Package org.apache.pig.data

Examples of org.apache.pig.data.ExampleTuple.copyFrom()

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            // sanity check:
            if (outputConstraint.arity() != schema.numFields()) throw new RuntimeException("Internal error: incorrect number of fields in constraint tuple.");
           
            Tuple inputT = new Tuple(outputConstraint.arity());
            ExampleTuple inputTuple = new ExampleTuple();
            inputTuple.copyFrom(inputT);
            for (int i = 0; i < inputTuple.arity(); i++) {
                Datum d = outputConstraint.getField(i);
                if (d == null) d = exampleTuple.getField(i);
                inputTuple.setField(i, d);
            }
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            if (outputConstraints.cardinality() > 0) {   // there's one or more output constraints; propagate them backwards through the projection
                for (Iterator<Tuple> it = outputConstraints.iterator(); it.hasNext(); ) {
                    Tuple outputConstraint = it.next();
                    Tuple inputConst = BackPropConstraint(outputConstraint, cols, inputSchema);
                    ExampleTuple inputConstraint = new ExampleTuple();
                    inputConstraint.copyFrom(inputConst);
                    if (inputConstraint != null) inputConstraints.add(inputConstraint);
                }
            }
           
            // note: if there are no output constraints, we don't have to do anything because the input operator
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            if (outputConstraints.cardinality() > 0) {     // there's one or more output constraints; generate corresponding input constraints
                for (Iterator<Tuple> it = outputConstraints.iterator(); it.hasNext(); ) {
                    Tuple outputConstraint = it.next();
                    Tuple inputConst = GenerateMatchingTuple(outputConstraint, filterCond);
                    ExampleTuple inputConstraint = new ExampleTuple();
                    inputConstraint.copyFrom(inputConst);
                    if (inputConstraint != null) inputConstraints.add(inputConstraint);
                }
            } else if (outputData.cardinality() == 0) {    // no output constraints, but output is empty; generate one input that will pass the filter
                Tuple inputConst = GenerateMatchingTuple(inputSchema, filterCond);
                ExampleTuple inputConstraint = new ExampleTuple();
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                    if (inputConstraint != null) inputConstraints.add(inputConstraint);
                }
            } else if (outputData.cardinality() == 0) {    // no output constraints, but output is empty; generate one input that will pass the filter
                Tuple inputConst = GenerateMatchingTuple(inputSchema, filterCond);
                ExampleTuple inputConstraint = new ExampleTuple();
                inputConstraint.copyFrom(inputConst);
                if (inputConstraint != null) inputConstraints.add(inputConstraint);
            }
           
            // if necessary, insert a negative example (i.e. a tuple that does not pass the filter)
            if (outputData.cardinality() == inputData.cardinality()) {     // all tuples pass the filter; generate one input that will not pass the filter
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            if (outputData.cardinality() == inputData.cardinality()) {     // all tuples pass the filter; generate one input that will not pass the filter
                ExampleTuple inputConstraint = new ExampleTuple();
                Tuple inputConst = GenerateMatchingTuple(inputSchema, new NotCond(filterCond));
                //inputConstraint.copyFrom(inputConst);
                if (inputConst != null) {
                  inputConstraint.copyFrom(inputConst);
                  inputConstraints.add(inputConstraint);
                }
            }

        } else {
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        int currentInput = 0;
        for (Iterator<Tuple> it = outputConstraints.iterator(); it.hasNext(); ) {
            Tuple outputConst = it.next();
            ExampleTuple outputConstraint = new ExampleTuple();
            outputConstraint.copyFrom(outputConst);
            inputConstraints.get(currentInput).add(outputConstraint);
            currentInput = (currentInput + 1) % numInputs;
        }
       
        // note: if there are no output constraints, don't have to do anything because inputs will ensure that
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    // HELPER METHODS:
   
    static Tuple GenerateGroupByInput(Datum groupLabel, List<Integer> groupCols, int numInputFields) throws IOException {
        Tuple inputConst = new Tuple(numInputFields);
      Tuple inputConstraint = new ExampleTuple();
      inputConstraint.copyFrom(inputConst);
        if (groupLabel != null) {
            if (groupCols.size() == 1) {   // group by one column, so group label is a data atom
                inputConstraint.setField(groupCols.get(0), groupLabel);
            } else {                       // group by multiple columns, so group label is a tuple
                if (!(groupLabel instanceof Tuple)) throw new RuntimeException("Unexpected group label type.");
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    }

    static Tuple BackPropConstraint(Tuple outputConstraint, List<Integer> cols, TupleSchema inputSchema) throws IOException {
        Tuple inputConst = new Tuple(inputSchema.numFields());
        Tuple inputConstraint = new ExampleTuple();
        inputConstraint.copyFrom(inputConst);

        for (int outCol = 0; outCol < outputConstraint.arity(); outCol++) {
            int inCol = cols.get(outCol);
            Datum outVal = outputConstraint.getField(outCol);
            Datum inVal = inputConstraint.getField(inCol);
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                    lastAdded = nextTuple;   // for lineage bookkeeping
                }
            }else{
                Tuple output = (Tuple)buf.removeFirst();
                ExampleTuple tOut = new ExampleTuple();
                tOut.copyFrom(output);
                if (lineageTracer != null) {
              List<Tuple> children = lineageTracer.getFlattenChildren(output);
              if(children != null) {
                //the output tuple we get is not a example tuple. so we take it out and put in the converted exampletuple
                lineageTracer.removeFlattenMap(output);
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            if (outputType == LogicalOperator.AMENDABLE) output = new AmendableTuple(1 + inputs.length, smallestGroup);
            else output = new Tuple(1 + inputs.length);

            // set first field to the group tuple
            output.setField(0, smallestGroup);
            tOut.copyFrom(output);
            if (lineageTracer != null) lineageTracer.insert(tOut);

            boolean done = true;
            for (int i = 0; i < inputs.length; i++) {
                DataBag b = BagFactory.getInstance().newDefaultBag();
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