nosql redis數據庫壓力測試基準工具redis

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這篇文章將為大家詳細講解有關 nosql redis 數據庫壓力測試基準工具 redis-benchmark 怎么用，丸趣 TV 小編覺得挺實用的，因此分享給大家做個參考，希望大家閱讀完這篇文章后可以有所收獲。

1, 查看基準測試工具的用法
[root@langfang src]# pwd
/redis_dir/redis-4.0.9/src
[root@langfang src]# ./redis-benchmark -help
Invalid option -help or option argument missing

Usage: redis-benchmark [-h host] [-p port] [-c clients] [-n requests] [-k boolean]

-h hostname Server hostname (default 127.0.0.1) -h 連接 REDIS 服務器 IP，默認為 127.0.0.1
-p port Server port (default 6379) -P REDIS 服務器端口，默認為 6379
-s socket Server socket (overrides host and port) -S 服務器套接字（覆蓋主機和端口）
-a password Password for Redis Auth –A REDIS AUTH 的認證密碼
-c clients Number of parallel connections (default 50) –c 表示并發連接數量，默認 50 個
-n requests Total number of requests (default 100000) —n 請求的總數量，默認為 100000，即 10 萬
-d size Data size of SET/GET value in bytes (default 3) –D 表示 SET GET 的大小，默認大小為 3
–dbnum db SELECT the specified db number (default 0) –dbnum 指定連接哪個數據庫，默認為 0 號數據庫
-k boolean 1=keep alive 0=reconnect (default 1) -k 布爾類型，1 為保持連接，默認值；0 為重連
-r keyspacelen Use random keys for SET/GET/INCR, random values for SADD –r 為 SET GET INCR 操作使用隨機的鍵，為 SADD 使用隨機值
Using this option the benchmark will expand the string __rand_int__
inside an argument with a 12 digits number in the specified range
from 0 to keyspacelen-1. The substitution changes every time a command
is executed. Default tests use this to hit random keys in the
specified range.
-P numreq Pipeline numreq requests. Default 1 (no pipeline). –P 與管道技術有關，請求的次數，默認為 1，即禁用管道技術，假如服務器報錯，顯示報錯信息
-e If server replies with errors, show them on stdout.
(no more than 1 error per second is displayed) – 僅僅每秒顯示 1 個報錯
-q Quiet. Just show query/sec values -Q 安靜，僅僅顯示每秒查詢值
–csv Output in CSV format –CSV 以 CSV 格式輸出
-l Loop. Run the tests forever -L 一直壓測，不停止
-t tests Only run the comma separated list of tests. The test -T -L 運行以逗號分隔的測試列表，指定具體的壓力測試場景，比如是 set or mget or get and so on
names are the same as the ones produced as output.
-I Idle mode. Just open N idle connections and wait. –L 空閑模式，只是打開 N 個空閑連接然后等待

Examples: — 示例

Run the benchmark with the default configuration against 127.0.0.1:6379:
$ redis-benchmark

Use 20 parallel clients, for a total of 100k requests, against 192.168.1.1:
$ redis-benchmark -h 192.168.1.1 -p 6379 -n 100000 -c 20

Fill 127.0.0.1:6379 with about 1 million keys only using the SET test:
$ redis-benchmark -t set -n 1000000 -r 100000000

Benchmark 127.0.0.1:6379 for a few commands producing CSV output:
$ redis-benchmark -t ping,set,get -n 100000 –csv

Benchmark a specific command line:
$ redis-benchmark -r 10000 -n 10000 eval return redis.call(ping) 0

Fill a list with 10000 random elements:
$ redis-benchmark -r 10000 -n 10000 lpush mylist __rand_int__

On user specified command lines __rand_int__ is replaced with a random integer
with a range of values selected by the -r option.
[root@langfang src]#

2,redis-benchmark 默認壓力測試
– 壓力測試結論包括壓力測試消耗時間及每秒最大處理的請求數以及各種的壓力測試場景的不同子節
[root@langfang src]# ./redis-benchmark
====== PING_INLINE ====== — 概述的名稱
100000 requests completed in 1.51 seconds – 概要結論，消耗 1.51 秒完成 10 萬次請求
50 parallel clients
3 bytes payload
keep alive: 1

96.26% = 1 milliseconds
99.96% = 2 milliseconds
100.00% = 2 milliseconds
66181.34 requests per second – 每秒完成 6.6 萬左右請求

====== PING_BULK ======
100000 requests completed in 1.70 seconds
50 parallel clients
3 bytes payload
keep alive: 1

93.00% = 1 milliseconds
99.98% = 2 milliseconds
100.00% = 2 milliseconds
58788.95 requests per second

====== SET ======
100000 requests completed in 1.69 seconds
50 parallel clients
3 bytes payload
keep alive: 1

92.51% = 1 milliseconds
99.95% = 2 milliseconds
100.00% = 2 milliseconds
59241.71 requests per second

====== GET ======
100000 requests completed in 1.53 seconds
50 parallel clients
3 bytes payload
keep alive: 1

96.22% = 1 milliseconds
99.97% = 2 milliseconds
100.00% = 2 milliseconds
65402.22 requests per second

====== INCR ======
100000 requests completed in 1.55 seconds
50 parallel clients
3 bytes payload
keep alive: 1

95.60% = 1 milliseconds
100.00% = 2 milliseconds
100.00% = 2 milliseconds
64683.05 requests per second

====== LPUSH ======
100000 requests completed in 1.52 seconds
50 parallel clients
3 bytes payload
keep alive: 1

94.17% = 1 milliseconds
99.99% = 2 milliseconds
100.00% = 2 milliseconds
65573.77 requests per second

====== RPUSH ======
100000 requests completed in 1.57 seconds
50 parallel clients
3 bytes payload
keep alive: 1

94.06% = 1 milliseconds
99.97% = 2 milliseconds
100.00% = 2 milliseconds
63734.86 requests per second

====== LPOP ======
100000 requests completed in 1.51 seconds
50 parallel clients
3 bytes payload
keep alive: 1

94.25% = 1 milliseconds
99.98% = 2 milliseconds
100.00% = 2 milliseconds
66269.05 requests per second

====== RPOP ======
100000 requests completed in 1.52 seconds
50 parallel clients
3 bytes payload
keep alive: 1

95.01% = 1 milliseconds
99.95% = 2 milliseconds
100.00% = 2 milliseconds
65919.58 requests per second
略

====== LPUSH (needed to benchmark LRANGE) ======
100000 requests completed in 1.50 seconds
50 parallel clients

====== LRANGE_500 (first 450 elements) ======
100000 requests completed in 10.29 seconds
50 parallel clients
3 bytes payload
keep alive: 1

3.16% = 1 milliseconds
22.80% = 2 milliseconds
46.08% = 3 milliseconds
65.75% = 4 milliseconds
81.96% = 5 milliseconds
94.78% = 6 milliseconds

====== MSET (10 keys) ======
100000 requests completed in 2.06 seconds
50 parallel clients
3 bytes payload
keep alive: 1

57.05% = 1 milliseconds
98.41% = 2 milliseconds
99.98% = 3 milliseconds
100.00% = 3 milliseconds
48567.27 requests per second

[root@langfang src]#

3，還是各種壓測場景，不過是 20 個并發，10 萬次請求，連接指定 REDIS 服務器
[root@langfang src]# ./redis-benchmark -h 127.0.0.1 -p 6379 -n 100000 -c 20
====== PING_INLINE ======
100000 requests completed in 1.63 seconds
20 parallel clients
3 bytes payload
keep alive: 1

99.69% = 1 milliseconds
100.00% = 1 milliseconds
61312.08 requests per second

====== PING_BULK ======
100000 requests completed in 1.67 seconds
20 parallel clients
3 bytes payload
keep alive: 1

4，指定測試場景比如 GET AND SET 以及隨機鍵的數量以及請求個數
[root@langfang src]# ./redis-benchmark -t set,get -n 100000 -r 1000
====== SET ======
100000 requests completed in 1.60 seconds
50 parallel clients
3 bytes payload
keep alive: 1

94.68% = 1 milliseconds
99.93% = 2 milliseconds
100.00% = 2 milliseconds
62656.64 requests per second

====== GET ======
100000 requests completed in 1.68 seconds
50 parallel clients
3 bytes payload
keep alive: 1

93.91% = 1 milliseconds
99.93% = 2 milliseconds
100.00% = 2 milliseconds
59488.40 requests per second

5，csv 格式輸出
[root@langfang src]# ./redis-benchmark -t ping,get,set -n 1000 –csv
PING_INLINE , 52631.58
PING_BULK , 55555.56
SET , 52631.58
GET , 52631.58

6，運行特定的命令行
[root@langfang src]# ./redis-benchmark -r 1000 -n 100000 eval return redis.call(ping)
====== eval return redis.call(ping) ======
100000 requests completed in 1.56 seconds
50 parallel clients
3 bytes payload
keep alive: 1

95.66% = 1 milliseconds
99.93% = 2 milliseconds
100.00% = 2 milliseconds
63979.53 requests per second

7，Fill a list with 10000 random elements 以隨機指定的范圍元素填充 list
[root@langfang src]# ./redis-benchmark -r 10000 -n 1000 lpush mylist _rand_init__
====== lpush mylist _rand_init__ ======
1000 requests completed in 0.02 seconds
50 parallel clients
3 bytes payload
keep alive: 1

87.20% = 1 milliseconds
99.50% = 2 milliseconds
100.00% = 2 milliseconds
47619.05 requests per second

[root@langfang src]# ./redis-benchmark -r 10000 -n 1000 set mylist _rand_init__
====== set mylist _rand_init__ ======
1000 requests completed in 0.02 seconds
50 parallel clients
3 bytes payload
keep alive: 1

86.40% = 1 milliseconds
100.00% = 1 milliseconds
47619.05 requests per second

8, 靜默方式壓力測試
[root@langfang src]# ./redis-benchmark -t set,get -n 100000 -r 1000
====== SET ======
100000 requests completed in 1.58 seconds
50 parallel clients
3 bytes payload
keep alive: 1

94.99% = 1 milliseconds
99.96% = 2 milliseconds
100.00% = 2 milliseconds
63211.12 requests per second

====== GET ======
100000 requests completed in 1.60 seconds
50 parallel clients
3 bytes payload
keep alive: 1

95.21% = 1 milliseconds
99.99% = 2 milliseconds
100.00% = 2 milliseconds
62617.41 requests per second

– 可見靜默方式只顯示每次處理的請求數以及壓力測試場景
[root@langfang src]# ./redis-benchmark -t set,get -n 100000 -r 1000 -q
SET: 63091.48 requests per second
GET: 64724.92 requests per second

9，redis-cli 可以直接附上操作命令
[root@langfang src]# ./redis-cli flushall
OK
[root@langfang src]# ./redis-cli dbsize
(integer) 0
[root@langfang src]#

10，–r 表示產生的隨機鍵的數量，數量大可以模擬鍵不命中情況
[root@langfang src]# ./redis-cli dbsize
(integer) 0
[root@langfang src]# ./redis-benchmark -t set -r 8888 -n 100000
====== SET ======
100000 requests completed in 1.61 seconds
50 parallel clients
3 bytes payload
keep alive: 1

94.16% = 1 milliseconds
99.93% = 2 milliseconds
99.95% = 3 milliseconds
100.00% = 3 milliseconds
62305.30 requests per second

[root@langfang src]# ./redis-cli dbsize
(integer) 8888
[root@langfang src]#

11，默認情況是處理 1 個請求然后順序接著處理下 1 個請求，但可以通過 -P 管道技術，并發處理多個請求，下述效果非常明顯，成 9 倍左右的差異
（同時處理多條命令需要 PIPELINE 管道技術）

[root@langfang src]# ./redis-benchmark -t get,set -n 100000 -q
SET: 64516.13 requests per second
GET: 64516.13 requests per second

[root@langfang src]# ./redis-benchmark -t get,set -n 100000 -P 16 -q
SET: 452488.69 requests per second
GET: 529100.56 requests per second

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