TL;DR : Trying to simulate Fabric DWH Bursting using Fabric Notebook and DuckDB. 

Setup

I am using TPCH_SF100 (600M rows) as an example, it does not matter you can use any dataset as long as you keep the same queries.

Fabric compute is rather serverless, you can’t customize it, except for Notebook where we can choose the number of CPU.

The test is very simple: run the same queries using a Fabric Notebook with different compute size, 4 Compute Unit, 8,16,32 and save the results in a OneLake.

To be very clear, Fabric SQL Engine is totally different from DuckDB, it does scale by adding more nodes, DuckDB is a single node and it scales by adding more CPU to the same node, but I think it is the same principle.

Scale is not linear.

• When increasing CU from 4 to 8, the duration decreased by 50 %
• From 8 CU to 16 , the Duration decreased by 24 %
• From 16 to 32, we reach a plateau, the duration stayed the same ( maybe OneLake throughput became the bottleneck)

I think it is fair to say regardless of the DB used at certain point adding more CPU will have diminishing returns.

Let’s now show the same graph but with $ values

For Me I will pick CU = 8 as the optimal solution, for the same cost as CU= 4 and half the duration, that’s what I call a manual bursting 🙂

Bursting in Fabric DWH is an automatic algorithm  to *predict* the optimum compute unit for a particular workload.

I don’t know how Fabric DWH increases resource allocations but I suspect it just adds ( and removes)  more nodes.

What’s not clear though, is how the system decided what’s the best solution, using the previous example; one can argue that for interactive Queries CU= 16 is the best option, yes you consume way more resources but it is faster. And for batch ETL jobs the only thing that matters is the lowest cost. 

Smaller SKU can do very big thing 

One of the most interesting implication of Bursting is that smaller SKU like F2 can do very big workload, F2 gives you 2 CU, but using the previous dataset as an example, it can scale just fine to 16 CU, but there is no free lunch, the total compute  you are entitled to is still 2 CU X 24 Hours = 48 CU(h) 

The challenge 

Cost based optimization in Database is a very complex problem, adding dynamic resource allocation makes it even harder ,otherwise everyone will be doing it 🙂

For me personally, SQL Engine so far seems to behave as expected, I did not see anything particularly extreme, some queries using as low as 2 CU for smaller workload. for a TB dataset bursting went till 72 CU.

What matters is continuous improvements.

I think what matters the most is that the efficiency keep improving, today Fabric DWH TPCH_SF100 cold run finish around 2 minutes for 6 cents ( that’s pretty much the same performance as Cloud DWH X)  which is great, but my hope in less than 5 years, the same workload will finish in less than 60 second for 3 cents.

And that’s the next big thing for Fabric, just efficiency improvement and bug fixes, nothing fancy. 

TL;DR: Fabric notebook uses a very nice functionality by mounting a remote Azure container as a “fake” local filesystem,  this works as the OS Level, for a lot of programs it is just another folder, you can learn more details here 

How it works 

When you mount a Fabric Lakehouse , you get something like this

In the file section, you can store any files, the tables section is special, if you create a Delta Table, Fabric will automatically expose it to all Fabric Engines ( SQL, Vertipaq etc), the data is stored in an azure storage container.

When you run this command in the notebook   !df -h, this is what you get

When a program send a command to delete, read, write etc, the system automatically translate those to an Azure storage API, it is not perfect, it is still not a real local filesystem, but so far it works well specially for read, I tested with 5 different SQL Engines and they all works fine ( DuckDB, Datafusion, databend,Glaredb and hyper) although the performance varied, I noticed that Hyper has some serious performance issues but I understand it may not be a priority for them to fix it 🙂

Writing DuckDB native file format

This one was  very confusing to me when I used it the first time, my understanding; files in a remote storage are immutable, you can create new files or delete but not modify an existing one, but somehow it works.

Open a remote DuckDB files in a write mode, the file is 25 GB

35 seconds is still slow, I thought it was supposed to be reading only some metadata !! I think that’s a DuckDB limitation.

Then I delete a random number from a table, it took 9 second to delete 85 million records ( this is an amazing performance)

Then I run a checkpoint and it works fine

BlobFuse2 will be a game changer 

Currently Fabric notebook runtime uses BlobFuseV1 For OneLake, which as far as I can tell does not support any cache, although in my experience the  throughput is rather good, it is still an object store and will never reach the speed of an SSD disk, but BlobFuse2 may well be the best thing that happen for Fabric Notebook, it has a native disc cache, and it works at the OS level, every program get a free cache, I hope the product team will upgrade soon.

TL;DR: wrote my first fabric notebook at work ( download some BOM data) and for some reason, I did find that working first in my laptop using VS code then deploying later in Fabric seemed a more natural workflow to me, maybe working with PowerBI desktop for years has something to do with it, you can download the notebook here

You can  just use pure Python in Fabric Notebook.

Fabric Notebook support Pyspark and R but vanilla Python works just fine, the only big limitation writing to Delta is currently in developpement , but there is a workaround, I created a function that try first to write using Spark, if not available it will write using Delta Python which works fine in my laptop, in that case the code will works in Fabric Notebook without any modification

Why Not PySpark ?

Obviously if you have Pyspark installed in your laptop and you are familiar with the syntax then go for it,  you can even make the argument it is a long term career advantage to be skilful in it, the catch is, I don’t see how I can install it in my laptop and to be honest, I feel DuckDB or pandas for that matter is substantially friendlier to use.

Python is very good to download public Data

I use a Python function to download data from BOM website to  csv files in a  local folder, I copied the same Folder Path as Fabric Lakehouse

I hope the code keeps working, I justed copied some code from the internet and mess with it till it worked, I have no idea how regex is working, and I don’t even want to know

DuckDI API has a vibe of PowerQuery

I used DuckDB to clean the data, you can use Pandas, Polars, Ibis or any library that you like, personally I never liked CTE in SQL, I much prefer step by step logic and seeing the results in every step, and this is exactly what I am doing here

Read everything as a text

Normalize_names will replace empty space with an underscore

All_varchar , will read everything as a text, you can cast the correct type later

DuckDB uses lazy evaluation by default, you need to use show() to see the result

Unpivot Other Columns

Unpivot other columns is one of the coolest feature of PoweQuery, DuckDB has a very nice SQL syntax to do the same

Write the final results as a Delta Table

That’s pretty much the core idea of fabric, if you write your Data into Delta Table then it will be usable by all the Engines, PowerBI, SQL DWH etc

Final Thoughts

The workflow did make sense to me, the only annoying thing is when you import a new notebook in a Fabric workspace it will not overwrite the existing one but create a copy, having the option will be nice.

I can see some advantages for this approach, like if you have a very small SKU and you want to optimize the resource usage, using your laptop for developpement can save you some precious compute, another case maybe if you were not given access to fabric from the admin, showing you have already a working code and you know what you are doing can make the conversation easier.

One thing for sure, users will use fabric in some unexpected ways and that’s just fine.