Simple Steps to Help You Optimize Your MongoDB Better 

When you are developing applications with MongoDB, you need to ensure that your database performs well. There are some instances where the whole process where the data is served becomes compromised for several reasons. You need to detect these issues to ensure they are arrested and your database’s performance is not hampered at all.

What are the issues that plague the performance of the MongoDB database? 

The following are some of the key issues that hinder the optimal performance of the MongoDB-

  1. The schema designs are not correct or proper 
  2. There are no proper strategies in place for indexing
  3. The hardware is not adequate 
  4. Replication lag 
  5. The query techniques perform poorly 

When it comes to MongoDB’s above issues, you may be compelled to take in more hardware infrastructure. This might resolve some of the problems listed above; however, it is not a solution for all of them. For example, a poor query structure might result in the query taking a very long time to be processed, leading to replica lag and, in some cases, even the loss of data.

You might believe that the storage memory is not sufficient in the above instance, and it needs a scale-up. Professionals in database administration and management like suggests you should always use the appropriate measures that ensure the optimal performance of your database. Now when it comes to the question as to how to optimize your MongoDB better, this guide will help you with the following recommendations to make your MongoDB database perform better- 

Eliminate the data you do not need

This suggestion seems obvious; however, for most business owners, it is challenging for you to incorporate. There are some data that you can remove easily. For instance, you can remove your clients’ documents that you do not use on your platform. In this way, you can reduce a substantial number of documents and free space.

The data that is hard for you to eliminate is generally the redundant data that you see in every document. Every document needs an update. Again, here there is another challenge as every update takes several hours for you to complete. Moreover, you would need to repeat this task for every replica separately. 

Schema design- Follow these optimization tips for schema design

  • Deploy document embedding as much as you can as it reduces the volume of queries you need to operate for a unique data set.
  • Do not use denormalization for those documents that you update frequently. If a field is needed to be updated frequently, you need to find all of the instances that need an update. This results in the query processing becoming slow and, in most cases, overwhelming, affecting the merits that are associated with the process of denormalization.
  • If you need to get a document separately, you do not have to use embedding as complex queries like aggregate pipelining takes a lot of time to be incorporated. 
  • Do not embed the range of documents that are large enough to be embedded. They should have a bound limit for the task. 

Replication lag

There are some instances where you might see some data is missing from the database, or when you delete a data, it surfaces again. No matter how efficiently you have designed the schema and conducted the proper indexing with sufficient resources, problems do arise after that. 

MongoDB depends on the concept of replication, where the data is copied redundantly for meeting an aspect of the design criteria. There is an assumption that this process is an instant one

However, experts from esteemed database administration and management company, caution you that delays might occur because of errors that have not been arrested or because of a failure in the system. In brief, there is a big gap between the time when the operation is processed at the primary node, and the time it takes to be incorporated inside the secondary node.

Replica lag and causes that lead to its setbacks

  • Data is inconsistent – This takes place when read operations get distributed across secondaries. 
  • If the pap gap is big enough, there can be a large volume of un-replicated data on the primary node, which needs to be reconciled on the secondary node. Later, the above can be impossible at some point in time, especially in those cases where recovery of the primary node is not possible. 
  • Recovery failure- When the primary node cannot be recovered, this forces the database to run a node with data that is not updated. This affects the whole database adversely for the primary node to recover. 

To reduce replication lag, the following steps will help you

  • Deploy indexes that are unique and use them beside the collection with the _id field. This prevents the process of replication from failing.
  • Consider the other backup types like filesystem snapshots and point-in-time that do not need to be necessarily locked
  • Do not build large indexes as they block the background operations 
  • The secondary node should be powerful. If your write operation is lightweight, use secondaries that are underpowered as they will be economical. However, for loads with heavy writes, the secondary node will lag behind the primary node. It should contain sufficient bandwidth to quickly aid the operation logs’ reading quickly enough to keep pace with the primary node.

These are just some of the simple steps you can embrace for enhancing the performance of your MongoDB. However, you must ensure you have a team of qualified and experienced DBAs in your company looking after its needs round-the-clock. Even if you do not have a full-time in-house team of qualified professionals, you should hire remote services to help you take care of all the needs of your database. You can discuss what it takes for performance tuning and ask the consultants to check your system to identify loopholes and deliver you a report with listed recommendations on how you can boost your business’s performance with success.  

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