| /* |
| * 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.pig.builtin; |
| |
| import java.io.IOException; |
| import java.util.List; |
| |
| import org.apache.pig.EvalFunc; |
| import org.apache.pig.backend.executionengine.ExecException; |
| import org.apache.pig.data.BagFactory; |
| import org.apache.pig.data.DataBag; |
| import org.apache.pig.data.DataType; |
| import org.apache.pig.data.Tuple; |
| import org.apache.pig.data.TupleFactory; |
| import org.apache.pig.impl.logicalLayer.FrontendException; |
| import org.apache.pig.impl.logicalLayer.schema.Schema; |
| import org.apache.pig.impl.logicalLayer.schema.Schema.FieldSchema; |
| |
| import com.google.common.collect.Lists; |
| |
| /** |
| * Produces a DataBag with all combinations of the argument tuple members |
| * as in a data cube. Meaning, (a, b, c) will produce the following bag: |
| * <pre> |
| * { (a, b, c), (null, null, null), (a, b, null), (a, null, c), |
| * (a, null, null), (null, b, c), (null, null, c), (null, b, null) } |
| * </pre> |
| * <p> |
| * The "all" marker is null by default, but can be set to an arbitrary string by |
| * invoking a constructor (via a DEFINE). The constructor takes a single argument, |
| * the string you want to represent "all". |
| * <p> |
| * Usage goes something like this: |
| * <pre>{@code |
| * events = load '/logs/events' using EventLoader() as (lang, event, app_id); |
| * cubed = foreach x generate |
| * FLATTEN(piggybank.CubeDimensions(lang, event, app_id)) |
| * as (lang, event, app_id), |
| * measure; |
| * cube = foreach (group cubed |
| * by (lang, event, app_id) parallel $P) |
| * generate |
| * flatten(group) as (lang, event, app_id), |
| * COUNT_STAR(cubed), |
| * SUM(measure); |
| * store cube into 'event_cube'; |
| * }</pre> |
| * <p> |
| * <b>Note</b>: doing this with non-algebraic aggregations on large data can result |
| * in very slow reducers, since one of the groups is going to get <i>all</i> the |
| * records in your relation. |
| */ |
| public class CubeDimensions extends EvalFunc<DataBag> { |
| |
| private static BagFactory bf = BagFactory.getInstance(); |
| private static TupleFactory tf = TupleFactory.getInstance(); |
| private final String allMarker; |
| private static final String unknown = "unknown"; |
| |
| public CubeDimensions() { |
| this(null); |
| } |
| public CubeDimensions(String allMarker) { |
| super(); |
| this.allMarker = allMarker; |
| } |
| @Override |
| public DataBag exec(Tuple tuple) throws IOException { |
| List<Tuple> result = Lists.newArrayListWithCapacity((int) Math.pow(2, tuple.size())); |
| convertNullToUnknown(tuple); |
| Tuple newt = tf.newTuple(tuple.size()); |
| recursivelyCube(result, tuple, 0, newt); |
| return bf.newDefaultBag(result); |
| } |
| |
| // if the dimension values contain null then replace it with "unknown" value |
| // since null will be used for rollups |
| public static void convertNullToUnknown(Tuple tuple) throws ExecException { |
| int idx = 0; |
| for(Object obj : tuple.getAll()) { |
| if( (obj == null) ) { |
| tuple.set(idx, unknown); |
| } |
| idx++; |
| } |
| } |
| |
| private void recursivelyCube(List<Tuple> result, Tuple input, int index, Tuple newt) throws ExecException { |
| newt.set(index, input.get(index)); |
| if (index == input.size() - 1 ) { |
| result.add(newt); |
| } else { |
| recursivelyCube(result, input, index + 1, newt); |
| } |
| // tf.newTuple makes a copy. tf.newTupleNoCopy doesn't. |
| Tuple newnewt = tf.newTuple(newt.getAll()); |
| newnewt.set(index, allMarker); |
| if (index == input.size() - 1) { |
| result.add(newnewt); |
| } else { |
| recursivelyCube(result, input, index + 1, newnewt); |
| } |
| } |
| |
| @Override |
| public Schema outputSchema(Schema input) { |
| try { |
| return new Schema(new FieldSchema("dimensions", input, DataType.BAG)); |
| } catch (FrontendException e) { |
| // we are specifying BAG explicitly, so this should not happen. |
| throw new RuntimeException(e); |
| } |
| } |
| |
| @Override |
| public boolean allowCompileTimeCalculation() { |
| return true; |
| } |
| } |