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Category: Cloud

Developing In The Cloud

Published 2016-11-04 by Kevin Feasel

Richie Rump has some nice pointers about developing for Azure or AWS:

Since we’re a bunch of data freaks, we wanted to make sure that our data and files are properly backed up. I set out to create a script that will backup DynamoDB to a file and copy the data in S3 to Azure. The reasoning for saving our backups into a different cloud provider is pretty straightforward. First, we wanted to keep the data in a separate cloud account from the application. We didn’t make the same mistakes that Code Spaces did. Secondly, I wanted to kick the tires of Azure a bit. Heck, why not?

I figure this script would take me a day to write and a morning to deploy. In the end it took four days to write and deploy. So here are some lessons that I learned the hard way from trying to bang out this backup code.

This is a must-read if you’re starting to look at using cloud providers for services.

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Azure SQL DW Statistics

Published 2016-11-02 by Kevin Feasel

Emma Stewart looks at how statistics are created in Azure SQL Data Warehouse:

In Azure SQL Data Warehouse, statistics have to be created manually. On previous SQL Server projects, creating and maintaining statistics wasn’t something that we had to incorporate into our design (and really think about!) however with SQL DW we need to make sure we think about how to include it in our process in order to make sure we take advantage of the benefits of working with Azure DW.

The major selling point of Azure SQL Data Warehouse is that it is capable of processing huge volumes of data, one of the specific performance optimisations that has been made is the distributed query optimiser. Using the information obtained from the statistics (information on data size and distribution), the service is able to optimize queries by assessing the cost of specific distributed query operations. Therefore, since the query optimiser is cost-based, SQL DW will always choose the plan with the lowest cost.

Azure SQL Data Warehouse is a bit of a strange animal, with differences in statistics being one of the smaller changes versus “classic” SQL Server.

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Details On Azure SSAS

Published 2016-11-02 by Kevin Feasel

James Serra breaks down what Azure Analysis Services has to offer:

  • Developers can use SQL Server Data Tools (SSDT) in Visual Studio for creating models and deploying them to the service.  Administrators can manage the models using SQL Server Management Studio (SSMS) and investigate issues using SQL Server Profiler

  • Business users can consume the models in any major BI tool.  Supported Microsoft tools include Power BI, Excel, and SQL Server Reporting Services.  Other MDX compliant BI tools can also be used, after downloading and installing the latest drivers

  • The service currently supports tabular models (compatibility level 1200 only).  Support for multidimensional models will be considered for a future release, based on customer demand

Between tabular-only support and the max size being 100 GB (if I’m reading this correctly), they’re not yet ready to push the product hard.  Given that it just came out, that makes sense, and hopefully the training wheels come off.

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Cached Azure Analysis Services Logins

Published 2016-10-31 by Kevin Feasel

Chris Webb shows how to log into Azure Analysis Services from Management Studio as a different user:

When Azure Analysis Services was announced I had to try it out right away. Of course I didn’t read the instructions properly so when I tried to log in to my Azure Analysis Services instance from SQL Server Management Studio, like an idiot I logged in with the wrong username. The problem is that once you’ve done this, with current versions of SQL Server Management Studio there’s no way of logging out and logging in as a different user. Luckily Igor Uzhviev of Microsoft had a solution for me and I thought I’d share it for anyone else who’s made the same mistake. Here’s what you need to do:

This seems a bit much, but should just be a temporary workaround.

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Polybase As Ersatz StretchDB

Published 2016-10-31 by Kevin Feasel

Ginger Grant has a great idea:

PolyBase, which was released with SQL Server 2016, provides another method to access live data either locally or in the cloud, very similar to the SQL Server Stretch database feature. Polybase can also provide the ability to provide a more cost-effective availability for cold data, streamlines on-premises data maintenance, and keeps data secure even during migration. Polybase differs from Stretch database in a few ways, as the SQL must be different, the speed is noticeably slower, and it is a lot less expensive. The cost is significantly less because storing data in a Azure blob store starts at 1 cent a month and Stretch database starts at $2.50 an hour. In this post,I will show how to take data which was archived due to the age of the data, which was created in 2012 and store it in an Azure Blob Storage file which will be available via Polybase when I needed.

The ideal scenario for this solution is extremely cold data which is nonetheless required as part of regulatory compliance, where having a query run for 3 hours once every six months or so is acceptable.

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Migrating To Azure SQL Database

Published 2016-10-27 by Kevin Feasel

Tim Radney walks us through steps to migrate an on-prem database to Azure SQL Database:

When planning to migrate on premises databases to V12, the size of the database is a huge factor in how long the migration will take. The export of the database, the transfer of the data, and the import will all increase in proportion to the size of the database.

Another big factor in the restore/import time when moving your databases to V12 is the performance tier you are restoring too. The restore/import process requires a lot of horsepower, so to help expedite your migration, you should consider restoring to a higher performance tier. When the database is online, you can easily and quickly drop down to a lesser tier that meets your daily performance needs. Being able to change performance tiers with a few mouse clicks is one of the big benefits of Azure SQL Database.

There are some design considerations for moving to Azure SQL Database, and once those are covered, Tim’s article helps with the actual migration process.

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Moving To The Cloud

Published 2016-10-27 by Kevin Feasel

Denny Cherry explains some of the important indicators that you might benefit from moving to a cloud provider:

The reality is that not all workloads are a right fit for the cloud. If you are running highly sustained workloads, then the cloud probably isn’t the right solution for your environment. The systems which work best in the cloud are the ones which can be converted to use the various PaaS services not just the IaaS services. The PaaS services will usually provide a more cost effective hosting solution, especially for workloads which vary over time; for example, ramping up during the day and becoming almost idle overnight.

Even if running in a PaaS environment isn’t an option this may be cost effective for running in an IaaS environment. It all depends on how bursty the workload is that you plan on moving to the cloud.

There are some good points here; check it out.

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Restoring An Azure SQL Database

Published 2016-10-26 by Kevin Feasel

I have a post on restoring a database in Azure SQL Database:

You will need to select your restore point as well.  In this case, I decided to restore back to midnight UTC on a particular date.  Note that the dates are UTC rather than your local timezone!

After selecting your restore point, you pick the target server and can decide a couple of things.  First, you can put this database into an elastic database pool, which makes cross-database connections a lot easier.  Second, you can choose a different pricing tier.  Because I only needed this database for a few minutes, keeping it at P2 Premium was fine; the total restore time meant that we spent less than a dollar bringing the data back to its pristine condition.

Be aware of the time for restoration; it can be very slow.

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Regular Expressions In Lucene

Published 2016-10-26 by Kevin Feasel

Kendra Little looks at Azure Search searches:

I wanted to be able to find all architect jobs using something like ‘%rchit%’ as well, because there’s not a lot of great ways to do this in SQL Server.

In SQL Server, you can use a traditional B-Tree index to seek, but only based on the letters at the beginning of a character column.  If I want to know every business title that contains ‘%rchit%’, I’m going to have to scan an entire index.

SQL Server fulltext indexes don’t solve the double-wildcard problem, either. Fulltext indexes support word prefix searches– so a fulltext index would be great at finding all job titles that contain a word that starts with ‘Arch%’.

Sometimes that’s enough. But a lot of times, you do need to find a substring anywhere in a word. And sometimes you do want to offload that from your database.

This is the kind of problem Lucene (and its follow-up implementations, like Elasticsearch) was designed to solve.  Read on for more details as Kendra solves the problem in Azure Search.

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Analysis Services In Azure

Published 2016-10-26 by Kevin Feasel

Chris Webb looks at SSAS in Azure:

Support for multidimensional models will be considered for a future release, based on customer demand.

I’m pretty sure there there will be plenty of demand for Multidimensional support given the installed base that’s out there.

I hope so.  Lack of multidimensional isn’t a deal-killer, but it’s a deal-harmer.

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