Cloud Forward Episode 3: Cloud-Native Database - PolarDB | Parallel Query

To truly resolve the issue of SQL execution time getting slower as data gets bigger, PolarDB for MySQL adopted the feature of Parallel Query at the kernel level.

Why is SQL execution time still getting slower, even after migrating to the cloud?

Hello and welcome to another episode of Cloud Forward.

Today, we'll look at how we can improve SQL performance.

MySQL, as an OLTP database, does not make full use of multiple CPU cores.

As a result, even if more CPU cores are added to the specification, the query still becomes quite slow when the data exceeds 500 gigabytes.

You may wonder, isn't cloud migration supposed to solve that problem?

We have to increase concurrency while decreasing latency to resolve the problem.

Then, there's Parallel Query, a technique for increasing the execution speed of SQL queries by dividing the workload of a SQL statement and executing it in parallel or at the same time.

At the time of its initial release, PolarDB for MySQL included the Parallel Query function, allowing PolarDB to break the limits of single-core execution performance and take full advantage of multiple CPU cores with parallel processing capabilities, reducing the time required to run some SQL queries on PolarDB exponentially.

Each query is executed by multiple threads in parallel to reduce the processing time (including IO and CPU computation) to achieve a significant decrease in response time.

Watch the full video here to learn more about PolarDB >>

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Cloud Forward Episode 3: Cloud-Native Database - PolarDB | Parallel Query

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