Anagram Solution Server in Different Languages/Frameworks

Algorithm

All standalone servers are using the same workflow:

Download a word list, i.e., words separated by line.
Turn the list into groups by size. For example, ['eat', 'god'] are in the same size of 3 but ['wood', 'fine'] are in group of size 4.
In each group, split the group into hash tables by "word sum". The sum means to add all letter ascii code in a word. For example, sum("bad") will be (b = 98) + (a = 97) + (d = 100) => 295.
Once these two simple hash indexes were built, the solution would be very easy, which is just pick up the same-size-same-sum word list, and perform a for-loop to compare the frequency tables to figure out the matches.
Start an http server to provide a solution JSON api. For example, $ curl http://localhost:8181/?anagram=eat should return ["tea","ate","eta"] if nothing wrong.

Such an algorithm is applying a balance between memory and cpu.

Firstly, two integer indices are light enough and can scope down the search quickly enough.

Secondly, the final frequency table match is only happening in a very narrow scope.

Last but not the least, frequency tables are calculated intermediately in the solution so it would never accumulate or waste too much cpu or memory.

Please visit Perfect for more information, if using Swift.

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
Java+Spark		Java+Spark
Java		Java
JavaScript+C++		JavaScript+C++
JavaScript		JavaScript
PHP		PHP
Python		Python
Swift		Swift
LICENSE		LICENSE
README.md		README.md