flac.tcl VS spyql

- https://github.com/wader/flac.tcl

In most cases i learned way more then i expected. Even made some friends.

fun fact: fq is kind of the spiritual predecessor of https://github.com/wader/flac.tcl, you can see traces of it in flac_frame.go in fq, was used to prototype some things :)

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