The Mystery of MongoDB Indexing

Perceive the ESR Rule and Index Intersection

Photograph by Jeremy Bishop on Unsplash

I consider we’re all conversant in MySQL’s indexing guidelines. As a result of it’s constructed from a B+ tree, MySQL indexes have a leftmost match rule. To ensure that the question to match the index, the columns utilized in MySQL question syntax are organized from left to proper. For instance:

SELECT * FROM desk WHERE a = 1 AND b > 2 ORDER BY c;

Probably the most environment friendly index for such a question is a compound index like (a, b, c). Nevertheless, such an index can’t be utilized to


It’s because it lacks the required b column.

That is the MySQL indexing rule, and normally, relational databases comply with just about the identical rule for implementation. Nevertheless, there are refined variations in MongoDB’s implementation with B-trees.

We’ll proceed to make use of the question talked about within the earlier part as an indication. 1, b: $gt: 2).type(c: 1)

A lot the identical because the MySQL question talked about earlier, however with MongoDB’s MQL rewritten. Nevertheless, the index that’s legitimate for such a question is (a, c, b) as a substitute of (a, b, c) as talked about within the earlier part.

It’s because MongoDB’s compound indexes should comply with the ESR (Equality, Type, Vary) rule. Therefore, within the above instance, b is used for vary matching and c is used for sorting, so the proper index order is c earlier than b.

Along with the ESR guidelines, that are completely different from relational databases, MongoDB has one other thriller: it might probably use a number of (let’s say 2) indexes in the identical question, which is named index intersection.

Proceed with the question within the earlier part for example. 1, b: $gt: 2).type(c: 1)

To enhance the question efficiency, we recommend to make use of (a, c, b) compound index, however in actual fact, we will obtain the identical consequence with two indexes.

  1. b
  2. (a, c)

We create a single index b and a compound index (a, c), which might additionally enhance the question efficiency.

Why can we use (a, c)? As a result of even index intersection should comply with ESR rule, so we separate ES and R.

What’s the benefit of this? The most important benefit is that the composition of the index turns into extra versatile. If just one index (a, c, b) is created, then if b is queried alone, there is no such thing as a matching index to make use of, so a further index of b have to be created. As everyone knows, indexes are literally a value, which can take up reminiscence and have an effect on writing effectivity. In different phrases, if a extra compact index can be utilized to cowl extra complicated question situations, then such an index can be extra useful.

When utilizing MySQL we use IN to do vary queries, however IN is definitely an equality in MySQL. That’s to say, when the question is WHERE a IN (2, 3) is definitely equal to WHERE a = 2 OR a = 3.

Nevertheless, in MongoDB, it’s not.

Should you merely use a single column $in then it nonetheless has the identical conduct as MySQL.

For instance, discover(a: $in: [2, 3]) and discover($or: [a: 2, a: 3]) are equal, and each belong to E of the ESR rule.

But when used with type, then $in is handled as a variety match, i.e. R. As an illustration:

discover(a: $in: [2, 3]).type(b: 1)

Right here a is handled as R, so to fulfill such a question, the index to be created needs to be (b, a) as a substitute of (a, b).

Should you solely have expertise with relational databases, it’s simple to get confused by feature-rich NoSQL databases, particularly because the underlying MongoDB is definitely a B-tree household just like relational databases. Nevertheless, there are nonetheless vital variations within the implementation particulars.

When creating MongoDB indexes, it’s particularly vital to concentrate to ESR rule. Many customers who’ve moved from MySQL can simply fall down on this rule with out noticing it.

The truth is, even indexes utilizing (a, b, c) don’t trigger issues when the info quantity is small; MongoDB kinds in reminiscence, however when the info quantity grows to a sure measurement, MongoDB can’t load your entire dataset in reminiscence and makes use of arduous disk accesses. Then the efficiency might be very tragic.

Moreover, the index intersection characteristic affords customers extra flexibility to create indexes and supplies them with extra numerous queries. Nevertheless, it’s also vital to pay attention to ESR rule when utilizing index intersection so as to not lose greater than you achieve.

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