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  val bonusForm = Form(
    mapping(
      "alias" -> optional(text),
      "selectedBonus" -> optional(mapping(
        "itemId" -> nonEmptyText,
        "bonus" -> number
      )(Bonus.apply)(Bonus.unapply)),
      "bonuses" -> list(
        mapping(
          "itemId" -> nonEmptyText,

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  val selectedBonusIdValidator = (form: Form[BonusForm]) =>
    (for {
      value <- form.value
      bonusId <- value.selectedBonusId
    } yield form
      ) getOrElse form.withError("selectedBonusId", "You should select an item!")

  val selectedBonusValueValidator = (form: Form[BonusForm]) =>
    (for {
      value <- form.value

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@segmentCriteria(filter: MongoFilter) = {
    @{
        filter match {
            case eql(column, childFilter) =>
                segmentCriterion(column.name, "=" , childFilter)
            case gt(column, childFilter) =>
                segmentCriterion(column.name, ">" , childFilter)
            case lt(column, childFilter) =>
                segmentCriterion(column.name, "<" , childFilter)
            case childFilter: and =>

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./run_etl_qa.sh t_user_session_load/initial_load
INFO  11-02 08:23:45,172 - Kitchen - Logging is at level : Detailed logging
INFO  11-02 08:23:45,174 - Kitchen - Start of run.
INFO  11-02 08:23:45,199 - RepositoriesMeta - Reading repositories XML file: /home/crm-user/.kettle/repositories.xml
INFO  11-02 08:23:45,407 - t_user_session_load/initial_load - Start of job execution
INFO  11-02 08:23:45,412 - t_user_session_load/initial_load - exec(0, 0, START.0)
INFO  11-02 08:23:45,414 - START - Starting job entry
INFO  11-02 08:23:45,415 - t_user_session_load/initial_load - Starting entry [Create empty table]
INFO  11-02 08:23:45,415 - t_user_session_load/initial_load - exec(1, 0, Create empty table.0)
INFO  11-02 08:23:45,416 - Create empty table - Starting job entry

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public class HTablePool {

    private static final int RESIZE_TABLE_POOL_INTERVAL_SEC = (int) TimeUnit.MINUTES.toSeconds(30);
    private static final int REPORT_CACHED_CONNECTIONS_COUNT_INTERVAL_SEC = (int) TimeUnit.MINUTES.toSeconds(2);
    private static final Log log = LogFactory.getLog(HTablePool.class);

    protected final ConcurrentMap<String, LinkedList<HTableInterface>> tables = new ConcurrentHashMap<String, LinkedList<HTableInterface>>();
    private final Configuration config;
    private final HTableInterfaceFactory tableFactory;

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import scala.collection.JavaConversions._
import java.lang.management.{ManagementFactory, MemoryMXBean}
import java.net.URI
import javax.management.JMX
import javax.management.remote.{JMXConnectorFactory, JMXServiceURL}
import scala.collection.JavaConversions._

object JmxDump {

def main(args: Array[String]) = 

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VL-0{n=5 c=[1003:3.400, 1006:3.400, 1008:3.200, 1009:3.200, 1012:3.200] r=[1003:1.744, 1006:1.744, 1008:1.600, 1009:1.600, 1012:1.600]}
	Top Terms: 
		1006                                    =>                 3.4
		1003                                    =>                 3.4
		1012                                    =>                 3.2
		1009                                    =>                 3.2
		1008                                    =>                 3.2


VL-15{n=1 c=[1016:4.000, 1019:3.000, 1020:3.000, 1021:3.000, 1022:3.000, 1023:3.000, 1024:3.000, 1025:3.000] r=[]}

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    // clusterization results
    val outputClustersPath = new Path("job/output-clusters")
    // textual dump of clusterization results
    val dumpPath = "job/dump"

    println("Running K-means...")
    // runs K-means algorithm with up to 20 iterations, to find clusters of colluding players (assumption of collusion is
    // made on the basis of number hand player together with other player[s])
    KMeansDriver.run(conf, vectorsPath, inputClustersPath, outputClustersPath,
      new CosineDistanceMeasure(), 0.01, 20, true, 0, false)

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    // randomly selected cluster the will be passed as an input to K-means
    val inputClustersPath = new Path("job/input-clusters")
    val distanceMeasure = new EuclideanDistanceMeasure

    println("Making random seeds...")
    // build 30 initial random clusters/centroids
    RandomSeedGenerator.buildRandom(conf, vectorsPath, inputClustersPath, 30, distanceMeasure)

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    val conf = new Configuration
    conf.set("io.serializations", "org.apache.hadoop.io.serializer.JavaSerialization,"
      + "org.apache.hadoop.io.serializer.WritableSerialization")

    // the path, where the vectors will be stored to
    val vectorsPath = new Path("job/vectors")
    // enumeration of all users involved in a selected subset of hand history records
    val dictionaryPath = new Path("job/dictionary")
    // text file with the dictionary size
    val dictionarySizePath = new Path("job/dictionary-size")