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首頁 云原生數(shù)據(jù)庫 PolarDB 云原生數(shù)據(jù)庫PolarDB MySQL版 用戶指南 查詢優(yōu)化（Query Optimizer) 計算下推 Bloom Filter For HashJoin下推

Bloom Filter For HashJoin下推

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當(dāng)需要查詢大量數(shù)據(jù)的時候，引擎層訪問數(shù)據(jù)、SQL層的交互和計算都會占用大量的開銷。PolarDB MySQL版會將Bloom Filter下推到引擎層進(jìn)行計算，極大的減少性能開銷，提升查詢性能。

前提條件

  • 集群版本需為PolarDB MySQL版8.0版本且修訂版本需為8.0.2.2.3或以上。如何查看集群版本，請參見查詢版本號。

  • 當(dāng)前僅INT類型支持Bloom Filter For HashJoin下推。

背景信息

Bloom Filter是一種減少存儲訪問，提升計算效率的成熟方法。PolarDB MySQL版會利用Bloom Filter對HashJoin進(jìn)行加速?；诖鷥r會對大數(shù)量場景，在build hash table的時候創(chuàng)建Bloom Filter，然后下推到Probe端的引擎中，在Probe的時候利用Bloom Filter過濾掉SQL層計算不需要的數(shù)據(jù)。這可以使得引擎層和SQL層的數(shù)據(jù)轉(zhuǎn)換、SQL層的計算都大幅減少，提升查詢性能。

使用方法

您可以通過loose_bloom_filter_enabled參數(shù)開啟Bloom Filter優(yōu)化功能。具體操作請參見設(shè)置集群參數(shù)和節(jié)點(diǎn)參數(shù)。

參數(shù)名稱

級別

描述

loose_bloom_filter_enabled

Global、Session

Bloom Filter優(yōu)化開關(guān)。取值范圍如下：

  • ON（默認(rèn)值）：開啟Bloom Filter優(yōu)化開關(guān)。

  • OFF：關(guān)閉Bloom Filter優(yōu)化開關(guān)。

示例

本文以TPCH不創(chuàng)建Primary key和Index的Schema為例。下文分別展示了TPCH中Q3、Q11和Q16的執(zhí)行計劃，在Extra列可以看到hash join with bloom filter的信息。

  • Q3：

    EXPLAIN
SELECT l_orderkey,
       SUM(l_extendedprice * (1 - l_discount)) AS revenue,
       o_orderdate,
       o_shippriority
FROM customer,
     orders,
     lineitem
WHERE c_mktsegment = 'MACHINERY'
  AND c_custkey = o_custkey
  AND l_orderkey = o_orderkey
  AND o_orderdate < '1995-03-10'
  AND l_shipdate > '1995-03-10'
GROUP BY l_orderkey,
         o_orderdate,
         o_shippriority
ORDER BY revenue DESC,
         o_orderdate
LIMIT 10\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: customer
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 148463
     filtered: 10.00
        Extra: Using where; Using temporary; Using filesort
*************************** 2. row ***************************
           id: 1
  select_type: SIMPLE
        table: orders
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 1486962
     filtered: 3.33
        Extra: Using where; Using join buffer (hash join with bloom filter)
*************************** 3. row ***************************
           id: 1
  select_type: SIMPLE
        table: lineitem
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 5948979
     filtered: 3.33
        Extra: Using where; Using join buffer (hash join with bloom filter)

  • Q11：

    EXPLAIN
SELECT ps_partkey,
       SUM(ps_supplycost * ps_availqty) AS value
FROM partsupp,
     supplier,
     nation
WHERE ps_suppkey = s_suppkey
  AND s_nationkey = n_nationkey
  AND n_name = 'INDIA'
GROUP BY ps_partkey
HAVING SUM(ps_supplycost * ps_availqty) >
  (SELECT SUM(ps_supplycost * ps_availqty) * 0.0001000000
   FROM partsupp,
        supplier,
        nation
   WHERE ps_suppkey = s_suppkey
     AND s_nationkey = n_nationkey
     AND n_name = 'INDIA' )
ORDER BY value DESC\G
*************************** 1. row ***************************
           id: 1
  select_type: PRIMARY
        table: nation
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 25
     filtered: 10.00
        Extra: Using where; Using temporary; Using filesort
*************************** 2. row ***************************
           id: 1
  select_type: PRIMARY
        table: supplier
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 10000
     filtered: 10.00
        Extra: Using where; Using join buffer (hash join with bloom filter)
*************************** 3. row ***************************
           id: 1
  select_type: PRIMARY
        table: partsupp
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 791815
     filtered: 10.00
        Extra: Using where; Using join buffer (hash join with bloom filter)
*************************** 4. row ***************************
           id: 2
  select_type: SUBQUERY
        table: nation
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 25
     filtered: 10.00
        Extra: Using where
*************************** 5. row ***************************
           id: 2
  select_type: SUBQUERY
        table: supplier
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 10000
     filtered: 10.00
        Extra: Using where; Using join buffer (hash join with bloom filter)
*************************** 6. row ***************************
           id: 2
  select_type: SUBQUERY
        table: partsupp
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 791815
     filtered: 10.00
        Extra: Using where; Using join buffer (hash join with bloom filter)

  • Q16：

    EXPLAIN SELECT p_brand,
       p_type,
       p_size,
       COUNT(DISTINCT ps_suppkey) AS supplier_cnt
FROM partsupp,
     part
WHERE p_partkey = ps_partkey
  AND p_brand <> 'Brand#33'
  AND p_type NOT LIKE 'PROMO POLISHED%'
  AND p_size IN (34,
                 45,
                 33,
                 42,
                 9,
                 24,
                 26,
                 7)
  AND ps_suppkey NOT IN
    (SELECT s_suppkey
     FROM supplier
     WHERE s_comment LIKE '%Customer%Complaints%' )
GROUP BY p_brand,
         p_type,
         p_size
ORDER BY supplier_cnt DESC,
         p_brand,
         p_type,
         p_size\G
*************************** 1. row ***************************
           id: 1
  select_type: PRIMARY
        table: part
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 198116
     filtered: 40.00
        Extra: Using where; Using temporary; Using filesort
*************************** 2. row ***************************
           id: 1
  select_type: PRIMARY
        table: partsupp
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 791815
     filtered: 10.00
        Extra: Using where; Using join buffer (hash join with bloom filter)
*************************** 3. row ***************************
           id: 2
  select_type: SUBQUERY
        table: supplier
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 10000
     filtered: 11.11
        Extra: Using where

性能效果

以TPCH不創(chuàng)建Primary key和Index為例，基于scale 1的數(shù)據(jù)，針對上文所述的查詢示例Q3、Q11、Q16。開啟與關(guān)閉Bloom Filter功能的性能對比如下：Bloom Filter