The AppDynamics for Databases MongoDB collector provides critical information
Monitoring of CPU usage percentages. High user CPU numbers might make a case for more CPU resources. High system CPU could indicate a more powerful processor should be implemented, and increased I/O wait states may show additional or faster disks are needed.
Automatically detected multi-shard configurations. AppDynamics will auto discover every shard and replica set.
Ongoing metrics. Metrics are collected for every shard in a cluster.
How MongoDB Differs From SQL Databases
MongoDB is a document-oriented database that allows both structured and unstructured data. Relational SQL databases arrange data in highly organized tables with explicit rules. They emerged in the 1970s because, at the time, there was no defined way to structure databases, fields, and data operations. Characteristics of relational databases include joins and support for both constraints and complex transactions.
This worked great until the current age of big data, where one application might handle millions of transactions. NoSQL databases like MongoDB provide the performance, availability, and scalability needed by modern apps. MongoDB also handles unstructured data, such as video, tweets, and audio files. With NoSQL, support for complex transactions and constraints must be dealt with by the application itself. At times, other languages are combined with NoSQL to handle these operations. That is why NoSQL is sometimes called “Not Only SQL” by some developers.
MongoDB Addresses Modern Data Challenges
Today’s apps are interactive, social and highly networked. They need to be able to handle tons of data, develop new features rapidly, and be deployed in a number of environments. They are storing significantly more data than ever before and accessing it at unprecedented speeds. At the same time, organizations running on a single server run into problems because they cannot scale.
MongoDB is a NoSQL database that was developed to address these challenges:
- Allows companies to scale by adding more servers as they are needed.
- Models data as documents, increasing query speed significantly.
- Capably handles fast-moving environments of continuous deployment and agile development, demands that can cripple older data models with less flexibility.
- Works well on lean servers, which saves money.
Storing and Managing Rapidly Growing Data Sets
Organizations using MongoDB rely on it to meet a variety of challenges, including:
- How to store and access large quantities of data.
- How to handle rapidly growing lists of diverse elements, such as server logs, twitter feeds and more.
- How to effectively use unstructured data or data that changes rapidly.
- How to develop applications quickly.
A MongoDB object is another name for the documents that store data. Objects are associated arrays and data structures represented in the database in a data interchange format called BSON (pronounced Bison). With MongoDB, data can be stored using multi-sharding, which is spreading data across several machines. Sharding is used to manage databases with rapid throughput demands and huge data sets.
MongoDB has grown rapidly in a few short years, and now only Oracle, MySQL and Microsoft SQL Server are ahead of it on the list of most popular databases. With AppDynamics for Databases now supporting MongoDB, you’ll be able to effectively monitor your critical applications to minimize errors, downtime and profit loss.