Can anyone tell me what is the unit of duration, found inside step.result captured inside cucumber reporting json? Is it millisecond, microsecond, nanosecond or anything else?
It's nanos. Divide to 1'000'000'000 to get seconds value.
1 000 000 000
second millis micros nanos
Source
For me at least its Microseconds
I.e.
"Duration": 8303000000
Means 8.303 Seconds
Related
Sorry for may be silly question but it is unclear from docs what is the unit of measurement for sliding window? Is it milliseconds, seconds or number of items in the stream?
I've noticed the aggregation operation was producing empty results and I had to filter them explicitly because probably there was no data available for that window, so I guess last point it not an option.
Jet doesn't specify a unit for windows, instead the windows are calculated based on the same unit that your timestamps are specified in. Typically if your timestamps are UNIX-style timestamps then it would be in milliseconds, but you could also use nanoseconds, seconds, or minutes if that's how your timestamps are defined. It refers to specifically event time and is not related to number of events in the stream, only to their timestamps.
On a Load Test with Loadrunner controller, I have a script which make 1029 Transactions per Hour with 1 Virtual User,with 1 "number of Iteration" ,"ignore think time", and pacing setting which start every iteration at a random interval every 3 to 4 seconds but I want to reduce the transactions to 45 Transactions per Hour. I set then the think time to "Limit think time to : 65", but without success.
Does anyone knows how to reduce the number of transactions. Is there any run time setting which must be changed to get this transactions reduced?
Use 'fixed' intervals of 80 seconds.
45 transactions per hour
3600 seconds in an hour
= 80 seconds per transaction
I am using Jmeter (started using it a few days ago) as a tool to simulate a load of 30 threads using a csv data file that contains login credentials for 3 system users.
The objective I set out to achieve was to measure 30 users (threads) logging in and navigating to a page via the menu over a time span of 30 seconds.
I have set my thread group as:
Number of threads: 30
Ramp-up Perod: 30
Loop Count: 10
I ran the test successfully. Now I'd like to understand what the results mean and what is classed as good/bad measurements, and what can be suggested to improve the results. Below is a table of the results collated in the Summary report of Jmeter.
I have conducted research only to find blogs/sites telling me the same info as what is defined on the jmeter.apache.org site. One blog (Nicolas Vahlas) that I came across gave me some very useful information,but still hasn't help me understand what to do next with my results.
Can anyone help me understand these results and what I could do next following the execution of this test plan? Or point me in the right direction of an informative blog/site that will help me understand what to do next.
Many thanks.
According to me, Deviation is high.
You know your application better than all of us.
you should focus on, avg response time you got and max response frequency and value are acceptable to you and your users? This applies to throughput also.
It shows average response time is below 0.5 seconds and maximum response time is also below 1 second which are generally acceptable but that should be defined by you (Is it acceptable by your users). If answer is yes, try with more load to check scaling.
In you requirement it is mentioned that you need have 30 concurrent users performing different actions. The response time of your requests is less and you have ramp-up of 30 seconds. Can you please check total active threads during the test. I believe the time for which there will be 30 concurrent users in system is pretty short so the average response time that you are seeing seems to be misleading. I would suggest you run a test for some more time so that there will be 30 concurrent users in the system and that would be correct reading as per your requirements.
You can use Aggregate report instead of summary report. In performance testing
Throughput - Requests/Second
Response Time - 90th Percentile and
Target application resource utilization (CPU, Processor Queue Length and Memory)
can be used for analysis. Normally SLA for websites is 3 seconds but this requirement changes from application to application.
Your test results are good, considering if the users are actually logging into system/portal.
Samples: This means the no. of requests sent on a particular module.
Average: Average Response Time, for 300 samples.
Min: Min Response Time, among 300 samples (fastest among 300 samples).
Max: Max Response Time, among 300 samples (slowest among 300 samples).
Standard Deviation: A measure of the variation (for 300 samples).
Error: failure %age
Throughput: No. of request processed per second.
Hope this will help.
I'm using JMeter client to test the throughtput of a certain workload (PHP+MySQL, 1 page) on a certain server. Basically I'm doing a "capacity test" with an increasing number of threads over the time.
I installed the "Statistical Aggregate Report" JMeter plugin and this was the result (ignore the "Response time" line):
At the same time I used the "Simple Data Writer" listener to write a log file ("JMeter.csv"). Then I tried to "manually" calculate the throughput for every second of the test.
Each line of "JMeter.csv" has this format:
timestamp elaspedtime responsecode success bytes
1385731020607 42 200 true 325
... ... ... ... ...
The timestamp is referred to the time when the request is made by the client, and not when the request is served by the server. So I simply did: totaltime = timestamp + elapsedtime.
In the next step I converted the totaltime to a date format, like: 13:17:01.
I have more than 14K samples and with Excel I was able to do this quickly.
Then I counted how many samples there were for each second. Example:
totaltime samples (requestsServed/second)
13:17:01 204
13:17:02 297
... ...
When I tried to plot the results I obtained the following graphic:
As you can notice it is far different from the first graphic.
Given that the first graphic is correct, what is the mistake of my formula/procedure to calculate the throughput?
It turns out that this plugin is plotting something that I don't know... I tried many times and my considerations were actually correct. Be careful with this plugin (or check its source code).
Throughput can be view in Jmeter Summary Report and you can calculate by saving your Test Results file in xml file in Summary Report.
Throughput = Number of samples/(Max (ts+t) - Min ts)*1000
Throughput = (Number of samples/The difference between Maximum and minimum response time)*1000
By this formula you can calculate Throughput for each and every http requests in Summary Report.
Example:
Max Response Time = 1485538701633+569 = 1485538702202
Min Response Time = 1485538143112
Throughput = (2/1485538702202-1485538143112)*1000
Throughput = (2/1505) *1000
Throughput = 0.00132890*1000
Throughput = 1.3/sec
You can read more with examples Here(http://www.wikishown.com/how-to-calculate-throughput-in-jmeter/), i got a good idea about Throughput Calculation.
We have a metric that we increment every time a user performs a certain action on our website, but the graphs don't seem to be accurate.
So going off this hunch, we invested the updates.log of carbon and discovered that the action had happened over 4 thousand times today(using grep and wc), but according the Integral result of the graph it returned only 220ish.
What could be the cause of this? Data is being reported to statsd using the statsd php library, and calling statsd::increment('metric'); and as stated above, the log confirms that 4,000+ updates to this key happened today.
We are using:
graphite 0.9.6 with statsD (etsy)
After some research through the documentation, and some conversations with others, I've found the problem - and the solution.
The way the whisper file format is designed, it expect you (or your application) to publish updates no faster than the minimum interval in your storage-schemas.conf file. This file is used to configure how much data retention you have at different time interval resolutions.
My storage-schemas.conf file was set with a minimum retention time of 1 minute. The default StatsD daemon (from etsy) is designed to update to carbon (the graphite daemon) every 10 seconds. The reason this is a problem is: over a 60 second period StatsD reports 6 times, each write overwrites the last one (in that 60 second interval, because you're updating faster than once per minute). This produces really weird results on your graph because the last 10 seconds in a minute could be completely dead and report a 0 for the activity during that period, which results in completely nuking all of the data you had written for that minute.
To fix this, I had to re-configure my storage-schemas.conf file to store data at a maximum resolution of 10 seconds, so every update from StatsD would be saved in the whisper database without being overwritten.
Etsy published the storage-schemas.conf configuration that they were using for their installation of carbon, which looks like this:
[stats]
priority = 110
pattern = ^stats\..*
retentions = 10:2160,60:10080,600:262974
This has a 10 second minimum retention time, and stores 6 hours worth of them. However, due to my next problem, I extended the retention periods significantly.
As I let this data collect for a few days, I noticed that it still looked off (and was under reporting). This was due to 2 problems.
StatsD (older versions) only reported an average number of events per second for each 10 second reporting period. This means, if you incremented a key 100 times in 1 second and 0 times for the next 9 seconds, at the end of the 10th second statsD would report 10 to graphite, instead of 100. (100/10 = 10). This failed to report the total number of events for a 10 second period (obviously).Newer versions of statsD fix this problem, as they introduced the stats_counts bucket, which logs the total # of events per metric for each 10 second period (so instead of reporting 10 in the previous example, it reports 100).After I upgraded StatsD, I noticed that the last 6 hours of data looked great, but as I looked beyond the last 6 hours - things looked weird, and the next reason is why:
As graphite stores data, it moves data from high precision retention to lower precision retention. This means, using the etsy storage-schemas.conf example, after 6 hours of 10 second precision, data was moved to 60 second (1 minute) precision. In order to move 6 data points from 10s to 60s precision, graphite does an average of the 6 data points. So it'd take the total value of the oldest 6 data points, and divide it by 6. This gives an average # of events per 10 seconds for that 60 second period (and not the total # of events, which is what we care about specifically).This is just how graphite is designed, and for some cases it might be useful, but in our case, it's not what we wanted. To "fix" this problem, I increased our 10 second precision retention time to 60 days. Beyond 60 days, I store the minutely and 10-minutely precisions, but they're essentially there for no reason, as that data isn't as useful to us.
I hope this helps someone, I know it annoyed me for a few days - and I know there isn't a huge community of people that are using this stack of software for this purpose, so it took a bit of research to really figure out what was going on and how to get a result that I wanted.
After posting my comment above I found Graphite 0.9.9 has a (new?) configuration file, storage-aggregation.conf, in which one can control the aggregation method per pattern. The available options are average, sum, min, max, and last.
http://readthedocs.org/docs/graphite/en/latest/config-carbon.html#storage-aggregation-conf