Database
Foglight for MongoDB
Document-oriented NoSQL monitoring, end to end
Description
MongoDB is a cross-platform, document-oriented database that falls into the class of NoSQL databases. It eschews the traditional table-based relational structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON). Instead of breaking a business subject into multiple relational structures, MongoDB can store the subject in the minimal number of documents, making the integration of data within certain applications easier and faster.
First developed by MongoDB Inc. in 2007 and shifted to an open-source development model in 2009, MongoDB has been adopted as backend software by major websites and services including Craigslist, eBay, and Foursquare. It is consistently one of the most popular database management systems and the most popular for document stores.
Business Challenge
MongoDB is built to be highly scalable. Deployments can grow to cover a large number of servers across network segments, horizontally scaling using sharding - the user chooses a shard key that determines how data in a collection is distributed, splitting it into ranges across multiple shards.
This helps balance the load and ensure redundancy, keeping the system running in case of hardware failure. The downside is that many elements need to be monitored and tuned to ensure optimum performance and availability.
Key Features
Foglight for MongoDB is designed to provide enterprises with a powerful tool that can act standalone or as part of broader cross-platform database monitoring requirements. It supports monitoring of key functions including the MongoDB Server, Queries, Indexing, Replication, Load Balancing, and File Storage.
As with all Foglight solutions, Foglight for MongoDB runs as a native Foglight process, leveraging the Foglight Agent Manager (FglAM) for communications with the agent. It can be run locally (Agent on the Host) or remotely (Agent on FMS or Proxy Server), and leverages the Foglight Rules Engine to evaluate and alert on potential issues before they significantly affect performance and availability.
Screenshots
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