主頁 > 資料庫 > Postgres根據自己的估計選擇一個更昂貴的查詢計劃

Postgres根據自己的估計選擇一個更昂貴的查詢計劃

2022-02-18 19:34:02 資料庫

對于特定查詢,我有以下 2 個查詢計劃(第二個是通過關閉 seqscan 獲得的):

Postgres 根據自己的估計選擇一個更昂貴的查詢計劃

Postgres 根據自己的估計選擇一個更昂貴的查詢計劃

第二個計劃的成本估計低于第一個計劃,但是,如果被迫這樣做(通過關閉 seqscan),pg 只會選擇第二個計劃。

什么可能導致這種行為?


編輯:使用評論中要求的資訊更新問題:

查詢 1 的輸出EXPLAIN (ANALYZE, BUFFERS, VERBOSE)(seqscan on;不使用索引)。也可以在https://explain.depesz.com/s/cGLY上查看:

QUERY PLAN
Limit  (cost=2449.76..840962.24 rows=1 width=87) (actual time=25701.021..26540.060 rows=10 loops=1)
  Output: books.id, books.title, books.authors, books.meta
  Buffers: shared hit=2254959
  ->  Nested Loop Left Join  (cost=2449.76..840962.24 rows=1 width=87) (actual time=25289.899..26128.923 rows=10 loops=1)
        Output: books.id, books.title, books.authors, books.meta
        Join Filter: (photos."bookId" = books.id)
        Rows Removed by Join Filter: 62876457
        Filter: (photos.id IS NULL)
        Rows Removed by Filter: 707
        Buffers: shared hit=2254959
        ->  Gather  (cost=2449.76..835403.18 rows=1 width=87) (actual time=391.874..494.669 rows=658 loops=1)
              Output: books.id, books.title, books.authors, books.meta
              Workers Planned: 2
              Workers Launched: 2
              Buffers: shared hit=11837
              ->  Parallel Bitmap Heap Scan on public.books  (cost=1449.76..834403.08 rows=1 width=87) (actual time=868.495..874.706 rows=554 loops=3)
                    Output: books.id, books.title, books.authors, books.meta
                    Recheck Cond: ((books.meta !~~ 'foo%'::text) AND (books.meta <> 'bar'::text))
                    Filter: ((books.meta ~~ 'baz%'::text) AND (books.id <> ALL ('{19643405,19702275,19784617,28454289,28491188,28491190,28491205,28521585,28521596,28521627,28521638,28521649,28521658,28521678,28521680,28521689,28521700,28518165,28515245,28515256,28515288,28515299,28515310,28515342,28515353,28515364,28515407,28515736,28518100,28518219,28518273,28518370,28518424,28518478,28518489}'::integer[])))
                    Rows Removed by Filter: 77897
                    Heap Blocks: exact=11567
                    Buffers: shared hit=11837
                    Worker 0:  actual time=1107.154..1115.320 rows=1113 loops=1
                      JIT:
                        Functions: 6
                        Options: Inlining true, Optimization true, Expressions true, Deforming true
                        Timing: Generation 5.001 ms, Inlining 471.271 ms, Optimization 365.866 ms, Emission 269.821 ms, Total 1111.959 ms
                      Buffers: shared hit=40
                    Worker 1:  actual time=1108.335..1108.975 rows=541 loops=1
                      JIT:
                        Functions: 6
                        Options: Inlining true, Optimization true, Expressions true, Deforming true
                        Timing: Generation 11.915 ms, Inlining 450.341 ms, Optimization 364.168 ms, Emission 293.461 ms, Total 1119.885 ms
                      Buffers: shared hit=21
                    ->  Bitmap Index Scan on books_meta_partial_exclude_foo_and_bar  (cost=0.00..1449.76 rows=2194002 width=0) (actual time=41.801..41.802 rows=238689 loops=1)
                          Buffers: shared hit=209
        ->  Seq Scan on public.photos  (cost=0.00..4364.58 rows=95558 width=8) (actual time=0.002..17.127 rows=95558 loops=658)
              Output: photos.id, photos.url, photos.type, photos."userId", photos."libraryId", photos."bookId", photos."libraryBookId", photos."isPrimaryPic", photos."processingStatus", photos."createdAt", photos."updatedAt", photos."otherData"
              Buffers: shared hit=2243122
Planning:
  Buffers: shared hit=17
Planning Time: 0.758 ms
JIT:
  Functions: 24
  Options: Inlining true, Optimization true, Expressions true, Deforming true
  Timing: Generation 20.953 ms, Inlining 1005.367 ms, Optimization 915.620 ms, Emission 705.338 ms, Total 2647.278 ms
Execution Time: 26544.310 ms

EXPLAIN (ANALYZE, BUFFERS, VERBOSE)查詢 2 的輸出(seqscan 關閉;使用索引)。也可以在https://explain.depesz.com/s/VDfP查看:

QUERY PLAN
Limit  (cost=2450.18..835405.63 rows=1 width=87) (actual time=1110.719..2424.086 rows=10 loops=1)
  Output: books.id, books.title, books.authors, books.meta
  Buffers: shared hit=16834
  ->  Nested Loop Left Join  (cost=2450.18..835405.63 rows=1 width=87) (actual time=464.812..1778.175 rows=10 loops=1)
        Output: books.id, books.title, books.authors, books.meta
        Filter: (photos.id IS NULL)
        Rows Removed by Filter: 1321
        Buffers: shared hit=16834
        ->  Gather  (cost=2449.76..835403.18 rows=1 width=87) (actual time=411.878..1753.914 rows=1232 loops=1)
              Output: books.id, books.title, books.authors, books.meta
              Workers Planned: 2
              Workers Launched: 2
              Buffers: shared hit=11822
              ->  Parallel Bitmap Heap Scan on public.books  (cost=1449.76..834403.08 rows=1 width=87) (actual time=653.691..663.053 rows=411 loops=3)
                    Output: books.id, books.title, books.authors, books.meta
                    Recheck Cond: ((books.meta !~~ 'foo%'::text) AND (books.meta <> 'bar'::text))
                    Filter: ((books.meta ~~ 'baz%'::text) AND (books.id <> ALL ('{19643405,19702275,19784617,28454289,28491188,28491190,28491205,28521585,28521596,28521627,28521638,28521649,28521658,28521678,28521680,28521689,28521700,28518165,28515245,28515256,28515288,28515299,28515310,28515342,28515353,28515364,28515407,28515736,28518100,28518219,28518273,28518370,28518424,28518478,28518489}'::integer[])))
                    Rows Removed by Filter: 77893
                    Heap Blocks: exact=11611
                    Buffers: shared hit=11822
                    Worker 0:  actual time=774.890..774.891 rows=1 loops=1
                      JIT:
                        Functions: 6
                        Options: Inlining true, Optimization true, Expressions true, Deforming true
                        Timing: Generation 14.889 ms, Inlining 364.167 ms, Optimization 205.348 ms, Emission 205.226 ms, Total 789.630 ms
                      Buffers: shared hit=1
                    Worker 1:  actual time=780.309..780.311 rows=1 loops=1
                      JIT:
                        Functions: 6
                        Options: Inlining true, Optimization true, Expressions true, Deforming true
                        Timing: Generation 4.595 ms, Inlining 362.465 ms, Optimization 209.509 ms, Emission 208.145 ms, Total 784.715 ms
                      Buffers: shared hit=1
                    ->  Bitmap Index Scan on books_meta_partial_exclude_foo_and_bar  (cost=0.00..1449.76 rows=2194002 width=0) (actual time=56.500..56.501 rows=238689 loops=1)
                          Buffers: shared hit=209
        ->  Index Scan using "photos_bookId_idx" on public.photos  (cost=0.42..2.44 rows=1 width=8) (actual time=0.012..0.013 rows=1 loops=1232)
              Output: photos.id, photos.url, photos.type, photos."userId", photos."libraryId", photos."bookId", photos."libraryBookId", photos."isPrimaryPic", photos."processingStatus", photos."createdAt", photos."updatedAt", photos."otherData"
              Index Cond: (photos."bookId" = books.id)
              Buffers: shared hit=5012
Planning:
  Buffers: shared hit=17
Planning Time: 2.640 ms
JIT:
  Functions: 25
  Options: Inlining true, Optimization true, Expressions true, Deforming true
  Timing: Generation 39.565 ms, Inlining 839.818 ms, Optimization 765.817 ms, Emission 599.027 ms, Total 2244.228 ms
Execution Time: 2455.226 ms

編輯 2:添加有關表結構、索引和查詢本身的資訊

-- Table: public.books

-- DROP TABLE IF EXISTS public.books;

CREATE TABLE IF NOT EXISTS public.books
(
    id integer NOT NULL DEFAULT nextval('books_id_seq'::regclass),
    title text COLLATE pg_catalog."default" NOT NULL,
    authors text COLLATE pg_catalog."default" NOT NULL,
    slug text COLLATE pg_catalog."default" NOT NULL,
    "desc" text COLLATE pg_catalog."default",
    meta text COLLATE pg_catalog."default",
    "createdAt" timestamp(3) without time zone NOT NULL DEFAULT CURRENT_TIMESTAMP,
    "updatedAt" timestamp(3) without time zone NOT NULL,
    tsv tsvector GENERATED ALWAYS AS (to_tsvector('english'::regconfig, ((COALESCE(title, ''::text) || ' '::text) || COALESCE(authors, ''::text)))) STORED,
    CONSTRAINT books_pkey PRIMARY KEY (id)
)

TABLESPACE pg_default;

ALTER TABLE IF EXISTS public.books
    OWNER to [REDACTED];
-- Index: books_fts_idx

-- DROP INDEX IF EXISTS public.books_fts_idx;

CREATE INDEX IF NOT EXISTS books_fts_idx
    ON public.books USING gin
    (tsv)
    TABLESPACE pg_default;
-- Index: books_meta_partial_exclude_foo_and_bar

-- DROP INDEX IF EXISTS public.books_meta_partial_exclude_foo_and_bar;

CREATE INDEX IF NOT EXISTS books_meta_partial_exclude_foo_and_bar
    ON public.books USING btree
    (meta COLLATE pg_catalog."default" ASC NULLS LAST)
    TABLESPACE pg_default
    WHERE meta !~~ 'foo%'::text AND meta <> 'bar'::text;
-- Index: books_slug_key

-- DROP INDEX IF EXISTS public.books_slug_key;

CREATE UNIQUE INDEX IF NOT EXISTS books_slug_key
    ON public.books USING btree
    (slug COLLATE pg_catalog."default" ASC NULLS LAST)
    TABLESPACE pg_default;
-- Table: public.photos

-- DROP TABLE IF EXISTS public.photos;

CREATE TABLE IF NOT EXISTS public.photos
(
    id integer NOT NULL DEFAULT nextval('photos_id_seq'::regclass),
    url text COLLATE pg_catalog."default" NOT NULL,
    type text COLLATE pg_catalog."default",
    "userId" integer,
    "libraryId" integer,
    "bookId" integer,
    "libraryBookId" integer,
    "isPrimaryPic" boolean DEFAULT false,
    "processingStatus" "PhotoProcessingStatus" NOT NULL DEFAULT 'UNPROCESSED'::"PhotoProcessingStatus",
    "createdAt" timestamp(3) without time zone NOT NULL DEFAULT CURRENT_TIMESTAMP,
    "updatedAt" timestamp(3) without time zone NOT NULL,
    "otherData" jsonb,
    CONSTRAINT photos_pkey PRIMARY KEY (id),
    CONSTRAINT "photos_bookId_fkey" FOREIGN KEY ("bookId")
        REFERENCES public.books (id) MATCH SIMPLE
        ON UPDATE CASCADE
        ON DELETE SET NULL,
    CONSTRAINT "photos_libraryBookId_fkey" FOREIGN KEY ("libraryBookId")
        REFERENCES public.library_books (id) MATCH SIMPLE
        ON UPDATE CASCADE
        ON DELETE SET NULL,
    CONSTRAINT "photos_libraryId_fkey" FOREIGN KEY ("libraryId")
        REFERENCES public.libraries (id) MATCH SIMPLE
        ON UPDATE CASCADE
        ON DELETE SET NULL,
    CONSTRAINT "photos_userId_fkey" FOREIGN KEY ("userId")
        REFERENCES public.users (id) MATCH SIMPLE
        ON UPDATE CASCADE
        ON DELETE SET NULL
)

TABLESPACE pg_default;

ALTER TABLE IF EXISTS public.photos
    OWNER to [REDACTED];
-- Index: photos_bookId_idx

-- DROP INDEX IF EXISTS public."photos_bookId_idx";

CREATE INDEX IF NOT EXISTS "photos_bookId_idx"
    ON public.photos USING btree
    ("bookId" ASC NULLS LAST)
    TABLESPACE pg_default;
-- Index: photos_libraryId_idx

-- DROP INDEX IF EXISTS public."photos_libraryId_idx";

CREATE INDEX IF NOT EXISTS "photos_libraryId_idx"
    ON public.photos USING btree
    ("libraryId" ASC NULLS LAST)
    TABLESPACE pg_default;
-- Index: photos_userId_idx

-- DROP INDEX IF EXISTS public."photos_userId_idx";

CREATE INDEX IF NOT EXISTS "photos_userId_idx"
    ON public.photos USING btree
    ("userId" ASC NULLS LAST)
    TABLESPACE pg_default;

查詢本身是:

SELECT
  books.id, books.title, books.authors, books.meta
FROM books
LEFT JOIN photos ON photos."bookId" = books.id
WHERE photos.id IS NULL
AND books.id NOT IN (19643405,19702275,19784617,28454289,28491188,28491190,28491205,28521585,28521596,28521627,28521638,28521649,28521658,28521678,28521680,28521689,28521700,28518165,28515245,28515256,28515288,28515299,28515310,28515342,28515353,28515364,28515407,28515736,28518100,28518219,28518273,28518370,28518424,28518478,28518489)
AND meta NOT LIKE 'foo%'
AND meta != 'bar'
AND meta LIKE 'baz%'
LIMIT 10; 

uj5u.com熱心網友回復:

這兩個計劃實際上是捆綁在一起的,它們的預期成本相差不到 1%。計劃者避免完全充實顯然捆綁的計劃,以避免額外的作業。

請參閱源代碼中的 compare_path_costs_fuzzily。

uj5u.com熱心網友回復:

你應該有這兩個索引來加速你的查詢:

CREATE INDEX X1 ON books (meta, id) INCLUDE  (title, authors);
CREATE INDEX X2 ON photos (id, bookId);

還要重寫您的查詢以消除過濾謂詞的冗余成員:

SELECT  books.id, books.title, books.authors, books.meta
FROM    books
        LEFT OUTER JOIN photos 
           ON photos.bookId = books.id
WHERE  photos.id IS NULL
       AND books.id NOT IN (19643405,19702275,19784617,28454289,28491188,28491190,28491205,28521585,28521596,28521627,28521638,28521649,28521658,28521678,28521680,28521689,28521700,28518165,28515245,28515256,28515288,28515299,28515310,28515342,28515353,28515364,28515407,28515736,28518100,28518219,28518273,28518370,28518424,28518478,28518489)
   --  AND books.meta NOT LIKE '[REDACTED-1]%' --> useless because books.meta LIKE '[REDACTED-3]%'
   --  AND books.meta != '[REDACTED-2]'        --> useless because books.meta LIKE '[REDACTED-3]%'
       AND books.meta LIKE '[REDACTED-3]%'
LIMIT 10;

最后使用臨時表可能會做得更好:

CREATE LOCAL TEMPORARY TABLE temp_books_NOT_IN
(id INT PRIMARY KEY);

INSERT INTO temp_books_NOT_IN VALUES 
(19643405),
(19702275),
(19784617),
(28454289),
(28491188),
(28491190),
(28491205),
(28521585),
(28521596),
(28521627),
(28521638),
(28521649),
(28521658),
(28521678),
(28521680),
(28521689),
(28521700),
(28518165),
(28515245),
(28515256),
(28515288),
(28515299),
(28515310),
(28515342),
(28515353),
(28515364),
(28515407),
(28515736),
(28518100),
(28518219),
(28518273),
(28518370),
(28518424),
(28518478),
(28518489);

SELECT  books.id, books.title, books.authors, books.meta
FROM    books
        LEFT OUTER JOIN photos 
           ON photos.bookId = books.id
WHERE  photos.id IS NULL
       AND books.meta LIKE '[REDACTED-3]%'
       AND books.id NOT IN (SELECT id FROM temp_books_NOT_IN)
LIMIT 10;

轉載請註明出處,本文鏈接:https://www.uj5u.com/shujuku/426892.html

標籤:PostgreSQL

上一篇:缺少值的SQLPostgres聯合資料

下一篇:為什么SQL/PostgreSQL允許在連接子句中不參考連接表中的列的情況下進行連接?

標籤雲
其他(157675) Python(38076) JavaScript(25376) Java(17977) C(15215) 區塊鏈(8255) C#(7972) AI(7469) 爪哇(7425) MySQL(7132) html(6777) 基礎類(6313) sql(6102) 熊猫(6058) PHP(5869) 数组(5741) R(5409) Linux(5327) 反应(5209) 腳本語言(PerlPython)(5129) 非技術區(4971) Android(4554) 数据框(4311) css(4259) 节点.js(4032) C語言(3288) json(3245) 列表(3129) 扑(3119) C++語言(3117) 安卓(2998) 打字稿(2995) VBA(2789) Java相關(2746) 疑難問題(2699) 细绳(2522) 單片機工控(2479) iOS(2429) ASP.NET(2402) MongoDB(2323) 麻木的(2285) 正则表达式(2254) 字典(2211) 循环(2198) 迅速(2185) 擅长(2169) 镖(2155) 功能(1967) .NET技术(1958) Web開發(1951) python-3.x(1918) HtmlCss(1915) 弹簧靴(1913) C++(1909) xml(1889) PostgreSQL(1872) .NETCore(1853) 谷歌表格(1846) Unity3D(1843) for循环(1842)

熱門瀏覽
  • GPU虛擬機創建時間深度優化

    **?桔妹導讀:**GPU虛擬機實體創建速度慢是公有云面臨的普遍問題,由于通常情況下創建虛擬機屬于低頻操作而未引起業界的重視,實際生產中還是存在對GPU實體創建時間有苛刻要求的業務場景。本文將介紹滴滴云在解決該問題時的思路、方法、并展示最終的優化成果。 從公有云服務商那里購買過虛擬主機的資深用戶,一 ......

    uj5u.com 2020-09-10 06:09:13 more
  • 可編程網卡芯片在滴滴云網路的應用實踐

    **?桔妹導讀:**隨著云規模不斷擴大以及業務層面對延遲、帶寬的要求越來越高,采用DPDK 加速網路報文處理的方式在橫向縱向擴展都出現了局限性。可編程芯片成為業界熱點。本文主要講述了可編程網卡芯片在滴滴云網路中的應用實踐,遇到的問題、帶來的收益以及開源社區貢獻。 #1. 資料中心面臨的問題 隨著滴滴 ......

    uj5u.com 2020-09-10 06:10:21 more
  • 滴滴資料通道服務演進之路

    **?桔妹導讀:**滴滴資料通道引擎承載著全公司的資料同步,為下游實時和離線場景提供了必不可少的源資料。隨著任務量的不斷增加,資料通道的整體架構也隨之發生改變。本文介紹了滴滴資料通道的發展歷程,遇到的問題以及今后的規劃。 #1. 背景 資料,對于任何一家互聯網公司來說都是非常重要的資產,公司的大資料 ......

    uj5u.com 2020-09-10 06:11:05 more
  • 滴滴AI Labs斬獲國際機器翻譯大賽中譯英方向世界第三

    **桔妹導讀:**深耕人工智能領域,致力于探索AI讓出行更美好的滴滴AI Labs再次斬獲國際大獎,這次獲獎的專案是什么呢?一起來看看詳細報道吧! 近日,由國際計算語言學協會ACL(The Association for Computational Linguistics)舉辦的世界最具影響力的機器 ......

    uj5u.com 2020-09-10 06:11:29 more
  • MPP (Massively Parallel Processing)大規模并行處理

    1、什么是mpp? MPP (Massively Parallel Processing),即大規模并行處理,在資料庫非共享集群中,每個節點都有獨立的磁盤存盤系統和記憶體系統,業務資料根據資料庫模型和應用特點劃分到各個節點上,每臺資料節點通過專用網路或者商業通用網路互相連接,彼此協同計算,作為整體提供 ......

    uj5u.com 2020-09-10 06:11:41 more
  • 滴滴資料倉庫指標體系建設實踐

    **桔妹導讀:**指標體系是什么?如何使用OSM模型和AARRR模型搭建指標體系?如何統一流程、規范化、工具化管理指標體系?本文會對建設的方法論結合滴滴資料指標體系建設實踐進行解答分析。 #1. 什么是指標體系 ##1.1 指標體系定義 指標體系是將零散單點的具有相互聯系的指標,系統化的組織起來,通 ......

    uj5u.com 2020-09-10 06:12:52 more
  • 單表千萬行資料庫 LIKE 搜索優化手記

    我們經常在資料庫中使用 LIKE 運算子來完成對資料的模糊搜索,LIKE 運算子用于在 WHERE 子句中搜索列中的指定模式。 如果需要查找客戶表中所有姓氏是“張”的資料,可以使用下面的 SQL 陳述句: SELECT * FROM Customer WHERE Name LIKE '張%' 如果需要 ......

    uj5u.com 2020-09-10 06:13:25 more
  • 滴滴Ceph分布式存盤系統優化之鎖優化

    **桔妹導讀:**Ceph是國際知名的開源分布式存盤系統,在工業界和學術界都有著重要的影響。Ceph的架構和演算法設計發表在國際系統領域頂級會議OSDI、SOSP、SC等上。Ceph社區得到Red Hat、SUSE、Intel等大公司的大力支持。Ceph是國際云計算領域應用最廣泛的開源分布式存盤系統, ......

    uj5u.com 2020-09-10 06:14:51 more
  • es~通過ElasticsearchTemplate進行聚合~嵌套聚合

    之前寫過《es~通過ElasticsearchTemplate進行聚合操作》的文章,這一次主要寫一個嵌套的聚合,例如先對sex集合,再對desc聚合,最后再對age求和,共三層嵌套。 Aggregations的部分特性類似于SQL語言中的group by,avg,sum等函式,Aggregation ......

    uj5u.com 2020-09-10 06:14:59 more
  • 爬蟲日志監控 -- Elastc Stack(ELK)部署

    傻瓜式部署,只需替換IP與用戶 導讀: 現ELK四大組件分別為:Elasticsearch(核心)、logstash(處理)、filebeat(采集)、kibana(可視化) 下載均在https://www.elastic.co/cn/downloads/下tar包,各組件版本最好一致,配合fdm會 ......

    uj5u.com 2020-09-10 06:15:05 more
最新发布
  • day02-2-商鋪查詢快取

    功能02-商鋪查詢快取 3.商鋪詳情快取查詢 3.1什么是快取? 快取就是資料交換的緩沖區(稱作Cache),是存盤資料的臨時地方,一般讀寫性能較高。 快取的作用: 降低后端負載 提高讀寫效率,降低回應時間 快取的成本: 資料一致性成本 代碼維護成本 運維成本 3.2需求說明 如下,當我們點擊商店詳 ......

    uj5u.com 2023-04-20 08:33:24 more
  • MySQL中binlog備份腳本分享

    關于MySQL的二進制日志(binlog),我們都知道二進制日志(binlog)非常重要,尤其當你需要point to point災難恢復的時侯,所以我們要對其進行備份。關于二進制日志(binlog)的備份,可以基于flush logs方式先切換binlog,然后拷貝&壓縮到到遠程服務器或本地服務器 ......

    uj5u.com 2023-04-20 08:28:06 more
  • day02-短信登錄

    功能實作02 2.功能01-短信登錄 2.1基于Session實作登錄 2.1.1思路分析 2.1.2代碼實作 2.1.2.1發送短信驗證碼 發送短信驗證碼: 發送驗證碼的介面為:http://127.0.0.1:8080/api/user/code?phone=xxxxx<手機號> 請求方式:PO ......

    uj5u.com 2023-04-20 08:27:27 more
  • 快取與資料庫雙寫一致性幾種策略分析

    本文將對幾種快取與資料庫保證資料一致性的使用方式進行分析。為保證高并發性能,以下分析場景不考慮執行的原子性及加鎖等強一致性要求的場景,僅追求最終一致性。 ......

    uj5u.com 2023-04-20 08:26:48 more
  • sql陳述句優化

    問題查找及措施 問題查找 需要找到具體的代碼,對其進行一對一優化,而非一直把關注點放在服務器和sql平臺 降低簡化每個事務中處理的問題,盡量不要讓一個事務拖太長的時間 例如檔案上傳時,應將檔案上傳這一步放在事務外面 微軟建議 4.啟動sql定時執行計劃 怎么啟動sqlserver代理服務-百度經驗 ......

    uj5u.com 2023-04-20 08:26:35 more
  • 云時代,MySQL到ClickHouse資料同步產品對比推薦

    ClickHouse 在執行分析查詢時的速度優勢很好的彌補了MySQL的不足,但是對于很多開發者和DBA來說,如何將MySQL穩定、高效、簡單的同步到 ClickHouse 卻很困難。本文對比了 NineData、MaterializeMySQL(ClickHouse自帶)、Bifrost 三款產品... ......

    uj5u.com 2023-04-20 08:26:29 more
  • sql陳述句優化

    問題查找及措施 問題查找 需要找到具體的代碼,對其進行一對一優化,而非一直把關注點放在服務器和sql平臺 降低簡化每個事務中處理的問題,盡量不要讓一個事務拖太長的時間 例如檔案上傳時,應將檔案上傳這一步放在事務外面 微軟建議 4.啟動sql定時執行計劃 怎么啟動sqlserver代理服務-百度經驗 ......

    uj5u.com 2023-04-20 08:25:13 more
  • Redis 報”OutOfDirectMemoryError“(堆外記憶體溢位)

    Redis 報錯“OutOfDirectMemoryError(堆外記憶體溢位) ”問題如下: 一、報錯資訊: 使用 Redis 的業務介面 ,產生 OutOfDirectMemoryError(堆外記憶體溢位),如圖: 格式化后的報錯資訊: { "timestamp": "2023-04-17 22: ......

    uj5u.com 2023-04-20 08:24:54 more
  • day02-2-商鋪查詢快取

    功能02-商鋪查詢快取 3.商鋪詳情快取查詢 3.1什么是快取? 快取就是資料交換的緩沖區(稱作Cache),是存盤資料的臨時地方,一般讀寫性能較高。 快取的作用: 降低后端負載 提高讀寫效率,降低回應時間 快取的成本: 資料一致性成本 代碼維護成本 運維成本 3.2需求說明 如下,當我們點擊商店詳 ......

    uj5u.com 2023-04-20 08:24:03 more
  • day02-短信登錄

    功能實作02 2.功能01-短信登錄 2.1基于Session實作登錄 2.1.1思路分析 2.1.2代碼實作 2.1.2.1發送短信驗證碼 發送短信驗證碼: 發送驗證碼的介面為:http://127.0.0.1:8080/api/user/code?phone=xxxxx<手機號> 請求方式:PO ......

    uj5u.com 2023-04-20 08:23:11 more