Zach Amsden has uploaded a new patch set (#17). Change subject: IMPALA-4864 Speed up single slot predicates with dictionaries ......................................................................
IMPALA-4864 Speed up single slot predicates with dictionaries When dictionaries are present we can pre-evaluate conjuncts against the dictionary values and simply look up the result. Status: Runs with ASAN, runs without crashes on ee tests. Performance results inconclusive; this may not be worth the complexity unless we get really selective or really expensive predicates. Basic idea: since we codegen so early, before we know enough details about the columns to know if they are dict filterable, if we do have dictionary filtering predicates, we codegen a guard around each dictionary filterable predicate evaluation. This guard skips evaluation of the predicate if it has already been evaluated by the dictionary. In this way, we can skip evaluation dynamically for each row group as we learn which columns are dictionary filterable, and then push predicate evaluation into the column reader. Since the branches will remain 100% predictable over the row group, this should give us the fastest way to skip over predicate evaluation without compromising the general case where we may be unable to evaluate against the dictionary. We can even do this with codegen turned off, as a side effect of the way we generate the codegen'd function when dictionary evaluation is enabled. If dictionaries aren't available for some predicates, we automatically fall back to evaluating those predicates in the original order, preserving selectivity. The overhead in this case is a perfectly predictable extra conditional per dictionary predicate. Performance: Hard to get! Simple predicates did not show improvement, in fact regressed. I used a TPC-H scale 30 dataset, duplicated 3x into a 'biglineitem' table. select count(*) from biglineitem WHERE l_returnflag = 'A'; 1.43s -> 1.53s select count(*) from biglineitem WHERE l_shipinstruct = 'DELIVER IN PERSON'; 1.43s -> 1.53s select count(*) from biglineitem WHERE l_quantity > 49; 0.93s -> 0.93s select count(*) from biglineitem WHERE instr(l_shipdate, '1994-11') > 0; 2.33s -> 1.03s So this appears to only be a win for expensive predicates. Update: I added changes to make computed predicate costs visible from the frontend to the backend, and tried a TPC-DS scale 10 dataset, which has better queries (lots of IN groups). Still there is a regression in raw query performance. Update again: reversing one branch to UNLIKELY in the ir compiled code gave these results on TPC-DSx10: Q46 (limit modified to 100000): 3.05 -> 2.89 sec Q27 (limit modified to 100000): 3.13 -> 3.09 sec Update (6.5.2017): Switched back to bitset evaluation for scratch batch rather than using an extra byte per tuple row. Surprisingly, this is the best performing diff yet for selective predicates. Using TPC-DS-10 with a filter: Query: select count(*) from store_sales, customer, customer_address where store_sales.ss_customer_sk = customer.c_customer_sk and customer.c_current_addr_sk = customer_address.ca_address_sk and customer_address.ca_city IN (... list of ~200 cities) This almost makes parity. I get 3836140 rows in 1.41-1.45s with this diff and in 1.39-1.45s with the same caching optimization for batch_size on top of the last change (Tim's parquest column reader optimizations). So in a totally fair comparison, we are still losing :( Update (6.8.2017): Tried Tim's suggestions. Best I could get on this query was now only 1.54s. My host OS kernel did change during this time so I re-measured baseline and got a best time of 1.53s. So we seem to be making parity, but not showcasing a big win. Maybe runtime filters will pay off better; they already are toggled dynamically so nothing should be lost. Change-Id: I65981c89e5292086809ec1268f5a273f4c1fe054 --- M be/src/codegen/gen_ir_descriptions.py M be/src/exec/exec-node.cc M be/src/exec/exec-node.h M be/src/exec/hdfs-parquet-scanner-ir.cc M be/src/exec/hdfs-parquet-scanner.cc M be/src/exec/hdfs-parquet-scanner.h M be/src/exec/hdfs-scan-node-base.cc M be/src/exec/hdfs-scan-node-base.h M be/src/exec/hdfs-scanner.h M be/src/exec/parquet-column-readers.cc M be/src/exec/parquet-column-readers.h M be/src/exec/parquet-scratch-tuple-batch.h M be/src/runtime/descriptors.h M be/src/runtime/row-batch.h M be/src/runtime/tuple.h M be/src/util/bitmap-test.cc M be/src/util/bitmap.h M be/src/util/dict-encoding.h M common/thrift/PlanNodes.thrift M fe/src/main/java/org/apache/impala/planner/PlanNode.java 20 files changed, 559 insertions(+), 165 deletions(-) git pull ssh://gerrit.cloudera.org:29418/Impala-ASF refs/changes/26/6726/17 -- To view, visit http://gerrit.cloudera.org:8080/6726 To unsubscribe, visit http://gerrit.cloudera.org:8080/settings Gerrit-MessageType: newpatchset Gerrit-Change-Id: I65981c89e5292086809ec1268f5a273f4c1fe054 Gerrit-PatchSet: 17 Gerrit-Project: Impala-ASF Gerrit-Branch: master Gerrit-Owner: Zach Amsden <zams...@cloudera.com> Gerrit-Reviewer: Joe McDonnell <joemcdonn...@cloudera.com> Gerrit-Reviewer: Marcel Kornacker <mar...@cloudera.com> Gerrit-Reviewer: Michael Ho <k...@cloudera.com> Gerrit-Reviewer: Tim Armstrong <tarmstr...@cloudera.com> Gerrit-Reviewer: Zach Amsden <zams...@cloudera.com>