Improve Java application reliability with Azure SQL Database using JDBC and connection pooling.
A growing number of Azure SQL Database customers are developing new applications in Java, or porting existing ones, using a JDBC driver from Microsoft or a 3rd party. While these drivers are providing quite extensive support and covering most of database service capabilities and performance expectations, there are a couple of areas that worth some special attention.JSON in your Azure SQL Database? Let’s benchmark some options!
So, in your solution you have to deal with a lot of JSON messages generated by IoT devices or microservice instances and you're looking for a database technology to store and query billions of these messages. Sure, you can pick up a trendy NoSQL store for that, but are you sure it will provide the best management, security, scalability and end-to-end development experience? Azure SQL Database can definitely be an interesting option that can offer a familiar T-SQL programming environment while providing powerful JSON manipulation capabilities. In this article we will explore various options at schema design and indexing level, and will compare results based on common scenarios and workloads. Hope you'll enjoy!Optimize Azure SQL Database Bulk Upsert scenarios
Customers often need to move a dataset from a source system into a new destination, inserting rows that doesn't exist in a target table and update with new values those where a given key already exists. This scenario is usually referred as "upsert", and can be very time consuming if executed row by row on 10s or 100s of thousands of records. With the technique described in this article, we've been able to reduce the time needed to upsert a dataset of 2M rows against a target table with 30M rows from 20 hours to 20 minutes.
