Press "Enter" to skip to content

Category: Spark

Databricks Power Tools in VS Code

Gerhard Brueckl has some tools for us:

As you probably know, we at paiqo have developed our Databricks extension for VSCode over the last years and are constantly adding new features and improving user experience. The most notable features are probably the execution of local notebooks against a Databricks cluster, a nice UI to manage clusters, jobs, secrets, repos, etc. and last but not least also a browser for your workspace and DBFS to sync files locally.

In February 2023 Databricks also published its own official VSCode extension which was definitely long awaited by a lot of customers (blogextension). It allows you to run a local file on a Databricks cluster and display the results in VSCode again. Alternatively you can also run the code as a workflow. I am sure we can expect much more features in the near future and Databricks investing in local IDE support is already a great step forward!

As you can imagine, I am working very closely with the people at Databricks and we are happy to also announce the next major release of our Databricks VSCode extension 2.0 which now also integrates with the official Databricks extension! To avoid confusion between the two extensions we also renamed ours to Databricks Power Tools so from now on you will see two Databricks icons on the very left bar in VSCode.

Click through to read more in the announcement and some of the things which have changed as a result of version 2.0.

Leave a Comment

The Legacy of Big Data

Adam Bellemare looks back:

Big Data was going to change the way everything worked. We were about to solve every financial, medical, scientific, and social problem known to humankind. All it would take was a great big pile of data and some way to process it all. 

But somewhere along the line, the big data revolution just sort of petered out, and today you barely hear anything about big data. 

Click through for Adam’s explanation, which is a more detailed form of “Some stuff worked out and became ubiquitous in other ways; others fell off the map.”

But I’m going to snag one more quotation here from Adam:

And finally, big data has shown us that no matter how hard we try, there’s simply no escaping from the inevitable convergence to a full SQL API.

Me: Laughs in Feasel’s Law.

Feasel’s Law – Any sufficiently advanced data retrieval process will eventually have a SQL interface.

1 Comment

Spark Application Dependency Caching

Shu Wang, Biao He, and Minchu Yang talk turkey about dependencies:

In this blog post, we will share our analysis of Spark Dependency Management at LinkedIn, highlight interesting findings, and present our design choice of using a simple user-level cache over more complex alternatives. We will also discuss our rollout experience and lessons learned. Finally, we will demonstrate the impact of accelerating all Spark applications at LinkedIn at the cluster level. Faster Spark jobs translate to increased data freshness, leading to an enhanced member experience by way of more relevant recommendations, timely insights, effective abuse protection, and other similar improvements.

If you work with Spark to any serious extent, you’ll want to read this post.

Leave a Comment

Unit Testing Spark Notebooks in Synapse

Arun Sethia grabs the oscilloscope:

In this blog post, we will cover how to test and create unit test cases for Spark jobs developed using Synapse Notebook. This is an extension of my previous blog, Synapse – Choosing Between Spark Notebook vs Spark Job Definition, where we discussed selecting between Spark Notebook and Spark Job Definition. Unit testing is an automated approach that developers use to test individual self-contained code units. By verifying code behavior early, it helps to streamline coding practices for larger systems.

Arun covers three major use cases: when your code is in an external library, when it is in a separate notebook, and when it is in the same notebook.

Leave a Comment

Spark Structured Streaming with Synapse

Ryan Adams builds a demo:

In this post we are going to look at an example of streaming IoT temperature data in Synapse Spark.  I have an IoT device that will stream temperature data from two sensors to IoT hub. We’ll use Synapse Spark to process the data, and finally write the output to persistent storage. Here is what our architecture will look like: 

Click through for the architectural diagram and step-by-step on how to put the demo together.

Comments closed

Parallel Loading in Spark Notebooks

Dustin Vannoy answers some questions:

I received many questions on my tutorial Ingest tables in parallel with an Apache Spark notebook using multithreading. In this video and post I address some of the questions that I couldn’t just answer in the YouTube comments. Watch the video for more complete answers but here are quick responses with links to examples where appropriate.

Click through for the video and some text versions. Dustin includes examples for Synapse and Databricks.

Comments closed

Executing Multiple Notebooks in one Spark Pool with Genie

Shalu Ganotra Chadra, et al, explain what Synapse Genie is:

The Genie framework is a metadata driven utility written in Python. It is implemented using threading (ThreadPoolExecutor module) and directed acyclic graph (Networkx library). It consists of a wrapper notebook, that reads metadata of notebooks and executes them within a single Spark session. Each notebook is invoked on a thread with command based on the available resources in the Spark pool. The dependencies between notebooks are understood and tracked through a directed acyclic graph.

Read on for more information about how you can use it and what the setup process looks like.

Comments closed

Converting Spark RDDs to DataFrames and Datasets

Ashish Chaudhary does a bit of swapping around:

In this blog, we will be talking about Spark RDD, Dataframe, Datasets, and how we can transform RDD into Dataframes and Datasets.

At this point, most of the libraries I know of accept and produce DataFrames. Occasionally you might need to “downshift” to an RDD to work with some specialty library. But in the event you do have one but want to get to another, Ashish has you covered.

Comments closed

Join Types in Spark SQL

Rituraj Khare makes some connections:

In Apache Spark, we can use the following types of joins in SQL:

Inner join: An inner join in Apache Spark is a type of join that returns only the rows that match a given predicate in both tables. To perform an inner join in Spark using Scala, we can use the join method on a DataFrame.

The set of options is the same as you’d see in a relational database: inner, left outer, right outer, full outer, and cross. The examples here are in Scala, though would apply just as easily to PySpark and, of course, writing classic SQL statements.

Comments closed

External Objects in Databricks Unity Catalog

Meagan Longoria adds external tables and views to an Azure Databricks Unity Catalog:

I’ve been busy defining objects in my Unity Catalog metastore to create a secure exploratory environment for analysts and data scientists. I’ve found a lack of examples for doing this in Azure with file types other than delta (maybe you’re reading this in the future and this is no longer a problem, but it was when I wrote this). So I wanted to get some more examples out there in case it helps others.

I’m not storing any data in Databricks – I’m leaving my data in the data lake and using Unity Catalog to put a tabular schema on top of it (hence the use of external tables vs managed tables. In order to reference an ADLS account, you need to define a storage credential and an external location.

Read on for examples of what you can do with this.

Comments closed