/** * 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.hadoop.hive.ql.optimizer.physical; import java.util.ArrayList; import java.util.List; import org.apache.hadoop.hive.conf.HiveConf; import org.apache.hadoop.hive.ql.metadata.HiveException; import org.apache.hadoop.hive.ql.parse.SemanticException; /** * A hierarchy physical optimizer, which contains a list of * PhysicalPlanResolver. Each resolver has its own set of optimization rule. */ public class PhysicalOptimizer { private PhysicalContext pctx; private List<PhysicalPlanResolver> resolvers; public PhysicalOptimizer(PhysicalContext pctx, HiveConf hiveConf) { super(); this.pctx = pctx; initialize(hiveConf); } /** * create the list of physical plan resolvers. * * @param hiveConf */ private void initialize(HiveConf hiveConf) { resolvers = new ArrayList<PhysicalPlanResolver>(); if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVESKEWJOIN)) { resolvers.add(new SkewJoinResolver()); } if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVECONVERTJOIN)) { resolvers.add(new CommonJoinResolver()); // The joins have been automatically converted to map-joins. // However, if the joins were converted to sort-merge joins automatically, // they should also be tried as map-joins. if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVE_AUTO_SORTMERGE_JOIN_TOMAPJOIN)) { resolvers.add(new SortMergeJoinResolver()); } } if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVEOPTINDEXFILTER)) { resolvers.add(new IndexWhereResolver()); } resolvers.add(new MapJoinResolver()); if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVEMETADATAONLYQUERIES)) { resolvers.add(new MetadataOnlyOptimizer()); } if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVENULLSCANOPTIMIZE)) { resolvers.add(new NullScanOptimizer()); } if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVESAMPLINGFORORDERBY)) { resolvers.add(new SamplingOptimizer()); } // Physical optimizers which follow this need to be careful not to invalidate the inferences // made by this optimizer. Only optimizers which depend on the results of this one should // follow it. if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVE_INFER_BUCKET_SORT)) { resolvers.add(new BucketingSortingInferenceOptimizer()); } if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVE_CHECK_CROSS_PRODUCT)) { resolvers.add(new CrossProductCheck()); } // Vectorization should be the last optimization, because it doesn't modify the plan // or any operators. It makes a very low level transformation to the expressions to // run in the vectorized mode. if (hiveConf.getBoolVar(HiveConf.ConfVars.HIVE_VECTORIZATION_ENABLED) && pctx.getContext().getExplainAnalyze() == null) { resolvers.add(new Vectorizer()); } if (!"none".equalsIgnoreCase(hiveConf.getVar(HiveConf.ConfVars.HIVESTAGEIDREARRANGE))) { resolvers.add(new StageIDsRearranger()); } if (pctx.getContext().getExplainAnalyze() != null) { resolvers.add(new AnnotateRunTimeStatsOptimizer()); } } /** * invoke all the resolvers one-by-one, and alter the physical plan. * * @return PhysicalContext * @throws HiveException */ public PhysicalContext optimize() throws SemanticException { for (PhysicalPlanResolver r : resolvers) { pctx = r.resolve(pctx); } return pctx; } }