tableconv 1.9685.20211120

CLI data plumbing tool

tableconv

tableconv converts tabular data from any format to any format.

Install

pip install tableconv

Usage

usage: tableconv SOURCE_URL [-q QUERY_SQL] [-o DEST_URL]

positional arguments:
  SOURCE_URL            Specify the data source URL.

optional arguments:
  -h, --help            show this help message and exit
  -q SOURCE_QUERY, --query SOURCE_QUERY
                        Query to run on the source. Even for non-SQL datasources (e.g. csv or json), SQL querying is still supported, try `SELECT * FROM data`.
  -F INTERMEDIATE_FILTER_SQL, --filter INTERMEDIATE_FILTER_SQL
                        Filter (aka transform) the input data using a SQL query operating on the dataset in memory using DuckDB SQL.
  -o DEST_URL, --dest DEST_URL, --out DEST_URL
                        Specify the data destination URL. If this destination already exists, be aware that the default behavior is to overwrite.
  -i, --interactive     Enter interactive REPL query mode
  --open                Open resulting file/url (not supported for all destination types)
  -v, --verbose, --debug
                        Show debug details, including all API calls.
  --quiet               Only display errors.

supported url schemes:
- ascii:- (dest only)
- asciibox:- (dest only)
- asciifancygrid:- (dest only)
- asciigrid:- (dest only)
- asciilite:- (dest only)
- asciipipe:- (dest only)
- asciiplain:- (dest only)
- asciipresto:- (dest only)
- asciipretty:- (dest only)
- asciipsql:- (dest only)
- asciisimple:- (dest only)
- awsathena://eu-central-1 
- awsdynamodb://eu-central-1/example_table (source only)
- csa:- 
- example.csv 
- example.dta 
- example.feather 
- example.h5 
- example.hdf5 
- example.json 
- example.jsonl 
- example.orc (source only)
- example.parquet 
- example.py 
- example.python 
- example.tsv 
- example.xls 
- example.xlsx 
- example.yaml 
- gsheets://:new: 
- html:- (dest only)
- jiracloud://mycorpname (source only)
- jsonarray:- 
- latex:- (dest only)
- list:- 
- markdown:- (dest only)
- md:- (dest only)
- mediawikiformat:- (dest only)
- moinmoinformat:- (dest only)
- mssql://127.0.0.1:5432/example_db 
- mysql://127.0.0.1:5432/example_db 
- oracle://127.0.0.1:5432/example_db 
- postgis://127.0.0.1:5432/example_db 
- postgres://127.0.0.1:5432/example_db 
- postgresql://127.0.0.1:5432/example_db 
- pylist:- 
- rst:- (dest only)
- smartsheet://SHEET_ID (source only)
- sqlite3://127.0.0.1:5432/example_db 
- sqlite://127.0.0.1:5432/example_db 
- sumologic://?from=2021-03-01T00:00:00Z&to=2021-05-03T00:00:00Z (source only)
- tex:- (dest only)
- yamlsequence:-

Examples

Basic conversion

Convert JSON to CSV

tableconv test.json -o test.csv

Convert CSV to JSON

tableconv test.csv -o test.json

Dump a postgres table as JSON

tableconv postgresql://192.168.0.10:5432/test_db/my_table -o my_table.json

Display a parquet file's data in a human-readable format

tableconv test.parquet -o ascii:-

Convert CSV to a Markdown table

tableconv test.csv -o md:-

Data transformation with SQL

Dump the first 100 rows of a postgres table as JSON

tableconv postgresql://192.168.0.10:5432/test_db -q 'SELECT * FROM my_table ORDER BY id LIMIT 100' -o my_table.json

Copy a few columns from one CSV into a new CSV. (in general, all functionality works on all of the supported data formats. So you can of course query with SQL on an Oracle database but it's also supported to query with SQL on JSON, SQL on Excel, and, here SQL on CSV)

tableconv test.csv -q 'SELECT time, name FROM data ORDER BY time DESC' -o output.csv

Append a few columns from a CSV into MySQL

tableconv test.csv -q 'SELECT time, name FROM data ORDER BY time DESC' -o mysql://localhost:3306/test_db/my_table?if_exists=append

Extract a report from a SQLite database into a new Google Spreadsheet

tableconv sqlite3://my_db.db -q 'SELECT name, COUNT(*) from occurences ORDER BY 2 DESC LIMIT 10' -o "gsheets://:new:/?name=top_occurences_$(date +'%Y_%m_%d')"

Interactive mode

Launch an interactive SQL shell to inspect data from a CSV file in the terminal

tableconv test.csv -i -o ascii:-

Arrays

Convert a copy/pasted newline-deliminated list into a python list

pbpaste | tableconv list:- -o python:-

Details

As a prototype, tableconv is usable as a quick and dirty CLI ETL tool for converting data between any of the formats, or usable for performing basic bulk data transformations and joins defined in a unified language (SQL) but operating across disparate data in wildly different formats. That is the immediate value proposition of tableconv, but it was created within the mental framework of a larger vision: The tableconv vision of computing is that all software fundamentally interfaces via data tables; that all UIs and APIs can be interpreted as data frames or data tables. Instead of requiring power users to learn interface after interface and build their own bespoke tooling to extract and manipulate the data at scale in each interface, the world needs a highly interoperable operating system level client for power users to directly interact with, join, and manipulate the data with SQL (or similar) using the universal "table" abstraction provided in a consistent UI across each service. Tableconv is that tool. It is meant to have adapters written to support any/all services and data formats.

However, this is just a prototype. The software is slow in all ways and memory+cpu intensive. It has no streaming support and loads all data into memory before converting it. Its most efficient adapters cannot handle tables over 10 million cells, and the least efficient cannot handle over 100000 cells. Schemas can migrate inconsistently depending upon the data available. It has experimental features that will not work reliably, such as schema management, the unorthodox URL scheme, and special array (1 dimensional table) support. All parts of the user interface are expected to be overhauled at some point. The code quality is mediocre and inconsistent. Most obscure adapter options are untested. It has an incomplete story on how to use it outside the CLI in other software, as a library. It has no story or documentation for service authentication, aside from SQL DBs. Lastly, the documentation is weak and no documentation has been written to document the standard options available for each adapter, nor documentation of any adapter-specific options.

Main Influences

• odo
• Singer
• ODBC/JDBC
• osquery