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Discusiones de Otro software de análisis
One of SolarWinds’ standout strengths is how quickly and clearly it surfaces performance issues across various database environments. Its support for SQL Server, Oracle, PostgreSQL, MySQL, and others is robust, with deep visibility into query performance, wait events, and resource bottlenecks.
The real-time analytics are actionable and easy to navigate, and I’ve found the historical performance trending and anomaly detection surprisingly powerful, even compared to some newer platforms. For teams dealing with mission-critical, high-throughput databases, SolarWinds offers the level of insight you need without overwhelming complexity. The platform also benefits from tight integration with the broader SolarWinds ecosystem, making it easy to correlate database behavior with app and infrastructure telemetry if you’re using other SolarWinds tools.
I’ve had a positive experience so far, but I’d love to hear from others:
- How has SolarWinds performed for your team at scale, especially in hybrid or multi-cloud environments?
- Any tips on getting the most out of its analytics and alerting capabilities?
- If you’ve compared TCO across observability platforms, how does SolarWinds stack up?
Oh, se me olvidó mencionar: una cosa que realmente he llegado a apreciar es lo intuitiva que es la interfaz de usuario, incluso para personas que no son expertos en bases de datos. Facilita la colaboración con desarrolladores y SREs, ya que pueden participar e interpretar los datos de rendimiento sin necesitar mucha capacitación. Además, las alertas son sorprendentemente fáciles de ajustar: no te quedas ahogado en ruido como con otras plataformas. Definitivamente un punto a favor que debería haber mencionado antes.
refers http://c.raqsoft.com/article/1687916213139 In addition to common static code, sometimes dynamic code is also needed to solve problems, such as generating code (or part of code) based on parameters and dynamically executing it. For programming languages that lack dynamic coding mechanisms, it is usually necessary to write the variable parts of the code in string form. For example, when referencing dataset field names in Python, it is necessary to write them as strings to achieve the effect of dynamic code. However, this will make it inconvenient to read and write more common static code. SQL, on the other hand, can directly write field names (as well as filter conditions, grouping expressions, etc.) in the code without having to write them into strings, making it easier to read and write static code, but it is difficult to handle dynamic code. SPL inherits the SQL style of static code, allowing for direct writing of code parts, such as field names, without the need to be written as strings. In addition, SPL also provides macros to achieve dynamic code effects. Example 1: Dynamically sort the order table based on the parameter pSortList, which contains an indefinite number of sorting fields separated by commas. This dynamic code can be implemented using SPL macros: T("Orders.txt").sort(${pSortList})
