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MySQL數據庫壓力測試報告

發布時間:2020-08-14 12:52:02 來源:ITPUB博客 閱讀:1245 作者:czxin788 欄目:MySQL數據庫


 


MySQL數據庫壓力測試報告


 


1       壓力測試環境


1.1     服務器配置


 


類別

名稱

OS

虛擬機 CentOS release 6.5 (Final)

DISK

765GB

MySQLl

v5.6.27

Sysbench

v0.5



 

2       性能需求


  1. 測試innodb buffer pool設置為24G44G這兩種情形下的性能差距;

  2. 測試操作系統cpu個數在812這兩種情形下的性能差距;

  3. 測試表加索引和不加索引時數據庫性能差距;

  4. 測試硬盤的隨機讀、隨機寫、隨機讀寫、順序寫、順序讀、順序讀寫等所有模式的iops、吞吐量。

     


3       準備工作


利用現在生產MySQL備庫搭建壓力測試環境,在測試時,停掉備庫的slave復制:


 


MySQL數據庫壓力測試報告


 


 


 


說明:本次測試,只測試在不同情況下的select查詢,用來測試的sql如下:


SELECT pad FROM test.sbtest1 where k = ‘xxxxxx; 
 


表結構為:


MySQL數據庫壓力測試報告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">


數據量為100萬行:


MySQL數據庫壓力測試報告 


4       專業術語


QPSQueries Per Second意思是“每秒查詢率”,是一臺服務器每秒能夠相應的查詢次數,是對一個特定的查詢服務器在規定時間內所處理流量多少的衡量標準。

    cpu負載:通過top命令的load average獲取1分鐘內的平均值,它代表了任務隊列的平均長度。


5       測試過程
 

執行下面的命令準備測試數據:


sysbench --test=/usr/local/src/sysbench-0.5/sysbench/tests/db/oltp.lua  \

              --mysql-host=localhost \

              --mysql-user=root \

              --mysql-password=xxxxx \

              --mysql-db=test \

              --oltp-tables-count=1 \

              --oltp-table-size=1000000 \

              --num-threads=50 \

              --max-requests=1000000 \

              --report-interval=1 \

              prepare

 


上述命令會在MySQLtest數據庫里面創建sbtest1表,數據量為100w行。


參數說明:


--mysql-host=locahost #數據庫host

--mysql-port=3306 #數據庫端口

--mysql-user=your_username #數據庫用戶名

--mysql-password=your_password #數據庫密碼

--mysql-db=your_db_for_test #數據庫名

--oltp-tables-count=1 #模擬的表的個數,規格越高該值越大

--oltp-table-size=1000000  #模擬的每張表的行數,規格越高該值越大

 --num-threads=50 #模擬的并發數量,規格越高該值越大

--max-requests=100000000 #最大請求次數

 --report-interval=1 #1秒打印一次當前的QPS等值

--test=/usr/local/src/sysbench-0.5/sysbench/tests/db/oltp.lua#選用的測試腳本(lua),此腳本可以從sysbench-0.5源代碼文件目錄下找 [prepare | run | cleanup] #prepare準備數據,run執行測試,cleanup清理數據


5.1     buffer pool =24Gcpu= 8時的壓力測試情況


sysbench --test=/usr/local/src/sysbench-0.5/sysbench/tests/db/select.lua \

              --mysql-host=localhost \

              --mysql-user=root \

              --mysql-password=xxxx  \

              --mysql-db=test \

              --oltp-tables-count=1 \

              --oltp-table-size=1000000 \

              --num-threads=16 \

              --max-requests=1000000 \

              --report-interval=1 \

              --max-time=60 \

              run



說明:--num-threads=16 #模擬數據庫線程并發數量,規格越高該值越大


--max-time=60#最大測試時間(與--max-requests只要有一個超過,則退出)。


 

利用sysbench測試了并發線程個數不同的情況下,分別執行最大請求次數為100w select操作,通過修改--num-threads可以獲得不同并發線程數。
   
測試有索引和無索引這兩種情況下的 QPSQPS越大,系統性能越好),每條sql平均執行時間(每條sql執行時間越小,系統性能越好), cpu負載,每組數據重復測試三次后取平均值,具體數據對比如下表所示:


 


buffer_pool24G
cpu
個數:8

線程數

請求數

數據量

cpu負載

qps(r/s)

min(ms)

avg(ms)

max(ms)

95%

有索引

16

701116

100萬行

3.21

11680

0.09

1.37

1274.83

0.73

沒有索引

16

720

100萬行

11

11

278

1345

5997

2373

有索引

32

707720

100萬行

4.46

11737

0.08

2.71

1995

1.38

沒有索引

32

688

100萬行

21

11

686

2829

12666

5700

有索引

64

746723

100萬行

18

12416

0.09

5.15

2253

9.52

沒有索引

64

726

100萬行

38

11

1587

5430

19856

11000

有索引

128

910519

100萬行

36

15145

0.09

8.43

3147

20.17

沒有索引

128

804

100萬行

43

12

1525

10379

68659

58056

有索引

256

896962

100萬行

80

14932

0.09

17.14

4945

38.97

沒有索引

256

917

100萬行

52

11

1673

20022

81671

77778

有索引

512

850414

100萬行

194

14161

0.1

36.15

5750

321

沒有索引

512

1133

100萬行

59

11

1252

38431

107995

103269

有索引

1024

818863

100萬行

252

13629

0.09

75

17780

401

沒有索引

1024

1667

100萬行

63

11

1535

66188

153542

147680

有索引

2048

799571

100萬行

652

13282

0.1

153

9599

563

沒有索引

2048

2595

100萬行

72

10

1325

132090

265936

255631



上表對應的QPS折線圖如下所示:


MySQL數據庫壓力測試報告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">


 

QPS折線圖可以看出,當sysbench的并發測試線程數小于128時,有索引的QPS12左右。這主要是因為當sysbench并發線程少時,數據庫性能沒有得到充分的發揮。


sysbench的并發測試線程到128時,此時MySQL的性能就得到了充分的發揮,有索引的QPS達到了15左右。如果繼續增加并發測試線程數,有索引的QPS稍有下降,但是還在13左右,還是不錯的。


這時,我們再觀察無索引的情況,無論并發測試線程數是多少,無索引的QPS都是11,也就是,無索引時數據庫每秒只能處理11select查詢,這對高并發的業務簡直不可接受。這說明了索引對數據庫的性能影響是多么巨大。


 

上表對應的每條SQL執行時間折線圖如下所示:


MySQL數據庫壓力測試報告



從每條SQL執行時間的折線圖來看,無索引的sql執行時間隨著并發測試線程數的增加而增加。也就是說,本來單條sql執行時間是1s,但是線程數越多,其執行時間越長,如上圖,當線程數128時,其執行時間已經由1s升到10.379s了。


這時,我們再觀察有索引的每條sql執行時間,不論線程數是多少,其執行時間都不會超過1s,可見有索引和無索引的性能差距太大了。



上表對應的cpu負載折線圖如下所示:


MySQL數據庫壓力測試報告

 
 

cpu折線圖來看,在無索引情況下,當線程數128時,cpu負載為43,這和我們生產系統發生故障情況是吻合的,即當我們數據庫cpu負載在40~50時,確實有100左右的并發線程在數據庫里面執行。


再來看有索引情況下cpu的負載情況,可以看到,當并發線程數128以上時,有索引的cpu負載驟然升高,甚至高于無索引的。關于這個現象,出乎我的預料,甚至很不理解。


后來,我仔細分析了linux 里面的cpu負載的含義,CPU負載顯示的是一段時間內正在使用和等待使用CPU的平均任務數。不過,我也不好解釋上述現象,只能列在這,供人參考。


 


小知識:

CPU負載怎么理解?是不是CPU利用率?
這里要區別CPU負載和CPU利用率,它們是不同的兩個概念,但它們的信息可以在同一個top命令中進行顯示。CPU利用率顯示的是程序在運行期間實時占用的CPU百分比,而CPU負載顯示的是一段時間內正在使用和等待使用CPU的平均任務數。CPU利用率高,并不意味著負載就一定大。網上有篇文章舉了一個有趣比喻,拿打電話來說明兩者的區別,我按自己的理解闡述一下。
某公用電話亭,有一個人在打電話,四個人在等待,每人限定使用電話一分鐘,若有人一分鐘之內沒有打完電話,只能掛掉電話去排隊,等待下一輪。電話在這里就相當于CPU,而正在或等待打電話的人就相當于任務數。
在電話亭使用過程中,肯定會有人打完電話走掉,有人沒有打完電話而選擇重新排隊,更會有新增的人在這兒排隊,這個人數的變化就相當于任務數的增減。為了統計平均負載情況,我們5秒鐘統計一次人數,并在第1515分鐘的時候對統計情況取平均值,從而形成第1515分鐘的平均負載。
有的人拿起電話就打,一直打完1分鐘,而有的人可能前三十秒在找電話號碼,或者在猶豫要不要打,后三十秒才真正在打電話。如果把電話看作CPU,人數看作任務,我們就說前一個人(任務)的CPU利用率高,后一個人(任務)的CPU利用率低。
當然, CPU并不會在前三十秒工作,后三十秒歇著,只是說,有的程序涉及到大量的計算,所以CPU利用率就高,而有的程序牽涉到計算的部分很少,CPU利用率自然就低。但無論CPU的利用率是高是低,跟后面有多少任務在排隊沒有必然關系。


 

5.2     buffer pool =24Gcpu= 12時的壓力測試情況


下面我們把cpu8加到12,其他配置都不變,再進行壓力測試,看有什么變化。


buffer_pool24G
cpu
個數:12

線程數

請求數

數據量

cpu負載

qps(r/s)

min(ms)

avg(ms)

max(ms)

95%

有索引

16

716917

100萬行

3.4

11948

0.09

1.34

944

1.02

沒有索引

16

873

100萬行

10.34

14

282

1104

3224

2474

有索引

32

708405

100萬行

7.48

11787

0.09

2.71

1159

1.71

沒有索引

32

888

100萬行

20

14

292

2193

7901

4749

有索引

64

719369

100萬行

18.84

11920

0.08

5.37

1156

15

沒有索引

64

898

100萬行

40

14

638

4416

15063

9432

有索引

128

696889

100萬行

10

11614

0.09

11

1157

29.32

沒有索引

128

943

100萬行

42

14

817

8686

67268

49821

有索引

256

681509

100萬行

59

11588

0.1

22.53

2495

55

沒有索引

256

1051

100萬行

47

13

730

16978

78149

75079

有索引

512

704611

100萬行

78

11730

0.1

43.63

2800

413

沒有索引

512

1267

100萬行

54

13

822

31718

96230

92691

有索引

1024

593684

100萬行

204

9868

0.1

103

6522

545

沒有索引

1024

1764

100萬行

59

12

662

58212

139380

133509

有索引

2048

571730

100萬行

196

8898

0.09

225

8748

948

沒有索引

2048

2769

100萬行

116

12

606

103518

214829

205576


 

上表對應的QPS折線圖如下所示:


MySQL數據庫壓力測試報告


 從上圖可以看到,當線程數為512時,有索引的qps開始驟降;但無索引的qps不論線程數是多少,都是14


             

上表對應的每條SQL執行時間折線圖如下所示:


MySQL數據庫壓力測試報告 
 

從上圖可以看到,隨著線程數的增加,無索引的每條sql執行時間在增加;而有索引的每條sql平均執行時間不到1s

 


上表對應的cpu負載折線圖如下所示:


MySQL數據庫壓力測試報告 
 

從上圖可以看到,隨著線程數的增加,cpu負載也在增加,但是當線程數為256時,有索引的cpu負載要比無索引的高,這個暫時沒法解釋。


5.3     buffer pool =44Gcpu= 12時的壓力測試情況


下面我們把innodb buffer pool24G升至44G,其他配置都不變,再進行壓力測試,看有什么變化。


buffer_pool44G
cpu
個數:12

線程數

請求數

數據量

cpu負載

qps(r/s)

min(ms)

avg(ms)

max(ms)

95%

有索引

16

709587

100萬行

3.56

11826

0.09

1.35

544

1.06

沒有索引

16

894

100萬行

11

14

279

1087

3211

2471

有索引

32

693606

100萬行

3.14

11503

0.08

2.77

1121

1.79

沒有索引

32

907

100萬行

20

14

549

2146

9473

4504

有索引

64

681148

100萬行

10.21

11352

0.08

5.64

1880

16

沒有索引

64

813

100萬行

42

14

722

4828

15398

9626

有索引

128

654430

100萬行

24

10906

0.09

11

1641

39

沒有索引

128

934

100萬行

40

14

774

12272

114780

61804

有索引

256

652889

100萬行

67

10828

0.09

23

2561

90

沒有索引

256

1091

100萬行

47

14

770

16203

76963

72930

有索引

512

651804

100萬行

143

10800

0.09

47

2816

448

沒有索引

512

1325

100萬行

88

14

506

30334

95839

90471

有索引

1024

466049

100萬行

220

7109

0.1

140

18282

573

沒有索引

1024

1818

100萬行

68

14

730

54924

132208

126847

有索引

2048

578978

100萬行

247

9574

0.09

212

5251

771

沒有索引

2048

2807

100萬行

105

14

616

100284

209988

200952


上表對應的QPS折線圖如下所示:


MySQL數據庫壓力測試報告


 

從上圖可以看到,當線程數為512時,有索引的qps開始驟降;但無索引的qps不論線程數是多少,都是14



上表對應的每條SQL執行時間折線圖如下所示:



MySQL數據庫壓力測試報告

 

從上圖可以看到,隨著線程數的增加,無索引的每條sql平均執行時間在增加;而有索引的每條sql平均執行時間不到1s



上表對應的cpu負載折線圖如下所示:



MySQL數據庫壓力測試報告

 

從上圖可以看到,隨著線程數的增加,cpu負載也在增加,但是當線程數為256時,有索引的cpu負載要比無索引的高,這種現象暫時沒法解釋。


5.4     磁盤io


io測試腳本:


[root@Mysql03 test]# cat iotest.sh


#!/bin/sh

set -u

set -x

set -e

for size in 2G ;do

    for mode in seqrd seqrw rndrd rndwr rndrw;do

        for blksize in 16384;do

            sysbench --test=fileio --file-num=64 --file-total-size=$size prepare

            for threads in 1 16 32 64 128 512 1024 2048;do

                echo "====== testing $blksize in $threads threads"

                echo PARAMS $size $mode $threads $blksize > sysbench-size-$size-mode-$mode-threads-$threads-blksz-$blksize

                for i in 1 ;do

                    sysbench --test=fileio --file-total-size=$size --file-test-mode=$mode --max-time=180 --max-requests=100000000\

                    --num-threads=$threads --init-rng=on --file-num=64 --file-extra-flags=direct --file-fsync-freq=0\

                    --file-block-size=$blksize run | tee -a sysbench-size-$size-mode-$mode-threads-$threads-blksz-$blksize 2>&1

                done

            done

            sysbench --test=fileio --file-total-size=$size cleanup

        done

    done

done

 

得到如下數據:



線程數

模式

數據塊大小

吞吐量(Mb/s

IOPS

1

順序讀

16k

59.639

3816.87

16

順序讀

16k

139.81

8948

32

順序讀

16k

158.85

10166.69

64

順序讀

16k

147

9451

128

順序讀

16k

149

9542

512

順序讀

16k

153

9853

1024

順序讀

16k

151

9712.16

2048

順序讀

16k

151

9666

1

隨機讀

16k

5

337

16

隨機讀

16k

41

2668

32

隨機讀

16k

61

3912.03

64

隨機讀

16k

61

3939.21

128

隨機讀

16k

61

3939

 

因為測試磁盤io會影響生產系統,所以只測試了上面幾組數據,沒有對順序讀、順序寫、順序讀寫、隨機讀、隨機寫、隨機讀寫等全面測試,即使測試可能意義也不大。


因為磁盤是機械硬盤,按理應該是220,上面出現1萬的情況,因為硬盤有閃存。


6       測試結論


  1. 有索引的qps12左右,沒索引的qps只有14,兩者相差1000倍;

  2. 有索引的sql執行時間不論線程數是多少都不到一秒,而無索引的sql隨著線程數的增加,其執行時間也會增加,最高到132s,相差倍數可是千倍萬倍;

  3. 數據庫的線程數達到128時,會使數據庫性能明顯下降;當增加cpu和內存時,也不能很好的解決這個問題,這可能是my.cnflinux 內核參數配置的不合適導致,后期仔細研究這些參數,使數據庫性能上一個新的臺階;

  4. 磁盤IO能力固定,只能從數據庫和操作系統參數著手。

     


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