I'm attempting to query our DATADOG hub and display some metric graphs. However, it appears the default way to do this is using an embed script generated by DATADOG and utilizing that in your app. I'm actually wanting to draw the graphs on my side, using their API data so I'm better able to control the size, look and flexibility of the graphs.
Is this something that is possible? Rather new to DATADOG and everything seems to be done in an iFrame, which I do not want.
Additionally, I found a package which I believe may be of use for Node?: http://brettlangdon.github.io/node-dogapi/#embed-create
D.D. Graphs Docs: http://docs.datadoghq.com/api/#graphs
Any advice would be greatly appreciated, I have not seen anything similar on S.O.
I think what you actually want is the metrics-query API endpoint? http://docs.datadoghq.com/api/#metrics-query
There are also a few Node.JS libraries that may be able to handle this kind of metric querying for you: http://docs.datadoghq.com/libraries/#community-node
Related
im currently working in a module of analysis of stadistics of videos from azure media services. I want to ask how can i get some data like average visualization time, number of visualizations and more stuff like that. im pretty sure it has to exist a very easy way to get this data but i cannot find it. I found that application insights could be useful. I have found that i may have to manually track this information. Im working on .net6. An example of code would be awesome. Thanks in advance!
pd: https://github.com/Azure-Samples/media-services-javascript-azure-media-player-application-insights-plugin/blob/master/options.md
I have found that Application Insights could be useful to my problem. Some classes like TelemetryClient (from the package Microsoft.ApplicationInsights) seems to be useful to my problem, but i cant find clear information about them.
No, there is no concept of client side analytics or viewer analytics in Azure Media Services. You have to track and log things on your own on the client side. App Insights is a good solution for this, and there are some older samples out there showing how to do that with a player application.
Take a look at this sample - https://learn.microsoft.com/en-us/samples/azure-samples/media-services-javascript-azure-media-player-application-insights-plugin/media-services-javascript-azure-media-player-application-insights-plugin/
Just WARNING: it is very old and probably very out of date. I would not use much of the code from that sample, as it is using SDK's from 4 year ago. Just use it as guidance at a high level for what the architecture might look like.
Another solution would be to look to a 3rd party service like Mux.com/Data that can plug into any player framework for client analytics.
I currently am creating a faster test harness for our team and will be recording a baseline from our prod sdk run and our staging sdk run. I am running the tests via jest and want to eventually fire the parsed requests and their query params to a datastore of sorts and have a nice UI around it for tracking.
I thought that Prometheus and Grafana would be able to provide that, but after getting a little POC for myself working yesterday it seems that this combo is more used for tracking application performance rather than request log handling/manipulation/tracking.
Is this the right tool to be using for what I am trying to achieve and if so might someone shed some light on where I might find some more reading aligned with what I am trying to do?
Prometheus does only one thing and it well. It collects metrics and store them. It is used for monitoring your infrastructure or applications to monitor performance, availability, error rates etc. You can write rules using PromQL expression to create alert based on conditions and send them to alert manager which can send it to Pager duty, slack, email or any ticketing system. Even though Prometheus comes with a UI for visualising the data it's better to use Grafana since it's pretty good with it and easy to analyse data.
If you are looking tools for distributed tracing you can check Jaeger
I need to upload registers into Dynamics365 using an AZDF pipeline.
Because of some requirements, I need to load the data through the web API.
I thought of using Functions/Durable Functions. They can use the API with no problem, but I'm concerned about the duration of the execution since I understand that these activities are not meant for long runs, like downloading/loading data, etcetera.
Is it really wrong to use a Functions like this?
Is there an alternative?
I want to write a series of small apps for myself as micro services. This is for practicality and self learning. I want these apps to be able to work independently, but build a separate frontend client that has a search bar that can find data across all of the services. I wanted to implement a live autocomplete with search results as the user is typing that will search across multiple databases.
My current approach was to split each app into two apps, backend API, and a frontend client. Have a common auth service that all of the apps utilize for authorization.
I think this approach would work fine except for speed and performance, which I am not sure about. It is a personal requirement of mine to be able to implement this search bar with autocomplete search results. This means it will have to make API requests to each service to get those results, it just feels like it might be too slow.
Also, in case anyone is wondering, I was planning on using node, express, and mongodb for backend. Probably go with node, express, vue or something for the front end.
Q. Does anyone have personal experience with the performance aspect of working with multiple APIs?
Q. If this approach is too slow, is there a better approach that still allows for a separation of the applications?
The performance will be depending on so many other factors before/with the number of rest calls. It is really hard to say anything beforehand.
But according to my previous experience if you need to provide such a search functionality for a autocomplete feature you may most likely need elastic search for that.
That means instead of sending a request to each and every service for each autocomplete request you should have an index to search in (partial or maybe all data you need to show in your frontend) and search that index first to find the corresponding items then ask for the remaining/full data to other services if necessary. Details are totally depends on your requirements.
Of course if you go that way you should also implement a data population/syncronization mechanism to elastic search.
Since I cannot modify builtin models (entities, intents..) as provided by the LUIS.ai, How can I import them into my own model in a way that I can modify them further specific to my scenario(s).
Some of the contextual information can be found here: https://github.com/Microsoft/BotBuilder/issues/1694#issuecomment-305531910
I am using Azure Bot Service with Node.js
If you are using the new prebuilt domains, once you add them to your model, you should be able to tweak them.
If you are using the Cortana prebuilt app, I don't think you will be able to update it; however, the documentation contains some information if you want to "mimic" it.
If you explain exactly what are your scenarios, we might be able to come up with other alternatives.
I can't think of a straight-forward way to go about doing this, but you could take the .csv logs from LUIS and incorporate it into your model; at the least the response column data is in json format.