You can track DB connections in Artifactory instances, the performance of remote repository HTTP connections, and Artifactory heap size. Real-time visual charts enable you to efficiently troubleshoot the performance issues.
Slow performance can be hard to track, and can also be caused by a variety of reasons, such as running out of Database or HTTP connections, slow database queries, slow remote requests or application level slowness (slow metadata calculation, blocking tasks, etc). The Slow Queries metrics enable you to gain insights, such as:
- If any single request to the database is taking an inordinate amount of time.
- If any one (aggregated) request to the database is taking an inordinate amount of time.
The JFrog Platform and services rely heavily on Database usage. If the Database has performance issues, it can cause slow performance in the system. The Database Pool metrics enable you to collect metrics on the the Database performance and interaction with JFrog products. It provides insights, such as:
- If a JFrog product is using up all of its Database connections.
- Which of the nodes or services is consuming the most Database connections.
- Number of free connections.
- Number of idle connections.
- Number of used connections.
- Number of max connections.
- TPS over time.
Heap size metrics give you insights on Artifactory storage memory, such as how much of the memory is used or free at a point in time.
Remote Repository HTTP Connections
A remote repository is associated with a remote site and acts as a cache for the content. The remote repository actively reaches out to the remote site it is pointing to, and pulls the content into Artifactory. This means there are HTTP connections made to external sites. The Remote Repository HTTP Connections metrics helps you gain insights such as:
- Who is responsible for the most traffic to a remote repository.
- How much data is being pulled in from the remote site over a period of time and how much of it is being saved.
- If all the connections available to a remote repository are being used up.
JFrog Xray works by downloading artifacts from JFrog Artifactory, breaking them down into one or more components, indexing, scanning, analyzing them and then performing actions based on user criteria. These actions are all done in separate microservices and It is possible for bottlenecks to arise in one or more of the microservices and these bottlenecks can result in slow performance.
The Xray Performance metrics enable you to gain insights such as how Xray is performing, the backlog of work, and the average time it takes to get scan results for an artifact.