Squid is a caching and forwarding HTTP web proxy. It has a wide variety of uses, including speeding up a web server by caching repeated requests, caching web, DNS and other computer network lookups for a group of people sharing network resources, and aiding security by filtering traffic. Squid Log Files are a valuable source of information about Squid workloads and performance. The logs record not only access information, but also system configuration errors and resource consumption (e.g. memory, disk space).
Netdata auto discovers hundreds of services, and for those it doesn’t turning on manual discovery is a one line configuration. For more information on configuring Netdata for Squid logs monitoring please read the collector documentation.
You should now see the Squid logs section on the Overview tab in Netdata Cloud already populated with charts about all the metrics you care about.
Netdata has a public demo space (no login required) where you can explore different monitoring use-cases and get a feel for Netdata.
Requests is a metric that measures the total number of requests received by the proxy. This metric is important for understanding the overall traffic load on the proxy and the effectiveness of the caching mechanism. It is also useful for identifying and diagnosing potential network performance issues or bottlenecks.
Excluded requests is a metric that measures the number of requests that were not accepted by the proxy. This metric is useful for understanding the effectiveness of the proxy and detecting any misconfigurations.
Type requests is a metric that measures the number of successful, bad, redirect, and error requests. This metric is important for understanding the types of requests that are being made to the proxy and can help identify potential performance issues.
HTTP status code class responses is a metric that measures the number of 1xx, 2xx, 3xx, 4xx, and 5xx responses. This metric is important for understanding the types of responses the proxy is receiving and can help identify potential performance issues.
HTTP status code responses is a metric that measures the number of requests per HTTP response code. This metric is important for understanding the types of responses being received from the proxy and can help identify potential performance issues.
Bandwidth is a metric that measures the amount of data sent in kilobits per second. This metric is important for understanding the overall data throughput of the proxy and can help identify potential performance issues.
Response time is a metric that measures the minimum, maximum, and average response times. This metric is important for understanding the latency of the proxy and can help identify potential performance issues.
Uniq clients is a metric that measures the number of unique clients making requests to the proxy. This metric is important for understanding the overall usage of the proxy and can help identify potential performance issues.
Cache result code requests is a metric that measures the number of requests per cache result code. This metric is important for understanding the effectiveness of the caching mechanism and can help identify potential performance issues.
Cache result code transport tag requests is a metric that measures the number of requests per cache result delivery transport tag. This metric is important for understanding the effectiveness of the caching mechanism and can help identify potential performance issues.
Cache result code handling tag requests is a metric that measures the number of requests per cache result handling tag. This metric is important for understanding the effectiveness of the caching mechanism and can help identify potential performance issues.
Cache code object tag requests is a metric that measures the number of requests per cache result produced object tag. This metric is important for understanding the effectiveness of the caching mechanism and can help identify potential performance issues.
Cache code load source tag requests is a metric that measures the number of requests per cache result load source tag. This metric is important for understanding the effectiveness of the caching mechanism and can help identify potential performance issues.
Cache code error tag requests is a metric that measures the number of requests per cache result error tag. This metric is important for understanding the effectiveness of the caching mechanism and can help identify potential performance issues.
HTTP method requests is a metric that measures the number of requests per HTTP method. This metric is important for understanding the types of requests being made to the proxy and can help identify potential performance issues.
MIME type requests is a metric that measures the number of requests per MIME type. This metric is important for understanding the types of requests being made to the proxy and can help identify potential performance issues.
Hier code requests is a metric that measures the number of requests per hierarchy code. This metric is important for understanding the types of requests being made to the proxy and can help identify potential performance issues.
Server address forwarded requests is a metric that measures the number of requests per server address. This metric is important for understanding the types of requests being made to the proxy and can help identify potential performance issues.
Effective monitoring of these metrics can help identify potential network performance issues, misconfigurations, and other issues that could reduce the efficiency of the proxy. By monitoring these metrics, it is possible to detect and address issues before they become a problem, thus preventing or reducing downtime.