Mongo query take a long time. How make it more fast?

Question

I use mongoose driver in node js. My schema:

let sendResultSchema = mongoose.Schema({
  emailId: String,      email: String,              
  letterId: String         


        

Answer 1

The index would need to cover all part of the query (equality part, sort part, and range part). This is because in a typical find() query, MongoDB only uses one index. E.g., it generally doesn't use one index for the equality part, and another index for the sort part.

In general, the sequence of fields in the index needs to follow the pattern of equality -> sort -> range.

This is described in detail in Optimizing MongoDB Compound Indexes.

For your query, the equality part is tag:..., letterId:... and the sort part is emailId:-1. There is no range part in your query.

Using this pattern, the compound index you need is:

db.test.createIndex({tag:1, letterId:1, emailId:-1})

Let's try to confirm how much performance improvement we can get using this index.

Test data

To confirm the suitability of the index, I inserted 1 million records into a test database using mgeneratejs, which is a tool to create a random document using a template.

Based on your example, the mgeneratejs template I'm using is:

$ cat template.json
{
  "emailId": "$hash",
  "email": "$email",
  "letterId": "$hash",
  "sendedFrom": "$email",
  "resultMsg": "$word",
  "owner": "$name",
  "created": "$date",
  "result": "$bool",
  "tag": "$word",
  "tryNum": {"$integer": {"min": 0, "max": 1e3}},
  "clickHash": "$word",
  "links": {"$array": {"of": "$url", "number": {"$integer": {"min": 1, "max": 5}}}}
}

and imported 1 million random documents into MongoDB:

$ mgeneratejs template.json -n 1000000 | mongoimport -d test -c test

Test 1: non-optimal index

Then I create the index you have, and tried to find a non-existent document and gathered 10 runs of the query with the collection containing only this index:

> db.test.createIndex({emailId: 1, letterId: 1, result: 1, owner: 1, tag: 1, clickHash: 1})

> db.test.find({"tag" : "xyz", "letterId" : "abc"}).sort({emailId: -1}).limit(1)
Fetched 0 record(s) in 3069ms
Fetched 0 record(s) in 2924ms
Fetched 0 record(s) in 2923ms
Fetched 0 record(s) in 3013ms
Fetched 0 record(s) in 2917ms
Fetched 0 record(s) in 2961ms
Fetched 0 record(s) in 2882ms
Fetched 0 record(s) in 2870ms
Fetched 0 record(s) in 2969ms
Fetched 0 record(s) in 2863ms

so using that index, the response times of the query are not great, with most execution close to 3 seconds.

Test 2: equality -> sort -> range index

By adding the optimal equality -> sort -> range index:

> db.test.createIndex({tag:1, letterId:1, emailId:-1})

> db.test.find({"tag" : "xyz", "letterId" : "abc"}).sort({emailId: -1}).limit(1)
Fetched 0 record(s) in 2ms
Fetched 0 record(s) in 1ms
Fetched 0 record(s) in 1ms
Fetched 0 record(s) in 1ms
Fetched 0 record(s) in 1ms
Fetched 0 record(s) in 1ms
Fetched 0 record(s) in 1ms
Fetched 0 record(s) in 1ms
Fetched 0 record(s) in 1ms
Fetched 0 record(s) in 3ms

In contrast, using the optimal index, performance was markedly improved. No query returned in more than 3ms, with the great majority of time it returns in 1ms.