Developing with Db2 Big SQL

Db2 Big SQL provides broad SQL support that is typical of commercial databases. You can issue queries using JDBC or ODBC drivers to access data that is stored in a cluster, in the same way that you access databases from your enterprise applications.

You can use the Db2 Big SQL server to execute standard SQL queries. Multiple queries can be executed concurrently. Db2 Big SQL provides support for large ad hoc queries and point queries, which are low-latency queries that return information quickly to reduce response time and provide improved access to data.

The Db2 Big SQL server is multi-threaded, so scalability is limited only by the performance and number of CPUs on the computer that runs the server. If you want to issue larger queries, you can increase the hardware performance of the server computer that Db2 Big SQL runs on.

Performance can be measured in terms of system response time, throughput, and resource utilization. To optimize the performance of Db2 Big SQL, consider the design of the database, as well as the cluster parameters.

Db2 Big SQL divides queries into parts, such as join and group-by. Depending on the query, the amount of data, the configuration settings, and other factors, Db2 Big SQL can run each of these parts in parallel or serially.

When Db2 Big SQL LOAD HADOOP runs, it uses the Hadoop MapReduce framework, which runs jobs by using multiple mappers and reducer tasks. Using the MapReduce framework to run queries over large data sets reduces query execution time.

For queries that operate over small data sets, the required resources might outweigh the benefits of parallel processing. In such cases, Db2 Big SQL supports running the query serially on a single node, sometimes referred to as local query execution.