Understanding indexing and cardinality for MongoDB
Bharat Kalluri / 2021-03-06
Cardinality is defined to be number of unique elements present in a set. The lower the cardinality, the more duplicated elements.
So if a set has 5 elements made of boolean values, then the cardinality of the set is going to be two. So, all sets made of booleans will have a max cardinality of two and a min cardinality of one.
How cardinality impacts indexing
If a boolean field is indexed, there is not much the index will improve in terms of performance. Imagine a column in the database to have just booleans with a 50/50 split. The index will allow you to skip 50% of the documents, but still be a sequential scan of the rest 50%. If there is a 80/20 split between true and false, then the index is pretty much useless when querying over the true part because you will have to do a sequential scan of 80% of the documents (But the queries looking for false will benefit from the index).This applies to any field with a low cardinality, if a field has an enum of five values and thousands of documents in each category, A similar effect can be observed. Indexes must be built carefully in conditions like these. One more side effect of having an index on such fields is that it impacts writes as well.
Indexing in MongoDB
MongoDB allows partial indexes, sparse indexes and compound indexes.
Imagine you wanted to have a index where a boolean, if the queries look for false and filter further. Then instead of creating a index on the complete field, mongoDB allows us to create a partial index with just the false values. This gets us the best of two worlds. Write speed is not impacted for true and false records are easily searchable.
This is one more common index types in mongoDB which is really popular (for a good reason), the idea is that sparse index allows to create an index where the value of the field is not undefined. This also helps when you have an optional unique field, also if the index is on a field where the majority of the fields are undefined.
Compound indexes really shine here. One note here is that, a compound index should never start with a field having a low cardinality. It defeats the purpose.
The idea is that, based on your queries, create a compound index with the field which has the max cardinality first and the least cardinality to the last. So, whenever a query is run, it will narrow down the funnel by filtering out the majority of the rows (since the first piece in the compound index has the highest cardinality) and progressively narrows down on the result.
Analyze all the created indexes and queries periodically to understand if any query can be improved. MongoDB also
provides a performance advisor in mongoDB atlas, which is really helpful. Keep an eye out for queries which do
COLLSCAN on a large amount of rows. Indexing is a very powerful feature, and used right will prove to massively