I have setup Analytics Export to BigQuery. Everytime when a new ga_sessions_yyyymmdd gets created I would like to run some queries aggregating some data for future use.
I can't figure out how to do this. Do I have to create a job and trigger it from outside or is there a way to trigger this in BigQuery directly (prefably using the Web UI).
You cannot schedule queries to run via the Web UI. You'll need to write a small piece of software to do this by using use the BigQuery API, and cron(s).
You may also want to check out Cloud Functions - bearing in mind that it's still in Alpha.
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I am developing mobile app with flutter framework in which I am using Firestore database and Firebase cloud functions. I have successfully implemented both in my project. However, I have some difficulties understanding how to run functions that are independent from my app. For example, if I have a Firestore database of 100 people and want to calculate average user age for my own analysis. I know that I can call the function calculateAverageAge() from my app, however is there a way to call this function without adding it in my app's code? Should I create a separate project (possibly in different language) and call those functions from there or I can somehow trigger this function from firebase website? Not sure what is the the right approach here.
Hopefully that makes sense.
Thanks!
In general there are three common ways to trigger a function:
Explicitly call it from your client-side application code, which applies to either HTTP functions or Callable functions.
Run them periodically on a schedule.
Run them when something in the data (or in another Firebase feature, or EventArc for v2) changes with background functions.
For your example of calculating the average age of your users, this means that you could combine the last two types and:
Recalculate the average whenever a new user gets added to the database, or a user gets removed from the database.
And then also run a scheduled function daily to update the average for any birthdays.
I'm wondering if there is a way to bulk update documents in firestore via the Firestore console. I'm aware that I am able to do this via code, as shown in answers such as this one and this one, but I'm mainly looking for a way to do this via the Firestore console/UI.
At the moment, I am able to update one document at a time, but is there a way for me to update all documnts (matching a filter for example) at the same time to have the same update?:
I find that I often want to do this when I'm prototyping, and having to write code just to do a bulk update adds additional time to something I would think would be simple to do via the UI. I'm currently on the free spark plan, so I also can't use cloud-functions, meaning I have to spin up a server to do the bulk update via the admin SDK. Is there a way to bulk update documents via the firestore console or some other UI?
Currently Cloud firestore supports editing single documents via console. but not in bulk. The only way to edit documents in bulk is either using Batch writes or Transactions. If it is necessary for you to use console, you can raise feature request here
I am working on a personal project to recreate the news feed of Facebook. So what I am trying to do is to recreate the scenario where when the user goes to the news feed, the user gets posts of everyone he follows only. Is there any way to run a query like that using the Firebase real-time database using an of "followings".
I can successfully generate single users posts in the android studio app using snapshot and recycler view.
If you're asking whether you can get posts from multiple userUID values with a single query, that is not possible.
If you're asking whether you can pass a list of postUID values to retrieve, that is also not possible.
In both cases the solution is to execute a separate query/read operation for each of the values, and merge the results in your application code. This is not nearly as slow as you may think, since Firebase pipelines the requests over a single web socket connection - which is quite efficient. For more on this, see Speed up fetching posts for my social network app by using query instead of observing a single event repeatedly
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.
I have data in BigQuery from my Google Analytics account, along with some extra tables where I have transformed some of this data.
I would like to export some of my transformed data from BigQuery and import it into Google Analytics as a custom dimension.
I have done this manually, by downloading a CSV from my table in BigQuery and importing this using the GA admin UI. I would like to automate the process, but not sure where to start.
What would be the most efficient tool to automate this process? The process being:
Run a SQL query on by BQ data every day and overwrite a table.
Export this table as a file and upload it to a GA account as a query time import.
Not sure why you'd want do this, but one (rather clunky) solution that pops into my head is to spin up a small GCE instance, and using the gcloud tool and some simple bash:
Run a BigQuery query job (SQL) to truncate your table
Monitor the progress of that query job i.e wait
When it's finished, trigger an export job and dump the table to GCS
Monitor the progress of that BigQuery export job i.e. wait
When it's finished, download the file from GCS
Upload the file to GA using the management API (https://developers.google.com/analytics/devguides/config/mgmt/v3/mgmtReference/management/uploads/uploadData)
Schedule a cron job to run the above bash script daily
A nicer way would be to use Cloud Functions listening on the GCS bucket, but in my opinion, CFs are not designed for performing long running batch/data workloads. They have e.g. time limits (540s). Also, if GA supported direct load from GCS it would be much better. But, I wasn't able to find support for that.