This straightforward GUI interface is appealing because it has a minimal learning curve and quickly enables you to start submitting benchmark tests against your Amazon Redshift cluster. Setting the maxrows field to a relatively low number also reduces the risk of SQLWorkbench running into an out-of-memory error from very large result sets. Although the resulting query times should still be considered as query runtimes, they certainly help you get closer to a query’s execution time. You can easily verify this by running the same query multiple times with different maxrows settings and observing that the number of rows returned for each query on the Amazon Redshift console query history page doesn’t change. Unlike the LIMIT clause in a SQL SELECT statement, which can alter (short-circuit) Amazon Redshift query processing, setting the maxrows field (whether to a value as low as 1 or something much higher) has no impact on query processing in Amazon Redshift maxrows only impacts SQLWorkbench’s rendering workload and overhead. The following screenshot shows what the SQLWorkbench connection dialog box might look like when populated with connection information.Īfter establishing a successful connection to your Amazon Redshift cluster, a query tab opens, in which you can write and run SQL queries similar to that shown in the following screenshot.įor benchmark tests, it’s highly recommended to set the maxrows field to a relatively low number to avoid noise from long transmission times of large result sets. For this post, I assume you’re familiar with the basics of JDBC connections. When you have SQLWorkbench running, setting up a connection to your Amazon Redshift cluster is quite easy. Typically, you stand up a Windows EC2 instance to serve as your benchmark host, and install SQLWorkbench on that machine. Also, I have found that customers that use SQLWorkbench often use it in a Windows environment (something to keep in mind if operating system has a determination on which open-source tool you use). I discuss both in this section, but in my experience, customers typically default to the GUI mode, so we explore that version first. SQLWorkbench can run in GUI or console mode. SQLWorkbench also requires a JDBC driver for the database (to download the latest Amazon Redshift JDBC driver, see Configuring a JDBC driver version 1.0 connection). ![]() It’s written in Java so it runs on Windows, Linux/UNIX, and macOS, and naturally requires a supported Java runtime environment (JRE). SQLWorkbench, also referred to as SQLWorkbench/J, is an open-source SQL query tool that you can freely download as a. ![]() I highly recommend you take advantage of that benefit by reaching out to your AWS account Solutions Architect. Analytics Specialists Solutions Architects such as myself frequently and happily engage with current and prospective customers to help you evaluate your benchmarking strategy and approach at no charge. One final point before we get started: there is a lot that could be said about benchmarking-more than can be accommodated in a single post. However, best practices for using these tools are scarce, possibly resulting in flawed compute profiles, flawed benchmark results, customer frustration, and bloated timelines. ![]() Open-source tools, with their cost-efficiency and vendor neutrality, are often the preferred choice for profiling production workloads and benchmark tests. In addition, you may also use benchmark tests to proactively monitor a production cluster’s performance in real time.įor prospective Amazon Redshift customers, benchmarking Amazon Redshift is often one of the main components of their evaluation and a key source of insight into the price-to-performance ratio of different Amazon Redshift configurations. You also need an approach to scale up with eyes wide open, and benchmarking different Amazon Redshift cluster configurations against various production workloads can help you appropriately accommodate workload expansion. When you use Amazon Redshift to scale compute and storage independently, a need arises to profile the compute requirements of various production workloads so that your Amazon Redshift cluster configuration reflects an appropriately balanced compute layer. Let’s first start with a quick review of the introductory installment. In this post, we discuss benchmarking Amazon Redshift with the SQLWorkbench and psql open-source tools. In the introductory post of this series, we discussed benchmarking benefits and best practices common across different open-source benchmarking tools.
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