mongodb - indexing vs normalization when optimizing for read speed -


having architecture discussion coworker , need find answer this. given set of millions of data points like: 

data = [{     "v" : 1.44,     "tags" : {         "account" : {             "v" : "1055",             "name" : "circle k"         }         "region" : "il-east"     } }, {     "v" : 2.25,     "tags" : {         "account" : {             "v" : "1055",             "name" : "circle k"         }         "region" : "il-west"     } }]

and need query on fields in tags collection (e.g. account.name == "circle k"), there speed benefit normalizing account field this: 

accounts = [{     _id : 507f1f77bcf86cd799439011,     v: "1055",     name : "circle k" }]  data = [{     "v" : 1.44,     "tags" : {         "account" : 507f1f77bcf86cd799439011         "region" : "il-east"     } }, {     "v" : 2.25,     "tags" : {         "account" : 507f1f77bcf86cd799439011         "region" : "il-west"     } }]

i suspect i'll have build 2 db's , see speed looks like. question is, mongo better @ querying on bson ids vs. strings? db in question 1:10 write vs. read.

the important thing here make sure have enough ram working set. includes space "tags.account.name" index , expected query result set.

as key size. use objectid-as-string above, should not do. leave real objectids in size quite bit smaller. if have lot of small documents, might want think shorting field names well.


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