Automating SSAS deployments

Matt Smith as introduced SQL Server Analysis Services deployments to Octopus Deploy:

The only thing missing was SSAS. After watching Chris Webb’s video tutorial –Cube Deployment, Processing and Admin on Project Botticelli, I decided it had to use Microsoft.AnalysisServices.Deployment.exe. After a bit of scripting and testing, I managed to write a PowerShell that updates the xml config files for the deployment – it sets the ProcessingOption to DoNotProcess’. It updates the Data source – where the cube will refresh the data from. The script isn’t perfect. For starters, what if you have more then one data source? Also what if your not using SQL Server 2014? Still the great thing about open source is that other can update it. Anyone can improve it, its not reliant on me having free time. So hopefully by the time we move to SQL 2016 someone will have already updated it to work with SQL 2016.

A big part of product maturation is automated deployment.  Good on Matt for introducing that to the community.

Specify Valid Network Protocols

Steve Jones shows how to specify the set of network protocols people can use to connect to a SQL Server instance:

I ran across a question on network protocols recently, which is something I rarely deal with. Often the default setup for SQL Server is fine, but there are certainly times you should add or remove network connectivity according to your environment.

Microsoft’s guidance on protocols pushes you toward TCP/IP and that’s a good default.

Delayed Durability Deletions

Melissa Connors looks at using Delayed Durability while deleting a large batch of records:

Recently, while considering possible use cases for Delayed Durability, it occurred to me that data loss might be entirely acceptable in cases where the data would not truly be lost. I have worked with a number of applications that have processes that purge old information from the database. If a purge process failed in these applications, data would simply live a little bit longer, and be purged the next time the process was successful – they have a recovery mechanism built in as it is. I decided to test Delayed Durability in a database with a long-running purge to observe the potential performance impact. I chose a process that was clearly contributing to transaction log waits, because that is where the real performance impact comes from when delaying durability. If you do not have notable waits or some level of a bottleneck there, you are not likely to improve anything simply by turning on this feature.

I was not aware that you could set durability at the transaction level; I was under the mistaken impression that once you flipped the switch, all transactions were subject to Delayed Durability.  Disk-heavy operations (like large batches of deletions) does seem like a good use case for this.

Result Sets

Kevin Feasel



Kenneth Fisher learns and teaches us about RESULT SETS:

Quick definition. A result set is the output of a query. It could result in a one row, one column output or a 100+ column, million+ row output. Either way that’s a result set. Note: you can have multiple result sets from a single object (stored procedure, function etc) call.

This was introduced in SQL Server 2012 and there are a couple of security-related scenarios in which RESULT SETS is helpful.  It also lets you rename columns in stored procedure calls, if you’re into that sort of thing.

Clear The Query Store

Grant Fritchey shows how to clear the Query Store in SQL Server 2016:

While setting up example code for my presentation at SQL Cruise (which is going to be a fantastic event), I realized I wanted to purge all the data from my Query Store, just for testing. I did a series of searches to try to track down the information and it just wasn’t there. So, I did what anyone who can phrase a question in less than 140 characters should do, I posted a question to Twitter using the #sqlhelp hash tag.

You can also call EXEC sp_query_store_remove_query to remove a specific query from the Query Store.

Mirrored Backups

Sean McCown talks about mirrored backups:

By mirroring backups, you’re saying that you want to backup to 2 locations simultaneously.  So let’s say you have the need to backup your DBs to a local SAN drive, but also you need to send them to another data center in case something happens to your local SAN.  The way to do that in SQL is with mirrored backups and the syntax looks like this:


So above you can see that SQL will write both of these files at once, and give you a good amount of redundancy for your DB backups.  However, this can go wrong when your network isn’t stable or when the link to the other data center is slow.  So you should only mirror backups when you can pretty much guarantee that it won’t fail or lag.  And as you can guess that’s a heavy burden to put on most networks.  In the situation last week that spawned this blog, the network went down for something like 9 hrs and caused the DB’s log to not be backed up that entire time, and hence the log grew and grew.  Now you’re in danger of bringing prod down and that’s clearly not what your backup strategy should do.

Sean talks about alternatives and then talks about how they’ve gotten around the problem with Minion Backup.  If you haven’t tried Minion Backup, it is well worth your time; it’s already a great product and I use it in a production environment I support.


January 2016
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