The R Core Team announced yesterday the release of R 3.5.3, and updated binaries for Windows and Linux are now available (with Mac sure to follow soon). This update fixes three minor bugs (to the functions
stopifnot), but you might want to upgrade just to avoid the “package built under R 3.5.4” warnings you might get for new CRAN packages in the future.
Click through for more info on this release, including where the name from each R release comes from.
For security purposes, Databricks Apache Spark clusters are deployed in an isolated VPC dedicated to Databricks within the customer’s account. In order to run their data workloads, there is a need to have secure connectivity between the Databricks Spark Clusters and the above data sources.
It is straightforward for Databricks clusters located within the Databricks VPC to access data from AWS S3 which is not a VPC specific service. However, we need a different solution to access data from sources deployed in other VPCs such as AWS Redshift, RDS databases, streaming data from Kinesis or Kafka. This blog will walk you through some of the options you have available to access data from these sources securely and their cost considerations for deployments on AWS. In order to establish a secure connection to these data sources, we will have to configure the Databricks VPC with either one of the following two available options :
Read on for those two options.
There are many ways of installing K8S as mentioned here. It can be installed in the Cloud, on-premise and also locally on the laptop using virtualization. But, installing K8S had never been easy. In this blog, we will look at one of the easiest way to get started with K8S using Play with Kubernetes (PWK). With this the whole K8S experience is within the browser and there is nothing to install on the laptop, everything is installed on the remote machine. PWK uses ‘Docker in Docker’ which is detailed here (1, 2).
This looks like a really useful way to get the hang of Kubernetes before trying it out on your own machines.
What’s achievable? I want to identify tables to extract from the database that won’t take years. Large monolithic systems can have a lot of dependencies to unravel.
So what tables in the database have the least dependencies? How do I tell without a trustworthy data model? Is it the ones with the fewest foreign keys (in or out)? Maybe, but foreign keys aren’t always defined properly or they can be missing all together.
My thought is that if two tables are joined together in some query, then they’re related or connected in some fashion. So that’s my idea. I can look at the procedure cache of a database in production to see where the connections are. And when I know that, I can figure out what tables are not connected.
Click through for the script to help you do it.
The locking story is not the same as with the primary and unique key constraints. First, there’s one extra piece: the transition will block access to
dbo.LookupTableas well as the table we create the constraint on. That’s to keep us from deleting rows in our lookup table before the key is in place.
Second, the locks begin as soon as we hit F5. Even
SELECTstatements get blocked requesting a
LCK_M_SCH_Slock. Bad news, people.
So what can we do to get around this problem? Two routes: the ineffectual way and the ugly way.
Despite my being a ray of sunshine here, you should still check this out. It’s shorter than the average Russian novel, at least.
Still, there are some complexities related to binary collations that you might not be aware of. To figure out what they are, we need to look at why there are so many binary collations in the first place. I mean, binary collations work on the underlying values of the characters, and comparing numbers doesn’t change between cultures or versions: 12 = 12, 12 > 11, and 12 <13, always. So, then what is the difference between:
Hebrew_100_BIN2(only the culture is different), or
Latin1_General_BIN2(only the version is different), or
Latin1_General_100_BIN(only the binary comparison type is different)
Read on to find out.
A couple of notes on the query. I cast the query_plan as xml so that I can use the XQuery to pull out the information. It is possible that the plan might be so large that you get an error because of the limit on nesting levels within XML. Also, I aggregate the information from the sys.query_store_runttime_stats. You may want to modify this to only look at limited ranges. I’ll leave that to you as an exercise.
Do read Grant’s warning in the conclusion.
In a traditional gaps and islands problem, the goal is to identify groups of continuous data sequences (islands) and groups of data where the sequence is missing (gaps).
While many people encounter gaps and islands problems when dealing with ranges of dates, and recently I did too but with an interesting twist:
How do you determine gaps and islands of data that has overlapping date ranges?
Check out Bert’s explanation of the solution; it’s a good one.
When I first started with VSTS and ultimately Azure DevOps, I went through many failed builds because the order of the jobs in your pipeline don’t run in the order that you’ve built them and how you would logically believe them to run. The image below shows two Build Pipeline jobs but when the build is queued, whether this be manual or via CI, the second job is running before job #1. In this example the build will fail because Job #2 is to deploy a dacpac to a SQL Server on Linux Docker Container (Using Ubuntu Agent Host) but obviously this cannot be done until the dacpac has been created in Job #1 which is running on a VS2017 Agent Host:
Click through to see how it’s done.