Search Results for "run sql command line"

A command-line benchmarking tool. Statistical analysis across multiple runs. Support for arbitrary shell commands. Constant feedback about the benchmark progress and current estimates. Warmup runs can be executed before the actual benchmark. Cache-clearing commands can be set up before each timing run. Statistical outlier detection to detect interference from other programs and caching effects.

...While it does not aim to support all of find's powerful functionality, it provides sensible (opinionated) defaults for a majority of use cases. First, to get an overview of all available command line options, you can either run fd -h for a concise help message or fd --help for a more detailed version. fd is designed to find entries in your filesystem. The most basic search you can perform is to run fd with a single argument: the search pattern. Instead of just showing the search results, you often want to do something with them. fd provides two ways to execute external commands for each of your search results: the -x/--exec option runs an external command for each of the search results (in parallel), or the -X/--exec-batch option launches the external command once, with all search results as arguments.

...Check out the manual for details on how to use it. MOROS is text-based and draws inspiration from Unix and DOS to provide a simple and efficient environment for computing and communication from the command line. Each program is designed to ensure a cohesive user interface across the entire system. The shell is the hearth of MOROS, used to navigate the filesystem and run other programs.

...Maybe you're interested in the population counts of each city in the world. So grab the data and start examining it. The next thing you might want to do is get an overview of the kind of data that appears in each column. The stats command will do this for you. The xsv table command takes any CSV data and formats it into aligned columns using elastic tabstops. These commands are instantaneous because they run in time and memory proportional to the size of the slice (which means they will scale to arbitrarily large CSV data).