Skip to main content

[1] Azure Tips and Tricks: SQL migration to Azure with near-zero downtime

Context:

You need to do a migration from an on-premises SQL Server 2019 instance to Microsoft Azure. There are 3 databases that need to be migrated, each of them has around 3-4 TB. The migration plan includes an Azure SQL Database Managed Instances.

Problem:

The challenges are around the data loss during migration and how to minimize the downtime. 


Solution

The first thing that you need to do is to include DMA (Database Migration Assistant) during the migration assessment. It would allow you to identify any compatibility issues between on-premises and Azure. Additional, DMA is able to provide a list of actions that can be done on the destination environments to do reliability and performance improvements. 

The migration of the schema, data, and uncontained objects can be done using DMA. Allowing to have a migration plan with near-zero downtime. 

On top of it is mandatory to consider DAG (Distributed Availability Groups) for the managed instances, which would enable you to have a strong DR (Disaster Recovery) plan. Think twice if you really need DAG versus the out of the shelve backup and replication capabilities offered by SQL in Azure.

At this moment in time configuration of DAG is done only from the command line and you need to be well documented. Why? Because it involves creating WSFC clusters, AG (Availability Groups) for each WSFC, an ILB (Internal Load Balancer), and configure in the write way the AG endpoint (port 5022 for SQ). Don't forget about port 1433 and 5022 that needs to be available between the clusters. 

The documentation is pretty well, but with testing, it will require you 2MD for the full configuration and testing of DAG.


Comments

Popular posts from this blog

Windows Docker Containers can make WIN32 API calls, use COM and ASP.NET WebForms

After the last post , I received two interesting questions related to Docker and Windows. People were interested if we do Win32 API calls from a Docker container and if there is support for COM. WIN32 Support To test calls to WIN32 API, let’s try to populate SYSTEM_INFO class. [StructLayout(LayoutKind.Sequential)] public struct SYSTEM_INFO { public uint dwOemId; public uint dwPageSize; public uint lpMinimumApplicationAddress; public uint lpMaximumApplicationAddress; public uint dwActiveProcessorMask; public uint dwNumberOfProcessors; public uint dwProcessorType; public uint dwAllocationGranularity; public uint dwProcessorLevel; public uint dwProcessorRevision; } ... [DllImport("kernel32")] static extern void GetSystemInfo(ref SYSTEM_INFO pSI); ... SYSTEM_INFO pSI = new SYSTEM_INFO(

Azure AD and AWS Cognito side-by-side

In the last few weeks, I was involved in multiple opportunities on Microsoft Azure and Amazon, where we had to analyse AWS Cognito, Azure AD and other solutions that are available on the market. I decided to consolidate in one post all features and differences that I identified for both of them that we should need to take into account. Take into account that Azure AD is an identity and access management services well integrated with Microsoft stack. In comparison, AWS Cognito is just a user sign-up, sign-in and access control and nothing more. The focus is not on the main features, is more on small things that can make a difference when you want to decide where we want to store and manage our users.  This information might be useful in the future when we need to decide where we want to keep and manage our users.  Feature Azure AD (B2C, B2C) AWS Cognito Access token lifetime Default 1h – the value is configurable 1h – cannot be modified

What to do when you hit the throughput limits of Azure Storage (Blobs)

In this post we will talk about how we can detect when we hit a throughput limit of Azure Storage and what we can do in that moment. Context If we take a look on Scalability Targets of Azure Storage ( https://azure.microsoft.com/en-us/documentation/articles/storage-scalability-targets/ ) we will observe that the limits are prety high. But, based on our business logic we can end up at this limits. If you create a system that is hitted by a high number of device, you can hit easily the total number of requests rate that can be done on a Storage Account. This limits on Azure is 20.000 IOPS (entities or messages per second) where (and this is very important) the size of the request is 1KB. Normally, if you make a load tests where 20.000 clients will hit different blobs storages from the same Azure Storage Account, this limits can be reached. How we can detect this problem? From client, we can detect that this limits was reached based on the HTTP error code that is returned by HTTP