1/6/2024 0 Comments Mongodb compass query array![]() ![]() How to query mongodb collection similar to 'NOT IN' with list of value returned from subquery. Let’s take a deeper look at what the database is doing using the EXPLAIN method we learned about during the investigation phase. Find all documents not having a keyword in array mongodb. 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( ) However, if a documents x field is an array, the document matches if there is an element of x that matches each part of the criteria but each query. ![]() With just a few simple commands, MongoDB will automatically sort these fields into separate entries to optimize your query lookups. 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. Indexes store a small portion of each collection’s data set into separate traversable data structures. Just like relational databases, NoSQL databases like MongoDB also utilize indexes to speed up queries. The examples on this page use the inventory collection. 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. This page provides examples of query operations on array fields using MongoDB Compass. 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 The second query returns the documents where all of the products in the results array are not 'xyz'. 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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