java.lang.UnsupportedOperationException: The DefaultMongoPartitioner requires MongoDB >= 3.2

Google Groups | Weng Shao Fong | 4 months ago
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    Pyspark DataFrameWriter.save() Error

    Google Groups | 4 months ago | Weng Shao Fong
    java.lang.UnsupportedOperationException: The DefaultMongoPartitioner requires MongoDB >= 3.2
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    Re: [iPOJO] Bug while calling dispose under knopflerfish

    felix-users | 5 years ago | Clement Escoffier
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    [iPOJO] Bug while calling dispose under knopflerfish

    felix-users | 5 years ago | Loic Petit
    java.lang.UnsupportedOperationException: This service requires an advanced creation policy. Before calling the service, call the getService(ComponentInstance) method to get the service object.
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    Re: Support for xpath/xquery?

    xml-xmlbeans-user | 1 decade ago | Dmitri_Colebatch@toyota.com.au
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    RE: Support for xpath/xquery?

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    java.lang.UnsupportedOperationException: This operation requires xqrl.jar

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    Root Cause Analysis

    1. java.lang.UnsupportedOperationException

      The DefaultMongoPartitioner requires MongoDB >= 3.2

      at com.mongodb.spark.rdd.partitioner.DefaultMongoPartitioner.partitions()
    2. com.mongodb.spark
      MongoRDD.getPartitions
      1. com.mongodb.spark.rdd.partitioner.DefaultMongoPartitioner.partitions(DefaultMongoPartitioner.scala:58)
      2. com.mongodb.spark.rdd.MongoRDD.getPartitions(MongoRDD.scala:137)
      2 frames
    3. Spark
      RDD$$anonfun$partitions$2.apply
      1. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
      2. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
      2 frames
    4. Scala
      Option.getOrElse
      1. scala.Option.getOrElse(Option.scala:121)
      1 frame
    5. Spark
      RDD$$anonfun$partitions$2.apply
      1. org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
      2. org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
      3. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
      4. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
      4 frames
    6. Scala
      Option.getOrElse
      1. scala.Option.getOrElse(Option.scala:121)
      1 frame
    7. Spark
      RDD$$anonfun$partitions$2.apply
      1. org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
      2. org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
      3. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
      4. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
      4 frames
    8. Scala
      Option.getOrElse
      1. scala.Option.getOrElse(Option.scala:121)
      1 frame
    9. Spark
      RDD$$anonfun$partitions$2.apply
      1. org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
      2. org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
      3. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
      4. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
      4 frames
    10. Scala
      Option.getOrElse
      1. scala.Option.getOrElse(Option.scala:121)
      1 frame
    11. Spark
      RDD$$anonfun$partitions$2.apply
      1. org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
      2. org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
      3. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:248)
      4. org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:246)
      4 frames
    12. Scala
      Option.getOrElse
      1. scala.Option.getOrElse(Option.scala:121)
      1 frame
    13. Spark
      RDD.partitions
      1. org.apache.spark.rdd.RDD.partitions(RDD.scala:246)
      1 frame
    14. Spark Project SQL
      Dataset.showString
      1. org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:326)
      2. org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
      3. org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2183)
      4. org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
      5. org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2532)
      6. org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2182)
      7. org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2189)
      8. org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1925)
      9. org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1924)
      10. org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2562)
      11. org.apache.spark.sql.Dataset.head(Dataset.scala:1924)
      12. org.apache.spark.sql.Dataset.take(Dataset.scala:2139)
      13. org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
      13 frames
    15. Java RT
      Method.invoke
      1. sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
      2. sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
      3. sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
      4. java.lang.reflect.Method.invoke(Method.java:606)
      4 frames
    16. Py4J
      GatewayConnection.run
      1. py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
      2. py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
      3. py4j.Gateway.invoke(Gateway.java:280)
      4. py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
      5. py4j.commands.CallCommand.execute(CallCommand.java:79)
      6. py4j.GatewayConnection.run(GatewayConnection.java:211)
      6 frames
    17. Java RT
      Thread.run
      1. java.lang.Thread.run(Thread.java:745)
      1 frame