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  • SoapUI压力测试的指标项说明以及对测试策略的诠释 1、Test Step:调用方法名称。  2、min、max、avg、last:调用时的最小、最大、平均、最近一次的响应时间  3、cnt总调用次数 ;tps平均每秒调用次数  4...

    如图:



    1、Test Step:调用方法名称。
      2、min、max、avg、last:调用时的最小、最大、平均、最近一次的响应时间
      3、cnt总调用次数 ;tps平均每秒调用次数
      4、bytes接口处理的字符数;bps平均每秒接口处理的字符数
      5、err报错次数;rat报错次数/执行次数
      或
      min,最小响应时间
      max,最大响应时间
      avg,平均响应时间
      last,上一次请求响应时间
      cnt,请求数
      tps,每秒处理请求数
      bps,吞吐率
      rat,错误率
    以下是对策略进行诠释:


    1. Simple Strategy - Baseline, Load and Soak Testing

    The Simple Strategy runs the specified number of threads with the specified delay between each run to simulate a "breathing space" for the server. For example if you want to run a functional test with 10 threads with 10 seconds delay, set Threads to 10, delay to 10000 and random to how much of the delay you want to randomize (ie setting it to 0.5 will result in delays between 5 and 10 seconds). When creating a new LoadTest this is the default strategy and set at a relatively low load (5 threads with 1000ms delay).

    default-simple-strategy

    The Simple Strategy is perfect for Baseline testing. Use it to assert the the basic performance of your service and validate that there are no threading or resource locking issues. Ramp up the number of threads when you want do more elaborate load testing or use the strategy for long-running soak tests.

    Since it isn't meant to bring your services to their knees, a setup like this can be used for continuous load-testing to ensure that your service performs as expected under moderate load; set up a baseline test with no randomization of the delay, add LoadTest Assertions that act as a safety net for unexpected results and automate its execution with the command-line LoadTest runner or maven plugins.


    2. Fixed Rate Strategy – Simple with a twist

    One thing that the Simple Strategy does not do is guarantee a number of executions within a certain time, for example if you might want to start your TestCase 10 times each second no matter how long it takes to execute. Using the Simple Strategy you could set up 10 Threads and a delay compensating for the average gap between the end of the TestCase and the start of the next second, but this would be highly unreliable over time. The Fixed-Rate strategy should be used instead; set the rate as desired (10 in our case) and off you go; the strategy will automatically start the required number threads for this setting attempting to maintain the configured value.


    As hinted in the headline, there are some twists here: what if our TestCase takes more than one second to execute? To maintain the configured TPS value, the strategy will internally start new threads to compensate for this; after a while you might have many more than 10 threads running due to the fact that the original ones had not finished within the set rate. And not surprisingly this could cause the target service to get even slower, resulting in more and more threads being started to "keep up" with the configured TPS value. As you probably guessed the "Max Threads" setting is her to prevent soapUI from overloading (both itself and the target services)  in this situation, specifying a value here will put a limit on the maximum number of threads the soapUI will be allowed to start to maintain the configured TPS, if reached the existing threads will have to finish before soapUI will start any new ones.

    The "Request Level" setting will attempt to maintain the TPS not on the TestCase execution level but on the request level instead, for example if you have a data-driven LoadTest or a TestCase with many requests, you want the TPS setting to apply not on the execution level of the entire TestCase but on the request level.

    In any case, the Fixed Rate strategy is useful for baseline, load and soak-testing if you don’t run into the "Thread Congestion" problem described above. On the other hand, you might provoke the congestion (maybe even in combination with another LoadTest) to see how your services handle this or how they recover after the congestion has been handled.

    3. Variable Load Strategies

    There are several strategies that can be used to vary load (the number of threads) over time, each simulating a different kind of behavior. They can be useful for recovery and stress testing, but just as well for baseline testing, either on their own or in combination with other strategies. Let's have a quick look:

    1. Variance strategy – this varies the number of threads over time in a “sawtooth” manor as configured; set the Interval to the desired value  and the Variance to the how much the number of threads should decrease and increase. For example if we start with 20 threads, set interval to 60 and Variance to 0.8, the number of threads will increase from 20 to 36 within the first 15 seconds, then decrease back to 20 and continue down to 4 threads after 45 seconds, and finally go back up to the initial value after 60 seconds. In the Statistics Diagrams we can follow this variance easily:
    1. variance-strategy-output

    2. Burst Strategy -  this strategy is specifically designed for Recovery Testing and takes variance to its extreme; it does nothing for the configured Burst Delay, then runs the configured number of threads for the “Burst Duration”  and goes back to sleep. Here you could (and should!) set the number of threads to a high value (20+) to simulate an onslaught of traffic during a short interval, then measure the recovery of your system with a standard base-line LoadTest containing basic performance-related assertions. Lets try this with a burst delay and duration of 10 seconds for 60 seconds;


      burst-strategy-output

      Here can see the bursts of activity in the diagram, please also note that the resolution has been changed to 250ms (from the default "data" value), otherwise we not have had any diagram updates during the "sleeping" periods of the execution (since no data would have been collected).

    3. The Thread Strategy lets you linearly change the number of threads from one level to another over the run of the LoadTest. It’s main purpose is as a means to identify at which level certain statistics change or events occur, for example to find at which ThreadCount the maximum TPS or BPS can be achieved or to find at which ThreadCount functional testing errors start occurring. Set the start and end thread values (for example 5 to 50) and set the duration to a relatively long duration (I  use at least 30 seconds per thread value, in this example that would be 1350 seconds) to get accurate measurements (more on this below).


      thread-strategy-output

    4. Grid Strategy – this strategy allows to specifically configure the relative change in number of threads over time, its main use for this is more advanced scenario and recovery testing, where you need to see the services behavior under varying loads and load changes. For example lets say you want to run for 60 seconds with 10, 20, 10, 40, 10 threads. Configure your LoadTest to start with 10 threads and then enter the following values in the Grid:

      grid-strategy-config

      Both values are stored relative to the duration and actual ThreadCount of the LoadTest; if you change these, the corresponding Grid Strategy values will be recalculated. Running the test shows the following output:

      grid-strategy-output

    5. Script Strategy – the script strategy is the ultimate customization possibility; the script you specify is called regularly (The "Strategy Interval" setting in the LoadTest Options dialog) and should return the desired number of threads at that current time. Returning a value other than the current one will start or stop threads to adjust for the change. This allows for any kind of variance of the number of threads, for example the following script randomizes the number of threads between 5 and 15.

      script-strategy-config

      Running this with the strategy interval set to 5000 the number of threads will change every 5 seconds:

      script-strategy-output

      The possibilities here are endless.

    4. Statistics Calculation and ThreadCount Changes

    Many of these strategies will change the number of threads which has an important impact on the statistics calculation that you need to be aware of; when the number of threads changes, this will usually change the response times of the target services, resulting in a change in avg, tps, etc. but since the LoadTest has already run at a previous number of threads the results for those runs will skew the result for the new ThreadCount.

    For example lets say you have been running at 5 threads and got and average to 500ms. Using the Thread Strategy you increase the number of threads gradually; when running 6 threads the average increases to 600ms but since the “old” values collected  for 5 threads are still there, these will in total result in a lower average. There are two easy ways to work around this; select the “Reset Statistics on ThreadCount change” value in the LoadTest Options dialog, or manually reset the statistics with the corresponding button in the LoadTest toolbar; in either case old statistics will be discarded. To see this in action lets do a ThreadCount Strategy test from 10 to 20 threads over 300 seconds (30 seconds per thread), below you can see results both with this setting unchecked and then checked;

    thread-strategy-stat-nochange

    thread-strategy-stat-change

    In the latter you see the “jumps” in statistics each time they are reset when the number of threads changes, gradually leveling out to a new value. The final TPS calculated at 20 threads differs about 10% between these two, showing how the lower results “impact” the higher ones.

    5. Running Multiple LoadTests Simultaneously

    Ok, lets have a quick look at this; we'll create one baseline test with the simple strategy and a low number of threads, and at the same time run a burst strategy to see how the baseline test performance "recovers" after the burst;

    multiple-strategies

    Here you can see the simple strategy (bottom diagram) recovering gradually after each burst of load.

    6. Final Words

    Hopefully you have gotten a good overview of the different strategies in soapUI and how they can be used to simulate different scenarios and  kinds of load. As you may have noticed, soapUI focuses more on "behavioral" LoadTesting (understanding how your services handler different loads) instead of exact numbers, which is anyhow hard to calculate since there are so many external factors influencing it.





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  •  2、min、max、avg、last:调用时最小、最大、平均、最近一次响应时间 3、cnt总调用次数 ;tps平均每秒调用次数 4、bytes接口处理字符数;bps平均每秒接口处理字符数 5、err报错次数;rat报错次数/执行...
          1、Test Step:调用方法名称。
      2、min、max、avg、last:调用时的最小、最大、平均、最近一次的响应时间
      3、cnt总调用次数 ;tps平均每秒调用次数
      4、bytes接口处理的字符数;bps平均每秒接口处理的字符数
      5、err报错次数;rat报错次数/执行次数
      或
      min,最小响应时间
      max,最大响应时间
      avg,平均响应时间
      last,上一次请求响应时间
      cnt,请求数
      tps,每秒处理请求数
      bps,吞吐率
      rat,错误率
    展开全文
  • SOAPUI 压力测试的指标项说明

    千次阅读 2016-06-22 16:35:11
    soapUI Pro指标项说明:   Test Step Sets the startup delay for each thread (in milliseconds), setting to 0 will start all threads simultaneously. min The shortest time the

    soapUI Pro指标项说明:

     

    Test Step

    Sets the startup delay for each thread (in milliseconds), setting to 0 will start all threads simultaneously.

    min

    The shortest time the step has taken (in milliseconds).

    max

    The longest time the step has taken (in milliseconds).

    avg

    The average time for the test step (in milliseconds).

    last

    The last time for the test step (in milliseconds).

    cnt

    The number of times the test step has been executed.

    tps

    The number of transactions per second for the test step, see Calculation of TPS/BPS below.

    bytes

    The number of bytes processed by the test step.

    bps

    The bytes per second processed by the test step.

    err

    The number of assertion errors for the test step.

    rat

    Failed requests ratio (the percentage of requests that failed).

    1、Test Step:调用方法名称。

    2、min、max、avg、last:调用时的最小、最大、平均、最近一次的响应时间
    3、cnt总调用次数 ;tps平均每秒调用次数
    4、bytes接口处理的字符数;bps平均每秒接口处理的字符数

    5、err报错次数;rat报错次数/执行次数

    min,最小响应时间
    max,最大响应时间
    avg,平均响应时间
    last,上一次请求响应时间
    cnt,请求数
    tps,每秒处理请求数
    bps,吞吐率
    rat,错误率


    soapui中添加随机参数  

    想让soapui每次访问带参数的Webservice时能随机生成参数,可以在需要填写参数的地方使用

             ${=(int)Math.random()*500}

        貌似这个是soapui的内联编译的特性.


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  • 压力测试的指标1 压力测试的指标1.1 TPS1.2 QPS1.3 平均处理时间(RT)1.4 并发用户数(并发量)1.5 换算关系1.6 TPS和QPS的区别2 压力测试方法3 名称概念解释1. QPS2. TPS3. RPS 1 压力测试的指标 1.1 TPS TPS ...

    1 压力测试中的指标

    1.1 TPS

    TPS 即Transactions Per Second的缩写,每秒处理的事务数目
    一个事务是指一个客户机向服务器发送请求然后服务器做出反应的过程(完整处理,即客户端发起请求到得到响应)。客户机在发送请求时开始计时,收到服务器响应后结束计时,以此来计算使用的时间和完成的事务个数,最终利用这些信息作出的评估分。一个事务可能对应多个请求,可以参考下数据库的事务操作

    1.2 QPS

    QPS 即Queries Per Second的缩写,每秒能处理查询数目(完整处理,即客户端发起请求到得到响应)
    是一台服务器每秒能够相应的查询次数,是对一个特定的查询服务器在规定时间内所处理流量多少的衡量标准。
    我们从它的英文全名可以得出它是查询意思,原来在因特网上,作为域名系统服务器的机器的性能经常用每秒查询率来衡量。对应fetches/sec,即每秒的响应请求数。
    虽然名义上是查询的意思,但实际上,现在习惯于对单一接口服务的处理能力用QPS进行表述(即使它并不是查询操作)。

    1.3 平均处理时间(RT)

    RT:响应时间,处理一次请求所需要的平均处理时间。
    我们一般还会关注90%请求的的平均处理时间,因为可能因网络情况出现极端情况。

    1.4 并发用户数(并发量)

    每秒对待测试接口发起请求的用户数量。

    1.5 换算关系

    QPS = 并发数/平均响应时间
    并发量 = QPS * 平均响应时间

    比如3000个用户(并发量)同时访问待测试接口,在用户端统计,3000个用户平均得到响应的时间为1188.538ms。所以QPS=3000/1.188538s= 2524.11 q/s。

    我们就可以这样描述本次测试,在3000个并发量的情况下,QPS为2524.11,平均响应事件为1188.538ms

    1.6 TPS和QPS的区别

    这个问题开始,我认为这两者应该是同一个东西,但在知乎上看到他们的英文名,现在我认为:
    QPS 每秒能处理查询数目,但现在一般也用于单服务接口每秒能处理请求数。

    TPS 每秒处理的事务数目,如果完成该事务仅为单个服务接口,我们也可以认为它就是QPS。

    PS:还有一个RPS的的概念 request per second 。每秒请求数,在一定条件下和QPS 和TPS类似。

    2 压力测试方法

    我们可以使用压测工具模拟多用户对系统进行压力测试。以一定请求总量,保持不变,逐步增加并发量,观察QPS的变化及平均响应时间的变化。

    比如10000的总请求数,然后测试100的并发量情况下的QPS值,然后200, 300, 400, 500等。

    一个系统吞吐量通常由TPS、并发数两个因素决定,每套系统这两个值都有一个相对极限值,在应用场景访问压力下,只要某一项达 到系统最高值,系统的吞吐量就上不去了,如果压力继续增大,系统的吞吐量反而会下降,原因是系统超负荷工作,上下文切换、内存等等其它消耗导致系统性能下降。这里给出一份使用ab工具的压测图。
    在这里插入图片描述
    从图中可以看出2000的并发量时,QPS已经达到2500左右,后续加大并发数仍维持在2500,说明该接口在该配置下,QPS为2500,即每秒该系统的能力只能处理2500个请求左右,后面加大的并发量,只会导致平均响应时间的增加。(PS:因为每秒只能处理2500个请求,而一次性有7000的并发,自然会造成请求堆积,导致平均响应时间会变长)我们看到超过14000之后连QPS也开始急剧下降,说明系统超负荷工作,导致性能开始急剧下降。
    而一般情况下,我们认为平均响应时间达到一定值,就已经不可以接受了。

    3 名称概念解释

    1. QPS

    Queries Per Second,每秒查询数。每秒能够响应的查询次数。QPS是对一个特定的查询服务器在规定时间内所处理流量多少的衡量标准,在因特网上,作为域名系统服务器的机器的性能经常用每秒查询率来衡量。每秒的响应请求数,也即是最大吞吐能力。

    2. TPS

    Transactions Per Second 的缩写,每秒处理的事务数目。一个事务是指一个客户机向服务器发送请求然后服务器做出反应的过程。客户机在发送请求时开始计时,收到服务器响应后结束计时,以此来计算使用的时间和完成的事务个数,最终利用这些信息作出的评估分。

    TPS 的过程包括:客户端请求服务端、服务端内部处理、服务端返回客户端。
    例如,访问一个 Index 页面会请求服务器 3 次,包括一次 html,一次 css,一次 js,那么访问这一个页面就会产生一个“T”,产生三个“Q”。

    3. RPS

    RPS 代表吞吐率,即 Requests Per Second 的缩写。吞吐率是服务器并发处理能力的量化描述,单位是 reqs/s,指的是某个并发用户数下单位时间内处理的请求数。某个并发用户数下单位时间内能处理的最大的请求数,称之为最大吞吐率。
    有人把 RPS 说等效于 QPS。其实可以看作同一个统计方式,只是叫法不同而已。RPS/QPS,可以使用 apache ab 工具进行测量。

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  • 重温压力测试指标

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    2018-05-08 06:43:24
    吞吐率(Requests per second),缩写RPS 计算公式: 吞吐率 = 总请求数 / 处理这些请求总完成时间 ...吞吐率是服务器并发处理能力量化描述,单位是reqs/s,指是某个并发用户数下单位时间...
  • jmeter(压力测试指标分析

    千次阅读 2019-09-17 10:01:54
    压力测试分两种场景:一种是单场景,压一个接口;第二种是混合场景,多个有关联接口。 压测时间,一般场景都运行10-15分钟。如果是疲劳测试,可以压一天或一周,根据实际情况来定。 压测任务需求确认 ...
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  • 性能/压力 测试指标

    2017-04-21 10:55:00
    一般情况下,当用户能够在2秒以内得到响应时,会感觉系统响应很快;当用户在2-5秒之间得到响应时,会感觉系统响应速度还可以;当用户在5-10秒以内得到响应时,会感觉系统响应速度很慢,但是还可以接受;而当...

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