Redis is an open source (BSD licensed), in-memory data structure store, used as database, cache and message broker. In 10.7, it will be replaced with the new fpm agent (see below)
MONIT DOCKER CONTAINERS FULL
The old full page monitor agent written in python and based on webkit. The Result broker support sending HTTP callbacks, but could be extended to support multiple callback protocols. This is only needed when the client provided a ?callback URL when requesting a check, something that the Scheduler currently does not do. The Result Broker is a component in the SmartPoP that is responsible for sending asynchronous check results back to the client callbacks. It will also keep track of expiry times (again via Redis), and purge the items that have expired. The Assets Manager will collect and persist them on disk. The Check agents can post assets to the Asset Manager via Redis. Next to a set of performance metrics, each check can output check detail objects. New agents (like webdriver) manage the asset storage themselves. It is also used for old agent only that cannot communicate with redis directly. The final component of the SmartPoP is the Assets Manager, which is responsible of storing Check Results and assets, and managing their lifetime. The HTTP Broker uses a set of empirically determined timeouts that should work well with the timeouts configured in the Check Agents, the SmartAPI and the Scheduler be careful when changing these. The function of the HTTP Broker is to listen on the Redis queue for the chosen agent, call the agent synchronously over HTTP, and push back the result into the Result Queue in Redis for the API to process. The HTTP Broker is used to communicate with historic Check Agents that communicate over HTTP themselves, instead of listen on a queue in Redis. The monitor checks are sent by the scheduler (running on ASM core servers) through this secure channel as a proxy to the API (see below)Īccepts check requests through the Tunnel Client, forwards them, maintains status.ĪPI goes through http broker and connects to PHP/CGI checkers. It is creating a connection between tunnel server and the OPMS. One instance is running only (using threads for concurrency) It prepares and runs the checks using selenoid (see selenoid-docker service below). This is the java agent that is responsible for webdriver monitor checks. This checks if the linux Xvfb daemon is running. Xvfb or X virtual framebuffer is a display server implementing the X11 display server protocol. the name is different on each OPMS) and checks the memory consumption is below 90% This service is named by the hostname (i.e. May we know which service is responsible for what ? Solutions like monit still have their place, but it is to monitor the long lived components like the VMs or bare metal hosting your swarm, but the containers themselves should be decoupled from that system to get the most benefits from a micro-services model.If we run monit summary, it lists multiple services. When systems are small and simple traditional polling based monitoring may provide enough metrics but it is preferable to have them in a format that is searchable and more importantly relational to other systems. If you use syslog, logstash or log4? to collect metrics that will be far more useful in the long run. It should also reduce down time and middle of the night Pagerduty alerts as the system self repairs.Īs for the overall systems metrics, which are needed to trace down issues like latency I would want them in a central location using a tool like elasticsearch. This also simplifies refactoring as you only need to replicate or modify this test with no external dependencies or state machine. If you need to add capacity you simply change the number of expected nodes. This method makes it easy to have the system self repair and to scale with a minimal amount of complexity.īasically you only have to check if the number of systems you expect are alive, and if not you spin up some more. In theory you don't care about the performance of an actual container, just that your overall performance is good. Tcp-check expect rstring ^HTTP/1.1\ 200\ Ok If the services are fine-grained and perform a single function, implement a health check at an upstream load balancer, then monitor the active pool members. While not set in stone the key differentiation between microservices and other variants of SOA is this loose coupling. This will break the loose coupling model and introduce inter-dependencies that inhibit the ease refactoring. Once you start to add Nagios, Zabbix or other types of monitoring to the system you start to build a large state machine. This is an area where Kubernetes has the correct model, there should be a load balancer between all systems which should have functional health checks.
0 kommentar(er)
