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52 lines
2.5 KiB
Markdown
52 lines
2.5 KiB
Markdown
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---
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layout: post
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title: Support for the PRQL in the database query prompt
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excerpt: >-
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PRQL is a database query language that is pipeline-oriented
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and easier to use interactively
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---
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The v0.12.1 release of lnav includes support for
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[PRQL](https://prql-lang.org). PRQL is a database query language
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that has a pipeline-oriented syntax. The main advantage of PRQL,
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in the context of lnav, is that it is easier to work with
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interactively compared to SQL. For example, lnav can provide
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previews of different stages of the pipeline and provide more
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accurate tab-completions for the columns in the result set. I'm
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hoping that the ease-of-use will make doing log analysis in lnav
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much easier.
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You can execute a PRQL query using the existing database prompt
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(press `;`). A query is interpreted as PRQL if it starts with
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the [`from`](https://prql-lang.org/book/reference/data/from.html)
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keyword. After `from`, the database table should be provided.
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The table for the focused log message will be suggested by default.
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You can accept the suggestion by pressing TAB. To add a new stage
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to the pipeline, enter a pipe symbol (`|`), followed by a
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[PRQL transform](https://prql-lang.org/book/reference/stdlib/transforms/index.html)
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and its arguments. In addition to the standard set of transforms,
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lnav provides some convenience transforms in the `stats` and `utils`
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namespaces. For example, `stats.count_by` can be passed one or more
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column names to group by and count, with the result sorted by most
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to least.
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As you enter a query, lnav will update various panels on the display
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to show help, preview data, and errors. The following is a
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screenshot of lnav viewing a web access log with a query in progress:
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The top half is the usual log message view. Below that is the online
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help panel showing the documentation for the `stats.count_by` PRQL
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function. lnav will show the help for what is currently under the
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cursor. The next panel shows the preview data for the pipeline stage
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that precedes the stage where the cursor is. In this case, the
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results of `from access_log`, which is the contents of the access
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log table. The second preview window shows the result of the
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pipeline stage where the cursor is located.
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There is still a lot of work to be done on the integration and PRQL
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itself, but I'm very hopeful this will work out well in the long
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term. Many thanks to the PRQL team for starting the project and
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keeping it going, it's not easy competing with SQL.
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