PostgreSQL 11用于分区表上的并行seq扫描，其中索引应该足够

Question

问题是我在一个相当简单的查询中不断进行seq扫描以进行非常简单的设置。我究竟做错了什么？

• Windows Server 2016上的Postgres 11
• 完成配置更改：constraint_exclusion = partition
• 一个表分区为200个子表，每个分区有数十万条记录。
• 有关字段的索引（假设一个字段也被分区）

这是create语句：

CREATE TABLE A (
    K int NOT NULL,
    X bigint NOT NULL,
    Date timestamp NOT NULL,
    fy smallint NOT NULL,
    fz decimal(18, 8) NOT NULL,
    fw decimal(18, 8) NOT NULL,
    fv decimal(18, 8) NULL,
    PRIMARY KEY (K, X)
) PARTITION BY LIST (K);

CREATE TABLE A_1 PARTITION OF A FOR VALUES IN (1);
CREATE TABLE A_2 PARTITION OF A FOR VALUES IN (2);
...
CREATE TABLE A_200 PARTITION OF A FOR VALUES IN (200);
CREATE TABLE A_Default PARTITION OF A DEFAULT;

CREATE INDEX IX_A_Date ON A (Date);

有问题的查询：

SELECT K, MIN(Date), MAX(Date)
FROM A 
GROUP BY K

这总是会产生一个需要几分钟的序列扫描，而很明显，由于Date字段被索引而根本不需要表数据，而我只是要求它的B树的第一个和最后一个叶子。

最初索引是在(K, Date)上，它很快就向我呈现Postgres不会在我认为它正在使用的任何查询中兑换一个.(Date)上的索引为其他查询做了伎俩，看起来Postgres声称自动分区索引。但是，这个特定的简单查询总是用于seq扫描。

任何想法赞赏！

UPDATE

查询计划(analyze, buffers)如下：

Finalize GroupAggregate  (cost=4058360.99..4058412.66 rows=200 width=20) (actual time=148448.183..148448.189 rows=5 loops=1)
  Group Key: a_16.k
  Buffers: shared hit=5970 read=548034 dirtied=4851 written=1446
  ->  Gather Merge  (cost=4058360.99..4058407.66 rows=400 width=20) (actual time=148448.166..148463.953 rows=8 loops=1)
    Workers Planned: 2
    Workers Launched: 2
    Buffers: shared hit=5998 read=1919356 dirtied=4865 written=1454
    ->  Sort  (cost=4057360.97..4057361.47 rows=200 width=20) (actual time=148302.271..148302.285 rows=3 loops=3)
        Sort Key: a_16.k
        Sort Method: quicksort  Memory: 25kB
        Worker 0:  Sort Method: quicksort  Memory: 25kB
        Worker 1:  Sort Method: quicksort  Memory: 25kB
        Buffers: shared hit=5998 read=1919356 dirtied=4865 written=1454
        ->  Partial HashAggregate  (cost=4057351.32..4057353.32 rows=200 width=20) (actual time=148302.199..148302.203 rows=3 loops=3)
            Group Key: a_16.k
            Buffers: shared hit=5984 read=1919356 dirtied=4865 written=1454
            ->  Parallel Append  (cost=0.00..3347409.96 rows=94658849 width=12) (actual time=1.678..116664.051 rows=75662243 loops=3)
                Buffers: shared hit=5984 read=1919356 dirtied=4865 written=1454
                ->  Parallel Seq Scan on a_16  (cost=0.00..1302601.32 rows=42870432 width=12) (actual time=0.320..41625.766 rows=34283419 loops=3)
                    Buffers: shared hit=14 read=873883 dirtied=14 written=8
                ->  Parallel Seq Scan on a_19  (cost=0.00..794121.94 rows=26070794 width=12) (actual time=0.603..54017.937 rows=31276617 loops=2)
                    Buffers: shared read=533414
                ->  Parallel Seq Scan on a_20  (cost=0.00..447025.50 rows=14900850 width=12) (actual time=0.347..52866.404 rows=35762000 loops=1)
                    Buffers: shared hit=5964 read=292053 dirtied=4850 written=1446
                ->  Parallel Seq Scan on a_18  (cost=0.00..198330.23 rows=6450422 width=12) (actual time=4.504..27197.706 rows=15481014 loops=1)
                    Buffers: shared read=133826
                ->  Parallel Seq Scan on a_17  (cost=0.00..129272.31 rows=4308631 width=12) (actual time=3.014..18423.307 rows=10340224 loops=1)
                    Buffers: shared hit=6 read=86180 dirtied=1
                ...
                ->  Parallel Seq Scan on a_197  (cost=0.00..14.18 rows=418 width=12) (actual time=0.000..0.000 rows=0 loops=1)
                ->  Parallel Seq Scan on a_198  (cost=0.00..14.18 rows=418 width=12) (actual time=0.001..0.002 rows=0 loops=1)
                ->  Parallel Seq Scan on a_199  (cost=0.00..14.18 rows=418 width=12) (actual time=0.001..0.001 rows=0 loops=1)
                ->  Parallel Seq Scan on a_default  (cost=0.00..14.18 rows=418 width=12) (actual time=0.001..0.002 rows=0 loops=1)
Planning Time: 16.893 ms
Execution Time: 148466.519 ms

更新2只是为了避免将来的评论，例如“你应该索引（K，日期）”：

两个索引的查询计划完全相同，分析数字相同，甚至缓冲区命中/读取几乎相同。

Answer 1

通过将enable_partitionwise_aggregate设置为on，可以启用向下推入并行计划的集合。

这可能会加快你的查询速度，因为PostgreSQL不必在并行工作者之间传递这么多数据。

但看起来PostgreSQL并不够智能，可以发现它可以使用索引来加速每个分区的min和max，尽管它非常聪明，可以使用非分区表来实现。

没有办法解决这个问题;您可以求助于查询每个分区：

SELECT k, min(min_date), max(max_date)
FROM (
   SELECT 1 AS k, MIN(date) AS min_date, MAX(date) AS max_date FROM a_1
   UNION ALL
   SELECT 2, MIN(date), MAX(date) FROM a_2
   UNION ALL
   ...
   SELECT 200, MIN(date), MAX(date) FROM a_200
   UNION ALL
   SELECT k, MIN(date), MAX(date) FROM a_default
) AS all_a
GROUP BY k;

呸！这里显然有改进的余地。

我挖掘代码并在src/backend/optimizer/plan/planagg.c中找到原因：

/*
 * preprocess_minmax_aggregates - preprocess MIN/MAX aggregates
 *
 * Check to see whether the query contains MIN/MAX aggregate functions that
 * might be optimizable via indexscans.  If it does, and all the aggregates
 * are potentially optimizable, then create a MinMaxAggPath and add it to
 * the (UPPERREL_GROUP_AGG, NULL) upperrel.
[...]
 */
void
preprocess_minmax_aggregates(PlannerInfo *root, List *tlist)
{
[...]                                                                                
    /*
     * Reject unoptimizable cases.
     *
     * We don't handle GROUP BY or windowing, because our current
     * implementations of grouping require looking at all the rows anyway, and
     * so there's not much point in optimizing MIN/MAX.
     */
    if (parse->groupClause || list_length(parse->groupingSets) > 1 ||
        parse->hasWindowFuncs)
        return;

基本上，PostgreSQL在看到GROUP BY子句时会发挥作用。