Our ASP.NET Core app logs trace messages to App Insights. We need to be able to query them and filter by some customDimentions. However, I have found 3 APIs and am not sure which one to use:
App Insights REST API
Azure Log Analytics REST API
Azure Data Explorer .NET SDK (Preview)
Firstly, I don't understand the relationships between these options. I thought that App Insights persisted its data to Log Analytics; but if that's the case I would expect to only be able to query through Log Analytics.
Regardless, I just need to know which is the best to use and I wish that documentation were clearer. My instinct says to use the App Insights API, since we only need data from App Insights and not from other sources.
The difference between #1 and #2 is mostly historical and converging.
Application Insights existed as a product before log analytics, and were based on different underlying database technologies
Both Application Insights and Log Analytics converged to use the same underlying database, based on ADX (Azure Data Explorer), and the same exact REST API service to query either. So while your #1 and #2 links are different, they point to effectively the same service backend by the same team, but the pathing/semantics are subtly different where the service looks depending on the inbound request.
both AI and LA introduce the concept of multi-tenancy and a specific set of tables/schema on top of their azure resources. They effectively hide the entire database from you, and make it look like one giant database.
there is now the possibility (suggested) to even have your Application Insights data placed in a Log Analytics Workspace:
https://learn.microsoft.com/en-us/azure/azure-monitor/app/create-workspace-resource
this lets you put the data for multiple AI applications/components into the SAME log analytics workspace, to simplify query across different apps, etc
Think of ADX as any other kind of database offering. If you create an ADX cluster instance, you have to create database, manage schema, manage users, etc. AI and LA do all that for you. So in your question above, the third link to ADX SDK would be used to talk to an ADX cluster/database directly. I don't believe you can use it to directly talk to any AI/LA resources, but there are ways to enable an ADX cluster to query AI/LA data:
https://learn.microsoft.com/en-us/azure/data-explorer/query-monitor-data
And ways to have a LA/AI query also join with an ADX cluster using the adx keyword in your query:
https://learn.microsoft.com/en-us/azure/azure-monitor/logs/azure-monitor-data-explorer-proxy
Related
In one of our apps we need to introduce a feature of users being able to choose friends, see friends activity, etc.
So far we use CosmosDb container with SQL API (for things app does beside this social network aspect).
I am wondering is there a reason not to model it with SQL API but to go strictly with Gremlin?
I’ve seen examples on Microsoft site about modeling basic social network done with ordinary SQL API but i am not sure if i am missing something that would bite me down the road in a case not going with Gremlin?
You should be safe in choosing either. From docs:
Each API operates independently, except the Gremlin and SQL API, which
are interoperable.
The stored data is JSON in both cases.
More on choosing an API.
tl;dr: I want to reference an external data source from a Kusto query in Application Insights.
My application is writing logs to Application Insights, and we're querying it using Kusto in the Azure portal. To give an example of what I'm trying to do:
We're currently looking at these logs to find an action that triggers when a visitor viewed a blog post on our site. This is working well on a per blog-post level, but now we want to group this data by the category these blog posts are in, or by the tags they have, but that's not information I have within the logs.
The information we log contains unique info about that blog post (unique url, our internal id, etc) that I could use to look up this information in another data source (e.g. our SQL DB where this relation is stored), but I have no idea if/how this is possible. So that's the question, is this possible? Can I query a SQL DB, or get data in JSON via a URL or something?
Alternative solutions would be to move the reporting elsewhere (e.g. PowerBI) and just use AI as a data source, or to actually log all the category/tag info, but I really don't want to go down that route.
Kusto supports accessing external data (blobs, Azure SQL, Cosmos DB), however
Application Insights / Azure Monitor and other multi-tenant services are blocking this functionality due to security and resource governance concerns.
You could try setting-up your own Azure Data Explorer (Kusto) cluster, where this functionality will be available, and then access your Application Insights data using cross-cluster query, or by exporting the data from Application Insights and hooking up EventGrid ingestion into your Kusto cluster.
Relevant links:
Kusto supporting external data:
https://learn.microsoft.com/en-us/azure/data-explorer/kusto/query/schema-entities/externaltables
Querying data inside Application Insights:
https://learn.microsoft.com/en-us/azure/data-explorer/query-monitor-data
Continuous export data from Application Insights:
https://learn.microsoft.com/en-us/azure/azure-monitor/app/export-telemetry
Data ingestion into Kusto from EventGrid:
https://learn.microsoft.com/en-us/azure/data-explorer/ingest-data-event-grid
I am using Firebase as my authentication and database platform in my React Native-Expo app. I have not yet decided if I will be using the realtime-database or Firestore database.
I need to perform statistical analysis on daily data gathered from my users, which is stored in the database. I.e. the users type in their daily intake of protein, from it I would like to calculate their weekly average, expected monthly average, provide suggestions of types of food if protein intake is too low and etc.
What would be the best approach in order to achieve the result wanted in my specific situation?
I am really unfamiliar and stepping into uncharted territory regarding on how I can accomplish this. I have read that Firebase Analytics generates different basic analytics regarding usage of the app, number crash-free users etc. But can it perform analytics on custom events? Can I create a custom event for Firebase analytics to keep track of a certain node in my database, and output analytics from that? And then of course, if yes, does it work with React Native-Expo or do I need to detach from Expo? In addition, I have read that Firebase Analytics can be combined with Google BigQuery. Would this be an alternative for my case?
Are there any other ways of performing such data analysis on my data stored in Firebase database? For example, export the data and use Python and SciKit Learn?
Whatever opinion or advice you may have, I would be grateful if you could share it!
You're not alone - many people building web apps on GCP have this question, and there is no single answer.
I'm not too familiar with Firebase Analytics, but can answer the question for Firestore and for your custom analytics (e.g. weekly avg protein consumption)
The first thing to point out is that Firestore, unlike other NoSQL databases, is storage only. You can't perform aggregations in real time like you can with MongoDB, so the calculations have to be done somewhere else.
The best practice recommended by GCP in this case is indeed to do a regular export of your Firestore data into BQ (BigQuery), and you can run analytical calculations there in the meantime. You could also, when a user inputs some data, send that to Pub/Sub and use one of GCP Dataflow's streaming templates to stream the data into BQ, and have everything in near real time.
Here's the issue with that however: while this solution gives you real time, and is very scalable, it gets expensive fast, and if you're more used to Python than SQL to run analytics it can be a steep learning curve. Here's an alternative I've used for smaller webapps, which scales well for <100k users and costs <$20 a month on GCP's current pricing:
Write a Python script that grabs the data from Firestore (using the Firestore Python SDK), generates the analytics you need on it, and writes the results back to a Firestore collection
Create an endpoint for that function using Flask or Django
Deploy that server application on Cloud Run, preventing unauthenticated invocations (you'll only be calling it from within GCP) - see this article, steps 1 and 2 only. You can also deploy the Python script(s) to GCP's Vertex AI or hosted Jupyter notebooks if you're more comfortable with that
Use Cloud Scheduler to call that function every x minutes - see these docs for authentication
Have your React app query the "analytics results" collection to get the results
My solution is a FlutterWeb based Dashboard that displays relevant data in (near) realtime like the Regular Flutter IOS/Android app and likewise some aggregated data.
The aggregated data is compiled using a few nodejs based triggers in the database that does any analytic lifting and hence is also near realtime. If you study pricing you will learn, that function invocations are pretty cheap unless of-course you happen to make a 'desphew' :)
I came up with a great solution.
I used the inbuilt firebase BigQuery plugin. Then I used Cube.js (deployed on GCP - cloud run on docker) on top of bigquery.
Cube.js just makes everything just so easy. You do need to make a manual query It tries to do optimize queries. On top of that, it uses caching so you won't get big bills on GCP. I think this is the best solution I was able to find. And this is infinitely scalable and totally real-time.
Also if you are a small startup then it is mostly free with GCP - free limits on cloud run and BigQuery.
Note:- This is not affiliated in any way with cubejs.
Wanting to validate my ARM template was deployed ok and to get an understanding of the telemetry options...
Under what circumstances do the following get logged to Log Analytics?
DataPlaneRequests
MongoRequests
QueryRuntimeStatistics
Metrics
From what I can tell arduously in the last few days connecting in different ways.
DataPlaneRequests are logged for:
SQL API calls
Table API calls even when the account was setup for SQL API
Graph API calls against an account setup for Graph API
Table API calls against an account setup for Table API
MongoRequests are logged for:
Mongo requests even when the account was setup for SQL API
However I haven't been able to see anything for QueryRuntimeStastics (even when turning on PopulateQueryMetrics) nor have I seen any AzureMetrics appear?
Thanks Alex for spending time and trying out different options of logging for Azure Cosmos DB.
There are primarily two types of monitoring paths for Azure Cosmos DB.
Metrics: These are low latency (<5 min) and aggregated metrics which are exposed on Azure Monitor API for consumption. THese metrics are primarily used for diagnosis of the app for any live site issues.
Logs: These are raw request logs coming at 2hours+ latency and are used for customer for primarily audit scenarios to understand who accessed the data.
Depending on your need you can choose either of the approaches.
DataPlaneRequests by default shows all the requests across all the API's and Mongo Requests only show Mongo specific calls. Please note Mongo requests would also be seen in Data Plane requests.
Metrics would not be see in Log Analytics due to a knowwn which our partner team is fixing.
Let me know if you have any further questions here.
Process App information is stored in LSW_PROJECT table
Human services and other "tasks" are designed in Process Designer to the Process App. I believe these are stored in LSW_PROCESS and LSW_PROCESS_ITEM.
How do I make a query associating a Process App to the services included in that App?
What is the significance of LSW_TASK table?
Is there any documentation which describe the tables used in IBM BPM ?
Querying the product database tables is not a supported method in IBM BPM. The database schema is not documented in the BPM knowledge center. There are REST API and Java Script API method to obtain information about the projects (which are stored in LSW_PROJECT). For the JS API, you could go through all process apps with the getAllProcessApps or the REST API with GET /rest/bpm/wle/v1/processApps
The LSW_TASK table holds information about the tasks which people (users) or the system processes. These are created for each activity on a BPD diagram. These are then deleted with the BPDProcessInstanceCleanup command is run.
If you describe your what problem you are trying to solve, I can direct you to specific resources that may already exist.