spark-instrumented-optimizer/sql/core
wangzhenhua e44274870d [SPARK-17080][SQL] join reorder
## What changes were proposed in this pull request?

Reorder the joins using a dynamic programming algorithm (Selinger paper):
First we put all items (basic joined nodes) into level 1, then we build all two-way joins at level 2 from plans at level 1 (single items), then build all 3-way joins from plans at previous levels (two-way joins and single items), then 4-way joins ... etc, until we build all n-way joins and pick the best plan among them.

When building m-way joins, we only keep the best plan (with the lowest cost) for the same set of m items. E.g., for 3-way joins, we keep only the best plan for items {A, B, C} among plans (A J B) J C, (A J C) J B and (B J C) J A. Thus, the plans maintained for each level when reordering four items A, B, C, D are as follows:
```
level 1: p({A}), p({B}), p({C}), p({D})
level 2: p({A, B}), p({A, C}), p({A, D}), p({B, C}), p({B, D}), p({C, D})
level 3: p({A, B, C}), p({A, B, D}), p({A, C, D}), p({B, C, D})
level 4: p({A, B, C, D})
```
where p({A, B, C, D}) is the final output plan.

For cost evaluation, since physical costs for operators are not available currently, we use cardinalities and sizes to compute costs.

## How was this patch tested?
add test cases

Author: wangzhenhua <wangzhenhua@huawei.com>
Author: Zhenhua Wang <wzh_zju@163.com>

Closes #17138 from wzhfy/joinReorder.
2017-03-08 16:01:28 +01:00
..
benchmarks [SPARK-17335][SQL] Fix ArrayType and MapType CatalogString. 2016-09-03 19:02:20 +02:00
src [SPARK-17080][SQL] join reorder 2017-03-08 16:01:28 +01:00
pom.xml [SPARK-19409][BUILD][TEST-MAVEN] Fix ParquetAvroCompatibilitySuite failure due to test dependency on avro 2017-02-08 12:21:49 +00:00