Module datarush.analytics
Package com.pervasive.datarush.analytics.stats

Class Rank

java.lang.Object
com.pervasive.datarush.operators.AbstractLogicalOperator
com.pervasive.datarush.operators.StreamingOperator
com.pervasive.datarush.operators.ExecutableOperator
com.pervasive.datarush.operators.AbstractExecutableRecordPipeline
com.pervasive.datarush.analytics.stats.Rank
All Implemented Interfaces:
LogicalOperator, PipelineOperator<RecordPort>, RecordPipelineOperator
public class Rank extends AbstractExecutableRecordPipeline
Rank data using the given rank mode. The data is grouped by the given partition field(s) and is sorted within the grouping by the ranking field(s). An example is to rank employees by salary per department. To rank the highest to lowest salary within department: partition by the department and rank by the salary in descending sort order.

Three different rank modes are supported:

  • STANDARD: also known as competition ranking, items with the same ranking values have the same rank and then a gap is left in the ranking numbers; for example: 1224
  • DENSE: items that compare equal receive the same ranking, items following receive the next ordinal ranking (i.e. ranks are not skipped); for example: 1223
  • ORDINAL: each item receives a distinct ranking, starting at 1 and increasing by one producing essentially a row number within the partition; for example: 1234

A new output field is created to contain the result of the ranking. The field is named "rank" by default. The name of the rank field can be set using setOutputField(String).

  • Constructor Details

    • Rank

      public Rank()
      Default constructor. The partitionBy and rankBy properties are required and must be set when using this operator.

  • Method Details

    • getInput

      public RecordPort getInput()
      Description copied from class: AbstractExecutableRecordPipeline
      Gets the record port providing the input data to the operation.
      Specified by:
      getInput in interface PipelineOperator<RecordPort>
      Overrides:
      getInput in class AbstractExecutableRecordPipeline
      Returns:
      the input port for the operation

    • getOutput

      public RecordPort getOutput()
      Description copied from class: AbstractExecutableRecordPipeline
      Gets the record port providing the output from the operation.
      Specified by:
      getOutput in interface PipelineOperator<RecordPort>
      Overrides:
      getOutput in class AbstractExecutableRecordPipeline
      Returns:
      the output port for the operation

    • setPartitionBy

      public void setPartitionBy(List<String> partitionKeys)
      Set the fields used for partitioning the input data.
      Parameters:
      partitionKeys - list of fields to use for partitioning

    • setPartitionBy

      public void setPartitionBy(String... partitionKeys)
      Set the fields used for partitioning the input data.
      Parameters:
      partitionKeys - list of fields to use for partitioning

    • getPartitionBy

      public List<String> getPartitionBy()
      Get the list of fields used to partition the input data.
      Returns:
      list of fields to use for partitioning

    • setRankBy

      public void setRankBy(SortKey... rankBy)
      Set the list of fields to use for ranking. The data within each partition is sorted by the specified order. This specifies the set of fields used to calculate the rating within each partitioned group.
      Parameters:
      rankBy - list of fields used to rank the data

    • setRankBy

      public void setRankBy(String... rankBy)
      Set the list of fields to use for ranking. The data within each partition is sorted by the specified order. This specifies the set of fields used to calculate the rating within each partitioned group. The rank order can be a list of fields with the sort order specified (optional) as 'asc' or 'desc'.
      Parameters:
      rankBy - list of fields used to rank the data along with sort order

    • setRankBy

      public void setRankBy(List<SortKey> rankBy)
      Set the list of fields to use for ranking. The data within each partition is sorted by the specified order. This specifies the set of fields used to calculate the rating within each partitioned group.
      Parameters:
      rankBy - list of fields used to rank the data

    • getRankBy

      public List<SortKey> getRankBy()
      Get the list of fields used for ranking within each partition.
      Returns:
      list of ranking fields

    • setMode

      public void setMode(Rank.RankMode mode)
      Set the ranking mode. Ordinal ranking is used by default.
      Parameters:
      mode - rank mode

    • getMode

      public Rank.RankMode getMode()
      Get the mode used for ranking.
      Returns:
      rank mode

    • setOutputField

      public void setOutputField(String outputField)
      Set the name of the output field that will contain the ranking order for each record.
      Parameters:
      outputField - name of the ranking results field

    • getOutputField

      public String getOutputField()
      Get the name of the output ranking field.
      Returns:
      name of the ranking results field

    • computeMetadata

      protected void computeMetadata(StreamingMetadataContext ctx)
      Description copied from class: StreamingOperator
      Implementations must adhere to the following contracts

      General

      Regardless of input ports/output port types, all implementations must do the following:

      1. Validation. Validation of configuration should always be performed first.
      2. Declare parallelizability.. Implementations must declare parallelizability by calling StreamingMetadataContext.parallelize(ParallelismStrategy).

      Input record ports

      Implementations with input record ports must declare the following:
      1. Required data ordering:
        2. Implementations that have data ordering requirements must declare them by calling RecordPort#setRequiredDataOrdering, otherwise data may arrive in any order.
      2. Required data distribution (only applies to parallelizable operators):
        3. Implementations that have data distribution requirements must declare them by calling RecordPort#setRequiredDataDistribution, otherwise data will arrive in an unspecified partial distribution.
      Note that if the upstream operator's output distribution/ordering is compatible with those required, we avoid a re-sort/re-distribution which is generally a very large savings from a performance standpoint. In addition, some operators may chose to query the upstream output distribution/ordering by calling RecordPort#getSourceDataDistribution and RecordPort#getSourceDataOrdering. These should be viewed as a hints to help chose a more efficient algorithm. In such cases, though, operators must still declare data ordering and data distribution requirements; otherwise there is no guarantee that data will arrive sorted/distributed as required.

      Output record ports

      Implementations with output record ports must declare the following:
      1. Type: Implementations must declare their output type by calling RecordPort#setType.
      Implementations with output record ports may declare the following:
      1. Output data ordering: Implementations that can make guarantees as to their output ordering may do so by calling RecordPort#setOutputDataOrdering
      2. Output data distribution (only applies to parallelizable operators): Implementations that can make guarantees as to their output distribution may do so by calling RecordPort#setOutputDataDistribution
      Note that both of these properties are optional; if unspecified, performance may suffer since the framework may unnecessarily re-sort/re-distributed the data.

      Input model ports

      In general, there is nothing special to declare for input model ports. Models are implicitly duplicated to all partitions when going from non-parallel to parallel operators. The case of a model going from a parallel to a non-parallel node is a special case of a "model reducer" operator. In the case of a model reducer, the downstream operator, must declare the following:
      1. Merge handler: Model reducers must declare a merge handler by calling AbstractModelPort#setMergeHandler.
      Note that MergeModel is a convenient, re-usable model reducer, parameterized with a merge-handler.

      Output model ports

      SimpleModelPort's have no associated metadata and therefore there is never any output metadata to declare. PMMLPort's, on the other hand, do have associated metadata. For all PMMLPorts, implementations must declare the following:
      1. pmmlModelSpec: Implementations must declare the PMML model spec by calling PMMLPort.setPMMLModelSpec.
      Specified by:
      computeMetadata in class StreamingOperator
      Parameters:
      ctx - the context

    • execute

      protected void execute(ExecutionContext ctx)
      Description copied from class: ExecutableOperator
      Executes the operator. Implementations should adhere to the following contracts:
      1. Following execution, all input ports must be at end-of-data.
      2. Following execution, all output ports must be at end-of-data.
      Specified by:
      execute in class ExecutableOperator
      Parameters:
      ctx - context in which to lookup physical ports bound to logical ports