Advanced index operation in MongoDB

Create an index on the array ie create separate indexes for each of its fields. So in this situation, when we create an index on the array of tags, individual indexes will be created for their values: music, cricket and blogs.

Advanced index operation in MongoDB

You follow the following Document of users collection:

 { "address" : { "city" : "Los Angeles" , "state" : "California" , "pincode" : "123" }, "tags" : [ "music" , "cricket" , "blogs" ], "name" : "Tom Benzamin" }

The above document contains a Subdocument as address and an array of tags .

Index array fields in MongoDB

Suppose that we want to search the user document based on tags. To do this, we will create an index on the array of tags in that Collection.

Create an index on the array ie create separate indexes for each of its fields. So in this situation, when we create an index on the array of tags, individual indexes will be created for their values: music, cricket and blogs.

To create an index on the array of tags, you use:

 > db . users . ensureIndex ({ "tags" : 1 })

After creating the index, we can perform a search on that Collection's tags field like this:

 > db . users . find ({ tags : "cricket" })

To check if a valid index has been used, you use the explain command.

 > db . users . find ({ tags : "cricket" }). explain ()

The explain command returns the result in "cursor": "BtreeCursor tags_1" which confirms that the logical index is used.

Field indexing is the Subdocument

Assume that you want to search in Documents based on city, state, and pincode fields. When all these fields are part of the address field, which is the Subdocument, you will create an index on all the fields of that Subdocument.

To create an index on all the fields of that Subdocument, you use:

 > db . users . ensureIndex ({ "address.city" : 1 , "address.state" : 1 , "address.pincode" : 1 })

Once the index has been created, you can search for any field in the fields of that Subdocument. Take advantage of this index as follows:

 > db . users . find ({ "address.city" : "Los Angeles" })

Remember, the query expression must follow the order of the specified index. Therefore, the index created above will support the following queries:

 > db . users . find ({ "address.city" : "Los Angeles" , "address.state" : "California" })

It will also support the following query:

 > db . users . find ({ "address.city" : "LosAngeles" , "address.state" : "California" , "address.pincode" : "123" })

According to Tutorialspoint

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Lesson: Limit of index in MongoDB

Index (Mong) in MongoDB
index (index) supports the resolution of queries more efficiently. without an index, mongodb must scan through all a collection's document to select the documents that connect to the query command. this scan may be ineffective and requires mongodb to handle a large amount of data.
Limitations of indexes in MongoDB
each index not only occupies part of the memory space but also causes an overhead on each insert, update, and delete operation. so, if you rarely use your collection for read operations, you should not use the index for it.
Text Search in MongoDB
starting with version 2.4, mongodb started supporting text indexes to search within the string content.
Advantages of MongoDB
any relation database has a unique schema design to index the data tables and relationships between those tables. meanwhile in mongodb there is no concept of relationship.
Covered Query in MongoDB
when all fields in the query are part of the index, mongodb connects query conditions and returns the result by using the same index without looking inside the document. when indexes are present in ram, retrieving data from indexes is faster when compared to retrieving data by scanning all documents.
Index (INDEX) in SQL
sql index (index) is a special lookup table that database search engines can use to quickly increase the time and performance of data retrieval.
Query analysis in MongoDB
analyzing queries is a very important aspect to assess the effectiveness of database and the effectiveness of the designed index. we will explore the two most frequently used queries, $ explain and $ hint.
MongoDB queries and tasks you should know
mongodb is one of the most coveted and admired nosql databases for professional development. its flexibility, scalability and handling of large volumes of data make it the first choice for modern applications.
Aggregation in MongoDB
aggregation can be understood as aggregation. the aggregation operation handles data records and returns calculated results. the operations group the values from multiple documents together, and can perform multiple operations on the grouped data to return a single result. in sql, count (*) and group by are equivalent to aggregation in mongodb.
Map Reduce in MongoDB
in mongodb documentation, map-reduce is a data processing system that condenses a large amount of data into useful overall results. mongodb uses mapreduce command for map-reduce operation. in general, map reduce is used to handle large data sets.
Instructions on 2 ways to install MongoDB on Raspberry Pi
in this tutorial, tipsmake will guide you through the process of installing and setting up the mongodb server software on your raspberry pi.
Atomic Operation in MongoDB
mongodb does not support atomic transaction over multiple documents. however, it provides atomic operations on a single document. therefore, if a document has hundreds of fields, the update command will either update all those fields or not update any fields, thus maintaining atomicity at the document level.