Query Performance Gauge

This is a little script to measure the performance of queries against a Trino or MySQL instance. You can use it to run several queries and measure how long it takes for results to come back to your local machine.

How to use

1. First, you need to install the package in your Python installation or a virtual environment. If you have our Google Drive Shared Drive replicated locally, you can do it like this:

pip install "git+file:///g:\shared drives\data drive\90 useful\10 query_performance_gauge@master"```

If not, you simply need to clone the repo somewhere in your machine and replace the path in the previous command.

2. Afterwards, you need to make a config file. See below details on how to compose one.

3. Once you have your config file ready, run the following command from the terminal.

measure_query_performance --config my_config_file.json

4. Results will be printed in your console as they are available. If instead you would like to store them in a file, a quick and easy hack is to redirect output in Powershell to a file. You can do it like this:

measure_query_performance --config my_config_file.json | Out-File - FilePath my_results.txt

Composing a config file

You can take a look at examples for different setups in config_examples. If you want to make a new config file, it will probably be easier for you to start from one of those templates.

A few notes:

• The valid engines are "trino" and "mysql".
• You can place as many queries as you would like in the queries_to_measure list.
• I advice you to make the first query a silly, fast query such as SELECT 1 to validate your connection and quickly confirm that everything is set up properly.

A few more details

• Queries are run sequentially, as in the second query will only start after the first query is finished.
• For this to work, your local machine must have access and permission to the connection you are targeting, so remember to set up VPNs and other necessary configs properly.
• A peculiarity: when using MySQL through an SSH tunnel, the port number used by the remote MySQL should be free in your local machine. That means that if the MySQL database is listening on port 3306, your local machine should have port 3306 free before running this.
• Queries in JSON must be stored in a single line. A bit of a headache, I know. JSON limitations. You can use this webpage to easily jump between prettified and one-line formats for any query.