metabase | integration of GLPI with Metabase | Business library

I try to deploy Matabase on my GKE cluster but I got Readiness probe failed.

I build on my local and get localhost:3000/api/health i got status 200 but on k8s it's not works.

Dockerfile. I create my own for push and build to my GitLab registry

I am using PostgreSQL in Metabase (I have read-only rights so functions won't work)

I have the following nested data in two columns:

I have a .NET Core 3.1 Application that's hosting REST services and I'm incorporating the SAP Connector utilities for some of the endpoints. I'm on Windows 10.

I'm running into an error through one of the endpoints when establishing new RfcConfigParameters by doing RfcConfigParameters parameters = new RfcConfigParameters()

The error trace is as follows:

I am trying to select only the past three months of data from a table. I've attempted the following:

I have a problem to change the metabase's port on linux

the ways described in https://www.metabase.com/docs/latest/operations-guide/running-the-metabase-jar-file.html , but i'm not clearly understand what I must do

the page's screenshot

after that the tutorial directed to https://www.metabase.com/docs/latest/operations-guide/environment-variables.html , but still i'm not clearly understand

if there are any other solution, please let me know, thank you very much

I have cumulative counts for two groups over time in this format:

And I'd like to reshape this data into this format:

So that I can get it to display accurately in a stacked area chart in metabase - any advice?

Using: Django==2.2.24, Python=3.6, PostgreSQL is underlying DB
Working with Django ORM, I can easily make all sort of queries, but I started using Metabase, and my SQL might be a bit rusty.

The problem:
I am trying to get a count of the items in a list, under a key in a dictionary, stored as a JSONField:

I'm trying to create filter widgets on metabase. I'm having an issue with my {{breakdown}} filter, when I put in 'year' instead of {{breakdown}} it works perfectly giving me a yearly breakdown report. But when I use {{breakdown}} and then input year into the widget I get the error:

column "analytics_moduleattemptanalyticsview.created" must appear in the GROUP BY clause or be used in an aggregate function Position: 27

does anyone know how to fix this? I want this as a widget so that I can see month and quarters too

In our Big Data project there are ~3000 tables to load, all of these tables should be processed by a separate DAG in Airflow.

In our solution a single python file generates every type of table loaders so they can be triggered separately via REST API on event-based manner via Cloud Function. Therefore we generate our DAGs by using:

• Airflow variables used for the DAG generator logic
  • list of table names to generate
  • table type: insert append, truncate load, scd1, scd2
• Airflow variables used by the specific operators of tables loader DAGs, e.g.:
  • RR_TableN = {} // python dict for operator handling RawToRaw
  • RC_TableN = {} // python dict for operator handling RawToCuration
• user defined macros:
  • we try not to "static python codes" in between task definitions, because they would be executed during DAG-generation process
  • user defined macros are evaluated only in DAG-execution time

Unfortunately we are bound to version Airflow v1.x.x

Problem:

We have noticed that the Airflow/Cloud Composer is signigicantly slower between the task executions when multiple DAGs are generated. When only 10-20 DAGs are generated the time between the Task executions is much faster then we have 100-200 DAGs. When 1000 DAGs are generated it takes minutes to start a new task after finishing a preceeding task for a given DAG even when a no other DAGs are executed.

We don't understand why the Task execution times are affected that hard by the number of generated DAGs. Shouldn't be near constant time for Airflow to search in it's metabase for the required parameters for the TaskInstances? We are not sure if the Cloud Composer is configured/scaled/managed properly by Google.

Questions:

• What's the reason behind this slowdown from Airflow's side?
• How could we reduce the waiting times between the task executions and speed up the whole process?
• Is this a "bad design pattern" what we are implementing (generator and user defined macros processing Airflow variables)?
  • If so, how could we do similar (table separated DAGs, single codebase etc.) in a more effective way?

This is a very simple example of the generator code what we use: