First Look at Google Malloy

Malloy is a new Modeling Language created by the original Author of Looker, it was released last year under an Open Source license, and made available to Windows users just last week, as someone who is enthusiast about Data Modeling I thought it is worth having a look at it.

Currently, Malloy is available under a free extension in Visual Studio Core, Malloy don’t have any “calculation Engine”, all it does, you build your model in a text where you define Source Tables, measures, relationship and dimension and then you write Queries, Malloy will parse those Queries and Generate SQL Code, it is more or less how every BI tools works behind the scene.

Interestingly, Malloy extension has DuckDB installed by default, it means, we have a full semantic layer and a fast OLAP engine as an open source offering, that’s a very big deal !!!

I spend sometime building a simple model, just one fact table and two dimensions, you can see the code here

When you run a Query, it will show the results in tabular format and you can see the SQL generated, you can even define some basic Visual like bar chart et

BI Engine not supported

Malloy has excellent support for BigQuery ( for obvious reason) and does support PostgreSQL too, but the SQL Generated is non trivial, BigQuery default Engine render the SQL extremely fast, no problem with that, but BI Engine struggle, basically any non trivial calculation are not supported. ( cross join and correlated variable etc)

Measure Behavior

let’s create three trivial measures

red is Quantity { where:  color = 'red' } 
black is Quantity { where:  color = 'BLACK' } 
red_or_black is Quantity { where:  color = 'red' or  color = 'black'}

then I run a simple group by

As a DAX users the results are rather unexpected

  • Measure Black : Return 0, it is case sensitive , maybe Malloy should maybe add a setting for DuckDB to ignore case sensitivity ( or it is by design, I don’t know)
  • Measure Red : return 2 only for red and 0 elsewhere, in DAX by default it will return 2 everywhere
  • red_or_black : was expecting to see the sum of both red and blacked which is 4 repeated in all rows

here is the same using DAX ( you can change that behavior by using keepfilters but I am interested only in the default behavior)

I don’t know enough about the language yet, but it would have being useful to have an option in the measure to ignore the group by ( it seems it is coming )

Why You Should care

For some historical reason, all Modeling language were proprietary and based only on vendor implementation, as far as I know, this is the first fully open source implementation, I am sure Google has a long term vision for Malloy and will show up in more service, I would not be surprised if BigQuery somehow integrated Malloy as a free semantic layer, after all it works well in a consumption model, I have not used it enough to have any good intuition, but I like the direction of the product, and Good on Google for making it Open Source, and DuckDB for being such an awesome SQL Engine.

Advance Geospatial analysis using location Parameter with Streamlit

This blog is a POC of something that I always wanted to have in a BI tool, and I tried Tableau, PowerBI and Data Studio, without success ( not interested in adding an invisible grid as a hack), The idea is extremely simple yet very powerful, retrieve data when you click on a map, you may think it should simple, it seems BI tool are good at retrieving data based on filter, but it is very hard to push a parameter from a map back to a source data.

Traditionally, if you want to have this kind of interactivity, you need to write code, to be honest the idea of writing javascript and learning how to deploy a web server was not very interesting for me, but luckily we have a new Option in Streamlit

Streamlit is a code first, web app platform using only Python, web page are generated behind the scene, and there are a lot of component where you need to write a minimum of code, and deployment is absolutely trivial using Streamlit Cloud, and because it is open source, you can deploy using alternative approach like Cloud Run, or Azure

I came across this component Streamlit-Folium recently, and it is magnificent work, when you click on a map, it does provide variable back on the last location clicked zoom, bounds etc, all for free, no code required !!!!

All I have done is copied the code from the source and built a SQL Query that take the last clicked item filter all the “cafe” in a radius of 500 m, the SQL Code is copied from this previous Blog

The Source Data is nearly half a Million, as you can imagine plotting a massive dataset just to see a small portion is a waste of computer resources.

here is the final results

Here an example of a SQL Query generated.

State management

I added the code here, again it was too easy to write as I nearly copied everything from the component sample code, the tricky part was how to update the value of a variable which was already declared, Streamlit has a brilliant solution using State Management, the solution is very simple

Assign a default value when the Streamlit run for the first time

if 'key' not in st.session_state:
    st.session_state.key = '( 153.024198,-27.467276)'
    st.session_state.key1 = [-27.467276, 153.024198]
    st.session_state.key2 = 16
point_clicked = st.session_state.key
location_ini  = st.session_state.key1
zoom_Start    = st.session_state.key2 

Update the values when a user click on the map, the next run in the same session will use those new values

 st.session_state.key = point_clicked
       st.session_state.key1 = location_ini
       st.session_state.key2 = map_data['zoom']

Currently I don’t know how to stop Streamlit from redrawing the map, as I am only interested in updating the markers.

Database

it works with Any Database as long as it has a minimum support for GIS functions, Currently I used bigQuery BI Engine as I am familiar with it and to speak freely :), it is very cheap for this kind of workload, small Data and potentially a lot of concurrency 🙂

I tried PowerBI Datamart but it seems Python access is blocked , DuckDB don’t support GIS functions yet, I am sure you can reproduce the results using only SQL, but I did not bother.

ST_DWithin(ST_GeogPoint(lng,lat),params.center,params.maxdist_m)

Take Away

I think there is a third way between no code and only Code, Streamlit managed to create a new category, maybe simple code 🙂 having said that BI Vendor should up their games, Location Parameter should not be that hard to implement.

Delta lake with Python, Local Storage and DuckDB

TL;DR : I added a streamlit app here

a new experimental support for Writing Delta storage format using only Python was added recently and I thought it is a nice opportunity to play with it.

Apache Spark had a native support since day one, but personally the volume of data I deal with does not justify running Spark, hence the excitement when I learned we can finally just use Python.

instead of another hot take on how Delta Works, I just built a Python Notebook, that download files from a web site ( Australian Energy Market), create a delta table, then I use DuckDB and vega lite to show a chart, all you need to do is to define the Location of the Delta Table, I thought it maybe a useful example, all the code are located here

And I added a PowerBI report using the delta Connector

Some Observations

Currently DuckDB don’t support Delta natively, instead we first read the Delta table using pyarrow which DuckDB can read automatically, at this stage, I am not sure if DuckDB can push down filter selection or read the stats saved in the Log file, and currently, it seems only AWS S3 is supported.

Loading 1 Billion New York Taxi Dataset into Datamart

Was chatting with David Eldersveld and he suggested that he wants to run a competition using the famous New York Taxi Dataset with Datamart, long story short, I did endup publishing my attempt before he had the time to start the competition, my sincere apology.

The report is using my personal PPU instance, the data is located here , personally I wanted to exclusively use tools available in PowerBI out of the box, no Synapse nor Azure stuff or BigQuery, Just pure self service tools.

Initially I didn’t really believed that PowerQuery can download such a big volume of data, my first set back , PowerQuery can not read parquet files available in public url, but as usual Chris Webb has an excellent blog explaining the reason, and gave a workaround.

I added the code here, it does work with any PowerQuery in Dataflow, Datamart and PowerBI desktop, unfortunately Excel is not supported yet, when prompted for authentication use anonymous

The only section of the code that you should pay attention to is selecting the number of files to download by default it is 2, but you can increase it

Only when I was writing this blog, I noticed the files for 2022 are using a slightly different URL, will update the code later. Code Updated.

To reduce the database size, I had to split Datetime to date and time, low cardinality is good for performance too.

Loading into Datamart

I don’t know how much it took datamart to load the data, currently Query refresh history is broken, but I think it is more than 6 hours, I maybe wrong, but Datamart take a bit of time to generate the tables with Clustered Columnstore Index

Initially I loaded only 2 then 30 files just to see how Datamart behave and finally I went for 100 files, and it did work again to my surprise.

and the Lineage View of the report

Performance

The Performance is not bad at all considering, the data was loaded as it is and it not sorted, although the parquet files are organized by month, unfortunately there are some outlier in every file see for example, so you get overlapping segments.

You can check the database size on disk by running this Query

EXEC sp_spaceused

Optimisation

Pretty much the only optimisation you can do in Datamart is to pre sort the data before loading it, but when you have 1 billion rows saved in parquet files, sorting is a very expensive operation, but there are options I think.

Create another Datamart and load it from the “raw” Datamart and define incremental refresh which will create partitions, yes partitions should improve the performance.

Hybrid table in PowerBI Dataset where only the recent data is cached in Vertipaq and the history kept in Datamart as a Direct Query Mode.

Final thoughts

The Publish to web report is here, a very big missing piece is the option to append data to an existing Datamart, this will make adding new data without a full refresh extremely trivial, I know about incremental refresh, and I am sure a hack like this may work, but we want the real deal, Dataflow people hurry up 🙂

I notice something interesting because the price of PPU is fixed, I felt I can experiment without the fear of getting a massive bill, maybe reserved pricing is not a bad thing after all.

My first reaction when I saw datamart was, that it will be a big validation for PowerQuery, and it is, as Alex said, PowerQuery everything !!!

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