Examples with SemanticRule org.apache.hadoop.hive.ql.lib.SemanticRule used on opensource projects

Example 31 with SemanticRule

use of org.apache.hadoop.hive.ql.lib.SemanticRule in project hive by apache.

the class SparkCompiler method runRemoveDynamicPruning.

private void runRemoveDynamicPruning(OptimizeSparkProcContext procCtx) throws SemanticException {
    ParseContext pCtx = procCtx.getParseContext();
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("Disabling Dynamic Partition Pruning", SparkPartitionPruningSinkOperator.getOperatorName() + "%"), new SparkRemoveDynamicPruning());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    SemanticDispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
    SemanticGraphWalker 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);
}

Example 32 with SemanticRule

use of org.apache.hadoop.hive.ql.lib.SemanticRule in project hive by apache.

the class SparkCompiler method runJoinOptimizations.

private void runJoinOptimizations(OptimizeSparkProcContext procCtx) throws SemanticException {
    ParseContext pCtx = procCtx.getParseContext();
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new TypeRule(JoinOperator.class), new SparkJoinOptimizer(pCtx));
    opRules.put(new TypeRule(MapJoinOperator.class), new SparkJoinHintOptimizer(pCtx));
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    SemanticDispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
    SemanticGraphWalker 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);
}

Example 33 with SemanticRule

use of org.apache.hadoop.hive.ql.lib.SemanticRule in project hive by apache.

the class SparkCompiler method generateTaskTree.

/**
 * TODO: need to turn on rules that's commented out and add more if necessary.
 */
@Override
protected void generateTaskTree(List<Task<?>> rootTasks, ParseContext pCtx, List<Task<MoveWork>> mvTask, Set<ReadEntity> inputs, Set<WriteEntity> outputs) throws SemanticException {
    PERF_LOGGER.perfLogBegin(CLASS_NAME, PerfLogger.SPARK_GENERATE_TASK_TREE);
    GenSparkUtils utils = GenSparkUtils.getUtils();
    utils.resetSequenceNumber();
    ParseContext tempParseContext = getParseContext(pCtx, rootTasks);
    GenSparkProcContext procCtx = new GenSparkProcContext(conf, tempParseContext, mvTask, rootTasks, inputs, outputs, pCtx.getTopOps());
    // -------------------------------- First Pass ---------------------------------- //
    // Identify SparkPartitionPruningSinkOperators, and break OP tree if necessary
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("Clone OP tree for PartitionPruningSink", SparkPartitionPruningSinkOperator.getOperatorName() + "%"), new SplitOpTreeForDPP());
    SemanticDispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
    SemanticGraphWalker ogw = new GenSparkWorkWalker(disp, procCtx);
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pCtx.getTopOps().values());
    ogw.startWalking(topNodes, null);
    // -------------------------------- Second Pass ---------------------------------- //
    // Process operator tree in two steps: first we process the extra op trees generated
    // in the first pass. Then we process the main op tree, and the result task will depend
    // on the task generated in the first pass.
    topNodes.clear();
    topNodes.addAll(procCtx.topOps.values());
    generateTaskTreeHelper(procCtx, topNodes);
    // the partitions used.
    if (!procCtx.clonedPruningTableScanSet.isEmpty()) {
        SparkTask pruningTask = SparkUtilities.createSparkTask(conf);
        SparkTask mainTask = procCtx.currentTask;
        pruningTask.addDependentTask(procCtx.currentTask);
        procCtx.rootTasks.remove(procCtx.currentTask);
        procCtx.rootTasks.add(pruningTask);
        procCtx.currentTask = pruningTask;
        topNodes.clear();
        topNodes.addAll(procCtx.clonedPruningTableScanSet);
        generateTaskTreeHelper(procCtx, topNodes);
        procCtx.currentTask = mainTask;
    }
    // we need to clone some operator plans and remove union operators still
    for (BaseWork w : procCtx.workWithUnionOperators) {
        GenSparkUtils.getUtils().removeUnionOperators(procCtx, w);
    }
    // we need to fill MapWork with 'local' work and bucket information for SMB Join.
    GenSparkUtils.getUtils().annotateMapWork(procCtx);
    // finally make sure the file sink operators are set up right
    for (FileSinkOperator fileSink : procCtx.fileSinkSet) {
        GenSparkUtils.getUtils().processFileSink(procCtx, fileSink);
    }
    // Process partition pruning sinks
    for (Operator<?> prunerSink : procCtx.pruningSinkSet) {
        utils.processPartitionPruningSink(procCtx, (SparkPartitionPruningSinkOperator) prunerSink);
    }
    PERF_LOGGER.perfLogEnd(CLASS_NAME, PerfLogger.SPARK_GENERATE_TASK_TREE);
}

Example 34 with SemanticRule

use of org.apache.hadoop.hive.ql.lib.SemanticRule in project hive by apache.

the class HiveOpConverterPostProc method transform.

@Override
public ParseContext transform(ParseContext pctx) throws SemanticException {
    // 0. We check the conditions to apply this transformation,
    // if we do not meet them we bail out
    final boolean cboEnabled = HiveConf.getBoolVar(pctx.getConf(), HiveConf.ConfVars.HIVE_CBO_ENABLED);
    final boolean returnPathEnabled = HiveConf.getBoolVar(pctx.getConf(), HiveConf.ConfVars.HIVE_CBO_RETPATH_HIVEOP);
    final boolean cboSucceeded = pctx.getContext().isCboSucceeded();
    if (!(cboEnabled && returnPathEnabled && cboSucceeded)) {
        return pctx;
    }
    // 1. Initialize aux data structures
    this.pctx = pctx;
    this.aliasToOpInfo = new HashMap<String, Operator<? extends OperatorDesc>>();
    // 2. Trigger transformation
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("R1", JoinOperator.getOperatorName() + "%"), new JoinAnnotate());
    opRules.put(new RuleRegExp("R2", TableScanOperator.getOperatorName() + "%"), new TableScanAnnotate());
    SemanticDispatcher disp = new DefaultRuleDispatcher(null, opRules, null);
    SemanticGraphWalker ogw = new ForwardWalker(disp);
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pctx.getTopOps().values());
    ogw.startWalking(topNodes, null);
    return pctx;
}

Example 35 with SemanticRule

use of org.apache.hadoop.hive.ql.lib.SemanticRule in project hive by apache.

the class AnnotateRunTimeStatsOptimizer method resolve.

@Override
public PhysicalContext resolve(PhysicalContext pctx) throws SemanticException {
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    SemanticDispatcher disp = new AnnotateRunTimeStatsDispatcher(pctx, opRules);
    SemanticGraphWalker ogw = new DefaultGraphWalker(disp);
    ArrayList<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pctx.getRootTasks());
    ogw.startWalking(topNodes, null);
    return pctx;
}