Geneos for big data: Monitoring MongoDB

Geneos for big data: Monitoring MongoDB

Database technologies have constantly evolved in response to changing storage and compute requirements. So far relational databases have dominated the market at a time when the development process followed the waterfall model and fixed schemas were the norm. Now with Agile development, using relational databases pose numerous limitations. NoSQL (NotOnlySQL) databases offer flexibility with dynamic schemas, rapid scaling, faster read write, fault tolerance and therefore quicker time to market.

NoSQL in the Financial Sector

Financial institutions generate huge amount of data like trades and transactions every day and need to store this data for regulatory and analysis purpose. Because relational databases are expensive, and their rigidity is a major bottleneck, banks and exchanges are now adopting NoSQL-based databases to store high volume of various kind of data. Application migration from on premises to cloud is another reason for such adoption. The cloud is a perfect home for NoSQL as it offers elastic horizonal scalability.

A popular NoSQL database within financial services is MongoDB, which is open source, high performance and document-oriented. MongoDB is very efficient at handing semi structured and unstructured data and has a very fast write speed. At ITRS, we’ve seen our clients use it for various use cases from storing time series based tick data to doing real-time analytics on data streams.

Geneos for MongoDB

ITRS has newly released integrations with various open source technologies within the big data space. Our MongoDB integration is useful for monitoring the health and availability of MongoDB database instances in real time.

The ITRS Geneos template for MongoDB integrates seamlessly with a MongoDB server and provides key performance indicators to your Support and DevOps teams in real-time.

Key Monitoring Metrics in MongoDB

The following are key MongoDB metrics monitored by Geneos, which ensure that your MongoDB implementation remains healthy:

  • Instance status: Summary of the Mongo server instance status
  • Assertions: Number of asserts
  • DB Stats: Storage statistics for a given database
  • Locks: MongoDB database-level lock use
  • Memory: Memory usage i.e. mapped, non mapped, page faults, virtual etc.
  • Concurrent Transactions: MongoDB version 3.0 is shipped with the WiredTiger storage engine. This view contains ticket details that the WiredTiger uses for its thread management.
  • Cursors: Data regarding cursor state and use
  • Replication: Data related to replication
  • Opcounters: Provides an overview of database operations (insert, query, update, delete, command, getmore) and makes it possible to analyse the load on the database in a more granular way.

Incorporating MongoDB monitoring along with existing application monitoring gives you a full picture of your technology stack using Geneos. Clients can download monitoring templates on ITRS' resources page and plug it in with their existing gateway setup.

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