ScaleArc Upgrades Its Software to Support Microsoft Azure SQL Database

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ScaleArc, a leading provider of database load balancing software, announced that it has integrated its ScaleArc for SQL Server software with Microsoft Azure SQL Database. Customers of Microsoft Azure Cloud Database-as-a-Service (DBaaS) software can now benefit from the seamless scale out, faster application performance, and high availability features of the ScaleArc software. With this enhancement, customers can now take advantage of the flexibility and simplicity of Azure SQL Database with no application code changes.

ScaleArc’s database load balancing software deploys between application and Azure SQL Database database servers to direct traffic into the database on behalf of the application. The software enables applications to leverage underlying database functionality without programming database logic into the app. ScaleArc also accelerates cloud-native application development, because developers no longer need to program database logic into the application. To fully support enterprise deployments, ScaleArc’s software upgrade also supports Azure Active Directory (AAD) for user authentication.

Azure SQL Database offers a level of simplicity and ease of administration that dramatically offloads IT resources,” said Justin Barney, president and CEO of ScaleArc. “The challenge has always been whether a company’s applications can take advantage of the flexibility and scalability of these cloud resources. With ScaleArc software, Azure SQL Database customers can now leverage that agility with increased application uptime and performance.”

Joanne Marone, director, database product marketing at Microsoft Corp. added, “When partners like ScaleArc enhance their software to support Microsoft Azure SQL Database, our customers gain access to a broader solution set on the platform. ScaleArc makes it easier for our customers to run database workloads on Azure SQL Database, extending the flexibility and simplicity of our platform to more applications.”

With this latest release, Azure SQL Database customers can leverage ScaleArc software to:

  • Automatically scale application performance with no code changes by automatically discovering all SQL Database cluster members, leveraging all available readable secondaries, and directing reads vs. writes to the appropriate servers – this capability eliminates the need to code “read intent” connect strings within apps to take advantage of readable secondaries;
  • Ensure application uptime and performance by monitoring health status and replication lag on all servers in the cluster, sending traffic to the fastest server based on Time to First Byte and avoiding any servers with performance or availability problems;
  • Eliminate the impact of transient errors by holding persistent client connections and directing queries to the new primary following a database server failover – the ScaleArc software will hold writes in queue until the failover is complete, shielding the application and the customer from seeing application errors;
  • Avoid downtime from Azure SQL Database maintenance by having ScaleArc front end Azure SQL Database clusters in multiple zones or regions and leveraging the software to route around downed database resources. ScaleArc can detect the new primary and always route the writes appropriately, avoiding potential split-brain issues with Azure SQL Database. Also, ScaleArc’s surge queue holds writes and waits for the server role change to happen, ensuring writes go to the new primary; and
  • Accelerate application performace by leveraging ScaleArc’s app-transparent caching capabilities – this feature allows Azure SQL database customers to identify queries that are good candidates for caching, add them to the cache via a single mouse click, and expire the cache based on time to live or auto-invalidation when the data changes.

ScaleArc performance testing demonstrates dramatically improved application performance when adding Database Transaction Units (DTUs or eDTUs) to the cluster. Adding a secondary doubled the Query per Second (QPS) throughput and adding two secondaries tripled the QPS rate – all with no changes needed at the application layer.


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