use of org.apache.hadoop.hive.ql.lib.GraphWalker in project hive by apache.
the class AnnotateWithOpTraits method transform.
@Override
public ParseContext transform(ParseContext pctx) throws SemanticException {
AnnotateOpTraitsProcCtx annotateCtx = new AnnotateOpTraitsProcCtx(pctx);
// create a walker which walks the tree in a BFS manner while maintaining the
// operator stack. The dispatcher generates the plan from the operator tree
Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
opRules.put(new RuleRegExp("TS", TableScanOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getTableScanRule());
opRules.put(new RuleRegExp("RS", ReduceSinkOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getReduceSinkRule());
opRules.put(new RuleRegExp("JOIN", JoinOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getJoinRule());
opRules.put(new RuleRegExp("MAPJOIN", MapJoinOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getMultiParentRule());
opRules.put(new RuleRegExp("SMB", SMBMapJoinOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getMultiParentRule());
opRules.put(new RuleRegExp("MUX", MuxOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getMultiParentRule());
opRules.put(new RuleRegExp("DEMUX", DemuxOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getMultiParentRule());
opRules.put(new RuleRegExp("UNION", UnionOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getMultiParentRule());
opRules.put(new RuleRegExp("GBY", GroupByOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getGroupByRule());
opRules.put(new RuleRegExp("SEL", SelectOperator.getOperatorName() + "%"), OpTraitsRulesProcFactory.getSelectRule());
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(OpTraitsRulesProcFactory.getDefaultRule(), opRules, annotateCtx);
GraphWalker ogw = new LevelOrderWalker(disp, 0);
// Create a list of topop nodes
ArrayList<Node> topNodes = new ArrayList<Node>();
topNodes.addAll(pctx.getTopOps().values());
ogw.startWalking(topNodes, null);
return pctx;
}
use of org.apache.hadoop.hive.ql.lib.GraphWalker in project hive by apache.
the class PartitionConditionRemover method transform.
/*
* (non-Javadoc)
*
* @see
* org.apache.hadoop.hive.ql.optimizer.Transform#transform(org.apache.hadoop
* .hive.ql.parse.ParseContext)
*/
@Override
public ParseContext transform(ParseContext pctx) throws SemanticException {
// create a the context for walking operators
List<PcrOpWalkerCtx.OpToDeleteInfo> opToRemove = new ArrayList<PcrOpWalkerCtx.OpToDeleteInfo>();
PcrOpWalkerCtx opWalkerCtx = new PcrOpWalkerCtx(pctx, opToRemove);
Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
opRules.put(new RuleRegExp("R1", "(" + TableScanOperator.getOperatorName() + "%" + FilterOperator.getOperatorName() + "%)|(" + TableScanOperator.getOperatorName() + "%" + FilterOperator.getOperatorName() + "%" + FilterOperator.getOperatorName() + "%)"), PcrOpProcFactory.getFilterProc());
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(PcrOpProcFactory.getDefaultProc(), opRules, opWalkerCtx);
GraphWalker ogw = new DefaultGraphWalker(disp);
// Create a list of topop nodes
ArrayList<Node> topNodes = new ArrayList<Node>();
topNodes.addAll(pctx.getTopOps().values());
ogw.startWalking(topNodes, null);
for (PcrOpWalkerCtx.OpToDeleteInfo entry : opToRemove) {
entry.getParent().removeChildAndAdoptItsChildren(entry.getOperator());
}
return pctx;
}
use of org.apache.hadoop.hive.ql.lib.GraphWalker in project hive by apache.
the class MapReduceCompiler method generateTaskTree.
@Override
protected void generateTaskTree(List<Task<? extends Serializable>> rootTasks, ParseContext pCtx, List<Task<MoveWork>> mvTask, Set<ReadEntity> inputs, Set<WriteEntity> outputs) throws SemanticException {
// generate map reduce plans
ParseContext tempParseContext = getParseContext(pCtx, rootTasks);
GenMRProcContext procCtx = new GenMRProcContext(conf, // Must be deterministic order map for consistent q-test output across Java versions
new LinkedHashMap<Operator<? extends OperatorDesc>, Task<? extends Serializable>>(), tempParseContext, mvTask, rootTasks, new LinkedHashMap<Operator<? extends OperatorDesc>, GenMapRedCtx>(), inputs, outputs);
// create a walker which walks the tree in a DFS manner while maintaining
// the operator stack.
// The dispatcher generates the plan from the operator tree
Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
opRules.put(new RuleRegExp(new String("R1"), TableScanOperator.getOperatorName() + "%"), new GenMRTableScan1());
opRules.put(new RuleRegExp(new String("R2"), TableScanOperator.getOperatorName() + "%.*" + ReduceSinkOperator.getOperatorName() + "%"), new GenMRRedSink1());
opRules.put(new RuleRegExp(new String("R3"), ReduceSinkOperator.getOperatorName() + "%.*" + ReduceSinkOperator.getOperatorName() + "%"), new GenMRRedSink2());
opRules.put(new RuleRegExp(new String("R4"), FileSinkOperator.getOperatorName() + "%"), new GenMRFileSink1());
opRules.put(new RuleRegExp(new String("R5"), UnionOperator.getOperatorName() + "%"), new GenMRUnion1());
opRules.put(new RuleRegExp(new String("R6"), UnionOperator.getOperatorName() + "%.*" + ReduceSinkOperator.getOperatorName() + "%"), new GenMRRedSink3());
opRules.put(new RuleRegExp(new String("R7"), MapJoinOperator.getOperatorName() + "%"), MapJoinFactory.getTableScanMapJoin());
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(new GenMROperator(), opRules, procCtx);
GraphWalker ogw = new GenMapRedWalker(disp);
ArrayList<Node> topNodes = new ArrayList<Node>();
topNodes.addAll(pCtx.getTopOps().values());
ogw.startWalking(topNodes, null);
}
use of org.apache.hadoop.hive.ql.lib.GraphWalker in project hive by apache.
the class SparkCompiler method runSetReducerParallelism.
private void runSetReducerParallelism(OptimizeSparkProcContext procCtx) throws SemanticException {
ParseContext pCtx = procCtx.getParseContext();
Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
opRules.put(new RuleRegExp("Set parallelism - ReduceSink", ReduceSinkOperator.getOperatorName() + "%"), new SetSparkReducerParallelism(pCtx.getConf()));
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
GraphWalker ogw = new PreOrderWalker(disp);
// Create a list of topop nodes
ArrayList<Node> topNodes = new ArrayList<Node>();
topNodes.addAll(pCtx.getTopOps().values());
ogw.startWalking(topNodes, null);
}
use of org.apache.hadoop.hive.ql.lib.GraphWalker in project hive by apache.
the class SparkCompiler method runDynamicPartitionPruning.
private void runDynamicPartitionPruning(OptimizeSparkProcContext procCtx) throws SemanticException {
if (!conf.getBoolVar(HiveConf.ConfVars.SPARK_DYNAMIC_PARTITION_PRUNING)) {
return;
}
ParseContext parseContext = procCtx.getParseContext();
Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
opRules.put(new RuleRegExp(new String("Dynamic Partition Pruning"), FilterOperator.getOperatorName() + "%"), new DynamicPartitionPruningOptimization());
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
GraphWalker ogw = new ForwardWalker(disp);
List<Node> topNodes = new ArrayList<Node>();
topNodes.addAll(parseContext.getTopOps().values());
ogw.startWalking(topNodes, null);
// of "and true and true" conditions.
if (procCtx.getConf().getBoolVar(HiveConf.ConfVars.HIVEOPTCONSTANTPROPAGATION)) {
new ConstantPropagate().transform(parseContext);
}
}
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