In the following sections, we examine each building block in greater detail. I hope this series has been helpful, and strongly encourage current and prospective customers to reach out to me or other AWS colleagues if you wish to delve deeper. The JDBC Request can be thought of as the benchmark query or SQL test query to be submitted non-stop by each simulated user in this thread group. The following table delves deeper into the purpose that each building block serves in our load test. However, outside Redshift SP, you have to prepare the SQL plan and execute that using EXECUTE command. You can use any of the mentioned statements in your dynamic query. Disabled short query acceleration but cranked up the default queue concurrency to 50. For prospective Amazon Redshift customers, benchmarking Amazon Redshift is often one of the main components of evaluation and a key source of insight into the price-to-performance ratio of different Amazon Redshift configurations. As with our first cluster, these five clusters had manually tuned WLMs and were operating well within our data SLAs. We use the Thread Schedule section to control how many users should be created and at what time interval. The following screenshot shows a test query on one of the TPC-H tables, customer. See also STL_DDLTEXT , STL_UTILITYTEXT , and SVL_STATEMENTTEXT . In this post, we discuss benchmarking Amazon Redshift with the Apache JMeter open-source tool. Amazon Redshift is built around industry-standard SQL, with added functionality to manage very large datasets and support high-performance analysis and reporting of those data. The following diagram illustrates the basic query lifecycle. We use the JDBC Connection Configuration building block to create a database connection pool that is used by the simulated users to submit queries to Amazon Redshift. An additional thing to note is that in the case of DW1, when running complex queries[1] against more than a TB of records of data, the query performance will take a few minutes, even when you increase the cluster size. I recommend creating a separate query queue for fast and slow queries, in our example fast_etl_execution . I take advantage of the User Defined Variables section to set my own custom variables that hold values needed by all components in the test case, such as the JDBC URL, test duration, and number of users submitting small, medium, and large queries. Let’s see what we can do with it. The Apache JMeter GUI saves test plans in .jmx files that can be used to run the same test plan in Apache JMeter’s console mode. select * from information_schema.tables; With the row entries in the following screenshot, an additional five users are created every 5 seconds. Now, you may verify that in Redshift using Redshift query editor in AWS console or third party IDE like SQL workbench, which is an open source JDBC IDE. You can run it on Windows and a number of different Linux/UNIX systems; for this post we run it in a Windows environment. executing this directly on the redshift via navicat results in 132 records being added. We rely on JMeter’s query throughput calculation, but we can also manually compute query throughput from the runtime metadata that is gathered if we so desire. This allows me to, for example, rapidly switch two different test clusters. Each thread group is named accordingly. The query speeds on DW2 can also increase as you add more DW2 nodes to your Redshift cluster. Asser Moustafa is an Analytics Specialist Solutions Architect at AWS based out of Dallas, Texas. For this post, I make the JDBC Connection Configuration a variable so that it’s customizable for each individual thread group (JDBC_Variable_Name_In_Pool). Redshift PREPARE Statement. Introduction. Redshift query editor. You can add a User Defined Variables building block in several places, and it’s best to use this capability to limit the scope of each variable. 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. It has has been built from ParAccel technology.… Interview Mocha’s Amazon Redshift online test is created by subject matter experts (SMEs) and contains questions on data warehouse, sort key, data migration, cluster query, query performance. Make sure you create at least one user defined query besides the Redshift query queue offered as a default. Redshift does not support all features that are supported in PostgreSQL. To query the linked server, click New Query in the toolbar: Enter your SQL query in the editor window and click Execute to run the query: As a result, you can see the contents of the selected table retrieved directly from the Amazon Redshift account you are connected to. As mentioned, this series is divided into multiple installments, with the first installment discussing general best practices for benchmarking, and the subsequent installments discussing the strengths and challenges with different open-source tools such as SQLWorkbench, psql, and Apache JMeter. I left all other fields at their default on this page. The \report-output\statistics.json file captures a lot of useful metrics, such as the total samples (like SQL queries) submitted during the test duration, achieved query throughput, and number of small, medium, and large queries and their individual throughput. With Redshift, you're simply charged for how long you use your nodes. If a query is sent to the Amazon Redshift instance while all concurrent connections are currently being used it will wait in the queue until there is an available connection. To open the query editor, click the editor from the clusters screen. It consists of a dataset of 8 tables and 22 queries that ar… why would this not work via EC2 / Lambda in a script with that role associated with them – Shenanigator Mar 23 at 20:46 Athena uses Presto and ANSI SQL to query on the data sets. For this post, I skip over discussing the possible Amazon Redshift cluster tweaks that you could use to squeeze every drop of performance out of Amazon Redshift, and instead rely on the strength of its default state to be optimized to achieve excellent query throughput on diverse workloads. The problem with doing a benchmark with “easy” queries is that every warehouse is going to do pretty well on this test; it doesn’t really matter if Snowflake does an easy query fast and Redshift does an easy query really, really fast. In this example, I named it RedshiftJDBCConfig. I left all other settings at their default. Alternatively, I could stagger user creation by creating multiple rows and setting the Initial Delay sec field to control each row’s startup delay. For this post, we use an instance of the User Defined Variables building block to hold the output file names of each listener in this test plan (if you look closely, you can see the values of these variables reference the baseDir variable, which was defined in our test plan). version - Redshift version; Rows. Using the CLI for large load tests also helps minimize any impact on the benchmark results. To install Apache JMeter on a Windows EC2 machine, complete the following steps: Apache JMeter supports both GUI and CLI modes, and although you may find the Apache JMeter GUI straightforward with a relatively small learning curve, it’s highly recommended that you use the Apache JMeter GUI primarily for defining benchmark tests, and perhaps running small-to-medium-sized benchmark tests. Towards the end, I input the database JDBC URL (which is actually a variable reference to a variable defined in the test plan), JDBC driver class name, and database username and password. In this particular post, we reviewed the strengths and appropriateness of Apache JMeter for conducting benchmark tests. This is achieved by a one-row entry in the Thread Schedule and setting the Start Threads Count thread group property to 20 users (or the matching variable, as we do in the following screenshot). For this post, the statistical metrics captured for the test run are located in a JSON file inside the report-output directory. You can clearly see how Redshift lives up to the promise of 10x faster querying speeds! The following screenshot shows the View Results in Table output. The queries below return the Redshift version and information about the installed version. A thread group can be thought of as a group of simulated users, which is why for this post, I create three separate thread groups: one to represent each of three previously mentioned user personas being simulated (small, medium, and large). Plenty for what we need to do. It creates external tables and therefore does not manipulate S3 data sources, working as a read-only service from an S3 perspective. It’s good enough to have a login to the Amazon AWS Console. 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