PgBouncer is an open-source connection pooler for PostgreSQL.
The prerequisites for monitoring PgBouncer with Netdata are to have PgBouncer and Netdata installed on your system.
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 PgBouncer monitoring please read the collector documentation.
You should now see the PgBouncer 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.
DB client connections is a metric that measures the active, waiting, and cancel_req connections. Monitoring this metric is important for understanding how many clients are actively connected to the database, how many are waiting to establish a connection, and how many are being cancelled. High values for active, waiting, or cancel_req connections can indicate issues with the connection pool, such as a bottleneck or misconfiguration. Monitoring this metric can help to prevent connection issues and performance bottlenecks.
DB server connections is a metric that measures the active, idle, used, tested, and login connections. Monitoring this metric is important for understanding how many connections are being used and how many are being tested or logged in. High values for active or used connections can indicate that the connection pool is reaching capacity and that the number of connections needs to be increased. Monitoring this metric can help to prevent performance issues such as timeouts or connection failures due to the pool being over capacity.
DB client wait time is a metric that measures the amount of time a client has to wait before being connected to the database. Monitoring this metric is important for understanding how quickly connections are being established. High wait times can indicate that the connection pool is reaching capacity and that the number of connections needs to be increased. Monitoring this metric can help to prevent performance issues such as timeouts or connection failures due to the pool being over capacity.
DB client max wait time is a metric that measures the maximum amount of time a client has to wait before being connected to the database. Monitoring this metric is important for understanding how quickly connections are being established. High max wait times can indicate that the connection pool is reaching capacity and that the number of connections needs to be increased. Monitoring this metric can help to prevent performance issues such as timeouts or connection failures due to the pool being over capacity.
DB transactions is a metric that measures the number of transactions per second. Monitoring this metric is important for understanding the load on the database and how efficiently it is processing transactions. High values can indicate that the database is reaching capacity and that the number of connections needs to be increased. Monitoring this metric can help to prevent performance issues such as timeouts or connection failures due to the pool being over capacity.
DB transactions time is a metric that measures the amount of time it takes for a transaction to complete. Monitoring this metric is important for understanding how quickly transactions are being processed. High transaction times can indicate that the database is reaching capacity and that the number of connections needs to be increased. Monitoring this metric can help to prevent performance issues such as timeouts or connection failures due to the pool being over capacity.
DB transaction avg time is a metric that measures the average amount of time it takes for a transaction to complete. Monitoring this metric is important for understanding how quickly transactions are being processed. High average transaction times can indicate that the database is reaching capacity and that the number of connections needs to be increased. Monitoring this metric can help to prevent performance issues such as timeouts or connection failures due to the pool being over capacity.
DB queries is a metric that measures the number of queries per second. Monitoring this metric is important for understanding the load on the database and how efficiently it is processing queries. High values can indicate that the database is reaching capacity and that the number of connections needs to be increased. Monitoring this metric can help to prevent performance issues such as timeouts or connection failures due to the pool being over capacity.
DB queries time is a metric that measures the amount of time it takes for a query to complete. Monitoring this metric is important for understanding how quickly queries are being processed. High query times can indicate that the database is reaching capacity and that the number of connections needs to be increased. Monitoring this metric can help to prevent performance issues such as timeouts or connection failures due to the pool being over capacity.
DB query avg time is a metric that measures the average amount of time it takes for a query to complete. Monitoring this metric is important for understanding how quickly queries are being processed. High average query times can indicate that
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