![]() ![]() ![]() Let’s take a deeper look at what the database is doing using the EXPLAIN method we learned about during the investigation phase. To create an index in MongoDB, simply use the following syntax: db.collection.createIndex(, )įor instance, the following command would create a single field index on the field color: db.collection.createIndex( ) Perform operations on the grouped data to return a single result. With just a few simple commands, MongoDB will automatically sort these fields into separate entries to optimize your query lookups. Group values from multiple documents together. When you know the queries ahead of time that you’re looking to speed up, you can create indexes from within MongoDB on the fields which you need faster access to. These indexes then enable your queries to perform at faster speeds by minimizing the number of disk accesses required with each request. Above will create collection (or table) (if collection already. ![]() For example, you can use aggregation pipelines to: Group values from multiple documents together. sum operator returns the sum of all numeric values of documents in the collection in MongoDB. Indexes store a small portion of each collection’s data set into separate traversable data structures. New in version 1.14.0 The Aggregation Pipeline Builder in MongoDB Compass lets you create aggregation pipelines to process documents from a collection or view and return computed results. Just like relational databases, NoSQL databases like MongoDB also utilize indexes to speed up queries. If you found during your investigation in Part One that your queries are being slowed down by unnecessary collection scans, you may want to consider using user-defined indexes in MongoDB. When operating at scale, most primary production databases cannot afford any collection scans at all unless the QPS is very low or the collection size itself is small. Avoiding Collection Scans using User-Defined Read Indexes In this blog post, we’ll discuss several other targeted strategies that we can use to speed up those problematic queries when the right circumstances are present. In Part One, we discussed how to first identify slow queries on MongoDB using the database profiler, and then investigated what the strategies the database took doing during the execution of those queries to understand why our queries were taking the time and resources that they were taking. ![]()
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