Acceptable requests per second throughput.
可接受的每秒请求的吞吐量。
The unit of measurement is requests per second.
的量度单位是每秒的请求。
Their MySQL server handles 2,400 requests per second!
他们的MySQL服务器每秒钟要处理2400个请求！
Acceptable backend requests per second throughput.
可接受的每秒后端请求的吞吐量。
Consider a site that averages 25 page requests per second.
可以考虑这种情况，一个站点平均每秒处理25个页面请求。
Otherwise, your application is sending too many requests per second.
否则，您的应用程序发送太多的每秒请求。
Handled 8% more requests per second (RPS) than the original.net application.
每秒处理请求数(requests per second,RPS)比原有。NET应用程序提高了8%。
Let's assume that a server or cluster is processing up to 100 requests per second.
假设服务器或集群每秒钟最多能处理100个请求。
You can see that all three governors have almost exactly the same number of requests per second.
您可以看到，所有3个调控器每秒的请求数几乎完全相同。
When you are handling thousands of requests per second, even small trivial lookups are expensive.
在每秒处理几千个请求的情况下，任何微小的查找所产生的代价都是高昂的。
The solution needs to scale to 1 million requests per second, with response time remaining at 6 ms.
该解决方案需要每秒扩展到100万次请求，响应时间仍然为6ms。
I initially guessed that requests per second would degrade at least a little bit as concurrency went up.
我原本猜想的是RPS(每秒完成请求数)会随着并发的增多而下降。
This would give a theoretical capacity of 154 requests per second, pushing the bottleneck to the database.
这会把理论容量提高到每秒154个请求，瓶颈位置就会转到数据库。
You can retrieve details such as the duration of the test, total number of requests per second, and failed requests.
您可以获取诸如测试的持续时间、每秒钟的总请求数、失败的请求之类的详细信息。
For example, 5 seconds of think time per request yields a maximum of 0.2 requests per second per virtual user.
例如，每个要求的考虑时间为5秒，会产生每个虚拟使用者每秒0.2个查询的上限。
Although a destination server can handle a limited number of requests per second, this number is usually very high.
尽管目标服务器每秒可以处理的请求的数量是有限制的，但是这个数量通常都很高。
The JMeter test plan was configured to deliver requests at an average rate of 16 requests per second per virtual machine.
JMeter测试计划配置为平均每秒每台虚拟机发送16个请求。
Using the inverse rule learned above, the capacity of the components are 142, 77, and 200 requests per second, respectively.
根据前面介绍的倒数规则，各个组件的容量分别是每秒142、77和200个请求。
Each application page performed 10 profile lookups from the database at 60 ms each, for a peak total of 80k requests per second.
每个应用程序页面每60ms从数据库执行10次配置文件查询，峰值每秒共80k次请求。
A huge number of requests per second can be handled, and a relatively low number of servers can handle a very large number of users.
每秒钟可以处理大量的请求，只需使用少量的服务器就可以处理很大数量的用户。
After analyzing the collected data, it was found that the peak load was 2,129 concurrent users averaging 18.8 Requests per second.
分析完收集的数据后发现，峰值负载为2,129个并发用户每秒平均发出18.8个请求。
If an application takes 1 second to respond to a request, then the number of requests per second must be 1 for the load to be in equilibrium.
如果一个应用程序需要1秒的时间来响应请求，那么每秒的请求数必须是1，负载才能均衡。
For example, a Web application might be able to handle 1,000 requests per second (throughput) and turn each request around in 2 seconds (response time).
例如，一个Web应用程序可能每秒处理1,000个请求(吞吐量)，每个请求需要2秒的处理时间(响应时间)。
Each server performs around 110 MB per second (8k requests per second with 10 KB record sizes) of total traffic (90 MB from requests, 20 MB for replication).
每个服务器每秒执行约110MB(每秒8k请求，10KB记录大小)的总通信量(请求90 MB，复制20 MB)。
Since we’ve made all these changes, we have been able to scale memcached to handle 200, 000 UDP requests per second with an average latency of 173 microseconds.
做了这些修改之后，我们可以将memcached提升到每秒处理20万个UDP请求，平均延迟降低为173微秒。
For this run, the conservative governor saved about 25w more than the performance governor while still serving almost exactly the same number of requests per second.
对于这次运行，conservative调控器比performance调控器节约了约25w，当它仍然可以服务几乎完全相同的每秒请求数。
If you have pages with high response times because of a database or CPU bottleneck, it will limit the number of requests per second that each virtual user can issue.
如果您因为资料库或CPU瓶颈而有高回应时间的页面，这会限制每个虚拟使用者发出的每秒查询数目。
With the more aggressive 20-second timeout value, the model computes an overall service rate of 1.19 seconds, which translates to a throughput of 84 requests per second.
通过使用更改为主动的20秒超时值，模型计算所得的总体服务速率为1.19秒，转换为吞吐量为每秒84个请求。
An XML parser might have a high megabytes-per-second rate, but if it is expensive to set up and tear down that parser, it will still have a low throughput in requests per second.
一个XML解析器可能每秒处理的百兆字节量很高，但是如果初始化和关闭该解析器的代价很大，那么它的每秒处理请求数仍旧很低。
An XML parser might have a high megabytes-per-second rate, but if it is expensive to set up and tear down that parser, it will still have a low throughput in requests per second.
一个XML解析器可能每秒处理的百兆字节量很高，但是如果初始化和关闭该解析器的代价很大，那么它的每秒处理请求数仍旧很低。
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