This is should be a fairly easy question for Logstash veterans.
When I use the metrics plugin, what does events.rate_5m mean?
Does it mean: Number of events per second in a 5 minute window?
Does it mean: Number of events every 5 minutes?
Also, what's the difference between using this over timer.rate_5m?
The documentation isn't very clear and I have problems understanding it.
Thanks in advance!
Logstash uses the Metriks library to generate the metrics.
According to that site:
A meter that measures the mean throughput and the one-, five-, and fifteen-minute exponentially-weighted moving average throughputs.
and
A timer measures the average time as well as throughput metrics via a meter.
A meter counts events and a timer is used to look at durations (you have to pass a name and a value into a timer).
To answer your specific question, the rate_5m is the per second rate over the last 5 minute sliding window.
Related
I have set up Hazelcast with JCache, where I want to get a more comprehensive statistic of my cache usage. Mainly I want to see the cache entries' stats over some period of time (e.g. past 24 hrs), but the graph seems only allows me to select a point of time, and show the 5 mins statistics of it.
Is there a way I can update this behavior and create a more meaningful graph? Thanks!
Hazelcast MC version: 5.0.2
Example:
I want this graph to allow me to select a period of time, instead of a point of time.
Could you please help understand how azure metrics are calculated. For an example, we can see the request/total request graph, and a number at a given point in time (say 6000 at 4.34 PM) for Azure API Management. The request count has no meaning at a given point in time, but a measure for a given "period" of time. When i research, i found that the number represent the number of request received during the sampling interval.However, no further data is available on what the sampling interval is. Azure portal/metrics graphs has no setting to view/change sampling interval either.
So what's the sampling interval used for Azure metrics? or what does the total request count meaning at a given point in time?
(However, the application insight metrics allow you to set the sampling interval)
could you please shed some light? thanks
I think the comment from AjayKumar-MSFT is correct, so I summarize if it could help others:
Typically - ‘Requests’ - The total number of requests regardless of their resulting HTTP status code and where as ‘Requests In Application Queue’ is the number of requests in the application request queue. You can always change the 'chart Setting' for much detailed info, by going into the metric and ellipsis (...) /settings
I have data in the format { host | metric | value | time-stamp }. We have hosts all around the world reporting metrics.
I'm a little confused about using window operations (say, 1 hour) to process data like this.
Can I tell my window when to start, or does it just start when the application starts? I want to ensure I'm aggregating all data from hour 11 of the day, for example. If my window starts at 10:50, I'll just get 10:50-11:50 and miss 10 minutes.
Even if the window is perfect, data may arrive late.
How do people handle this kind of issue? Do they make windows far bigger than needed and just grab the data they care about on every batch cycle (kind of sliding)?
In the past, I worked on a large-scale IoT platform and solved that problem by considering that the windows were only partial calculations. I modeled the backend (Cassandra) to receive more than 1 record for each window. The actual value of any given window would be the addition of all -potentially partial- records found for that window.
So, a perfect window would be 1 record, a split window would be 2 records, late-arrivals are naturally supported but only accepted up to a certain 'age' threshold. Reconciliation was done at read time. As this platform was orders of magnitude heavier in terms of writes vs reads, it made for a good compromise.
After speaking with people in depth on MapR forums, the consensus seems to be that hourly and daily aggregations should not be done in a stream, but rather in a separate batch job once the data is ready.
When doing streaming you should stick to small batches with windows that are relatively small multiples of the streaming interval. Sliding windows can be useful for, say, trends over the last 50 batches. Using them for tasks as large as an hour or a day doesn't seem sensible though.
Also, I don't believe you can tell your batches when to start/stop, etc.
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.
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