I need to find the average values of web transaction times, that change their value by 3*x in 15 minutes. I. can’t find a suitable query. Can you help me?
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Imagine I'm a bank and I want a dashboard that shows our "top spenders" with data updating throughout the day.
Currently I query the database for all our customer IDs, and pass all those IDs to a service which calculates how much they've spent today. If I have 10,000 customers, it must make 10,000 calculations.
Then I pick the Top 10 and show them in the dashboard. 9,990 calculations were useless, but we can't know which ones until they're complete.
Is there some way to improve the performance? I can't pre-calculate because customers are making new purchases all the time, and the list should be dynamic.
What if the results are very consistent day-to-day? As in, the Top 10 spenders are almost always in yesterday's Top 20. We could store the prior day's top spenders, and only calculate those 20 to find the Top 10, but if someone jumps from #25 to #9, they would be missing from our Top 10 if we use that algorithm.
Any advice is appreciated!
Instead of asking another service for all customers, have this service subscribe to customer created/deleted events from that service, and have it store the customer info in its database. So, it will not need to inefficiently query all the customers every time to compute the top spenders.
I want to create a SQL Server table that has a Department and a Maximum Capacity columns (assume 10 for this scenario). When users add them selves to a department the system will check the current assignment count (assume 9 for this scenario) in the department and compare it to the maximum value. If it is below the maximum, they will be added.
The issue is this: what if two users submit at the same time and the when the code retrieves the current assignment count it will be 9 for both. One user updates the row sooner so now its 10 but the other user has already retrieved the previous value before the update (9) and so both are valid when compared and we end up with 11 users in the department.
Is this even possible and how can one solve it?
The answer to your problem lies in understanding "Database Concurrency" and then choosing the correct solution to your specific scenario.
It too large a topic to cover in a single SO answer so I would recommend doing some reading and coming back with specific questions.
However in simple form you either block the assignments out to the first person who tries to obtain them (pessimistic locking), or you throw an error after someone tries to assign over the limit (optimistic locking).
In the pessimistic case you then need ways to unblock them if the user fails to complete the transaction e.g. a timeout. A bit like on a ticket booking website it says "These tickets are being held for you for the next 10 minutes, you must complete your booking within that time else you may lose them".
And when you're down to the last few positions you are going to be turning everyone after the first away... no other way around it if you require this level of locking. (Well you could then create a waiting list, but that's another issue in itself).
Can anyone explain to me how to calculate your capacity in Rally. I am new to Rally and I am trying to figure out how most of the functions work.
There is a capacity column next to each developer with a pencil where you can type in your capacity. I am assuming it's number of working days in the Sprint * hours of work you are currently assigned or some sort.
Any help is appreciated!
Thanks
Yes, that is correct.
The capacity you find on the Track->Team Status per person is the number of available hours in the selected iteration.
What you need to decide is, how many "productive" hours do each person have per work day.
You can find the help documentation here: https://help.rallydev.com/view-team-member-capacity
I have a batch job that runs once per day.
At the end of the job I submit a meter metric with a count of the items processed.
I want to alert if one day this metric is not updated.
On http://metrics.librato.com the maximum time I can check "not reported for" when creating an alert is 60 minutes.
I thought maybe I can create a composite metric and take the avg rate of change over the past 24 hours, and alert if that reaches zero.
I've been trying:
derive(s("my.metric", "%", {function:"sum", period:"86400"}))
However it seems that, because I log only a single event, above quite small values of period (~250s) my rate of change simply drops to zero ...I guess the low frequency means my single value is completely lost by the sampling.
Maybe I am using the wrong tool for the job...
Is there a way to achieve this in Librato?
There currently is not a way to achieve this as composite metrics are subject to the 60 minute limitation of alerts as well (as of 5/15/2015). You may need to look into configuring the metric (or a similar metric) to report within the 60m time range if possible.
I have a MySQL table LOGIN_LOG with fields ID, PLAYER, TIMESTAMP and ACTION. ACTION can be either 'login' or 'logout'. Only around 20% of the logins have an accompanying logout row. For those that do, I want to calculate the average duration.
I'm thinking of something like
select avg(LL2.TIMESTAMP - LL1.TIMESTAMP)
from LOGIN_LOG LL1
inner join LOGIN_LOG LL2 on LL1.PLAYER = LL2.PLAYER and LL2.TIMESTAMP > LL1.TIMESTAMP
left join LOGIN_LOG LL3 on LL3.PLAYER = LL1.PLAYER
and LL3.TIMESTAMP between LL1.TIMESTAMP + 1 and LL2.TIMESTAMP - 1
and LL3.ACTION = 'login'
where LL1.ACTION = 'login' and LL2.ACTION = 'logout' and isnull(LL3.ID)
is this the best way to do it, or is there one more efficient?
Given the data you have, there probably isn't anything much faster you can do because you have to look at a LOGIN and a LOGOUT record, and ensure there is no other LOGIN (or LOGOUT?) record for the same user between the two.
Alternatively, find a way to ensure that a disconnect records a logout, so that the data is complete (instead of 20% complete). However, the query probably still has to ensure that the criteria are all met, so it won't help the query all that much.
If you can get the data into a format where the LOGIN and corresponding LOGOUT times are both in the same record, then you can simplify the query immensely. I'm not clear if the SessionManager does that for you.
Do you have a SessionManager type object that can timeout sessions? Because a timeout could be logged there, and you could get the last activity time from that and the timeout period.
Or you log all activity on the website/service, and thus you can query website/service visit duration directly, and see what activities they performed. For a website, Apache log analysers can probably generate the required stats.
I agree with JeeBee, but another advantage to a SessionManager type object is that you can handle the sessionEnd event and write a logout row with the active time in it. This way you would likely go from 20% accompanying logout rows to 100% accompanying logout rows. Querying for the activity time would then be trivial and consistent for all sessions.
If only 20% of your users actually log out, this search will not give you a very accurate time of each session. A better way to gauge how long an average user session is would be to take the average time between actions, or avg. time per page. This, then, can multiplied by the average number of pages/actions per visit to give a more accurate time.
Additionally, you can determine avg. time for each page, and then get your session end time = session time to that point + avg time spent on their last page. This will give you a much more fine-grained(and accurate) measure of time spent per session.
Regarding the given SQL, it seems to be more complicated than you really need. This sort of statistical operation can often be better handled/more maintainable in code external to the database where you can have the full power of whichever language you choose, and not just the rather convoluted abilities of SQL for statistical calculations