SolarWinds Database Performance Analyzer is a fast and easy way to optimize MySQL databases

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Analyze SQL queries

Analyze SQL queries

Database Performance Analyzer enables you to access collated, second-by-second data pulled from SQL statements. This information is ranked based on response times, and is used by developers, admins, and database analysts to identify which SQL query to focus on. Response time analysis is an approach to measure the time spent on query execution and end-user impact. Response time analysis helps identify bottlenecks, pinpoint root causes and prioritize your actions, based on the impact that poor database performance has on end-users.
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Align resource provisioning with database performance

Align resource provisioning with database performance

Database analysis helps you make informed hardware investment decisions. SolarWinds Database Performance Analyzer lets you align resource provisioning with database performance. This provides useful information so you can be more prescriptive with hardware purchases. Database Performance Analyzer collates data from all SQL statements every second and helps rank them based on response time. Database Performance Analyzer provides a detailed, single-pane-of-glass view of your resource utilization, which helps identify how resource provisioning impacts database response time.
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View queries running in production

View queries running in production

Unfortunately, application performance is often put off until the deployment phase. Accessing query performance on production servers improves SQL performance because it incorporates application performance into development cycles. This MySQL optimization tool monitors development, test, and production servers with less than a 1% load. With the Database Performance Analyzer dashboard, developers can drill down into a production database to learn how a code is performing.
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Database Performance Analyzer for MySQL

  • Una auténtica herramienta de análisis de desempeño que se centra en el tiempo de respuesta para identificar los cuellos de botella y comprender dónde la base de datos dedica el tiempo.

  • Brinda una única versión de la verdad en las bases de datos y la infraestructura que respalda el desempeño de las bases de datos.

  • Utilice la detección de anomalías basada en aprendizaje automático para saber cuándo el comportamiento es diferente de lo esperado.

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