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Mastering MongoDB 3.x

Mastering MongoDB 3.x

By : Alex Giamas
4.1 (13)
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Mastering MongoDB 3.x

Mastering MongoDB 3.x

4.1 (13)
By: Alex Giamas

Overview of this book

MongoDB has grown to become the de facto NoSQL database with millions of users—from small startups to Fortune 500 companies. Addressing the limitations of SQL schema-based databases, MongoDB pioneered a shift of focus for DevOps and offered sharding and replication maintainable by DevOps teams. The book is based on MongoDB 3.x and covers topics ranging from database querying using the shell, built in drivers, and popular ODM mappers to more advanced topics such as sharding, high availability, and integration with big data sources. You will get an overview of MongoDB and how to play to its strengths, with relevant use cases. After that, you will learn how to query MongoDB effectively and make use of indexes as much as possible. The next part deals with the administration of MongoDB installations on-premise or in the cloud. We deal with database internals in the next section, explaining storage systems and how they can affect performance. The last section of this book deals with replication and MongoDB scaling, along with integration with heterogeneous data sources. By the end this book, you will be equipped with all the required industry skills and knowledge to become a certified MongoDB developer and administrator.
Table of Contents (13 chapters)
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Operations

When connecting to our production MongoDB servers, we want to make sure that our operations are as lightweight as possible (and certainly non-destructive) and do not alter the database state in any sense.

Two useful utilities we can chain to our queries are as follows:

> db.collection.find(query).maxTimeMS(999)

Our query will only take up to 999 ms and then return an exceeded time limit error.

> db.collection.find(query).maxScan(1000)

Our query will examine at most 1,000 documents to find results and then return (no error raised).

Whenever we can, we should bind our queries by time or document result size to avoid running unexpectedly long queries that may affect our production database.

A common reason for accessing our production database is troubleshooting degraded cluster performance. This can be investigated via cloud monitoring tools as described in previous...

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