content: /*
   * Licensed to the Apache Software Foundation (ASF) under one or more
   * contributor license agreements.  See the NOTICE file distributed with
   * this work for additional information regarding copyright ownership.
   * The ASF licenses this file to you under the Apache License, Version 2.0
   * (the "License"); you may not use this file except in compliance with
   * the License.  You may obtain a copy of the License at
   *
   * http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package org.apache.calcite.rel.core;
  
  import org.apache.calcite.plan.Convention;
  import org.apache.calcite.plan.RelOptCluster;
  import org.apache.calcite.plan.RelOptSamplingParameters;
  import org.apache.calcite.plan.RelTraitSet;
  import org.apache.calcite.rel.RelInput;
  import org.apache.calcite.rel.RelNode;
  import org.apache.calcite.rel.RelWriter;
  import org.apache.calcite.rel.SingleRel;
  
  import java.math.BigDecimal;
  import java.util.List;
  
  /**
   * Relational expression that returns a sample of the rows from its input.
   *
   * <p>In SQL, a sample is expressed using the {@code TABLESAMPLE BERNOULLI} or
   * {@code SYSTEM} keyword applied to a table, view or sub-query.
   */
  public class Sample extends SingleRel {
    //~ Instance fields --------------------------------------------------------
  
    private final RelOptSamplingParameters params;
  
    //~ Constructors -----------------------------------------------------------
  
    public Sample(RelOptCluster cluster, RelNode child,
        RelOptSamplingParameters params) {
      super(cluster, cluster.traitSetOf(Convention.NONE), child);
      this.params = params;
    }
  
    /**
     * Creates a Sample by parsing serialized output.
     */
    public Sample(RelInput input) {
      this(input.getCluster(), input.getInput(), getSamplingParameters(input));
    }
  
    //~ Methods ----------------------------------------------------------------
  
    private static RelOptSamplingParameters getSamplingParameters(
        RelInput input) {
      String mode = input.getString("mode");
      final boolean bernoulli = "bernoulli".equals(mode);
      final BigDecimal rate = input.getBigDecimal("rate");
      final Object repeatableSeed = input.get("repeatableSeed");
      final int seed;
      final boolean repeatable;
      if (repeatableSeed instanceof Number) {
        repeatable = true;
        seed = ((Number) repeatableSeed).intValue();
      } else {
        repeatable = false;
        seed = 0;
      }
      return new RelOptSamplingParameters(bernoulli, rate, repeatable, seed);
    }
  
    @Override public RelNode copy(RelTraitSet traitSet, List<RelNode> inputs) {
      assert traitSet.containsIfApplicable(Convention.NONE);
      return new Sample(getCluster(), sole(inputs), params);
    }
  
    /**
     * Retrieve the sampling parameters for this Sample.
     */
    public RelOptSamplingParameters getSamplingParameters() {
      return params;
    }
  
    @Override public RelWriter explainTerms(RelWriter pw) {
      return super.explainTerms(pw)
          .item("mode", params.isBernoulli() ? "bernoulli" : "system")
          .item("rate", params.sampleRate)
          .item("repeatableSeed",
              params.isRepeatable() ? params.getRepeatableSeed() : "-");
    }
  }
  