You can use prof:track() to time your insertion operation for enough iterations to get a reasonable time and then multiply by 2.5 million to get an approximate time to completion.

On my machine I’m finding times around 0.05 seconds for my operations, which are more than just attribute insertions, where I need to do 40K iterations. I would expect attribute insertion to be faster, especially if you can batch up the insertions into a small number of transactions.

But five hours to do the update doesn’t seem entirely out of spec if your machine is significantly slower. Doing the math, I get 7ms per insertion:

That seems pretty fast on a per-operation standpoint.

If you can break your content into multiple databases you could parallelize the updates across multiple BaseX instances and then combine the result back at the end.

So spin up one server for each core, have a master server that provides a REST API to kick off the processing and then use the REST method to farm jobs out to each of the servers (using REST to make it easy to target each of the servers via a port. Could also do it from a shell script through the baseclient command-line.).

With that should be able to reduce the processing to the time it takes one server to process its share, which will be total objects/number of cores (its share, that is).

Cheers,

E.

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