"# searching_in_trie_in_microseconds_1000wdata_python"
just a very simple using of trie Tree. very useful in big data of matching substring with the words in dict. you can assume the solving is a powerful version of kmp in big data.
if your keywords have overlap you can select there method below as your situation. you can check them in 1.py with example . you can test them. the test defualt not activate you can change them in 1.py:309
pipei_shortest : if you match the shortest word in dict, after that and you just skip this word and match the else string with the word in dict.
pipei_all: you find all the matching with the word in dict.
pipei_longest:you can find the lognest word in dict , after that and you just skip this word and match the else string with the word in dict.
i hope you can find the explaning code in the 1.py