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Focus on mean time to recovery

更新于 : May 05, 2015
这一条目不在当前版本的技术雷达中。如果它出现在最近几期中,那么它很有可能仍然具有相关参考价值。如果这一条目出现在更早的雷达中,那么它很有可能已经不再具有相关性,我们的评估将不再适用于当下。很遗憾我们没有足够的带宽来持续评估以往的雷达内容。 了解更多
May 2015
采纳 ? 我们强烈建议业界采用这些技术,我们将会在任何合适的项目中使用它们。

Traditionally operations groups look to improve the mean time between failures. While avoiding failures is obviously still important, lessons from cloud computing have taught us to expect failure and instead to focus on mean time to recovery. Continuous Delivery automation makes rolling out rapid fixes easier and we are also seeing a growth in monitoring techniques to spot failures quickly through a ‘production immune system’. Teams are also successfully using semantic monitoring and synthetic transactions to exercise production systems in non-destructive ways. This combined focus allows teams to move rapidly with higher confidence, it can also reduce the emphasis on expensive test-execution in pre-production environments and is particularly important in responding to the ever-growing list of security vulnerabilities that are being discovered.

Jan 2015
采纳 ? 我们强烈建议业界采用这些技术,我们将会在任何合适的项目中使用它们。
Jul 2014
试验 ? 值得一试。了解为何要构建这一能力是很重要的。企业应当在风险可控的前提下在项目中尝试应用此项技术。
In DevOps-savvy organizations delivery teams often configure production monitoring and respond to incidents themselves. This visibility and access into production environments allows those teams to make changes to their systems to improve their ability to recover quickly when something goes wrong. This focus on mean time to recovery improves quality of service overall, and allows teams to safely deploy more frequently. This can also reduce the emphasis on expensive test execution in non-production environments. Techniques we've used include end-to-end 'semantic monitoring' or reconciliation of real business transactions, and the injection of 'synthetic transactions' which exercise systems in non-destructive ways in production.
Jan 2014
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索
In previous radars we recommended arranging automated acceptance tests into longer journeys and, in what we call semantic monitoring, running these tests continuously against a production environment. We still believe that this is an important technique for scenarios the team can anticipate in advance. A variation of this approach, seen especially with startups, is to reduce the number of tests while increasing monitoring and automatic alarms. This shifts the focus from avoiding problems that can be anticipated to reducing mean time to recovery for all problems.
May 2013
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索
发布于 : May 22, 2013


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