Opening data

A file

datasight sales.csv
datasight events.parquet

datasight goes by the file extension:

A file with no extension is read like piped input (below).

Other separators

Use -d for anything that isn’t a comma. It also opens files with any other extension as delimited text:

datasight -d ';' european.csv
datasight -d '|' export.psv
datasight -d '\t' data.txt

Piping data in

Leave the file name off and datasight reads whatever you pipe to it:

curl -s https://api.example.com/orders | datasight
gzip -dc sales.csv.gz | datasight
psql -c "\copy (SELECT * FROM orders) TO STDOUT CSV HEADER" | datasight

It looks at the first character to work out the format: [ means JSON, { means NDJSON, and anything else is treated as CSV. Piped tab-separated data needs -d '\t', because there’s no extension to say so.

You can also pass a command’s output as if it were a file:

datasight <(head -n 1000 huge.csv)

Dates

Text columns that hold dates are turned into real dates automatically, so they sort and chart properly. Recognised formats:

A column is only converted when every value matches. Something like 03/04/2024 could mean 3 April or 4 March. If every value in a column could be read either way, datasight leaves it as text rather than guess.

How big a file?

The whole file is loaded into memory. A 1-million-row, 42 MB CSV opens in under a tenth of a second and uses about 115 MB of RAM. After that, scrolling and filtering stay fast no matter how many rows there are.