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Category: Spark

Azure Synapse Analytics R Language Support

Published 2022-11-01 by Kevin Feasel

Ryan Majidimehr has a short list of updates for Azure Synapse Analytics but it includes a big one:

Azure Synapse Analytics provides built-in R support for Apache Spark. As part of this, data scientists can leverage Azure Synapse Analytics notebooks to write and run their R code. This also includes support for SparkR and SparklyR, which allows users to interact with Spark using familiar Spark or R interfaces. To learn more read the official how-to Use R for Apache Spark with Azure Synapse Analytics (Preview).

That it took this long for R support was a bit weird, but I’m glad it’s there now.

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Choosing between Synapse Spark Notebooks or Job Definitions

Published 2022-10-28 by Kevin Feasel

Arun Sethia and Arshad Ali explain when you might use a Spark notebook versus a job definition:

A Synapse Spark Notebook is a web-based (HTTP/HTTPS) interactive interface to create files that contain live code, narrative text, and visualizes output with rich libraries for spark based applications. Data engineers can collaborate, schedule, run, and test their spark application code using Notebooks. Notebooks are a good place to validate ideas and do quick experiments to get insight into the data. You can integrate the Synapse Notebook into Synapse pipeline.

The Notebook allows you to combine programming code with markdown text and perform simple visualizations (using Synapse Notebook chart options and open-source libraries). In addition, running code will supply immediate feedback, output, and progress tracking within Notebook.

Click through for the comparison.

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Transferring Data between Dedicated SQL and Spark Pools in Synapse

Published 2022-10-24 by Kevin Feasel

Sidney Cirqueria shows off a connector available to us in Azure Synapse Analytics:

Usually, customers do this kind of operation using Synapse Apache Spark to load data to Dedicated Pool within Azure Synapse Workspace, but today, I would like to reproduce a different scenario that I was working on one of my support cases.  Consider a scenario where you are trying to load data from Synapse Spark to Dedicated pool (formerly SQL DW) using Synapse Pipelines, and additionally you are using Synapse Workspace deployed with Managed Virtual Network.

The intention of this guide is to help you with which configuration will be required if you need to load data from Azure Synapse Apache Spark to Dedicated SQL Pool (formerly SQL DW). If you prefer take advantage of the new feature-rich capabilities now available via the Synapse workspace and Studio and load data directly from Azure Apache Spark to Dedicated Pool in Azure Synapse Workspace is recommended that you enable Synapse workspace features on an existing dedicated SQL pool (formerly SQL DW).

Read on for a few tips a nd a step-by-step walkthrough of the process.

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Named Entity Encryption in Spark

Published 2022-10-07 by Kevin Feasel

Arshad Ali wants to secure some data being used in a Synapse Spark pool:

As a data engineer, we often get requirements to encrypt, decrypt, mask, or anonymize certain columns of data in files sitting in the data lake when preparing and transforming data with Apache Spark. The extensibility feature of Spark allows us to leverage a library which is not native to Spark. One such library is Microsoft Presidio, which provides fast identification and anonymization modules for private entities in text such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers, financial data, and more. It facilitates both fully automated and semi-automated PII (Personal Identifiable Information) de-identification and anonymization flows on multiple platforms.

In this blog post, I am going to demonstrate step by step how to download and use this library to meet the above requirements with Spark pool of Azure Synapse Analytics.

Read on to see how it works.

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Spark Query Optimization in Synapse

Published 2022-09-13 by Kevin Feasel

Daniel Coelho lays out a few optimizations in Azure Synapse Analytics Spark pools:

The Azure Synapse Analytics team has prominent engineers enhancing and contributing back to the Apache Spark project. One of our focus areas is Spark query optimization techniques, where Microsoft has decades of experience and is making significant contributions to the Apache Spark open source engine.

The attachment at the bottom of this blog post will be presented at the 48th International Conference on Very Large Databases (#VLDB2022) and covers the latest developments in query optimization for Apache Spark 3. Those optimizations were developed by Microsoft engineers and are available today in the Azure Synapse runtime for Apache Spark versions 3.1 and 3.2.

Check out the high-level updates as well as a complete technical paper laying out the changes.

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Creating Multiple Output Files per Spark Task

Published 2022-09-01 by Kevin Feasel

Dmitry Tolpeko has a quick but helpful post:

It is highly recommended that you try to evenly distribute the work among multiple tasks so every task produces a single output file and job is completed in parallel.

But sometimes it still may be useful when a task generates multiple output files with the limited number of records in each file […]

I had to cut it off right there to keep from spilling the beans here. Click through for Dmitry’s post to see what setting controls records per file, allowing you to keep opening those Spark output files in Excel.

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Overly Large Executors in ElasticMapReduce

Published 2022-08-29 by Kevin Feasel

Dmitry Tolpeko notes a change to Amazon ElasticMapReduce:

So 50 executors were initially requested with the required memory 22528 and 4 vcores as expected, but actually 9 executors were created with 112640 memory and 20 cores that is 5x larger. It should have created 10 executors but my cluster does not have resources to run more containers.

Note: The second log row specifies allocated vCores:5, it is because of using DefaultResourceCalculator in my YARN cluster that ignores CPU and uses memory resource only. Do not pay attention to this, the Spark executor will still use 20 cores as it reported in the third log record above.

Click through for the reason.

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Watermarking in Spark Structured Streaming

Published 2022-08-26 by Kevin Feasel

Max Fisher takes us through an important feature for Spark streaming:

When building real-time pipelines, one of the realities that teams have to work with is that distributed data ingestion is inherently unordered. Additionally, in the context of stateful streaming operations, teams need to be able to properly track event time progress in the stream of data they are ingesting for the proper calculation of time-window aggregations and other stateful operations. We can solve for all of this using Structured Streaming.

For example, let’s say we are a team working on building a pipeline to help our company do proactive maintenance on our mining machines that we lease to our customers. These machines always need to be running in top condition so we monitor them in real-time. We will need to perform stateful aggregations on the streaming data to understand and identify problems in the machines.

This is where we need to leverage Structured Streaming and Watermarking to produce the necessary stateful aggregations that will help inform decisions around predictive maintenance and more for these machines.

Read on to see how watermarking works in various scenarios, including when you join together streams.

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Useful Design Patterns for Apache Spark Projects

Published 2022-08-18 by Kevin Feasel

Alexander Eleseev applies some design patterns:

When I participated in a big data project, I needed to program Spark applications to move and transform data from/to relational and distributed databases, like Apache Hive. I found such applications to have a number of pitfalls, so all “hard to read code,” “method is too large to fit into a single screen,” etc. problems need to be avoided for us to focus on deeper issues. Also, Spark jobs are similar: data is loaded from a single or multiple databases, gets transformed, then saved to a single or multiple databases. So it seems reasonable to try to use GoF patterns to program Spark applications. 

Specifically, this covers Spark code written in Java (or Python). I’d argue that Scala-based code would profit by following a different set of functional patterns rather than Gang of Four object-oriented design patterns.

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