alamb
commented
8 months ago cc @sunchao @viirya and @kazuyukitanimura whom I mention this technique the other day
Open alamb opened 8 months ago
alamb
commented
8 months ago cc @sunchao @viirya and @kazuyukitanimura whom I mention this technique the other day
acking-you
commented
8 months ago Is your feature request related to a problem or challenge?
If we want to make DataFusion the engine of choice for fast OLAP processing, eventually we will need to make joins faster. In addition to making sure the join order is not disastrous (e.g. #7949) we can consider other advanced OLAP techniques improve joins (especially queries with multiple joins)
Describe the solution you'd like
I would like to propose we look into pushing "join predicate" into scans (which I know of as "sideways information passing")
As an example, consider the joins from TPCH Q17
select sum(l_extendedprice) / 7.0 as avg_yearly from part, lineitem where p_partkey = l_partkey and p_brand = 'Brand#23' and p_container = 'MED BOX' and l_quantity < ( select 0.2 * avg(l_quantity) from lineitem where l_partkey = p_partkey );The first join (should) look like this. The observation is there are no predicates on the
lineitemtable (the big one), which means all the filtering happens in the join, which is bad because the scan can't do optimizations like "late materialization" and instead must decode all 60M values of selected columns, even though very few (2044!) are actually used│ │ 2044 Rows │ │ ▼ ┌────────────────┐ │ HashJoin │ │ p_partkey = │ │ l_partkey │ └──┬─────────┬───┘ This scan decodes 60M values 2M Rows │ │ 60M Rows of l_quantity and ┌────────┘ └─────────┐ l_extendedprice, even though │ │ all but 2044 are filtered by ▼ ▼ the join ┌──────────────────┐ ┌─────────────────────┐ │Scan: part │ │Scan: lineitem │ │ │projection: │ │projection: │ │ p_partkey │ │ l_quantity, │ │ │filters: │ │ l_extendedprice, │◀─ ─ ─ ─ ─ ─ ─ ─ ─ │ p_brand = .. │ │ l_partkey │ │ p_container = ..│ │filters: │ │ │ │ NONE │ └──────────────────┘ └─────────────────────┘The idea is to push the predicate into the join, by making something that acts like
l_partkey IN (...)that can be applied during the scan1. The HashJoin completely reads the build side before starting the probe side. Thus, all 2M known matching values of │ l_partkey are in a hash table prior to │ scanning lineitem 2044 Rows │ │ │ ▼ ┌────────────────┐ │ │ HashJoin │ │ p_partkey = │◀─ ─ ─ ─ ─ ─ ─ ─ ─ ┘ │ l_partkey │ └──┬─────────┬───┘ │ │ 60M Rows ┌────────┘ └────────────┐ The idea is to introduce a filter │ │ that is effectively "l_partkey IN ▼ ▼ (HASH TABLE)" or something similar ┌──────────────────┐ ┌──────────────────────────┐ that is applied during the scan │Scan: part │ │Scan: lineitem │┌ ─ ─ │projection: │ │projection: │ If the scan can avoid decoding │ p_partkey │ │ l_quantity, ││ l_quantity and l_extended that do │filters: │ │ l_extendedprice, │ not match, there is significant │ p_brand = .. │ │ l_partkey ││ savings │ p_container = ..│ │filters: │ │ │ │ l_partkey IN (....) ◀─│┘ └──────────────────┘ └──────────────────────────┘In a query with a single selective join (that filters many values) the savings is likely minimal as it depends on how much work can be saved in materialization (decoding). The only scan that does late materialization in DataFusion at the time of writing is the
ParquetExecHowever, in a query with multiple selective joins the savings becomes much more pronounced, because we can save the effort of creating intermediate join outputs which are filtered out by joins later in the plan
For example:
Pass down in multiple joins While this doesn't happen in TPCH Q17 (the subquery has no predicates) the SIPS approach can be even more effective with multiple selective joins │ │ │ Filters on both join keys can be applied │ at this level, which can be even more ▼ effective as it avoids the work to create ┌────────────────┐ the intermediate output of HashJoin(2) ─ ┐ │ HashJoin (1) │ which is then filtered by HashJoin(1) │ d1.key = │ │ │ f.d1_key │ └──┬─────────┬───┘ │ │ │ ┌──────────┘ └────────────┐ │ │ │ ▼ ▼ │ ┌──────────────────┐ ┌────────────────┐ │Scan: D1 │ │ HashJoin (2) │ │ │filters: │ │ d2.key = │ │ ... │ │ f.d2_key │ │ └──────────────────┘ └───┬─────────┬──┘ │ │ │ ┌───────────┘ └─────────────┐ │ │ │ ▼ ▼ ┌────────────────┐ ┌─────────────────────┐ │ │Scan: D2 │ │Scan: F │ │filters: │ │filters: │ │ │ ... │ │ f.d1_key IN (...) │◀ ─ ─ ─ ─ └────────────────┘ │ f.d2_key IN (...) │ │ │ └─────────────────────┘Describe alternatives you've considered
Some version of this technique is described in "Bloom Filter Joins" in Spark: https://issues.apache.org/jira/browse/SPARK-32268
Building a seprate Bloom Filter has the nice property that you can distribute them in a networked cluster, however, the overhead of creating the bloom filter would likely be non trivial
Additional context
See a description of how DataFusion HashJoins work here: #7953
Here is an industrial paper that describes industrial experience with using SIPS techniques here: https://15721.courses.cs.cmu.edu/spring2020/papers/13-execution/shrinivas-icde2013.pdf
I'm very curious about how this kind of graph is drawn😯
alamb
commented
8 months ago I'm very curious about how this kind of graph is drawn😯
@ACking-you I draw it by hand using https://monodraw.helftone.com/ (and it unfortunately takes me a long time)
@devinjdangelo has suggested https://asciiflow.com/#/ works for making something quick in the browser
@westonpace pointed out that if you're writing on Github then you can use mermaid syntax in comments / issues / prs and it will render automatically: https://github.blog/2022-02-14-include-diagrams-markdown-files-mermaid/
Is your feature request related to a problem or challenge?
If we want to make DataFusion the engine of choice for fast OLAP processing, eventually we will need to make joins faster. In addition to making sure the join order is not disastrous (e.g. https://github.com/apache/arrow-datafusion/issues/7949) we can consider other advanced OLAP techniques improve joins (especially queries with multiple joins)
Describe the solution you'd like
I would like to propose we look into pushing "join predicate" into scans (which I know of as "sideways information passing")
As an example, consider the joins from TPCH Q17
The first join (should) look like this. The observation is there are no predicates on the
lineitemtable (the big one), which means all the filtering happens in the join, which is bad because the scan can't do optimizations like "late materialization" and instead must decode all 60M values of selected columns, even though very few (2044!) are actually usedThe idea is to push the predicate into the join, by making something that acts like
l_partkey IN (...)that can be applied during the scanIn a query with a single selective join (that filters many values) the savings is likely minimal as it depends on how much work can be saved in materialization (decoding). The only scan that does late materialization in DataFusion at the time of writing is the
ParquetExecHowever, in a query with multiple selective joins the savings becomes much more pronounced, because we can save the effort of creating intermediate join outputs which are filtered out by joins later in the plan
For example:
Describe alternatives you've considered
Some version of this technique is described in "Bloom Filter Joins" in Spark: https://issues.apache.org/jira/browse/SPARK-32268
Building a seprate Bloom Filter has the nice property that you can distribute them in a networked cluster, however, the overhead of creating the bloom filter would likely be non trivial
Additional context
See a description of how DataFusion HashJoins work here: https://github.com/apache/arrow-datafusion/pull/7953
Here is an industrial paper that describes industrial experience with using SIPS techniques here: https://15721.courses.cs.cmu.edu/spring2020/papers/13-execution/shrinivas-icde2013.pdf