MongoDB largely relies upon open-source DBMS (database management system), which functions with a document-oriented DB model, supporting all different forms of data. MongoDB is one among the top nonrelational database models, which got introduced in the mid of 2000s as NoSQL for big data applications. It was highly functional in processing the jobs involving data of different forms, which don’t fit in well in the rigid form of the relational model. Instead of the conventional mode of rows and tables in relational databases, the architecture of MongoDB is more based on documents and collections.
Optimizing MongoDB for enterprise database management
If you ever faced any performance issues on using MongoDB database, then read on. The major issue with MongoDB as faced by many is the performance issues while running queries. The immediate solution to think of is “Let’s create an index.” Even though this works in many cases, there are some other options too we need to consider in order to optimize MongoDB.
Database Performance is not simply the matter of having some big machines, expensive disks, and speedy networks. Performance enhancement of MongoDB is owed more to its fairly new concepts in terms of database management, organization or data, and distribution of data. Further, in this article, we will discuss some of the industry best practice in terms of MongoDB optimization. This is not; however, an exhaustive list of solutions, but there are many variables to consider. In fact, this can be only a good start.
Keeping the documents simple
As we have read above, unlike conventional relational databases, MongoDB is more of a schema-free database. There is no such predefined schema for MongoDB by default. The users have the option to add any predefined schemas in the latest versions; however, this is not mandatory. One needs to beware of the troubles involved while working with the embedded documents and various arrays and it may sometimes become complicated to parse data in the ETL process and application side.
Besides this, the arrays may also hurt the replication performance. For any single change in the array, each array values get replicated. In terms of MMAPv1, it is very important to choose the right field names as the database gets saved in the field name in every document. This is totally different from saving schema in relational databases. Documents of big size aren’t desirable as the DB will need more pages for a single document. This further means more CPU cycles are required to finish the operation and it adversely affects optimal performance.
Hardware is important
It is essential to use good hardware featuring multiple processors and a significant amount of memory to ensure optimum performance. The advanced solutions in terms of MongoDB management offer multiple processors to ensure better performance. These storage engines feature some kinds of per-document locking algorithms, which enable simultaneous processing of as many operations as possible.
There is every possibility of a failover in some typical environments, i.e., if there are any big machines. In fact, this can be further sorted out by having many small and medium machines in the distributed environment which will further make sure that the outages will affect only some parts with no perception by the application. At the same time, more machines in the environment will lessen the probability of any failure. As RemoteDBA.com suggest, you consider this effective tradeoff while designing your distributed database environment.
Read preference and write Concern
Read preferences and write Concern may vary based on the organization’s priorities. However, one should remember that the latest MongoDB (version 3.6) uses
write Concern: “majority”
read Concern: “primary”
This implies that it should acknowledge all write in at least floor ((N/0.5)+1), in which ‘N’ is the total number of instances in a replica set. This may be slow. However, this will be an ideal trade-off in terms of speed and consistency. Always make sure that you are using the most appropriate write concern and read preference for your purpose. The drivers by default read from primary, but sometimes it may not be the need for your environment so one can consider distributing queries among other instances too.
Working Set
You need to consider the size of the working set. An application will not use the entire data; some data gets updated often whereas some other left untouched. So, you need to consider at the first point whether the working dataset fit in the RAM. Optimal performance can be ensured only when the working data set runs smoothly in the RAM. Any possible slowing down like the page faults may hurt performance based on what actions you are performing.
The ‘Reads’ like ETL, backup, or reporting from primaries may hurt your performance as there is a competition to have pages in the cache. The same is true in the case of large reports. In this case, it will be helpful to have multiple collections for various purposes and usage of specific machines for different purposes. You may use different zones to save the documents which are no longer used. This will help to simplify the working set and optimize the overall performance.
MongoDB GUI tools
There are a lot of management tools for MongoDB. These improve the overall productivity of your MongoDB administrators. Here is a handy list of the latest MongoDB tools.
1) NoSQLBooster
This is a cross-platform shell-centric GUI tool, which is available for free. This tool features built-in language service to know all methods, completions, variables, keywords, properties, and also the MongoDB collection names, operators, and field names, etc.
2) MongoDB Compass
It is another effective GUI tool to provide the users with a handy graphical view of the MongoDB schema. It also can analyze the documents and display the structures inside the GUI itself. A visual exploration of data makes it easy for the administrators to get an instant insight into DB.
3) Studio 3T
It is a handy tool for developers to explore the local database and to work more effectively with replica sets and shards. This till is compatible not only with the latest but also the releases of MongoDB.
Some other tools to explore are Nucleon Database Master, NoSQL Manager, Mongo Management Studio, MongoJS Query Analyzer, Nosqlclient, Cluster control, etc., which will make MongoDB administration much easier and flexible in 2019.
