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Category: Versions

A Measure of Utilization by SQL Server Version

Published 2021-07-12 by Kevin Feasel

Steve Stedman some numbers:

Here listed is the current percentages of SQL server versions running our Daily Check-up with Database Health Monitor.

Do note that the population for this is “People who use Steve Steadman’s SQL Daily Checkup product” and not organizations which use SQL Server as a whole, so it’s not wise to apply findings from the first directly onto the second. That said, it doesn’t surprise me that 2016 is the most in-use version of SQL Server in this sample. It’s a little surprising how many 2014 instances there are, but that might be related to cardinality estimator changes.

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SQL Server 2016 Leaving Mainstream Support July 2021

Published 2021-06-15 by Kevin Feasel

Glenn Berry reminds us that time flies:

SQL Server 2016 falls out of Mainstream Support on July 13, 2021. What this means is that there won’t be any new Service Packs or Cumulative Updates released for SQL Server 2016 after that date. It is still in Extended Support until July 14th, 2026. While in Extended Support, there will still be security and critical functional updates, if any are needed. This post is about SQL Server 2016 falling out of Mainstream Support.

Read on for more information about what this means, as it’s not a situation to panic and immediately change everything.

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Azure Synapse Analytics Supports Apache Spark 3.0

Published 2021-05-26 by Kevin Feasel

Euan Garden has some great news for us:

Starting today, the Apache Spark 3.0 runtime is now available in Azure Synapse. This version builds on top of existing open source and Microsoft specific enhancements to include additional unique improvements listed below. The combination of these enhancements results in a significantly faster processing capability than the open-source Spark 3.0.2 and 2.4.

The public preview announced today starts with the foundation based on the open-source Apache Spark 3.0 branch with subsequent updates leading up to a Generally Available version derived from the latest 3.1 branch.

It still won’t be as fast as Databricks, but it should be a good bit faster than the Spark 2 they were running.

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New Features in R 4.1.0

Published 2021-05-19 by Kevin Feasel

The Jumping Rivers folks have some good news for us:

The stability of the base packages is a great strength of the R ecosystem: both underpinning, and contrasting with, the rapid pace at which contributed packages (CRAN, BioConductor) evolve.

Imagine introducing a new feature into the R language. Even if problems arise with the usability of that feature, it would need to be maintained until at least the next major release, by which time thousands of developers and analysts may depend upon it. Unsurprisingly, the R maintainers are exceedingly cautious when introducing new syntax.

Similarly, you should employ caution when using new syntax in your own code. If you do use syntax that was introduced in R-4.1, be aware that your code will not run on versions of R that precede this. For example, this may prevent your new analysis scripts from running on your colleague’s computer, or prevent users from installing your new package.

Given how many third-party packages have regular breaking changes, I do wish more people would follow this advice.

Getting into the meat of things, I really like the F#-style pipe in R: |> makes a lot of intuitive sense, though I do wish they had included a placeholder element with the native pipe.

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Apache Kafka 2.8 Released

Published 2021-04-20 by Kevin Feasel

John Roesler announces Apache Kafka 2.8:

We are excited to announce that 2.8 introduces an early-access look at Kafka without ZooKeeper! The implementation is not yet feature complete and should not be used in production, but it is possible to start new clusters without ZooKeeper and go through basic produce and consume use cases.

At a high level, KIP-500 works by moving topic metadata and configurations out of ZooKeeper and into a new internal topic named @metadata. This topic is managed by an internal Raft quorum of “controllers” and is replicated to all brokers in the cluster. The leader of the Raft quorum serves the same role as the controller in clusters today. A node in the KIP-500 world can serve as a controller, a broker, or both, depending on the new process.roles configuration. See the README for quickstart instructions and additional details.

In addition to the headline item, there are plenty of other bugfixes and additions as well.

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spkarlyr 1.6 Released

Published 2021-04-06 by Kevin Feasel

Carly Driggers announces a new release of sparklyr:

Sparklyr, an LF AI & Data Foundation Incubation Project, has released version 1.6! Sparklyr is an R Language package that lets you analyze data in Apache Spark, the well-known engine for big data processing, while using familiar tools in R. The R Language is widely used by data scientists and statisticians around the world and is known for its advanced features in statistical computing and graphics. 

Click through to see the changes.

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Kafka Sans ZooKeeper

Published 2021-04-05 by Kevin Feasel

Ben Stopford and Ismael Juma give us a preview:

So we’re very pleased to say that the early access of the KIP-500 code has been committed to trunk and is expected to be included in the upcoming 2.8 release. For the first time, you can run Kafka without ZooKeeper. We call this the Kafka Raft Metadata mode, typically shortened to KRaft (pronounced like craft) mode.

Beware, there are some features that are not available in this early-access release. We do not yet support the use of ACLs and other security features or transactions. Also, both partition reassignment and JBOD are unsupported in KRaft mode (these are anticipated to be available in an Apache Kafka release later in the year). Hence, consider the quorum controller experimental software—we don’t advise subjecting it to production workloads. If you do try out the software, however, you’ll find a host of new advantages: It’s simpler to deploy and operate, you can run Kafka in its entirety as a single process, and it can accommodate significantly more partitions per cluster (see measurements below).

Read on for more information. This is a big deal for Kafka.

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