Ah, I didn't see the N records originally. That makes sense! Because I'm enthusiastic about data structures, and now that I've finished my work day, I thought

Ah, I didn't see the N records originally. That makes sense! Because I'm enthusiastic about data structures, and now that I've finished my work day, I thought I'd come back with a few numbers to support my earlier comment. The package size can be reduced by 90% by saving the names as compressed plaintext. 5192 KB gender.json 1484 KB gender.txt (71% savings) 488 KB gender.txt.gz (90% savings) On my reasonably modern laptop, it takes over 700 ms to unmarshal 5MB worth of JSON. But it takes less than 100 ms (85% time savings) to read the whole file and compare strings. $ time jq -r . >/dev/null gender.json real 0m0.764s $ time grep -E '^(NIC|PAUL)' gender.txt real 0m0.091s When working with large datasets of items that are mostly the same size, sometimes it's useful to use fixed-width records to enable random indexing into the file. Of course, this is a small data set so it's not really worthwhile to pursue such optimizations. So just for fun, here's an analysis of how long the names are. 99% of names in this set are 13 characters or less. Representing short names as fixed-width records and long names as an appendix would use about 2.5 MB (50% savings compared to JSON). 2 222 3 1785 4 8913 5 27862 6 45986 7 44311 8 28291 9 14355 10 7167 11 4072 12 2611 13 1512 PS. Here's how I prepared the text file: <gender.json jq -r '. | keys[]' > gender.txt