Working with RedisGraph: your brain and old habits are your worst enemy

One of the things I noticed in my hobby project working with Redis I started overthinking and over optimising:

Consider old code here — it makes two calls to RedisGraph, one to fetch edges and then another one to turn node ids to list of dictionary `{id:node_id,name:node_name}`, both queries are trivial:

#fetch edges
WITH $ids as ids MATCH (e:entity)-[r]->(t:entity) where in ids RETURN DISTINCT,,max(r.rank) ORDER BY r.rank
#fetch nodes
WITH $ids as ids MATCH (e:entity) where in ids RETURN DISTINCT,,max(e.rank)

But when I added years to nodes properties I decided to “optimise” and fetch node names in the same query, this is probably what you would normally do for SQL server:

WITH $ids as ids MATCH (e:entity)-[r]->(t:entity) where ( in ids) and (r.year in $years) RETURN DISTINCT,,e.rank,,, t.rank, max(r.rank), r.year ORDER BY r.rank DESC

Full code here. But then now I need to traverse via RedisGraph output to shape dict, and then flatten dict in python so I can serialize it to JSON and return to three.js.

#flatten dict
node_list=[{'name':k,'id':node_dict[k]['id'],'rank':node_dict[k]['rank']} for k in node_dict]

Hold on a second, all this python code to save on query which takes between 30 to 100 milliseconds response?

Now I am putting back the old code.

I am a systems thinker with a deep understanding of technology and a methodological approach to innovation