HashPartition.java

/*
 * Copyright © 2014 Cask Data, Inc.
 *
 * Licensed 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 co.cask.cdap.api.annotation;

import java.lang.annotation.ElementType;
import java.lang.annotation.Retention;
import java.lang.annotation.RetentionPolicy;
import java.lang.annotation.Target;

/**
 * Defines the strategy used when reading data from a {@link co.cask.cdap.api.flow.flowlet.Flowlet Flowlet's} input
 * as hash partitioning.
 *
 * <p>
 * The input is partitioned among the running {@link co.cask.cdap.api.flow.flowlet.Flowlet Flowlets} using the
 * partitioning key. If the partitioning key is not emitted, then the input is treated as part of partition zero.
 * </p>
 * 
 * <p>
 * If we have 3 instances of a Flowlet, with round-robin partitioning, every instance will receive every third word. 
 * For example, for the sequence of words in the sentence, "I scream, you scream, we all scream for ice 
 * cream":
 * </p>
 * 
 * <ul>
 * <li>
 * The first instance receives these words: I scream scream cream
 * </li>
 * <li>
 * The second instance receives these words: scream we for
 * </li>
 * </ul>
 *
 * <p>
 * The potential problem with this is that both instances might attempt to increment the counter for the word 
 * "scream" at the same time, and that may lead to a write conflict. To avoid write conflicts, we can use 
 * hash partitioning:
 * </p>
 * <pre><code>
 *    {@literal @}HashPartition("wordHash")
 *    {@literal @}ProcessInput("wordOut")
 *    public void process(String word) throws OperationException {
 *       this.wordCountsTable.increment(Bytes.toBytes(word), 1L);
 *    }
 * </code></pre>
 * <p>
 * Now only one of the Flowlet instances will receive the word "scream", and there will be no write 
 * conflicts. Note that in order to use hash partitioning, the emitting Flowlet must annotate each data object with the
 * partitioning key: 
 * </p>
 * <pre><code>
 *    {@literal @}Output("wordOut")
 *    private OutputEmitter{@literal <}String> wordOutput;
 *      ...
 *    public void process(StreamEvent event) throws OperationException {
 *      ...
 *      // emit the word with the partitioning key “wordHash” 
 *      // (the partitioning key "wordHash" was declared in the earlier code snippet above)
 *      wordOutput.emit(word, "wordHash", word.hashCode());
 *    }
 * </code></pre>
 * <p>
 * Note that the emitter must use the same name ("wordHash") for the key that the consuming Flowlet specifies 
 * as the partitioning key. If the output is connected to more than one Flowlet, you can also annotate a data 
 * object with multiple hash keys – each consuming Flowlet can then use different partitioning. This is useful 
 * if you want to aggregate by multiple keys; for example, if you want to count purchases by product ID as well 
 * as by customer ID.
 * </p>
 * <p>
 * Partitioning can be combined with batch execution: 
 * </p>
 *
 * <pre><code>
 *    {@literal @}Batch(100)
 *    {@literal @}HashPartition("wordHash")
 *    {@literal @}ProcessInput("wordOut")
 *    public void process(Iterator{@literal <}String> words) throws OperationException {
 *      ...
 *    }
 * </code></pre>
 *
 * @see Batch
 * @see RoundRobin
 * @see ProcessInput
 */
@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.METHOD)
public @interface HashPartition {
  /**
   * Declare the name of the partitioning key to the process methods
   * across multiple instances of a {@link co.cask.cdap.api.flow.flowlet.Flowlet}.
   */
  String value();

}