Question

当我在具有自定义操作的节点上执行JcrExportCommand时，我想为其过滤掉木兰中JcrExportCommand的“ mgnl：page”节点。

我在下面的代码中编写的过滤器不起作用。仍然在导出文件中为我提供了mgnl：page子节点。

//set filter to only export mgnl:area subnodes
    DefaultFilter filter = new JcrExportCommand.DefaultFilter();
    NodeFilteringPredicate nodePredicate = new NodeFilteringPredicate();
    nodePredicate.setNodeTypes(Lists.newArrayList("mgnl:area"));
    filter.setNodePredicate(nodePredicate);

如何设置正确的过滤器以导出除“ mgnl：page”子节点以外的所有内容？我相信将NodeFilteringPredicate设置为“ mgnl：area”只会得到那种类型的节点。

Answer 1

您必须在JcrExportCommand上设置过滤器才能生效：

DefaultFilter filter = new DefaultFilter();
filter.getNodePredicate().getNodeTypes().add("mgnl:page");
jcrExport.setFilter(Collections.singletonMap("website", filter));

Answer 2

*这不是我的问题的答案，而是注释的答案，因为代码的注释格式不正确*

按照@michid的建议，我创建了一个自定义谓词，并使用JcrExportCommand.DefaultFilter＃setNodePredicate（）对其进行了应用。

我希望根据谓词获得带有已过滤节点的导出YAML，但我仍然会获得所有节点（包括mgnl：page类型的子节点）。

我的自定义谓词类是：

19/12/04 13:21:15 INFO Client: Application report for application_1574748298453_0917 (state: FINISHED)
19/12/04 13:21:15 INFO Client: 
     client token: N/A
     diagnostics: User class threw exception: org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory hdfs://bigdatalab/user/username/output/outfile already exists
    at org.apache.hadoop.mapred.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:131)
    at org.apache.spark.internal.io.HadoopMapRedWriteConfigUtil.assertConf(SparkHadoopWriter.scala:283)
    at org.apache.spark.internal.io.SparkHadoopWriter$.write(SparkHadoopWriter.scala:71)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply$mcV$sp(PairRDDFunctions.scala:1096)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1094)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1094)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopDataset(PairRDDFunctions.scala:1094)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply$mcV$sp(PairRDDFunctions.scala:1067)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply(PairRDDFunctions.scala:1032)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply(PairRDDFunctions.scala:1032)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:1032)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$1.apply$mcV$sp(PairRDDFunctions.scala:958)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$1.apply(PairRDDFunctions.scala:958)
    at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$1.apply(PairRDDFunctions.scala:958)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:957)
    at org.apache.spark.rdd.RDD$$anonfun$saveAsTextFile$1.apply$mcV$sp(RDD.scala:1493)
    at org.apache.spark.rdd.RDD$$anonfun$saveAsTextFile$1.apply(RDD.scala:1472)
    at org.apache.spark.rdd.RDD$$anonfun$saveAsTextFile$1.apply(RDD.scala:1472)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.saveAsTextFile(RDD.scala:1472)
    at com.read.scispark.ReadSci$.main(ReadSci.scala:126)
    at com.read.scispark.ReadSci.main(ReadSci.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$4.run(ApplicationMaster.scala:721)

我的自定义Action类是：

public class MyPredicate extends NodeFilteringPredicate {

    public boolean evaluate(Node node) throws AccessDeniedException, ItemNotFoundException, RepositoryException {
            //only nodes that are not of type mgnl:page 
            if((node.getParent().getPrimaryNodeType().getName().contains("mgnl:page"))&&(node.getPrimaryNodeType().getName().contains("mgnl:page"))) {
                return false;
            }else{
                return true;
            }
    }
}

JcrExportCommand过滤器在木兰cms中排除“ mgnl：page”

2 个答案: