I was trying to retrieve certain nodes via Overpass API that are newly created within a defined time frame (or at least since a certain point in time). So far I only managed to end up with basically this query:
[out:json][timeout:30];
node
[amenity=atm]
({{bbox}})
(newer:"2018-04-01T00:00:00Z");
out;
However this also shows nodes that have been updated since that timestamp but might have been created several years ago.
Just add a version filter and you're all set. New nodes will have version = 1.
[out:json][timeout:30];
node
[amenity=atm]
({{bbox}})
(newer:"2018-04-01T00:00:00Z")
(if:version() == 1);
out meta;
Related
Trying to understand the difference between the two: Aggregator vs Aligner.
Docs was not helpful for me.
What I'm trying to achieve is to get the bytes of logs generated within a week per each namespace and container combination. For example, I want to see that container C in namespace N generated 10Gb of logs during the last 7 days.
This is how far I got:
Resource type = Kubernetes Container
Metric = Log bytes
Group by = namespace_name and container_name
Aggregator = sum(?) mean(?)
Minimum alignment period = 1(?) 7(?) days
Aligner = sum(?) mean(?)
I was confused with this until I realized that a single metric, such as kubernetes.io/container/cpu/core_usage_time is available in multiple different resources in my cluster.
So when you search for that metric, you'll get a whole lot of different resources that emit that metric. Aggregation is adding up all the data from those different resources WITH THAT SAME METRIC.
This all combines into one "time series" for that metric, an aggregation of all the individual time series from each of those different resources.
Now, alignment is the process of using that time series and putting all the data points through a function (over a period of time, known as the alignment period) which results in one single data point (per alignment period).
So aggregation combines the same metric across multiple resources, while alignment combines multiple data points in the same time series into one data point (per alignment period, which is why that field is required when using alignment).
I am just starting to get into PostGIS and pgRouting and stumbled upon the following problem:
I imported a Network-Graph into my DB, and the routing inside of that Table (from node A to node B) works fine. However I would like to find a route between Points that are in another table (e.g. Customers and Shops). Can someone help me out on how to use Points (Nodes) from a different table than the network as start/end (or source/target)?
Thanks
My atomist client exposes metrics on commands that are run. Each command is a metric with a username element as well a status element.
I've been scraping this data for months without resetting the counts.
My requirement is to show the number of active users over a time period. i.e 1h, 1d, 7d and 30d in Grafana.
The original query was:
count(count({Username=~".+"}) by (Username))
this is an issue because I dont clear the metrics so its always a count since inception.
I then tried this:
count(max_over_time(help_command{job=“Application
Name”,Username=~“.+“}[1w]) -
max_over_time(help_command{job=“Application name”,Username=~“.+“}[1w]
offset 1w) > 0)
which works but only for one command I have about 50 other commands that need to be added to that count.
I tried the:
"{__name__=~".+_command",job="app name"}[1w] offset 1w"
but this is obviously very expensive (timeout in browser) and has issues with integrating max_over_time which doesn't support it.
Any help, am I using the metric in the wrong way. Is there a better way to query... my only option at the moment is the count (format working above for each command)
Thanks in advance.
To start, I will point out a number of issues with your approach.
First, the Prometheus documentation recommends against using arbitrarily large sets of values for labels (as your usernames are). As you can see (based on your experience with the query timing out) they're not entirely wrong to advise against it.
Second, Prometheus may not be the right tool for analytics (such as active users). Partly due to the above, partly because it is inherently limited by the fact that it samples the metrics (which does not appear to be an issue in your case, but may turn out to be).
Third, you collect separate metrics per command (i.e. help_command, foo_command) instead of a single metric with the command name as label (i.e. command_usage{commmand="help"}, command_usage{commmand="foo"})
To get back to your question though, you don't need the max_over_time, you can simply write your query as:
count by(__name__)(
(
{__name__=~".+_command",job=“Application Name”}
-
{__name__=~".+_command",job=“Application name”} offset 1w
) > 0
)
This only works though because you say that whatever exports the counts never resets them. If this is simply because that exporter never restarted and when it will the counts will drop to zero, then you'd need to use increase instead of minus and you'd run into the exact same performance issues as with max_over_time.
count by(__name__)(
increase({__name__=~".+_command",job=“Application Name”}[1w]) > 0
)
Consider raw events (alpha set in Druid parlance) of the form timestamp | compoundId | dimension 1 | dimension 2 | metric 1 | metric 2
Normally in Druid data can be loaded in Realtime nodes and historic nodes based on some rules. These rules seem to be related to time-ranges. E.g.:
load the last day of data on boxes A
load the last week (except last day) on boxes B
keep the rest in deep storage but don't load segments.
In contrast I want to support the use-case of:
load the last event for each given compoundId on boxes A. Regardless if that last event happened to be loaded today or yesterday.
Is this possible?
Alternatively, if the above is not possible, I figured it would perhaps be possible as a workaround to create a betaset (finest granulation level as follows):
Given an alphaset with schema as defined above, create a betaset so that:
all events for a given compoundId are rolled-up.
metric1 and metric2 are set to the metrics from the last occurring (largest timestamp) event.
Any advice much appreciated.
I believe the first and last aggregators is what you are looking for.
I am working on a simple application that I need to be run as a service and report gps position every 3 minutes. I already have a working example based on the tutorial, but still have the followin doubts.
The starting of the service GPS1.Start(5*60*1000, 0)
Says first parameter is time lapse, and 2nd parameter is distance difference, How is determined, based on prior position ?
If I want to do what I stated before and I am scheduling / starting service every 3 minutes, this means I will need to ask a GPS1.Start(0,0) to get latest fix? what would be the gain to use the parameters?
I trying in a NexusOne and the Time object comes with local time, I have to do this to make it UTC but this is a tweak to the code. Is this a standard or could It change based on Phone model ? hora=DateTime.Date(Location1.Time + 6*DateTime.TicksPerHour)
thanks
If you are only interested in a single fix each time then you should pass 0, 0. These values affect the frequency of subsequent events.
You can find the time zone with the code posted here: GetTimeZone