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Category: Spark

New in SQL Server Big Data Clusters

Published 2021-10-07 by Kevin Feasel

Daniel Coelho has an update on what’s available in SQL Server Big Data Clusters:

SQL Server Big Data Clusters (BDC) is a capability brought to market as part of the SQL Server 2019 release. Big Data Clusters extends SQL Server’s analytical capabilities beyond in-database processing of transactional and analytical workloads by uniting the SQL engine with Apache Spark and Apache Hadoop to create a single, secure, and unified data platform. It is available exclusively to run on Linux containers, orchestrated by Kubernetes, and can be deployed in multiple-cloud providers or on-premises.

Today, we’re proud to announce the release of the latest cumulative update, CU13, for SQL Server Big Data Clusters which includes important changes and capabilities:

Updating to the most recent production-ready version of Spark (as of today) is a nice upgrade.

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pyspark.pandas in Apache Spark 3.2

Published 2021-10-06 by Kevin Feasel

Hyukjin Kwon and Xinrong Meng announce a built-in pandas API for Apache Spark 3.2:

We’re thrilled to announce the pandas API as part of the upcoming Apache Spark™ 3.2 release. pandas is a powerful, flexible library and has grown rapidly to become one of the standard data science libraries. Now pandas users can leverage the pandas API on their existing Spark clusters.

A few years ago, we launched Koalas, an open source project that implements the pandas DataFrame API on top of Spark, which became widely adopted among data scientists. Recently, Koalas was officially merged into PySpark by SPIP: Support pandas API layer on PySpark as part of Project Zen (see also Project Zen: Making Data Science Easier in PySpark from Data + AI Summit 2021).

pandas users can now scale their workloads with one simple line change in the upcoming Spark 3.2 release:

Click through to see more details on the change.

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Adaptive Query Execution in Spark 3

Published 2021-10-01 by Kevin Feasel

Amarjeet Singh explains what Adaptive Query Execution is in Apache Spark:

As we all know optimization plays an important role in the success of spark SQL. Therefore, a lot of work has been done in this direction. Before spark 3.0, cost-based optimization was a major hit in which different stages related to cost (based on time efficiency and estimated CPU and I/O usage) are compared and executes the strategy which minimizes the cost. But, because of outdated statistics, it has become a sub-optimal technique. Therefore in spark 3.0, Adaptive Query Execution was introduced which aims to solve this by reoptimizing and adjusts the query plans based on runtime statistics collected during query execution. Thus re-optimization of the execution plan occurs after every stage as each stage gives the best place to do the re-optimization.

Item number 2 from the list is also available in SQL Server, giving you an idea that this is an active battleground for query processing in data platform technologies.

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Why 200 Tasks for a Spark Execution?

Published 2021-09-30 by Kevin Feasel

The Hadoop in Real World team explains why you might see 200 tasks when running a Spark job:

It is quite common to see 200 tasks in one of your stages and more specifically at a stage which requires wide transformation. The reason for this is, wide transformations in Spark requires a shuffle. Operations like join, group by etc. are wide transform operations and they trigger a shuffle.

Read on to learn why 200, and whether 200 is the right number for you.

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SCD Type 2 with Delta Lake

Published 2021-09-28 by Kevin Feasel

Chris Williams continues a series on slowly changing dimensions in Delta Lake:

A Type 2 SCD is probably one of the most common examples to easily preserve history in a dimension table and is commonly used throughout any Data Warehousing/Modelling architecture. Active rows can be indicated with a boolean flag or a start and end date. In this example from the table above, all active rows can be displayed simply by returning a query where the end date is null.

Read on to see how you can implement this pattern using Delta Lake’s capabilities.

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Databricks Notebook Discovery via Notebooks

Published 2021-09-27 by Kevin Feasel

Darin McBeth creates a meta-noterbook to keep track of notebooks:

Elsevier has been a customer of Databricks for about six years. There are now hundreds of users and tens of thousands of notebooks across their workspace. To some extent, Elsevier’s Databricks users have been a victim of their own success, as there are now too many notebooks to search through to find some earlier work.

The Databricks workspace does provide a keyword search, but we often find the need to define advanced search criteria, such as creator, last updated, programming language, notebook commands and results.

Interestingly, we managed to achieve this functionality using a 100% notebook-based solution with Databricks functionalities. As you will see, this makes it easy to set up in a customer’s Databricks environment.

Read on to see how.

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Visualization in Spark with Drsti

Published 2021-09-23 by Kevin Feasel

Jean-Georges Perrin shows off a Spark library:

I was looking for an effortless data visualization that would interface easily with Apache Spark. I found a few interesting tools, but nothing that would not require some complex interfacing, setup, or infrastructure. In a good geek way, I then decided to write the tool. This lack of simple tools is how Drsti (pronounced drishti) was born.

Aren’t you tired of looking at dataframes that looked like they came straight from a 1980 VT100? Sure, if you use notebooks, either standalone or hosted (IBM Watson Studio, Databricks…), you are not (or less) confronted with the issue. However, if you are building pipelines outside of the Data Science toys, oops, tools, you may need to visualize data in a graph.

Read on to see how it works and some of what you can do with Drsti.

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CI/CD with Databricks Notebooks and Azure DevOps

Published 2021-09-22 by Kevin Feasel

Michael Shtelma and Piotr Majer get us started on an MLOps journey:

This is the first part of a two-part series of blog posts that show how to configure and build end-to-end MLOps solutions on Databricks with notebooks and Repos API. This post presents a CI/CD framework on Databricks, which is based on Notebooks. The pipeline integrates with the Microsoft Azure DevOps ecosystem for the Continuous Integration (CI) part and Repos API for the Continuous Delivery (CD).In the second post, we’ll show how to leverage the Repos API functionality to implement a full CI/CD lifecycle on Databricks and extend it to the fully-blown MLOps solution.

Click through for the article and a link to code. You can also see the pipeline YAML (and Python code it calls) in the repo.

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Spark Performance Improvements in Azure Synapse

Published 2021-09-22 by Kevin Feasel

Balaji Sankaran shows improvements Microsoft has made over open-source Apache Spark 3 in Azure Synapse Analytics:

Azure Synapse Analytics is continually focused on delivering a highly performant and scalable platform for supporting Spark Workload. We are focused on improving the query performance for the typical workload patterns that we see with our customers. By combining the latest open-source updates in Apache Spark with our team’s focus on performance updates we have made significant performance gains in standard TPC-DS benchmarking tests.

I expect it will never be as fast as what Databricks can do, but getting a 2x performance improvement over the open source version of Spark is nothing to sneeze at.

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