I Failed the Turing Test

Indexes are crucial for the efficient execution of queries and aggregations in MongoDB. Without indexes, MongoDB must perform a collection scan, i.e., scan every document in a collection.

If a write operation modifies an indexed field, MongoDB updates all indexes that have the modified field as a key. So, be careful while choosing indexes.

Types Of Indexes

ref:
https://docs.mongodb.com/manual/indexes/
https://docs.mongodb.com/manual/applications/indexes/

Single Field Index

For a single field index and sort operations, the sort order (i.e. ascending or descending) of the index key doesn't matter. With index intersetion, single field indexs could be powerful.

ref:
https://docs.mongodb.com/manual/core/index-single/

Compound Index

The order of the fields listed in a compound index is very important.

ref:
https://docs.mongodb.com/manual/core/index-compound/
https://docs.mongodb.com/manual/tutorial/create-indexes-to-support-queries/

TTL Index

When the TTL thread is active, a background thread in mongod reads the values in the index and removes expired documents from the collection. You will see delete operations in the output of db.currentOp().

TTL indexes are a single-field indexes. Compound indexes do not support TTL and ignore the expireAfterSeconds option.

import datetime

class JournalEntry(db.Document):
    users = db.ListField(db.ReferenceField('User'))
    event = db.StringField()
    context = db.DynamicField()
    timestamp = db.DateTimeField(default=datetime.datetime.utcnow)

    meta = {
        'index_background': True,
        'indexes': [
            {
                'fields': ['timestamp'],
                'cls': False,
                'expireAfterSeconds': int(datetime.timedelta(days=90).total_seconds()),
            },
        ],
    }

ref:
https://docs.mongodb.com/manual/core/index-ttl/

Index Intersection

MongoDB can use multiple single field indexes to fulfill queries.

db.orders.createIndex({tags: 1});
db.orders.createIndex({key: { created_at: -1 }, background: true});

db.orders.find({item: 'abc123', qty: {$gt: 15}});

ref:
https://docs.mongodb.com/manual/core/index-intersection/

Covered Queries

ref:
https://docs.mongodb.com/manual/core/query-optimization/#read-operations-covered-query

Index Limits

The size of an index entry for an indexed field must be less than 1024 bytes. For instance, an arbitrary URL field can easily exceed 1024 bytes.

MongoDB will not insert into an indexed collection any document with an indexed field whose corresponding index entry would exceed the index key limit, and instead, will return an error; Updates to the indexed field will error if the updated value causes the index entry to exceed the indexkey limit.

ref:
https://docs.mongodb.com/manual/reference/limits/#indexes

List Indexes

db.message.getIndexes()

// show collection statistics
db.message.stats()
db.message.stats().indexSizes

// scale defaults to 1 to return size data in bytes
// 1024 * 1024 means MB
db.getCollection('message').stats({'scale': 1024 * 1024}).indexSizes

ref:
https://docs.mongodb.com/manual/tutorial/manage-indexes/

Add Indexes

TODO:
It seems like creating indexes on empty collection, even with background will cause DB latency.

An index which contains array fields might consume a lot of disk space.

db.message.createIndex({
    '_cls': 1,
    'sender': 1,
    'posted_at': 1
}, {'background': true, 'sparse': true})

db.message.createIndex({
    '_cls': 1,
    'includes': 1,
    'posted_at': 1
}, {'background': true, 'sparse': true})

db.getCollection('message').find({
    '$or': [
        // sent by cp
        {
            '_cls': 'Message.ChatMessage',
            'sender': ObjectId('582ee32a5b9c861c87dc297e'),
            'posted_at': {
                '$gte': ISODate('2018-01-08T00:00:00.000Z'),
                '$lt': ISODate('2018-01-14T00:00:00.000Z')
            }
        },
        // sent by payer
        {
            '_cls': 'Message.GiftMessage',
            'includes': ObjectId('582ee32a5b9c861c87dc297e'),
            'posted_at': {
                '$gte': ISODate('2018-01-08T00:00:00.000Z'),
                '$lt': ISODate('2018-01-14T00:00:00.000Z')
            }
        }
    ]
})

import pymongo
from your_app.models import YourModel

YourModel._get_collection().create_index(
    [
        ('users', pymongo.ASCENDING),
        ('timestamp', pymongo.DESCENDING),
    ], 
    background=True,
    partialFilterExpression={'timestamp': {'$exists': True}},
)

ref:
http://api.mongodb.com/python/current/api/pymongo/collection.html#pymongo.collection.Collection.create_index

You can't index two arrays together, in this example: includes and unlocks.

// it doesn't work
db.message.createIndex({
    '_cls': 1,
    'sender': 1,
    'includes': 1,
    'unlocks': 1
}, {'background': true, 'sparse': true})

The Order Of Fields of Compound Indexes

The order of fields in an index matters, you must consider Index Cardinality and Selectivity. Instead, the order of fields in a find() query or $match in an aggregation doesn't affect whether it can use an index or not.

The order of fields in a compound index should be:

• First, fields on which you will query for exact values.
• Second, fields on which you will sort.
• Finally, fields on which you will query for a range of values.

ref:
https://docs.mongodb.com/manual/core/index-compound/#prefixes
https://emptysqua.re/blog/optimizing-mongodb-compound-indexes/
https://blog.mlab.com/2012/06/cardinal-ins/
https://stackoverflow.com/questions/33545339/how-does-the-order-of-compound-indexes-matter-in-mongodb-performance-wise
https://stackoverflow.com/questions/5245737/mongodb-indexes-order-and-query-order-must-match

Partial Indexes v.s. Sparse Indexes

Partial indexes should be preferred over sparse indexes. However, partial indexes only support a very small set of filter operators:

• $exists
• $eq or field: value
• $gt, $gte, $lt, $lte
• $type
• $and

If you use 'partialFilterExpression': {'includes': {'$exists': true}}, MongoDB also indexes documents whose includes field has null value.

db.collection('message').createIndex(
    {'_cls': 1, 'includes': 1, 'posted_at': 1},
    {'background': true, 'partialFilterExpression': {'includes': {'$exists': true}}}
)

db.collection('message').createIndex(
  {'created_at': -1},
  {'background': true, 'partialFilterExpression': {'created_at': {'$gte': new Date("2018-01-01T16:00:00Z")}}}
)

ref:
https://docs.mongodb.com/manual/core/index-partial/
https://docs.mongodb.com/manual/core/index-sparse/

Create An Index On An Array Field

Querying will certainly be a lot easier in an array field index than a object field.

ref:
https://stackoverflow.com/questions/9589856/mongo-indexing-on-object-arrays-vs-objects

Create An Unique Index On An Array Field

Create an unique index on an array field.

The unique constraint applies to separate documents in the collection. That is, the unique index prevents separate documents from having the same value for the indexed key. It prevents different documents have the same transaction ID but allows one document has multiple identical transaction IDs.

db.getCollection('test1').createIndex({purchases.transaction_id: 1}, {unique: true})

db.getCollection('test1').insert({ _id: 1, purchases: [
    {transaction_id: 'A'}
]})

db.getCollection('test1').insert({ _id: 5, purchases: [
    {transaction_id: 'A'}
]})

db.getCollection('test1').update({ _id: 1}, {$push: {purchases: {transaction_id: 'A'}}})

To prevent one document has multiple identical transaction IDs, We would have atomic updates on single documents.

user = User(id=bson.ObjectId(user_id))
purchase = DirectPurchase(
    user=user,
    timestamp=timestamp,
    transaction_id=transaction_id,
)
MessagePackProduct.objects \
    .filter(id=message_pack_id, __raw__={
        'purchases': {'$not': {'$elemMatch': {
            '_cls': purchase._cls,
            'user': purchase.user.id,
        }}},
    }) \
    .update_one(push__purchases=purchase)

ref:
https://docs.mongodb.com/manual/core/index-unique/#unique-constraint-across-separate-documents

Sort With Indexes

ref:
https://docs.mongodb.com/manual/tutorial/sort-results-with-indexes/

Drop Indexes

db.message.dropIndex({
    'includes': 1
})

db.message.dropIndex({
    '_cls': 1,
    'posted_at': 1,
    'includes': 1
})

Remove Unused Indexes

You can use db.getCollection('COLLECTION_NAME').aggregate({$indexStats: {}}) to find unused indexes, there is a accesses.ops field which indicates the number of operations that have used the index. Also, you might want to remove indexes which have the same prefix.

db.getCollection('message').aggregate(
    {
        '$indexStats': {}
    },
    {
        '$match': {
            'accesses.ops': {'$gt': 0}
        }
    }
);

Result:

{
    "name" : "_cls_1_sender_1_posted_at_1",
    "key" : {
        "_cls" : 1,
        "sender" : 1,
        "posted_at" : 1
    },
    "host" : "a6ea11893605:27017",
    "accesses" : {
        "ops" : 3,
        "since" : "2018-01-26T07:04:51.137Z"
    }
}

ref:
https://blog.mlab.com/2017/01/using-mongodb-indexstats-to-identify-and-remove-unused-indexes/
https://scalegrid.io/blog/how-to-find-unused-indexes-in-mongodb/

Profiling

// enable
db.setProfilingLevel(2)

// disable
db.setProfilingLevel(0)

// see profiling data after you issues some queries
db.system.profile.find().limit(10).sort( { ts : -1 } ).pretty()

// delete profiling data
db.system.profile.drop()

Query Explain

There are both collection.find().explain() and collection.explain().find(). It's recommended to use collection.find().explain('executionStats') for getting more information, like total documents examined.

db.getCollection('message').find({
    '$or': [
        // sent by cp
        {
            '_cls': 'Message.ChatMessage',
            'sender': ObjectId('582ee32a5b9c861c87dc297e'),
            'posted_at': {
                '$gte': ISODate('2018-01-08T00:00:00.000Z'),
                '$lt': ISODate('2018-01-14T00:00:00.000Z')
            }
        },
        {
            '_cls': 'Message',
            'sender': ObjectId('582ee32a5b9c861c87dc297e'),
            'posted_at': {
                '$gte': ISODate('2018-01-08T00:00:00.000Z'),
                '$lt': ISODate('2018-01-14T00:00:00.000Z')
            }
        },
        // sent by payer
        {
            '_cls': 'Message.ChatMessage',
            'includes': ObjectId('582ee32a5b9c861c87dc297e'),
            'posted_at': {
                '$gte': ISODate('2018-01-08T00:00:00.000Z'),
                '$lt': ISODate('2018-01-14T00:00:00.000Z')
            }
        },
        {
            '_cls': 'Message.ReplyMessage',
            'includes': ObjectId('582ee32a5b9c861c87dc297e'),
            'posted_at': {
                '$gte': ISODate('2018-01-08T00:00:00.000Z'),
                '$lt': ISODate('2018-01-14T00:00:00.000Z')
            }
        },
        {
            '_cls': 'Message.GiftMessage',
            'includes': ObjectId('582ee32a5b9c861c87dc297e'),
            'posted_at': {
                '$gte': ISODate('2018-01-08T00:00:00.000Z'),
                '$lt': ISODate('2018-01-14T00:00:00.000Z')
            }
        }
    ]
})
// .explain()
// .explain('allPlansExecution')
.explain('executionStats')

ref:
https://docs.mongodb.com/manual/reference/method/cursor.explain/
https://docs.mongodb.com/manual/reference/method/db.collection.explain/#db.collection.explain

You could also explain a .update() query. However, .updateMany() and .updateOne() don't support .explain().

db.getCollection('user').explain().update(
    {'follows.user': ObjectId("57985b784af4124063f090d3")},
    {'$set': {'created_at': ISODate('2018-01-01 00:00:00.000Z')}},
    {'multi': true}
)

Some important fields to look at in the result of explain():

• executionStats.totalKeysExamined
• executionStats.totalDocsExamined
• queryPlanner.winningPlan.stage
• queryPlanner.winningPlan.inputStage.stage
• queryPlanner.winningPlan.inputStage.indexName
• queryPlanner.winningPlan.inputStage.direction

Possible values of stage:

• COLLSCAN: scanning the entire collection
• IXSCAN: scanning index keys
• FETCH: retrieving documents
• SHARD_MERGE: merging results from shards

ref:
https://docs.mongodb.com/manual/reference/explain-results/

Aggregation Explain

db.getCollection('message').explain().aggregate()

ref:
https://stackoverflow.com/questions/12702080/mongodb-explain-for-aggregation-framework
https://docs.mongodb.com/manual/reference/method/db.collection.explain/

If $project, $unwind, or $group occur prior to the $sort operation, $sort cannot use any indexes. Additionally, $sort can only use fields defined in previous $project stage.

Basically, you could just consider the $match part when you want to create new indexes.

ref:
https://docs.mongodb.com/manual/reference/operator/aggregation/sort/#sort-operator-and-performance

MongoEngine

_cls creation on indexes is automatically included if allow_inheritance is on. If you want to disable, set kwarg cls: False.

ref:
http://docs.mongoengine.org/guide/defining-documents.html#indexes