![]() It covers CPU used by modules such as user, kernel. This is the percentage of time the CPU spent on application software (MongoDB code) servicing this MongoDB process, scaled to a range of 0-100% by dividing by the number of CPU cores. High kernel or user CPU might indicate an exhaustion of CPU by the MongoDB operations (software) while high iowait will most likely be related to storage exhaustion being the root cause for CPU exhaustion. It covers CPU used by modules such as user, kernel, iowait, steal, etc. This is the percentage of time the CPU spent on system calls servicing this MongoDB process, scaled to a range of 0-100% by dividing by the number of CPU cores. Let’s cover the main metrics for hardware monitoring. This refers to the average rate per second over the selected sample period of queries that return sorted results that cannot perform the sort operation using an index. High queues may indicate the existence of conflicting writing paths or suboptimal schema design, which force high competition over database resources. Queues describe the number of operations waiting for a lock, either read or write. High numbers or spikes might indicate a suboptimal connection strategy from the client side or unresponsive server. This describes the number of open connections to the instance. A high number ratio may indicate suboptimal operations which scan a lot of documents to return a smaller portion. The query targeting represents the ratio between the number of documents scanned and the number of documents returned. Query Executors represent the average rate per second over the selected sample period of scanned documents during queries and query-plan evaluation. This is the average operation time (read and write operations) performed over the selected sample period. Opcounters graph/metric shows the operations velocity and breakdown of operation types for the instance. The average rate of operations performed per second over the selected sample period. Let’s cover the main metrics for operations and connection monitoring. Once you connect via compass to your instance, you can use the MongoDB Compass Performance Tab, which is similar to Atlas RealTime Performance panel.Use MongoDB’s built-in free monitoring feature to get information on Operation Execution Times and Operation Counts.You can leverage tools like mongostat and mongotop.How to monitor with self-managed MongoDB instances: I will highlight and elaborate on the important ones further in this article: Additionally, the Metrics tab provides many graphs that plot operations and number of connections. How to monitor with MongoDB Atlas: Atlas provides various built-in features like Performance Advisor, Real-Time Performance Panel, and Query Profiler to track operations and highlight slow/heavy spotted operations. ![]() ![]() On top of the active and proactive monitoring tools, Atlas provides a full alerting system and log gathering is available. MongoDB provides various metrics and mechanisms to identify its connections and operations patterns. ![]() Since the application issues connections and operations against the database, we should pay close attention to their behavior. When our application is struggling or underperforming, we need to rule out the database layer as the bottleneck. MongoDB Cluster’s Operations and Connection Metrics
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