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PostgreSQL is an open-source, object-relational database popular for its reliability, data integrity, and extensibility. Features like write-ahead logs (WALs) and Multi-Version Concurrency Control (MVCC) help make PostgreSQL highly useful for application developers and administrators alike.

PostgreSQL uses built-in replication—both synchronous and asynchronous—to manage database traffic more effectively. For this reason, it’s recommended to have an effective PostgreSQL performance monitoring solution. The MVCC function also uses various lock modes to manage concurrent access to database tables, which makes PostgreSQL lock monitoring necessary, as well.

PostgreSQL includes a statistics collector and Postgres activity monitor. The statistics collector provides some Postgres performance monitoring capabilities and can automatically collect and organize certain database performance metrics (monitoring for some metrics can also be enabled manually).

However, using database monitoring software designed to increase system performance, team efficiency, and infrastructure cost savings can help you improve PostgreSQL activity along with your overall infrastructure by providing fuller visibility into major open-source databases. Postgres database monitoring tools capable of providing not only granular visibility but also a unified performance view across database types can help support the kinds of powerful analysis and deep insights to improve problem resolution time.

Effective Postgres performance monitoring often requires collecting and analyzing substantial amounts of data. While the statistics collector can allow you to monitor PostgreSQL activity and database performance, purpose-built Postgres database monitoring tools are designed to increase the effectiveness of your lol世界总决赛下注 capabilities, especially when used to track vital performance metrics, by transforming raw data into easy-to-understand graphs and tables.

Here are some of the most important metrics to track with your PostgreSQL performance monitoring tools:

• Read query throughput and performance: An essential aspect of this is monitoring PostgreSQL query throughput, which provides a sense of how efficiently applications can access information from the database. If the PostgreSQL monitoring tool indicates throughput is low, it’s likely a sign of an underlying issue or inefficiency needing to be examined. One example of this involves sequential scans and opportunities for indexing. If you detect a database is performing an increasing number of scans, for instance, creating a new index may help improve database performance. Sequential scans can also significantly increase a query’s response time, as they read each row of the database tables in sequence. Some PostgreSQL monitoring solutions can also suggest new indexes directing the query to a specific set of rows and help identify trends in database performance by creating an archive of performance data for comparison and analysis.

• Write query throughput and performance: Monitoring PostgreSQL queries writing changes to the database is crucial—not only because the inability to efficiently update a database can quickly cause issues, but also because changes in write query throughput are often signs of other database issues. PostgreSQL monitoring should track the number of rows deleted, inserted, or updated to help provide a deeper understanding of the types of write queries being sent to your databases. Monitoring write throughput is key for maintaining database health and availability.

• Replication and reliability: PostgreSQL writes query update information by recording transactions in the WALs. The database systems then commit the WAL to disk rather than the updated database page or block—a process providing data reliability and integrity (in case the master database fails, for instance) without notable sacrifices in database performance. PostgreSQL replication monitoring is key to ensuring the system is effectively directing queries to read-only standby servers. If your PostgreSQL deployment is running asynchronous replication, replication delay is also an important metric to monitor.

• Resource utilization: Like all databases, PostgreSQL relies on system infrastructure and resources to operate. PostgreSQL monitoring also needs to track how the database uses CPU, disk space, memory, bandwidth, and other common system resources to ensure it can respond to read and write queries efficiently.

DPM includes Postgres performance monitoring tools built to provide crucial visibility into the operations of databases and systems. The PSQL monitor function in DPM can collect detailed information for thousands of metrics across your deployment with one-second resolution, with intuitive, customizable dashboards allowing you to examine your database workload holistically—and drill down to the granular detail easier.

Features like Top Queries are designed to show a master-detail view across servers, with per-second drill-down into samples of queries, EXPLAIN plans, and cross-correlations with other metrics like I/O and CPU. You can also thin-slice queries, users, and databases, and compare across time periods quickly for before-and-after change analysis. DPM also uses powerful analytics to achieve faster, more accurate troubleshooting, which can help speed up time to resolution.