Applications based on data can be based on bringing together various storage and search technologies. MongoDB is most effective in the storage of flexible documents whereas Elasticsearch is most effective in providing advanced search and analytics. In order to get the two close to each other, the developers are relying on Mongo Connector, a tool that is used to synchronize data between the MongoDB and Elastic in almost real-time.
The Reason to Reconnect MongoDB to Elasticsearch
Development teams have benefited by using a combination of these two technologies and they have:
- The use of advanced functionality of full-text search in addition to those provided by MongoDB.
- Real time synch of formatted and unformatted data.
- High traffic search capability
- Allowing analytics without having to strain the main MongoDB server.
This incorporation is common in e-commerce, social applications, and enterprise analytics applications.
Setting Up Mongo Connector
It can be done in a number of steps:
Install Mongo connector through the package manager of Python.- The MongoDB replica set is configured even when it is operated in a single node.
- Install Elasticsearch and ensure that it is operational.
- Write a Configuration File that maps MongoDB collections to Elasticsearch indexes.
- Start Mongo connector in order to start data synchronization.
Install Mongo connector through the package manager of Python.Mongo Connector then keeps track of the oplog of MongoDB and makes updates to Elasticsearch.
Common Use Cases
The following applications reflect the usefulness of connecting Mongo into Elasticsearch:
Search-Driven Applications
Driving product or content search.
Analytics Dashboards
Using Elasticsearch query to gain insights.
Log Management
Keeping application logs in a searchable index.
The Best Practices in Using Mongo Conversion into Elasticsearch
- It is always important to keep a check on the size of oplog in order to prevent loss of data during intensive updates.
- Manage Tune Elasticsearch index mappings to gain the best query performance.
- You can think about specific hardware when deploying on a large scale.
- Perform frequent testing of failover scenarios to maintain reliability.
Conclusion
Mongo Connector is an effective method of integrating the storage flexibility offered by MongoDB with Elasticsearch powerful search engine. With this kind of integration, business organizations can gain access to the more sophisticated search and analytics solutions, which boost the functionality and user experience in applications.