Mendoza: Use stack machines to compute efficient JSON diffs

Post by nerdponx » Fri, Oct 30, 2020, 8:07 PM UTC

Arqu wrote: post #19

I'm super interested in this topic. Recently (and still ongoing) I started on hashing out how to diff large datasets and what that even means. I would love to get an understanding of how the HN crowd sees diffing datasets should be (lets say >1GB in size). Are you more interested in a "patch" quality diff of the data which is more machine tailored? Or is a change report/summary/highlights more interesting in that cas…

Post by jiofih » Fri, Oct 30, 2020, 9:58 PM UTC

feanaro wrote: post #15

This is an interesting tool for computing minimal diffs, but the result is not very human friendly. If this is your goal and you are looking for something better than diff, have a look at graphtage: https://github.com/trailofbits/graphtage Also works for XML, HTML, YAML and CSV.

Post by jdc » Fri, Oct 30, 2020, 10:37 PM UTC

feanaro wrote: post #15

This is an interesting tool for computing minimal diffs, but the result is not very human friendly. If this is your goal and you are looking for something better than diff, have a look at graphtage: https://github.com/trailofbits/graphtage Also works for XML, HTML, YAML and CSV.

Post by mjgp2 » Fri, Oct 30, 2020, 11:07 PM UTC

Post by konda » Sat, Oct 31, 2020, 1:58 AM UTC

nerdponx wrote: post #21

Arqu wrote: post #19

I'm super interested in this topic. Recently (and still ongoing) I started on hashing out how to diff large datasets and what that even means. I would love to get an understanding of how the HN crowd sees diffing datasets should be (lets say >1GB in size). Are you more interested in a "patch" quality diff of the data which is more machine tailored? Or is a change report/summary/highlights more interesting in that cas…

Both! I need to be able to handle merge conflicts for data, but I also need the machine to be able to apply the changes.