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Category: Administration

Azure Redis Cache Geo-Replication

Arun Sirpal shows how to set up geo-replication in Azure Redis Cache:

The concept of a geo-replicated partnership between a primary and secondary node is very similar to that of something you may have seen with Azure SQL DB, where the primary handles all R/W and then the changes are pushed to secondary ( async). This is no different with Redis.

Read on to see what limitations exist and how you can set up geo-replication.

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Finding Azure SQL DB Backup History

Taiob Ali takes us through a new DMV:

There is a new DMV currently in preview which returns information about backups of Azure SQL databases except for the Hyperscale tier. Microsoft official documentation is here.

If you run the example query as-is from the above documentation some of the columns do not make sense.

Taiob includes a better query which provides the type of information you’re used to in on-premises SQL Server.

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Finding the Busiest Database

David Fowler looks for the biggest, meanest database on a SQL Server instance:

I’m sure we’ve all been there at some point, sometimes it’s easy to tell. We might only have a handful of databases on the server with one known to be the heaviest utilised. But sometimes things might not be so obvious, there could be a large numbers of databases or no obvious resource hog.

In those instances we need some way to figure out what how much time each database is spending on the CPU if that’s what we’re interested in or perhaps the total number of page reads or writes if IO is our problem.

Read on for one way to do this, assuming that the instance has been up long enough to give you reliable results.

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Automated Partitioned Table Management

Eitan Blumin automates creation and deletion of partitions in SQL Server:

Before we begin, there are a few “ground rules” we should understand first:

1 – Partition Functions define the partition ranges

This means that whenever we want to eliminate an old partition range or add a new partition range, the PARTITION FUNCTION is the object that we actually need to modify.

Click through for Eitan’s entire process and a couple of scripts. This is an area that SQL Server could have made a lot easier, especially for periodic processes, by including options like “Daily” or “Monthly” or “Weekly(start on Monday)” for intervals rather than making people specify every partition separately.

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High Availability in SQL Managed Instance General Purpose Tier

Niko Neugebauer clears up what options you have for high availability in SQL MI’s General Purpose tier:

The two main requirements around high availability are commonly known as RTO and RPO.


RTO
 – stands for Recovery Time Objective and is the maximum allowable downtime when a failure occurs. In other words, how much time it takes for your databases to be up and running.


RPO
 – stands for Recovery Point Objective and is the maximum allowable data-loss when a failure occurs. Of course, the ideal scenario is not to lose any data, but a more realistic (and also ideal) scenario is to not lose any committed data, also known as Zero Committed Data Loss.

With those definitions out of the way, read on to learn more.

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Persisting Data in Azure Redis Cache

Arun Sirpal feeds the mogwai after midnight:

I mentioned before that you could use the idea of data persistency to rebuild your data from total failure. There are two types. RDB and AOF.

RDB – persists a snapshot of your cache in a binary format. The snapshot is saved in an Azure Storage account. AOF – saves every write operation to a log. The log is saved at least once per second into an Azure Storage account. 

I’m a big proponent of using Redis as a caching service. I’m not a big proponent of using Redis as a persisted database, mostly because I’ve had a lot of bad experiences with persistent Redis…

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Debugging a Production Failure

Roel Hogervorst diagnoses trouble:

When you are in panic mode you focus on what is right in front of you and make suboptimal decisions. Here is some I have made.

Read on for a couple stories as well as a practical implementation of debugging as an OODA loop. Something that Sean McCown mentioned before has always stuck with me: it’s amazing just how few people know how to troubleshoot issues. Our inclination seems to be one of two things: adduce a conclusion from the first piece of evidence (usually just a flimsy error message) or immediately give up.

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