[jira] [Created] (IMPALA-6461) *DataStreamSender::Channel::AddRow needs some micro-optimizations to remove per row function call and data dependency

["Mostafa Mokhtar (JIRA)" <ji...@apache.org>]

Mostafa Mokhtar created IMPALA-6461:
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             Summary: *DataStreamSender::Channel::AddRow needs some micro-optimizations to remove per row function call and data dependency 
                 Key: IMPALA-6461
                 URL: https://issues.apache.org/jira/browse/IMPALA-6461
             Project: IMPALA
          Issue Type: Improvement
          Components: Distributed Exec
            Reporter: Mostafa Mokhtar
            Assignee: Mostafa Mokhtar


While analyzing performance of partition exchange operator I noticed that there is dependency and a function call per row in the hot path.

{code}

// hash-partition batch's rows across channels
 // TODO: encapsulate this in an Expr as we've done for Kudu above and remove this case
 // once we have codegen here.
 int num_channels = channels_.size();
 for (int i = 0; i < batch->num_rows(); ++i) {
 TupleRow* row = batch->GetRow(i);
 uint64_t hash_val = EXCHANGE_HASH_SEED;
 for (int j = 0; j < partition_exprs_.size(); ++j) {
 ScalarExprEvaluator* eval = partition_expr_evals_[j];
 void* partition_val = eval->GetValue(row);
 // We can't use the crc hash function here because it does not result in
 // uncorrelated hashes with different seeds. Instead we use FastHash.
 // TODO: fix crc hash/GetHashValue()
 DCHECK(&(eval->root()) == partition_exprs_[j]);
 hash_val = RawValue::GetHashValueFastHash(
 partition_val, partition_exprs_[j]->type(), hash_val);
 }
 RETURN_IF_ERROR(channels_[hash_val % num_channels]->AddRow(row));
 }

{code}

Force inlining DataStreamSender::Channel::AddRow and breaking up the loop improves partition exchange performance by 5%

Code-generation of the hash computation IMPALA-5168 should give another 10% speedup. 



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