spyql
gron
spyql | gron | |
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23 | 64 | |
902 | 13,520 | |
- | - | |
0.0 | 0.0 | |
over 1 year ago | 6 months ago | |
Jupyter Notebook | Go | |
MIT License | MIT License |
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spyql
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Fq: Jq for Binary Formats
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!
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Command-line data analytics made easy with SPyQL
SPyQL documentation: spyql.readthedocs.io
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This Week In Python
spyql – Query data on the command line with SQL-like SELECTs powered by Python expressions
- Command-line data analytics made easy
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Jc – JSONifies the output of many CLI tools
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
- The fastest command-line tools for querying large JSON datasets
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Working with more than 10gb csv
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).
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ClickHouse Cloud is now in Public Beta
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.
- A SQLite extension for reading large files line-by-line
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I want to convert a large JSON file into Tabular Format.
I thought this library was pretty nifty for json. It's also relatively fast compared to most json parsers: https://github.com/dcmoura/spyql
gron
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Frawk: An efficient Awk-like programming language. (2021)
gron (https://github.com/tomnomnom/gron) to transform it and query and then invert the transformation?
- Show HN: Flatito, grep for YAML and JSON files
- Gron: Make JSON greppable
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Make JSON Greppable
It buffers all of its output statements in memory before writing to stdout:
https://github.com/tomnomnom/gron/blob/master/main.go#L204
- Ask HN: What are some unpopular technologies you wish people knew more about?
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Jaq – A jq clone focused on correctness, speed, and simplicity
Have you tried `gron`?
It converts your nested json into a line by line format which plays better with tools like `grep`
From the project's README:
▶ gron "https://api.github.com/repos/tomnomnom/gron/commits?per_page..." | fgrep "commit.author"
json[0].commit.author = {};
json[0].commit.author.date = "2016-07-02T10:51:21Z";
json[0].commit.author.email = "[email protected]";
json[0].commit.author.name = "Tom Hudson";
https://github.com/tomnomnom/gron
It was suggested to me in HN comments on an article I wrote about `jq`, and I have found myself using it a lot in my day to day workflow
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Interactive Examples for Learning Jq
> So all I want is a tool to go from json => line oriented and I will do the rest with the vast library of experience I already have at transformations on the command line.*
The tool for that is likely https://github.com/tomnomnom/gron
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Modern Linux Tools vs. Unix Classics: Which Would I Choose?
If JQ is too much, see GRON &| Miller
gron transforms JSON into discrete assignments to make it easier to grep for what you want https://github.com/tomnomnom/gron
Miller is like awk, sed, cut, join, and sort for data formats such as CSV, TSV, JSON, JSON https://github.com/johnkerl/miller
- XML is better than YAML
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jq 1.7 Released
And jless [1] and gron [2].
This is the first I'm hearing of gron, but adding here for completeness sake. Meanwhile, JSON seems to be becoming a standard for CLI tools. Ideal scenario would be if every CLI tool has a --json flag or something similar, so that jc is not needed anymore.
[1] https://jless.io/
[2] https://github.com/tomnomnom/gron
What are some alternatives?
prql - PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement
jq - Command-line JSON processor [Moved to: https://github.com/jqlang/jq]
malloy - Malloy is an experimental language for describing data relationships and transformations.
jfq - JSONata on the command line
tresql - Shorthand SQL/JDBC wrapper language, providing nested results as JSON and more
xidel - Command line tool to download and extract data from HTML/XML pages or JSON-APIs, using CSS, XPath 3.0, XQuery 3.0, JSONiq or pattern matching. It can also create new or transformed XML/HTML/JSON documents.
Preql - An interpreted relational query language that compiles to SQL.
pup - Parsing HTML at the command line
prosto - Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby
JsonPath - Java JsonPath implementation
pxi - 🧚 pxi (pixie) is a small, fast, and magical command-line data processor similar to jq, mlr, and awk.
fx - Terminal JSON viewer & processor