A Fine Slice Of SQL Server
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 closedDustin 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 closedShalu 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 MSSparkutils.run() 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 closedAshish 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 closedRituraj 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
joinmethod 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 closedMeagan 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 closedAmit Kumar shares a few tips with us:
RDD does serialisation and de-serialisation of data whenever it distributes the data across clusters such as during repartition and shuffle, and we all know that serialisation and de-serialisation are very expensive operations in spark.
On the other hand, DataFrame stores the data as binary using off-heap storage, no need for deserialization and serialization of data when it distributes to clusters. We see a big performance improvement in DataFrame over RDD
Click through for several additional tips.
Comments closedUst Oldfield sneaks in a file:
File seeding is a useful way of maintaining and deploying static, or very slowly changing, reference data for a data model to use repeatably and reliably across environments, whilst benefitting from source control.
If you aren’t in the Databricks world, this also feels like a job for DVC.
Comments closedLiliam Leme provides an answer to a common Synapse Spark pool question:
I’ve been reviewing customer questions centered around “Have I tried using MSSparkUtils to solve the problem?”
One of the questions asked was how to share results between notebooks. Every time you hit “run” in a notebook, it starts a new Spark cluster which means that each notebook would be using different sessions. Making it impossible to share results between executions of notebooks. MSSparkUtils offers a solution to handle this exact scenario.
Read on to see what MSSparkUtils is and how it helps in this case.
Comments closedEstera Kot has an announcement:
We are excited to announce the preview availability of Apache Spark™ 3.3 on Synapse Analytics. The essential changes include features which come from upgrading Apache Spark to version 3.3.1 and upgrading Delta Lake to version 2.1.0.
Check out the official release notes for Apache Spark 3.3.0 and Apache Spark 3.3.1 for the complete list of fixes and features. In addition, review the migration guidelines between Spark 3.2 and 3.3 to assess potential changes to your applications, jobs and notebooks.
There’s a lot in there, though I did snicker a bit at log4j 2 being more secure than log4j v1 given what we saw last year, though that gaping hole was fixed.
Comments closed