 63m0.730s
sys 3m17.950s
Java 5 Threads:
real 0m24.961s
user 1m52.650s
sys 0m20.920s
In addition, top reports that the Scala application has about 10x the resident memory size. So we're talking about orders of magnitude more CPU and memory here for orders of magnitude worse performance, and I just can't figure out what is causing this. Is it a GC issue, or am I somehow creating far more copies of objects than I realize?
Additional details that may or may not be of importance:
The scala application is wrapped by a Java class so that I could
deliver a self-contained executable JAR file (I don't have the Scala
jars on every machine that I might want to run this app).
The application is being invoked as follows: gunzip -c gzFilename |
java -jar StatParser.jar
Here is the code:
Main Driver:
import scala.actors.Actor._
import scala.collection.{ immutable, mutable }
import scala.io.Source
class StatCollector (numParsers : Int ) {
private val parsers = new mutable.ArrayBuffer[StatParser]()
private val aggregator = new StatAggregator()
def generateParsers {
for ( i <- 1 to numParsers ) {
val parser = new StatParser( i, aggregator )
parser.start
parsers += parser
}
}
def readStdin {
var nextParserIdx = 0
var lineNo = 1
for ( line <- Source.stdin.getLines() ) {
parsers( nextParserIdx ) ! line
nextParserIdx += 1
if ( nextParserIdx >= numParsers ) {
nextParserIdx = 0
}
lineNo += 1
}
}
def informParsers {
for ( parser <- parsers ) {
parser ! true
}
}
def printCounts {
val countMap = aggregator.getCounts()
println( "ID,Count" )
/*
for ( key <- countMap.keySet ) {
println( key + "," + countMap.getOrElse( key, 0 ) )
//println( "Campaign '" + key + "': " + countMap.getOrElse( key, 0 ) )
}
*/
countMap.toList.sorted foreach {
case (key, value) =>
println( key + "," + value )
}
}
def processFromStdIn {
aggregator.start
generateParsers
readStdin
process
}
def process {
informParsers
var completedParserCount = aggregator.getNumParsersAggregated
while ( completedParserCount < numParsers ) {
Thread.sleep( 250 )
completedParserCount = aggregator.getNumParsersAggregated
}
printCounts
}
}
The Parser Actor:
import scala.actors.Actor
import collection.mutable.HashMap
import scala.util.matching
class StatParser( val id: Int, val aggregator: StatAggregator ) extends Actor {
private var countMap = new HashMap[String, Int]()
private val sep1 = "\t"
private val sep2 = ","
def getCounts(): HashMap[String, Int] = {
return countMap
}
def act() {
loop {
react {
case line: String =>
{
val idx = line.indexOf( sep1 )
var currentCount = 0
if ( idx > 0 ) {
val tokens = line.substring( idx + 1 ).split( sep2 )
for ( token <- tokens ) {
if ( !token.equals( "" ) ) {
currentCount = countMap.getOrElse( token, 0 )
countMap( token ) = ( 1 + currentCount )
}
}
}
}
case doneProcessing: Boolean =>
{
if ( doneProcessing ) {
// Send my stats to Aggregator
aggregator ! this
}
}
}
}
}
}
The Aggregator Actor:
import scala.actors.Actor
import collection.mutable.HashMap
class StatAggregator extends Actor {
private var countMap = new HashMap[String, Int]()
private var parsersAggregated = 0
def act() {
loop {
react {
case parser: StatParser =>
{
val cm = parser.getCounts()
for ( key <- cm.keySet ) {
val currentCount = countMap.getOrElse( key, 0 )
val incAmt = cm.getOrElse( key, 0 )
countMap( key ) = ( currentCount + incAmt )
}
parsersAggregated += 1
}
}
}
}
def getNumParsersAggregated: Int = {
return parsersAggregated
}
def getCounts(): HashMap[String, Int] = {
return countMap
}
}
Any help that could be offered in understanding what is going on here would be greatly appreciated.
Thanks in advance!
---- Edit ---
Since many people responded and asked for the Java code, here is the simple Java app that I created for comparison purposes. I realize that this is not great Java code, but when I saw the performance of the Scala application, I just whipped up something quick to see how a Java Thread-based implementation would perform as a base-line:
Parsing Thread:
import java.util.Hashtable;
import java.util.Map;
import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.TimeUnit;
public class JStatParser extends Thread
{
private ArrayBlockingQueue<String> queue;
private Map<String, Integer> countMap;
private boolean done;
public JStatParser( ArrayBlockingQueue<String> q )
{
super( );
queue = q;
countMap = new Hashtable<String, Integer>( );
done = false;
}
public Map<String, Integer> getCountMap( )
{
return countMap;
}
public void alldone( )
{
done = true;
}
#Override
public void run( )
{
String line = null;
while( !done || queue.size( ) > 0 )
{
try
{
// line = queue.take( );
line = queue.poll( 100, TimeUnit.MILLISECONDS );
if( line != null )
{
int idx = line.indexOf( "\t" ) + 1;
for( String token : line.substring( idx ).split( "," ) )
{
if( !token.equals( "" ) )
{
if( countMap.containsKey( token ) )
{
Integer currentCount = countMap.get( token );
currentCount++;
countMap.put( token, currentCount );
}
else
{
countMap.put( token, new Integer( 1 ) );
}
}
}
}
}
catch( InterruptedException e )
{
// TODO Auto-generated catch block
System.err.println( "Failed to get something off the queue: "
+ e.getMessage( ) );
e.printStackTrace( );
}
}
}
}
Driver:
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Hashtable;
import java.util.List;
import java.util.Map;
import java.util.TreeSet;
import java.util.concurrent.ArrayBlockingQueue;
public class JPS
{
public static void main( String[] args )
{
if( args.length <= 0 || args.length > 2 || args[0].equals( "-?" ) )
{
System.err.println( "Usage: JPS [filename]" );
System.exit( -1 );
}
int numParsers = Integer.parseInt( args[0] );
ArrayBlockingQueue<String> q = new ArrayBlockingQueue<String>( 1000 );
List<JStatParser> parsers = new ArrayList<JStatParser>( );
BufferedReader reader = null;
try
{
if( args.length == 2 )
{
reader = new BufferedReader( new FileReader( args[1] ) );
}
else
{
reader = new BufferedReader( new InputStreamReader( System.in ) );
}
for( int i = 0; i < numParsers; i++ )
{
JStatParser parser = new JStatParser( q );
parser.start( );
parsers.add( parser );
}
String line = null;
while( (line = reader.readLine( )) != null )
{
try
{
q.put( line );
}
catch( InterruptedException e )
{
// TODO Auto-generated catch block
System.err.println( "Failed to add line to q: "
+ e.getMessage( ) );
e.printStackTrace( );
}
}
// At this point, we've put everything on the queue, now we just
// need to wait for it to be processed.
while( q.size( ) > 0 )
{
try
{
Thread.sleep( 250 );
}
catch( InterruptedException e )
{
}
}
Map<String,Integer> countMap = new Hashtable<String,Integer>( );
for( JStatParser jsp : parsers )
{
jsp.alldone( );
Map<String,Integer> cm = jsp.getCountMap( );
for( String key : cm.keySet( ) )
{
if( countMap.containsKey( key ))
{
Integer currentCount = countMap.get( key );
currentCount += cm.get( key );
countMap.put( key, currentCount );
}
else
{
countMap.put( key, cm.get( key ) );
}
}
}
System.out.println( "ID,Count" );
for( String key : new TreeSet<String>(countMap.keySet( )) )
{
System.out.println( key + "," + countMap.get( key ) );
}
for( JStatParser parser : parsers )
{
try
{
parser.join( 100 );
}
catch( InterruptedException e )
{
// TODO Auto-generated catch block
e.printStackTrace();
}
}
System.exit( 0 );
}
catch( IOException e )
{
System.err.println( "Caught exception: " + e.getMessage( ) );
e.printStackTrace( );
}
}
}

I'm not sure this is a good test case for actors. For one thing, there's almost no interaction between actors. This is a simple map/reduce, which calls for parallelism, not concurrency.
The overhead on the actors is also pretty heavy, and I don't know how many actual threads are being allocated. Depending on how many processors you have, you might have less threads than on the Java program -- which seems to be the case, given that the speed-up is 4x instead of 5x.
And the way you wrote the actors is optimized for idle actors, the kind of situation where you have hundreds or thousands or actors, but only few of them doing actual work at any time. If you wrote the actors with while/receive instead of loop/react, they'd perform better.
Now, actors would make it easy to distribute the application over many computers, except that you violated one of the tenets of actors: you are calling methods on the actor object. You should never do that with actors and, in fact, Akka prevents you from doing so. A more actor-ish way of doing this would be for the aggregator to ask each actor for their key sets, compute their union, and then, for each key, ask all actors to send their count for that key.
I'm not sure, however, that the actor overhead is what you are seeing. You provided no information about the Java implementation, but I daresay you use mutable maps, and maybe even a single concurrent mutable map -- a very different implementation than what you are doing in Scala.
There's also no information on how the file is read (such a big file might have buffering issues), or how it is parsed in Java. Since most of the work is reading and parsing the file, not counting the tokens, differences in implementation there can easily overcome any other issue.
Finally, about resident memory size, Scala has a 9 MB library (in addition to what JVM brings), which might be what you are seeing. Of course, if you are using a single concurrent map in Java vs 6 immutable maps in Scala, that will certainly make a big difference in memory usage patterns.

Scala actors give way Akka actors last days... and more is coming - Viktor is hAkking further to make last the best: https://twitter.com/viktorklang/status/229694698397257728
BTW: Open Source is great power! This day should be holiday of all JVM-based community:
http://www.marketwire.com/press-release/azul-systems-announces-new-initiative-support-open-source-community-with-free-zing-jvm-1684899.htm

How do I query existing applications on JBoss AS 7?

I'm working on automatically deploying to a remote JBoss AS 7.1.1 server from a Jenkins build server as part of a build pipeline and have a small jar file that I call from ant (based on this).
My question is how do I find out if an application is already installed? Doing a deploy plan will fail if the application is already deployed(I could catch the exception that's thrown but that's not great).

You can read the resource before doing the deploy. From there you can either redploy it or do nothing.
Here is an example that would work on a standalone server.
private boolean exists(final ModelControllerClient client, final String deploymentName) {
final ModelNode op = new ModelNode();
op.get(OP).set("read-children-names");
op.get("child-type").set(ClientConstants.DEPLOYMENT);
final ModelNode result;
try {
result = client.execute(op);
// Check to make sure there is an outcome
if (result.hasDefined(ClientConstants.OUTCOME)) {
if (result.get(ClientConstants.OUTCOME).asString().equals(ClientConstants.SUCCESS)) {
final List<ModelNode> deployments = (result.hasDefined(ClientConstants.RESULT) ? result.get(ClientConstants.RESULT).asList() : Collections.<ModelNode>emptyList());
for (ModelNode n : deployments) {
if (n.asString().equals(deploymentName)) {
return true;
}
}
} else if (result.get(ClientConstants.OUTCOME).asString().equals(ClientConstants.FAILED)) {
throw new IllegalStateException(String.format("A failure occurred when checking existing deployments. Error: %s",
(result.hasDefined(ClientConstants.FAILURE_DESCRIPTION) ? result.get(ClientConstants.FAILURE_DESCRIPTION).asString() : "Unknown")));
}
} else {
throw new IllegalStateException(String.format("An unexpected response was found checking the deployment. Result: %s", result));
}
} catch (IOException e) {
throw new IllegalStateException(String.format("Could not execute operation '%s'", op), e);
}
return false;
}
If you're using maven, there is a maven plugin you could use too.

An alternative:
ModelNode res = AS7CliUtils.executeRequest("/deployment=* /:read-resource", ctx.getAS7Client() );
{
"outcome" => "success",
"result" => [{
"address" => [("deployment" => "jboss-as-wicket-ear-ear.ear")],
"outcome" => "success",
"result" => {
"content" => [{"hash" => bytes { ... }}],
"enabled" => true,
"name" => "jboss-as-wicket-ear-ear.ear",
"persistent" => true,
"runtime-name" => "jboss-as-wicket-ear-ear.ear",
"subdeployment" => {
"jboss-as-wicket-ear-ejb.jar" => undefined,
"jboss-as-wicket-ear-war.war" => undefined
},
"subsystem" => {"datasources" => undefined}
}
}]
}
JBoss AS CLI client lib contains some API for that, can't find it right now.
This is a primitive implementation of query parsing (doesn't support nested values and doesn't care about escaping etc.).
/**
* Parse CLI command into a ModelNode - /foo=a/bar=b/:operation(param=value,...) .
*
* TODO: Support nested params.
*/
public static ModelNode parseCommand( String command ) {
return parseCommand( command, true );
}
public static ModelNode parseCommand( String command, boolean needOp ) {
String[] parts = StringUtils.split( command, ':' );
if( needOp && parts.length < 2 ) throw new IllegalArgumentException("Missing CLI command operation: " + command);
String addr = parts[0];
ModelNode query = new ModelNode();
// Addr
String[] partsAddr = StringUtils.split( addr, '/' );
for( String segment : partsAddr ) {
String[] partsSegment = StringUtils.split( segment, "=", 2);
if( partsSegment.length != 2 ) throw new IllegalArgumentException("Wrong addr segment format - need '=': " + command);
query.get(ClientConstants.OP_ADDR).add( partsSegment[0], partsSegment[1] );
}
// No op?
if( parts.length < 2 ) return query;
// Op
String[] partsOp = StringUtils.split( parts[1], '(' );
String opName = partsOp[0];
query.get(ClientConstants.OP).set(opName);
// Op args
if( partsOp.length > 1 ){
String args = StringUtils.removeEnd( partsOp[1], ")" );
for( String arg : args.split(",") ) {
String[] partsArg = arg.split("=", 2);
query.get(partsArg[0]).set( unquote( partsArg[1] ) );
}
}
return query;
}// parseCommand()