了解为什么我的 Postgres 查询在没有索引的情况下会更快答案

【问题标题】：Understanding why my Postgres queries are faster without indexes了解为什么我的 Postgres 查询在没有索引的情况下会更快
【发布时间】：2022-01-14 09:24:27
【问题描述】：

我们继承了一个 Postgresql 驱动的数据仓库，但存在一些严重的性能问题。我们已经为我们的一位客户选择了一个数据库，并且正在对查询进行基准测试。我们选择了查询，发现了从所有查询中选择的几个常见表，我们认为这是我们性能不佳的核心。我们特别关注冷启动性能，当没有数据加载到共享缓冲区时，因为这是我们客户的常见情况。我们进行了一个大型查询并将其剥离到最慢的部分；

explain (analyze, buffers, costs, format json)
        select
            poi."SKUId" as "SKUId",
            poi."ConvertedLineTotal" as "totalrevenue",
            poi."TotalDispachCost" as "totaldispachcost",
            poi."Quantity" as "quantitysold"
        from public.processedorder o
        join public.processedorder_item poi on poi."OrderId" = o."OrderId"
        WHERE o."ReceivedDate" >= '2020-09-01' and o."ReceivedDate" <= '2021-01-01';

事实上，将其进一步简化为一个看起来特别慢的表；

explain (analyze, buffers, costs, format json)
select o."OrderId", o."ChannelId"
from public.processedorder o
WHERE o."ReceivedDate" >= '2020-09-01' and o."ReceivedDate" <= '2021-01-01'

这个已处理订单表有大约 210 万行，每月添加大约 35/40k 行。

桌子看起来像这样——它相当宽；

CREATE TABLE public.processedorder (
    "OrderId" int4 NOT NULL,
    "ChannelId" int4 NOT NULL,
    "ShippingId" int4 NOT NULL,
    "CountryId" int4 NOT NULL,
    "LocationId" int4 NOT NULL,
    "PackagingId" int4 NOT NULL,
    "ConvertedTotal" numeric(18, 6) NOT NULL,
    "ConvertedSubtotal" numeric(18, 6) NOT NULL,
    "ConvertedShippingCost" numeric(18, 6) NOT NULL,
    "ConvertedShippingTax" numeric(18, 6) NOT NULL,
    "ConvertedTax" numeric(18, 6) NOT NULL,
    "ConvertedDiscount" numeric(18, 6) NOT NULL,
    "ConversionRate" numeric(18, 6) NOT NULL,
    "Currency" varchar(3) NOT NULL,
    "OriginalTotal" numeric(18, 6) NOT NULL,
    "OriginalSubtotal" numeric(18, 6) NOT NULL,
    "OriginalShippingCost" numeric(18, 6) NOT NULL,
    "OrignalShippingTax" numeric(18, 6) NOT NULL,
    "OriginalTax" numeric(18, 6) NOT NULL,
    "OriginalDiscount" numeric(18, 6) NOT NULL,
    "ReceivedDate" timestamp NOT NULL,
    "DispatchByDate" timestamp NOT NULL,
    "ProcessedDate" timestamp NOT NULL,
    "HoldOrCancel" bool NOT NULL,
    "CustomerHash" varchar(100) NOT NULL,
    "EmailHash" varchar(100) NOT NULL,
    "GetPostalCode" varchar(10) NOT NULL,
    "TagId" uuid NOT NULL,
    "timestamp" timestamp NOT NULL,
    "IsRMA" bool NOT NULL DEFAULT false,
    "ConversionType" int4 NOT NULL DEFAULT 0,
    "ItemWeight" numeric(18, 6) NULL,
    "TotalWeight" numeric(18, 6) NULL,
    "PackageWeight" numeric(18, 6) NULL,
    "PackageCount" int4 NULL,
    CONSTRAINT processedorder_tagid_unique UNIQUE ("TagId")
)
WITH (
    fillfactor=50
);

我们的困惑是，在数据库的本地副本上，我们使用 receivedDate 上的简单索引运行最小查询，它会在 4 秒内返回结果-

create INDEX if not exists ix_processedorder_btree_receieveddate ON public.processedorder USING btree ("ReceivedDate" DESC);

执行计划可以看这里https://explain.tensor.ru/archive/explain/639d403ef7bf772f698502ed98ae3f63:0:2021-12-08#explain

Hash Join  (cost=166953.18..286705.36 rows=201855 width=19) (actual time=3078.441..4176.251 rows=198552 loops=1)
  Hash Cond: (poi."OrderId" = o."OrderId")
  Buffers: shared hit=3 read=160623
  ->  Seq Scan on processedorder_item poi  (cost=0.00..108605.28 rows=2434228 width=23) (actual time=0.158..667.435 rows=2434228 loops=1)
        Buffers: shared read=84263
  ->  Hash  (cost=164773.85..164773.85 rows=174346 width=4) (actual time=3077.990..3077.991 rows=173668 loops=1)
        Buckets: 262144 (originally 262144)  Batches: 1 (originally 1)  Memory Usage: 8154kB
        Buffers: shared read=76360
        ->  Bitmap Heap Scan on processedorder o  (cost=3703.48..164773.85 rows=174346 width=4) (actual time=27.285..3028.721 rows=173668 loops=1)
              Recheck Cond: (("ReceivedDate" >= '2020-09-01 00:00:00'::timestamp without time zone) AND ("ReceivedDate" <= '2021-01-01 00:00:00'::timestamp without time zone))
              Heap Blocks: exact=75882
              Buffers: shared read=76360
              ->  Bitmap Index Scan on ix_receiveddate  (cost=0.00..3659.89 rows=174346 width=0) (actual time=17.815..17.815 rows=173668 loops=1)
                    Index Cond: (("ReceivedDate" >= '2020-09-01 00:00:00'::timestamp without time zone) AND ("ReceivedDate" <= '2021-01-01 00:00:00'::timestamp without time zone))
                    Buffers: shared read=478

然后我们将此索引应用到我们的登台服务器（数据库的同一副本）并运行查询，但这一次需要 44 秒； https://explain.tensor.ru/archive/explain/9610c603972ba89aac4e223072f27575:0:2021-12-08

Gather  (cost=112168.21..275565.45 rows=174360 width=19) (actual time=42401.776..44549.996 rows=145082 loops=1)
  Workers Planned: 2
  Workers Launched: 2
  Buffers: shared hit=149058 read=50771
  ->  Hash Join  (cost=111168.21..257129.45 rows=72650 width=19) (actual time=42397.903..44518.824 rows=48361 loops=3)
        Hash Cond: (poi."OrderId" = o."OrderId")
        Inner Unique: true
        Buffers: shared hit=445001 read=161024
        ->  Parallel Seq Scan on processedorder_item poi  (cost=0.00..117223.50 rows=880850 width=23) (actual time=0.302..1426.753 rows=702469 loops=3)
              Filter: ((NOT "ContainsComposites") AND ("SKUId" <> 0))
              Rows Removed by Filter: 108940
              Buffers: shared read=84260
        ->  Hash  (cost=105532.45..105532.45 rows=173408 width=4) (actual time=42396.156..42396.156 rows=173668 loops=3)
              Buckets: 262144 (originally 262144)  Batches: 1 (originally 1)  Memory Usage: 8154kB
              Buffers: shared hit=444920 read=76764
              ->  Index Scan using ix_processedorder_receieveddate on processedorder o  (cost=0.43..105532.45 rows=173408 width=4) (actual time=0.827..42152.428 rows=173668 loops=3)
                    Index Cond: (("ReceivedDate" >= '2020-09-01 00:00:00'::timestamp without time zone) AND ("ReceivedDate" <= '2021-01-01 00:00:00'::timestamp without time zone))
                    Buffers: shared hit=444920 read=76764

最后，常识显然不起作用，我们只是删除临时服务器上的索引并发现它在 4 秒内返回数据（就像我们的本地机器索引） https://explain.tensor.ru/archive/explain/486512e19b45d5cbe4b893fdecc434b8:0:2021-12-08

Gather  (cost=1000.00..200395.65 rows=177078 width=4) (actual time=2.556..4695.986 rows=173668 loops=1)
  Workers Planned: 2
  Workers Launched: 2
  Buffers: shared read=41868
  ->  Parallel Seq Scan on processedorder o  (cost=0.00..181687.85 rows=73782 width=4) (actual time=0.796..4663.511 rows=57889 loops=3)
        Filter: (("ReceivedDate" >= '2020-09-01 00:00:00'::timestamp without time zone) AND ("ReceivedDate" <= '2021-01-01 00:00:00'::timestamp without time zone))
        Rows Removed by Filter: 642941
        Buffers: shared read=151028

在每个查询运行之前，我从 SSH 执行： echo 3 > /proc/sys/vm/drop_caches; service postgresql restart;

我们还在测试前运行了vacuum full; analyze;。

是否有人能够解释为什么会发生这种情况，因为这对我们来说毫无意义 - 我希望查询使用索引执行得最快，因为我们正在查询一小部分数据（订单记录跨越 9 年和我们只选择了 3 个月）。

服务器本身是在 Amazon AWS E2 i3.2xlarge 实例上运行的 Postgres 10.4，带有两个运行在 RAID 0 中的 io2 EBS 块存储驱动器，用于托管 psql 数据。

work_mem is 150MB
shared_buffers is set to 15Gb (60gb server total ram)
effective_io_concurrency = 256 
effective_cache_size=45GB

----- 更新 1 根据 Franks 的建议，我们尝试添加一个似乎没有帮助的新索引

Gather  (cost=1000.86..218445.54 rows=201063 width=19) (actual time=4.412..61616.676 rows=198552 loops=1)
  Output: poi."SKUId", poi."ConvertedLineTotal", poi."TotalDispachCost", poi."Quantity"
  Workers Planned: 2
  Workers Launched: 2
  Buffers: shared hit=239331 read=45293
  ->  Nested Loop  (cost=0.86..197339.24 rows=83776 width=19) (actual time=3.214..61548.378 rows=66184 loops=3)
        Output: poi."SKUId", poi."ConvertedLineTotal", poi."TotalDispachCost", poi."Quantity"
        Buffers: shared hit=748815 read=136548
        Worker 0: actual time=2.494..61658.415 rows=65876 loops=1
          Buffers: shared hit=247252 read=45606
        Worker 1: actual time=3.033..61667.490 rows=69100 loops=1
          Buffers: shared hit=262232 read=45649
        ->  Parallel Index Only Scan using ix_processedorder_btree_receieveddate_orderid on public.processedorder o  (cost=0.43..112293.34 rows=72359 width=4) (actual time=1.811..40429.474 rows=57889 loops=3)
              Output: o."ReceivedDate", o."OrderId"
              Index Cond: ((o."ReceivedDate" >= '2020-09-01 00:00:00'::timestamp without time zone) AND (o."ReceivedDate" <= '2021-01-01 00:00:00'::timestamp without time zone))
              Buffers: shared hit=97131 read=76571
              Worker 0: actual time=1.195..40625.809 rows=57420 loops=1
                Buffers: shared hit=31847 read=25583
              Worker 1: actual time=1.850..40463.813 rows=60469 loops=1
                Buffers: shared hit=34898 read=25584
        ->  Index Scan using ix_processedorder_item_orderid on public.processedorder_item poi  (cost=0.43..1.12 rows=2 width=23) (actual time=0.316..0.361 rows=1 loops=173668)
              Output: poi."OrderItemId", poi."OrderId", poi."SKUId", poi."Quantity", poi."ConvertedLineTotal", poi."ConvertedLineSubtotal", poi."ConvertedLineTax", poi."ConvertedLineDiscount", poi."ConversionRate", poi."OriginalLineTotal", poi."OriginalLineSubtotal", poi."OriginalLineTax", poi."OriginalLineDiscount", poi."Currency", poi."ContainsComposites", poi."TotalDispachCost", poi."TagId", poi."timestamp"
              Index Cond: (poi."OrderId" = o."OrderId")
              Buffers: shared hit=651684 read=59977
              Worker 0: actual time=0.316..0.362 rows=1 loops=57420
                Buffers: shared hit=215405 read=20023
              Worker 1: actual time=0.303..0.347 rows=1 loops=60469
                Buffers: shared hit=227334 read=20065

----更新2

我们重新运行了两个查询中较大的一个

explain (analyze, buffers, verbose, costs, format json)
        select
            poi."SKUId" as "SKUId",
            poi."ConvertedLineTotal" as "totalrevenue",
            poi."TotalDispachCost" as "totaldispachcost",
            poi."Quantity" as "quantitysold"
        from public.processedorder o
        join public.processedorder_item poi on poi."OrderId" = o."OrderId"
        WHERE o."ReceivedDate" >= '2020-09-01' and o."ReceivedDate" <= '2021-01-01';

这次启用了跟踪 io 计时 - 我们运行了两次，一次使用索引，另一次没有索引 - 这些是计划；

带索引： https://explain.tensor.ru/archive/explain/d763d7e1754c4ddac8bb61e403b135d2:0:2021-12-09

Gather  (cost=111153.17..252254.44 rows=201029 width=19) (actual time=36978.100..39083.705 rows=198552 loops=1)
  Output: poi."SKUId", poi."ConvertedLineTotal", poi."TotalDispachCost", poi."Quantity"
  Workers Planned: 2
  Workers Launched: 2
  Buffers: shared hit=138354 read=60159
  I/O Timings: read=16453.227
  ->  Hash Join  (cost=110153.17..231151.54 rows=83762 width=19) (actual time=36974.257..39044.116 rows=66184 loops=3)
        Output: poi."SKUId", poi."ConvertedLineTotal", poi."TotalDispachCost", poi."Quantity"
        Hash Cond: (poi."OrderId" = o."OrderId")
        Buffers: shared hit=444230 read=160640
        I/O Timings: read=37254.816
        Worker 0: actual time=36972.516..39140.086 rows=79787 loops=1
          Buffers: shared hit=155175 read=48507
          I/O Timings: read=9382.648
        Worker 1: actual time=36972.438..39138.754 rows=77496 loops=1
          Buffers: shared hit=150701 read=51974
          I/O Timings: read=11418.941
        ->  Parallel Seq Scan on public.processedorder_item poi  (cost=0.00..114682.68 rows=1014089 width=23) (actual time=0.262..1212.102 rows=811409 loops=3)
              Output: poi."OrderItemId", poi."OrderId", poi."SKUId", poi."Quantity", poi."ConvertedLineTotal", poi."ConvertedLineSubtotal", poi."ConvertedLineTax", poi."ConvertedLineDiscount", poi."ConversionRate", poi."OriginalLineTotal", poi."OriginalLineSubtotal", poi."OriginalLineTax", poi."OriginalLineDiscount", poi."Currency", poi."ContainsComposites", poi."TotalDispachCost", poi."TagId", poi."timestamp"
              Buffers: shared read=84260
              I/O Timings: read=1574.316
              Worker 0: actual time=0.073..1258.453 rows=870378 loops=1
                Buffers: shared read=30133
                I/O Timings: read=535.914
              Worker 1: actual time=0.021..1256.769 rows=841299 loops=1
                Buffers: shared read=29126
                I/O Timings: read=538.814
        ->  Hash  (cost=104509.15..104509.15 rows=173662 width=4) (actual time=36972.511..36972.511 rows=173668 loops=3)
              Output: o."OrderId"
              Buckets: 262144 (originally 262144)  Batches: 1 (originally 1)  Memory Usage: 8154kB
              Buffers: shared hit=444149 read=76380
              I/O Timings: read=35680.500
              Worker 0: actual time=36970.996..36970.996 rows=173668 loops=1
                Buffers: shared hit=155136 read=18374
                I/O Timings: read=8846.735
              Worker 1: actual time=36970.783..36970.783 rows=173668 loops=1
                Buffers: shared hit=150662 read=22848
                I/O Timings: read=10880.127
              ->  Index Scan using ix_processedorder_btree_receieveddate on public.processedorder o  (cost=0.43..104509.15 rows=173662 width=4) (actual time=0.617..36736.881 rows=173668 loops=3)
                    Output: o."OrderId"
                    Index Cond: ((o."ReceivedDate" >= '2020-09-01 00:00:00'::timestamp without time zone) AND (o."ReceivedDate" <= '2021-01-01 00:00:00'::timestamp without time zone))
                    Buffers: shared hit=444149 read=76380
                    I/O Timings: read=35680.500
                    Worker 0: actual time=0.018..36741.158 rows=173668 loops=1
                      Buffers: shared hit=155136 read=18374
                      I/O Timings: read=8846.735
                    Worker 1: actual time=0.035..36733.684 rows=173668 loops=1
                      Buffers: shared hit=150662 read=22848
                      I/O Timings: read=10880.127

然后，运行速度更快，没有索引； https://explain.tensor.ru/archive/explain/d0815f4b0c9baf3bdd512bc94051e768:0:2021-12-09

Gather  (cost=231259.20..372360.47 rows=201029 width=19) (actual time=4829.302..7920.614 rows=198552 loops=1)
  Output: poi."SKUId", poi."ConvertedLineTotal", poi."TotalDispachCost", poi."Quantity"
  Workers Planned: 2
  Workers Launched: 2
  Buffers: shared hit=106720 read=69983
  I/O Timings: read=2431.673
  ->  Hash Join  (cost=230259.20..351257.57 rows=83762 width=19) (actual time=4825.285..7877.981 rows=66184 loops=3)
        Output: poi."SKUId", poi."ConvertedLineTotal", poi."TotalDispachCost", poi."Quantity"
        Hash Cond: (poi."OrderId" = o."OrderId")
        Buffers: shared hit=302179 read=235288
        I/O Timings: read=7545.729
        Worker 0: actual time=4823.181..7978.501 rows=81394 loops=1
          Buffers: shared hit=87613 read=93424
          I/O Timings: read=2556.079
        Worker 1: actual time=4823.645..7979.241 rows=76022 loops=1
          Buffers: shared hit=107846 read=71881
          I/O Timings: read=2557.977
        ->  Parallel Seq Scan on public.processedorder_item poi  (cost=0.00..114682.68 rows=1014089 width=23) (actual time=0.267..2122.529 rows=811409 loops=3)
              Output: poi."OrderItemId", poi."OrderId", poi."SKUId", poi."Quantity", poi."ConvertedLineTotal", poi."ConvertedLineSubtotal", poi."ConvertedLineTax", poi."ConvertedLineDiscount", poi."ConversionRate", poi."OriginalLineTotal", poi."OriginalLineSubtotal", poi."OriginalLineTax", poi."OriginalLineDiscount", poi."Currency", poi."ContainsComposites", poi."TotalDispachCost", poi."TagId", poi."timestamp"
              Buffers: shared read=84260
              I/O Timings: read=4135.860
              Worker 0: actual time=0.034..2171.677 rows=865244 loops=1
                Buffers: shared read=29949
                I/O Timings: read=1394.407
              Worker 1: actual time=0.068..2174.408 rows=827170 loops=1
                Buffers: shared read=28639
                I/O Timings: read=1395.723
        ->  Hash  (cost=224615.18..224615.18 rows=173662 width=4) (actual time=4823.318..4823.318 rows=173668 loops=3)
              Output: o."OrderId"
              Buckets: 262144 (originally 262144)  Batches: 1 (originally 1)  Memory Usage: 8154kB
              Buffers: shared hit=302056 read=151028
              I/O Timings: read=3409.869
              Worker 0: actual time=4820.884..4820.884 rows=173668 loops=1
                Buffers: shared hit=87553 read=63475
                I/O Timings: read=1161.672
              Worker 1: actual time=4822.104..4822.104 rows=173668 loops=1
                Buffers: shared hit=107786 read=43242
                I/O Timings: read=1162.254
              ->  Seq Scan on public.processedorder o  (cost=0.00..224615.18 rows=173662 width=4) (actual time=0.744..4644.291 rows=173668 loops=3)
                    Output: o."OrderId"
                    Filter: ((o."ReceivedDate" >= '2020-09-01 00:00:00'::timestamp without time zone) AND (o."ReceivedDate" <= '2021-01-01 00:00:00'::timestamp without time zone))
                    Rows Removed by Filter: 1928823
                    Buffers: shared hit=302056 read=151028
                    I/O Timings: read=3409.869
                    Worker 0: actual time=0.040..4651.203 rows=173668 loops=1
                      Buffers: shared hit=87553 read=63475
                      I/O Timings: read=1161.672
                    Worker 1: actual time=0.035..4637.415 rows=173668 loops=1
                      Buffers: shared hit=107786 read=43242
                      I/O Timings: read=1162.254

【问题讨论】：

这些链接都不起作用（在此处），请在此处以纯文本形式发布解释结果。
很抱歉，弗兰克刚刚添加了它们！
我没有看到任何异常 - 你能 SET track_io_timing = on 并重复 EXPLAIN 运行吗？这应该可以澄清一些事情。
嗨 @LaurenzAlbe 刚刚添加了显示 IO 计时的更新 2！

标签： postgresql amazon-ec2 indexing query-optimization amazon-ebs

【解决方案1】：

第一个查询将受益于数据索引和单个索引中的 id：

CREATE INDEX ix_processedorder_btree_receieveddate ON public.processedorder USING btree ("ReceivedDate" DESC, "OrderId");

这会改变查询计划吗？

【讨论】：

它确实使用了索引，但没有更快 - 仍然是 60 秒 explain.tensor.ru/archive/explain/… （如果该链接有效） - 我将使用此输出更新原始问题）
你能告诉我查询计划吗？我希望只扫描索引
当然，将计划正文作为对原始问题的更新。
值得一提的是，我们确实得到了 staging 以匹配本地数据库上看到的性能，但只能通过使用接收到的日期索引对表进行聚类，这感觉有点矫枉过正，因为我们在没有聚类的情况下在本地看到了相同的性能。
这实际上看起来像是您的 IO 和数据库配置的限制。如果顺序扫描比索引扫描快，看起来 random_page_cost 设置得太低了。

【解决方案2】：

从您最近的两个执行计划中，我收集了以下信息：

使用索引的慢速计划为processedorder从磁盘读取22848个块，耗时10.88秒
使用顺序扫描的快速计划从磁盘中为processedorder 读取 43242 个块，耗时 1.162 秒

所以可以有两种解释：

在快速的情况下，这些块实际上是缓存在内核缓存中的，所以是一种缓存效果

通过清除内核缓存进行实验。
随机 I/O 比优化器认为的要昂贵

在这种情况下，您可以考虑提高random_page_cost。

【讨论】：

有趣的是，我们的 random_page_cost 已设置为 1.1- 我将其重置为默认值 4 并使用索引重新运行查询，它现在比没有索引的查询快一秒.所以看起来那部分配置有问题 - 谢谢！