bash_kernel VS spyql

For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel

And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber

Something like this? https://github.com/takluyver/bash_kernel

I wrote a Crystal kernel for Jupyter, just a modified bash_kernel, 140 lines of code, but it was tiring because I don't have enough Python ability. icrystal is the widely used Jupyter kernel for Crystal, which uses ICR . On the other hand, this crystal_kernel uses the official crystal interpreter.

Thank you! One of my main goals was making data processing in the command-line more accessible and intuitive. If you use a shell you can leverage an extensive array of tools. please take a look at the recipes in the Readme. The shell is many times underrated for data processing!

Right now you can use it in Jupiter Notebooks using a shell kernel like: https://github.com/takluyver/bash_kernel

On the mid-term, developing a spyql kernel is appealing because of syntax highlighting, code autocompleting, and more. But unless several people show interest on this, I should tackle other features first.

I recently learned of jupyter+bash and it seemed like a step toward rundeck.

I prefer a SQL-like format. It’s not as complete but it cover most of the day-to-day use cases. Take a look at https://github.com/dcmoura/spyql (I am the author). Congrats on fq!

SPyQL documentation: spyql.readthedocs.io

spyql – Query data on the command line with SQL-like SELECTs powered by Python expressions

This is great!

I am the author of SPyQL [1]. Combining JC with SPyQL you can easily query the json output and run python commands on top of it from the command-line :-) You can do aggregations and so forth in a much simpler and intuitive way than with jq.

I just wrote a blogpost [2] that illustrates it. It is more focused on CSV, but the commands would be the same if you were working with JSON.

[1] https://github.com/dcmoura/spyql

You can import the data into a PostgreSQL/MySQL/SQLite/... database and then query the database. However, even with the right choice of indexes, it might take a while to run queries on a table with hundreds of millions of records. You can easily import your data to these databases with SpyQL: $ spyql "SELECT * FROM csv TO sql(table=my_table_name) | sqlite3 my.db" (you would need to create the table my_table_name before running the command).

https://github.com/dcmoura/spyql/blob/master/notebooks/json_...

And ClickHouse looks like a normal relational database - there is no need for multiple components for different tiers (like in Druid), no need for manual partitioning into "daily", "hourly" tables (like you do in Spark and Bigquery), no need for lambda architecture... It's refreshing how something can be both simple and fast.

I thought this library was pretty nifty for json. It's also relatively fast compared to most json parsers: https://github.com/dcmoura/spyql