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Use Cases

Optimizing MySQL Performance with MetricsHub

  • 2 min read
Database MySQL

Optimizing MySQL Performance with MetricsHub: Monitor Queries, Locks, and Cache Efficiency

MetricsHub allows you to collect and analyze key metrics from your MySQL databases, helping you identify performance bottlenecks and optimize query execution. Whether your databases run on Windows or Linux, MetricsHub provides deep visibility into critical performance indicators, enabling proactive troubleshooting and efficiency improvements.

With MetricsHub, you can monitor and address common MySQL issues that impact database performance and application responsiveness. The table below outlines frequent challenges and the corresponding metrics that help diagnose and resolve them:

Issue Description Metrics
High Query Latency Queries take too long to execute, causing delays in application response times. db.server.queries{db.query.state="slow"}, db.server.queries{db.query.state="normal"}, db.server.row_lock.time{db.mysql.engine="innodb"}
High Cache Miss Rate Inefficient use of MySQL’s cache leads to frequent disk I/O, slowing down performance. db.server.cache.operations{db.io.direction="read", db.cache.state="hit"}, db.server.cache.operations{db.io.direction="read", db.cache.state="miss"}
Lock Contention High contention on row or table locks can block queries and degrade performance. db.server.row_lock.current_waits{db.mysql.engine="innodb"}, db.server.row_lock.time{db.mysql.engine="innodb"}, db.server.row_lock.waits{db.mysql.engine="innodb"}
Disk I/O Bottlenecks Excessive disk reads and writes slow down database performance, especially for InnoDB. db.server.io{db.io.direction="read"}, db.server.io{db.io.direction="write"}, db.server.operations{db.io.direction="read", db.mysql.engine="innodb"}
Inefficient Cache Configuration Suboptimal cache configuration leads to excessive memory usage or inefficient queries. db.server.cache.usage{db.cache.state="data_clean"}, db.server.cache.usage{db.cache.state="data_dirty"}, db.server.cache.usage{db.cache.state="free"}
Failed Connections Users or applications experience connection failures, resulting in degraded service. db.server.connections{error.type="client_disconnected", db.connection.state="aborted"}, db.server.connections{error.type="failed_attempt", db.connection.state="aborted"}
Query Failures Queries fail due to timeouts or misconfigurations, causing service disruptions. db.server.queries{db.query.state="failed", error.type="timeout"}, db.server.queries{db.query.state="failed", error.type="set_timeout_failed"}
Insufficient Threads The database struggles to handle simultaneous queries, leading to thread starvation. db.server.threads{db.thread.state="cached"}, db.server.threads{db.thread.state="connected"}, db.server.threads{db.thread.state="created"}
Resource Utilization Inefficiencies Inefficient use of CPU, memory, or disk resources slows down the database. db.server.uptime, db.server.active_connections, db.server.cache.read_head.evicted{db.mysql.engine="innodb"}
Schema or Data Design Issues Poor schema design or excessive table operations can degrade database performance. db.server.row_operations{db.row_operation.type="delete", db.mysql.engine="innodb"}, db.server.row_operations{db.row_operation.type="insert", db.mysql.engine="innodb"}

MetricsHub collects MySQL database metrics

In the example below, we configured MetricsHub to collect metrics about the MySQL-Linux resource hosted on nb-docker and push the collected metrics to Prometheus.

Procedure

To monitor a MySQL database with MetricsHub:

  1. In the config/metricshub.yaml file, we configure the MySQL-Linux resource who is hosted on nb-docker, a Linux-based system:

      MySQL-Linux:
    attributes:
      host.name: nb-docker
      host.type: linux

  2. We configure the jdbc protocol by providing the credentials to connect to the database, the database type and the name of the instance to connect to:

        protocols:
      jdbc:
        timeout: 120s
        database: information_schema # Name of the instance to connect to. 
        type: mysql # Type of database
        port: 3306
        username: root
        password: mypassword

    Here is the complete YAML configuration to be added to config/metricshub.yaml to monitor a MySQL database with MetricsHub

      MySQL-Linux:
    attributes:
      host.name: nb-docker
      host.type: linux
    protocols:
      jdbc:
        timeout: 120s
        database: information_schema # Name of the instance to connect to. 
        type: mysql # Type of database
        port: 3306
        username: root
        password: mypassword

  3. Finally we configure the Prometheus integration.

After completing the configuration, we restart MetricsHub and check the logs and Prometheus to confirm that the MySQL metrics are being collected and exported successfully.

MetricsHub pushes MySQL metrics to Prometheus

Database MySQL