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Production Issues Take Longer to Solve Despite Advancements in Observability

Despite all of the advancement and maturity of observability systems, production issues take longer to resolve, according to the DevOps Pulse 2022 Report from Logz.io.

DevOps Pulse 2022 respondents consistently reported increasing complexity across their cloud environments — driven by everything from expanding microservices architecture to the proliferation and complexity of observability tools themselves — meaning they are struggling to maintain clear visibility, quickly resolve production issues and manage related monitoring costs.

64% of respondents reported an average MTTR of over an hour compared to 47% reported in the DevOps Pulse 2021.

What's more, 53.4% of respondents surveyed last year claimed to have resolved production issues within an hour on average — this year, that number dropped to 35.94%. This trend is being driven by factors ranging from growing cloud data volumes and systems complexity, to issues of observability tool sprawl, and the need for greater expertise among DevOps teams.

Additional key findings identified in the report include:

Observability costs and data volumes are growing concerns

27% of respondents ranked total cost of ownership and the large volumes of data being ingested into the tools among their main challenges in maintaining effective observability.

Observability is maturing

Some 77% of respondents rated their efforts over 3 on a scale of 1-5 when asked to indicate how extensive and mature their observability strategy has become — compared to last year, when more than 30% of respondents indicated a low score of two or less.

This is combined with a palpable increase in observability tool sprawl. 22% of those surveyed indicated their organization uses 5 or more observability tools, compared to 11% last year.


Open source capabilities are widely used

Open source capabilities are widely used by 90% of respondents. Open Source observability is the most common way to deploy observability.

A shared services model is growing in popularity

Over 85% of respondents indicated that their organizations operate using a shared services observability model in which a central team is responsible for implementing and maintaining tooling for other stakeholders such as app developers, SREs and DevOps teams.

Tools are further diversifying

This year’s survey responses highlighted the full range of observability tools in use by practitioners including log management and analysis (67%), and infrastructure monitoring (59%), followed somewhat behind by distributed tracing (27%) and APM (22%). Meanwhile some 21% said that they have deployed all of these capabilities

"Essentially, the 2022 DevOps Pulse Report reveals that there is too much data and the current model for observability is broken," says Tomer Levy, CEO of Logz.io. "As practices and implementations expand, organizations are becoming more concerned about the impact of data volumes on production quality and cost. In addition to offering an analysis of the evolving landscape, DevOps Pulse 2022 calls on organizations to think carefully about the impact of Kubernetes and Microservices and constantly evaluate telemetry data value and hygiene."

By closely tracking and analyzing data that is central to core observability requirements and finding ways to reduce MTTR despite identified challenges, organizations can better calculate associated spending and ROI. Emphasizing these factors combined with an increased focus on application and data security solve the challenges identified by DevOps teams and observability practitioners.

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Production Issues Take Longer to Solve Despite Advancements in Observability

Despite all of the advancement and maturity of observability systems, production issues take longer to resolve, according to the DevOps Pulse 2022 Report from Logz.io.

DevOps Pulse 2022 respondents consistently reported increasing complexity across their cloud environments — driven by everything from expanding microservices architecture to the proliferation and complexity of observability tools themselves — meaning they are struggling to maintain clear visibility, quickly resolve production issues and manage related monitoring costs.

64% of respondents reported an average MTTR of over an hour compared to 47% reported in the DevOps Pulse 2021.

What's more, 53.4% of respondents surveyed last year claimed to have resolved production issues within an hour on average — this year, that number dropped to 35.94%. This trend is being driven by factors ranging from growing cloud data volumes and systems complexity, to issues of observability tool sprawl, and the need for greater expertise among DevOps teams.

Additional key findings identified in the report include:

Observability costs and data volumes are growing concerns

27% of respondents ranked total cost of ownership and the large volumes of data being ingested into the tools among their main challenges in maintaining effective observability.

Observability is maturing

Some 77% of respondents rated their efforts over 3 on a scale of 1-5 when asked to indicate how extensive and mature their observability strategy has become — compared to last year, when more than 30% of respondents indicated a low score of two or less.

This is combined with a palpable increase in observability tool sprawl. 22% of those surveyed indicated their organization uses 5 or more observability tools, compared to 11% last year.


Open source capabilities are widely used

Open source capabilities are widely used by 90% of respondents. Open Source observability is the most common way to deploy observability.

A shared services model is growing in popularity

Over 85% of respondents indicated that their organizations operate using a shared services observability model in which a central team is responsible for implementing and maintaining tooling for other stakeholders such as app developers, SREs and DevOps teams.

Tools are further diversifying

This year’s survey responses highlighted the full range of observability tools in use by practitioners including log management and analysis (67%), and infrastructure monitoring (59%), followed somewhat behind by distributed tracing (27%) and APM (22%). Meanwhile some 21% said that they have deployed all of these capabilities

"Essentially, the 2022 DevOps Pulse Report reveals that there is too much data and the current model for observability is broken," says Tomer Levy, CEO of Logz.io. "As practices and implementations expand, organizations are becoming more concerned about the impact of data volumes on production quality and cost. In addition to offering an analysis of the evolving landscape, DevOps Pulse 2022 calls on organizations to think carefully about the impact of Kubernetes and Microservices and constantly evaluate telemetry data value and hygiene."

By closely tracking and analyzing data that is central to core observability requirements and finding ways to reduce MTTR despite identified challenges, organizations can better calculate associated spending and ROI. Emphasizing these factors combined with an increased focus on application and data security solve the challenges identified by DevOps teams and observability practitioners.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...