Examples with PhysicalContext org.apache.hadoop.hive.ql.optimizer.physical.PhysicalContext used on opensource projects

Example 1 with PhysicalContext

use of org.apache.hadoop.hive.ql.optimizer.physical.PhysicalContext in project hive by apache.

the class MapReduceCompiler method optimizeTaskPlan.

@Override
protected void optimizeTaskPlan(List<Task<? extends Serializable>> rootTasks, ParseContext pCtx, Context ctx) throws SemanticException {
    // For each task, go over all operators recursively
    for (Task<? extends Serializable> rootTask : rootTasks) {
        breakTaskTree(rootTask);
    }
    PhysicalContext physicalContext = new PhysicalContext(conf, getParseContext(pCtx, rootTasks), ctx, rootTasks, pCtx.getFetchTask());
    PhysicalOptimizer physicalOptimizer = new PhysicalOptimizer(physicalContext, conf);
    physicalOptimizer.optimize();
}

Example 2 with PhysicalContext

use of org.apache.hadoop.hive.ql.optimizer.physical.PhysicalContext in project hive by apache.

the class SparkCompiler method optimizeTaskPlan.

@Override
protected void optimizeTaskPlan(List<Task<? extends Serializable>> rootTasks, ParseContext pCtx, Context ctx) throws SemanticException {
    PERF_LOGGER.PerfLogBegin(CLASS_NAME, PerfLogger.SPARK_OPTIMIZE_TASK_TREE);
    PhysicalContext physicalCtx = new PhysicalContext(conf, pCtx, pCtx.getContext(), rootTasks, pCtx.getFetchTask());
    physicalCtx = new SplitSparkWorkResolver().resolve(physicalCtx);
    if (conf.getBoolVar(HiveConf.ConfVars.HIVESKEWJOIN)) {
        (new SparkSkewJoinResolver()).resolve(physicalCtx);
    } else {
        LOG.debug("Skipping runtime skew join optimization");
    }
    physicalCtx = new SparkMapJoinResolver().resolve(physicalCtx);
    if (conf.getBoolVar(HiveConf.ConfVars.HIVENULLSCANOPTIMIZE)) {
        physicalCtx = new NullScanOptimizer().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping null scan query optimization");
    }
    if (conf.getBoolVar(HiveConf.ConfVars.HIVEMETADATAONLYQUERIES)) {
        physicalCtx = new MetadataOnlyOptimizer().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping metadata only query optimization");
    }
    if (conf.getBoolVar(HiveConf.ConfVars.HIVE_CHECK_CROSS_PRODUCT)) {
        physicalCtx = new SparkCrossProductCheck().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping cross product analysis");
    }
    if (conf.getBoolVar(HiveConf.ConfVars.HIVE_VECTORIZATION_ENABLED) && ctx.getExplainAnalyze() == null) {
        (new Vectorizer()).resolve(physicalCtx);
    } else {
        LOG.debug("Skipping vectorization");
    }
    if (!"none".equalsIgnoreCase(conf.getVar(HiveConf.ConfVars.HIVESTAGEIDREARRANGE))) {
        (new StageIDsRearranger()).resolve(physicalCtx);
    } else {
        LOG.debug("Skipping stage id rearranger");
    }
    new CombineEquivalentWorkResolver().resolve(physicalCtx);
    if (physicalCtx.getContext().getExplainAnalyze() != null) {
        new AnnotateRunTimeStatsOptimizer().resolve(physicalCtx);
    }
    PERF_LOGGER.PerfLogEnd(CLASS_NAME, PerfLogger.SPARK_OPTIMIZE_TASK_TREE);
    return;
}

Example 3 with PhysicalContext

use of org.apache.hadoop.hive.ql.optimizer.physical.PhysicalContext in project hive by apache.

the class TezCompiler method optimizeTaskPlan.

@Override
protected void optimizeTaskPlan(List<Task<? extends Serializable>> rootTasks, ParseContext pCtx, Context ctx) throws SemanticException {
    PerfLogger perfLogger = SessionState.getPerfLogger();
    perfLogger.PerfLogBegin(this.getClass().getName(), PerfLogger.TEZ_COMPILER);
    PhysicalContext physicalCtx = new PhysicalContext(conf, pCtx, pCtx.getContext(), rootTasks, pCtx.getFetchTask());
    if (conf.getBoolVar(HiveConf.ConfVars.HIVENULLSCANOPTIMIZE)) {
        physicalCtx = new NullScanOptimizer().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping null scan query optimization");
    }
    if (conf.getBoolVar(HiveConf.ConfVars.HIVEMETADATAONLYQUERIES)) {
        physicalCtx = new MetadataOnlyOptimizer().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping metadata only query optimization");
    }
    if (conf.getBoolVar(HiveConf.ConfVars.HIVE_CHECK_CROSS_PRODUCT)) {
        physicalCtx = new CrossProductCheck().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping cross product analysis");
    }
    if (conf.getBoolVar(HiveConf.ConfVars.HIVE_VECTORIZATION_ENABLED) && ctx.getExplainAnalyze() == null) {
        physicalCtx = new Vectorizer().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping vectorization");
    }
    if (!"none".equalsIgnoreCase(conf.getVar(HiveConf.ConfVars.HIVESTAGEIDREARRANGE))) {
        physicalCtx = new StageIDsRearranger().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping stage id rearranger");
    }
    if ((conf.getBoolVar(HiveConf.ConfVars.HIVE_TEZ_ENABLE_MEMORY_MANAGER)) && (conf.getBoolVar(HiveConf.ConfVars.HIVEUSEHYBRIDGRACEHASHJOIN))) {
        physicalCtx = new MemoryDecider().resolve(physicalCtx);
    }
    if ("llap".equalsIgnoreCase(conf.getVar(HiveConf.ConfVars.HIVE_EXECUTION_MODE))) {
        physicalCtx = new LlapDecider().resolve(physicalCtx);
    } else {
        LOG.debug("Skipping llap decider");
    }
    //  This optimizer will serialize all filters that made it to the
    //  table scan operator to avoid having to do it multiple times on
    //  the backend. If you have a physical optimization that changes
    //  table scans or filters, you have to invoke it before this one.
    physicalCtx = new SerializeFilter().resolve(physicalCtx);
    if (physicalCtx.getContext().getExplainAnalyze() != null) {
        new AnnotateRunTimeStatsOptimizer().resolve(physicalCtx);
    }
    perfLogger.PerfLogEnd(this.getClass().getName(), PerfLogger.TEZ_COMPILER, "optimizeTaskPlan");
    return;
}