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Tag: observability

  • Three Strategies for Reducing MTTD and MTTR as Outage Costs Spiral

    Three Strategies for Reducing MTTD and MTTR as Outage Costs Spiral

    According to a New Relic survey of IT professionals, the median annual cost of IT outages has reached an astronomical $7.75 million. Over one-third of the 1,700 respondents said critical business application outages now cost more than $500,000 per hour. In a business environment where most interactions with customers, suppliers and business partners are conducted digitally, downtime has become a problem and an existential threat.

    Most IT organizations carefully monitor two key metrics — mean time to detection (MTTD) and mean time to resolution (MTTR) — to track their success in identifying and remediating critical systems issues. The figures represent, respectively, the average time it takes administrators to identify that a problem has occurred and to remediate the underlying issues. They serve as a benchmark for understanding the organization’s level of awareness of the status of its systems and the speed with which problems can be diagnosed and acted upon.

    There are many ways to improve performance against these metrics, but three stand out as the most effective, as evidenced by New Relic and the survey results.

    Monitor Everything

    The less you know about a problem, the longer it takes to diagnose and fix. The New Relic survey measured 17 different observability categories, such as network monitoring, alerts, log management, browser monitoring and distributed tracing. Across the board, about two-thirds of the companies that used even one of those monitoring tools reported reduced MTTR, with many saying resolution times had fallen by more than 25%.

    One-third of respondents whose organizations implemented full-stack observability—which is the ability to see everything in the tech stack that could affect the customer experience—reported the fewest outages, fastest MTTD and MTTR, lowest outage costs and highest median annual return on investment compared to all respondents. For example, those who have full-stack observability experience a median outage cost that is 37% less than those without.

    As the complexity of enterprise technology stacks increases, so does the difficulty of pinpointing the root cause of an outage. Any monitoring is better than no monitoring at all, and the more visibility you have into applications and infrastructure the faster you can identify and resolve incidents. Strive for full-stack observability around integrated observability data and a comprehensive dashboard.

    Have an Incident Management Action Plan

    The worst time to figure out how to resolve a problem is when you’re in the midst of a crisis. Organizations with mature observability practices also have extensive and documented response plans.

    Your incident plan will be unique to your organization. Small companies and startups tend to have ad hoc response strategies based on the skills within the organization. Large enterprises may use a more formal methodology like IT service management that defines strict procedures and protocols. Companies that have embraced modern DevOps and Lean principles often use a hybrid approach that depends heavily on teamwork and collaborative problem-solving. All of these approaches can work if the team fully understands the chosen strategy.

    Document your IT architecture, identifying known vulnerabilities and outlining procedures for responding to the most common types of incidents. Plans should cover obvious disruptions like fires and power outages, but you should also think about “unicorn” scenarios such as a rogue query that ties up a database server or a communication outage at a key supplier. Mature observability teams devote a portion of their resources to constantly testing new and unpredictable scenarios and use chaos engineering to randomly inject problems in a controlled manner to see if systems respond as expected.

    As you develop incident response procedures, document the processes in runbooks, which tell responders exactly what to do when a specific problem occurs. This not only creates a database of organizational knowledge about response strategies but also helps with onboarding new employees and covers for skills gaps that occur when team members are unavailable or leave the company.

    Fix for the Future

    Technical debt is a long-term obligation that is created when a short-term – and incomplete – solution is applied. You should aim to minimize it in your observability operation.

    When a problem occurs, the temptation is to fix it as quickly as possible. Human nature is to move on to the next task without taking the time to go back and understand why a problem occurred. “Quick and dirty” fixes are an invitation to larger problems in the future. Your observability plan should include a forensics stage that documents root causes and the fixes that were applied. Even if it isn’t convenient to implement a long-term solution immediately, the documentation serves as a guide for implementing a permanent fix down the road.

    Building a resilient IT architecture is a marathon, not a sprint. The more rigor you apply to identifying and resolving problems when they happen, the better prepared you will be to prevent their reoccurrence and the more informed your team will be about the resources they manage. Reducing your MTTD and MTTR will not only improve the bottom line of your business, it will ensure that your customers have an amazing digital experience.

  • Observability: The Central Watchtower Your All-Seeing IT Needs

    Observability: The Central Watchtower Your All-Seeing IT Needs

    If there’s something you need to keep secure, it’s natural to want to keep it sealed away under lock and key. However, even keeping something in a vault, far from prying eyes and ill intent, may not be enough to offer peace of mind. After all, how can you have the peace of mind of knowing it’s safe if you don’t regularly check to ensure it hasn’t bolted, been burgled or broken down?

    Now, multiply the volume and value of things being monitored tenfold — and you get a sense of the daily inefficiencies and anxieties IT professionals battle.

    Instead of jewels, these teams safeguard, manage and maintain something far more valuable: The data that underpins everything from business transactions to system performance to threat detection to service delivery and beyond. To make matters more complicated, today’s digital ecosystems are more complicated, distributed and disparate than ever before.

    Databases are critical to managing a successful IT department — and it’s time to discard the widespread industry’s perception of them as an unknowable black box. To be sure, databases represent the most difficult component of the IT ecosystems to observe, tune, manage and scale. But the database isn’t a Magic 8 ball in terms of its internal processes; We no longer have to shake the mystery toy and accept one of the six answers it’s designed to spit out.

    The fact of the matter is that database specialists and IT teams need a clear view of database performance telemetry if they want any hope of maintaining the health, stability and scalability of their services.

    Observability Tools

    This is where observability tools can play a transformative role. Taking its cues from the all-seeing panopticon watchtower, observability eliminates the dark corners of the database dungeon by providing a comprehensive view across the full cloud-native, on-premises and hybrid technology stacks.

    The word panopticon derives from the Greek word panoptes, or “all-seeing.” The panopticon model features a central, lighted guard tower at the center of a circular room — allowing 360-degree lines of sight that allow the guards to observe every surrounding cell from a single, centralized vantage point.

    English philosopher Jeremy Bentham had no way of foreseeing the challenges of today’s database professionals when he first conceived of the panopticon in the 1700s. The concept would lay the blueprint for a prison system designed around one core concept: Enabling the minimum number of guards to effectively monitor the maximum number of inmates. Bentham’s system also works as a strategy for ensuring the health and performance of your organization’s critical databases.

    The panopticon was designed so that every cell could be monitored from one central point — ensuring not only better and more straightforward security but preserving resources in saved time, manpower and effort expended. Think of your ITOps, database, DataOps and DevOps teams as the wardens of your IT performance. Traditional monitoring can be likened to patrolling a traditional prison: Rows of cells facing out into a hallway that is patrolled on a schedule or used to spot and address misbehavior as it’s noticed.

    How can they be sure they’re focusing their efforts in the right place? Or trust that Cell Block A won’t stage a breakout while they’re breaking up an argument in Cell Block B?

    In short: They can’t. Odds are, the team is either running from outage to outage in an everlasting firefighting mode or relying on random spot checks for something as vital as the health of our IT systems.

    Observability, as opposed to monitoring, puts teams into that central panopticon watchtower, allowing them to not just see everything, but to see it within the context of the bigger picture. This comprehensive visibility and always-on observation enable IT teams to identify critical issues as they occur, even those caused by complex dependencies between the database, operating system, storage subsystem and network.

    Database Performance Issues

    Furthermore, database performance issues are a starting gun for costly bottlenecks or serious outages that can handcuff your company’s ability to compete or grow. Without complete and precise database monitoring and observability, IT operations struggle to accurately determine an application’s root cause of performance issues. This increases the risk of downtime, data loss and poor customer experiences.

    Discovering the root cause of performance issues is quite literally a system-critical undertaking, whether you’re simply running your enterprise, scaling your operations or deploying new code. Observability also offers teams the rare opportunity to get ahead of performance issues since you can see performance anomalies in advance of a service disruption rather than relying on real-time remediation.

    With traditional methods, DevOps and IT teams must manually analyze the data presented to them, correlate it to the problem, and locate the error before they can finally begin addressing it. In contrast, observability collects data to provide information on what’s not performing as expected and why. This allows teams to take a proactive approach to solving downtime and bottlenecks and proactively preventing them.

    In the software world, that translates directly to more time spent where it matters: Developing new business value within your applications and infrastructures, fueling innovation and exceeding customer expectations.

    Given how complicated the modern database has — and continues to — become, arming your enterprise with the right observability solutions can provide a boost to efficiency and performance and pardon your teams from a sentence to time-consuming, mundane and error-prone work.

  • How to Alleviate Your Observability Challenges

    How to Alleviate Your Observability Challenges

    Observability is becoming a keystone of contemporary DevOps practices. Even departments that weren’t traditionally a part of DevOps are seeing the benefits of being brought under the auspices of observability teams. In 2023, however, organizations are finding that the road to adoption is bumpier than expected. Here are seven of the biggest challenges DevOps teams face with observability and some suggestions for mitigating them.

    Increasing MTTR

    MTTR, or mean time to recovery, is the time it takes to get a system back up and running after an outage or bug. A longer MTTR means more downtime and poorer service for clients. Worryingly, the DevOps Pulse Report indicates that the average MTTR is increasing. This year, 73% of its respondents reported an MTTR of multiple hours; last year’s figure was only 64%.

    MTTR often results from an inability to diagnose incidents due to data siloes impairing observability. This can be improved by an observability platform that allows engineers to see the big picture.

    Costs of Telemetry Data

    Along with increasing MTTR, many organizations have to deal with costs incurred by high telemetry data volumes. It’s a big problem. An IDC survey of 200 companies found that 53% of respondents highlighted the costs of storing log data.

    Much of the problem is due to an outmoded tiered pricing model. Many vendors charge per GB of data, so if your data volumes fluctuate, so will your data costs. They also have obscure pricing models, meaning many organizations find it difficult to know what they’re paying for. At Coralogix, we’ve created a new business model that is one-third of the cost of standard log storage solutions.

    Tool Sprawl

    To understand why this is a challenge, we need to answer the question: What is observability? Effective observability requires integrating data from every aspect of your application. Because many organizations implement monitoring with multiple tools, they suffer from tool sprawl. This has the effect of siloing telemetry data, making it harder to correlate data and extract insights into system performance.

    There are a range of options for mitigating tool sprawl, such as thoroughly evaluating a tool’s costs and benefits before including it in your DevOps strategy. The most effective solution is a “single pane of glass” tool that combines insights synoptically on a single dashboard.

    Kubernetes Complexity

    Elastic reports that organizations are increasingly turning to cloud-based solutions such as Kubernetes for their DevOps. Kubernetes can supercharge organizations with its ability to dynamically scale infrastructure as needed, eliminating the cost overheads of dedicated servers.

    However, Kubernetes is complex and comes with its own set of challenges. Kubernetes’ scalable architecture comes from containerization, a paradigm in which applications are hosted in objects called containers. This means that developing in Kubernetes requires the ability to work with a lot of spinning plates.

    A good way to combat this is better training in organizations. Additionally, breaking down silos allows different teams to transfer knowledge.

    Security Challenges

    Kubernetes’ popularity brings security challenges. These can include privilege escalation, where a user manages to gain privileges such as write access, and security misconfigurations, where developers forget to change the non-secure default configurations.

    There are several strategies for mitigating Kubernetes security risks. These include scoping roles to particular namespaces, using service meshes, and enhancing security with Coralogix’s Kubernetes Operator.

    Beyond Kubernetes, there’s the larger issue of integrating security into an observability strategy, which is becoming a challenge for an increasing number of IT businesses. To counter this, more and more businesses are starting to incorporate observability and security monitoring under a single umbrella. Solutions such as infrastructure and application metrics can enhance security and monitoring.

    Scaling Platforms

    To meet the challenges posed by rising data costs and increasing cloud complexity, businesses are turning to open-source solutions. These come with their own challenges, however. According to the DevOps Pulse Report, around 30% of enterprises surveyed had problems with infrastructure management, scaling and upgrading relevant components. Because many open-source platforms require specialized knowledge to maintain, businesses have trouble sourcing the skills and expertise for them.

    Tools such as OpenTelemetry can make scaling easier by integrating with platforms such as Coralogix.

    Troubleshooting Data Pipeline Performance

    Implementing observability requires having a reliable and high-performance pipeline for telemetry data. However, organizations using open-source platforms often have trouble monitoring and troubleshooting the performance of their data pipeline. This can impair observability as telemetry data is of lower quality.

    Data engineer Abraham Alcantara suggests ten key steps to troubleshoot data pipelines successfully. These include identifying data pipeline software and infrastructure, reproducing and isolating issues and automating issue scenarios. Another strategy is to apply machine learning, such as Coralogix uses.

  • How to Build a Data Platform for Self-Service, Ad-Hoc Analytics

    How to Build a Data Platform for Self-Service, Ad-Hoc Analytics

    In a digital world, data is often the differentiator between success and failure. Whether it’s defending against cybersecurity threats, improving application performance, or resolving full-blown outages, data is critical to the modern DevOps team.

    In fact, there are plenty of situations where DevOps engineers, SRE teams and other observability-focused departments need to explore reams of data rapidly and flexibly. An SRE trying to figure out the root cause of latency may need to dissect the movement of data across endpoints to find the malfunctioning one. Another engineer may need to compare historical and current performance to find anomalous behavior. Lastly, a DevOps engineer may have to dissect user performance metrics to understand the scope of a slowdown—whether it is global or regional.

    Every use case requires dashboards that can provide detailed data through a wide variety of visualizations, including choropleth maps, stack areas, pie charts and bar graphs. Ideally, a dashboard will enable teams to isolate dimensions, apply filters, and dive into data for deeper insights.

    Unfortunately, not all dashboards can support this type of fast, flexible analysis. Many still utilize outdated technologies, which lack the flexibility, agility and scalability required to seamlessly explore data. Many were also designed without the urgency that today’s data requires—after all, in previous years, most uses for dashboards (such as internal reporting) were not time-sensitive.

    Other dashboards are constrained by templates, which offer a finite array of widgets, tools and drill-down capabilities. These dashboards may feel unwieldy and sluggish, unsuited for a rapidly evolving situation like an application outage.

    Requirements

    To truly fulfill the promise of self-service, ad-hoc analytics across huge datasets, teams need to work in real-time—and so they need a database capable of timely responses.

    When an application goes down, an SRE may not know what to look for and, hence, needs to quickly comb through lots of data. While some dashboards rely on workarounds for faster queries, such as pre-aggregations, precomputing, or rollups, this is not possible in this instance because the SRE simply won’t know what to look for. After all, they can’t necessarily predict what will go wrong, and even if they work on assumptions from previous failures, this current issue could be much different.

    Therefore, dashboards must provide plenty of functionalities and visualizations. Users should be able to filter data by time, isolate variables like location, and zoom into specific time intervals with a few clicks. Dashboards should also accommodate diverse data types, including intricate parent-child relationships and nested columns.

    Further, dashboards must offer a depth of insight. An online streaming media platform may need to assess user metrics (such as latency or load times) across different devices, operating systems, and regions to fine-tune performance. A cloud provider has to monitor their physical hardware for high temperatures, slowed network switches or devices, and other anomalies.

    Because data is now so important to success, many more people across a company, including data scientists, product managers and external users, themselves require data-driven insights. In these situations, dashboards must handle the increased user traffic and query activity, maintaining fast responses even under load. This is especially important considering that a single-user operation (such as a zoom) will require multiple queries on the backend to execute.

    A database also must scale seamlessly. If an organization’s environment generates millions of events an hour, that equates to billions of events per day or week—challenging for any database to ingest, store, analyze and query. In fact, many databases cannot successfully provide fast response times while managing large datasets and high query volume. As an example, transactional databases (OLTP) can often query rapidly but cannot execute analytics at scale, while analytical databases (OLAP) can analyze massive volumes of data but not at speed.

    Apache Druid for Independent, Ad-hoc Data Exploration

    This is where open source Apache Druid comes in. Combining the scale and advanced analytics of an OLAP database with the speed of an OLTP database, Druid offers swift, real-time data exploration.

    Upon ingesting data, Druid makes it immediately available for querying and analysis, removing the need to first batch or aggregate the data in some way. In addition, Druid natively integrates with streaming technologies like Apache Kafka and Amazon Kinesis, removing the need for workarounds or connectors.

    Druid powers interactive visualizations, providing millisecond response times, enabling more versatile exploration, expanding the range of available dimensions and filters, and even maintaining subsecond speeds in the face of surging user and query volumes.

    Druid’s unique architecture also enables easy scaling. By separating key duties among separate node types—data nodes for storage, master nodes for data ingestion and availability, and query nodes for executing queries and returning results via the scatter/gather method—Druid ensures that nodes can be independently scaled based on need. Afterward, Druid also automatically rebalances traffic to ensure consistent performance.

    Salesforce: A Druid Success Story

    Salesforce pioneered the customer relationship management (CRM) space, serves 150,000 customers worldwide and earns billions in annual revenue. 

    The Edge Intelligence Team is the division of Salesforce that tackles the massive task of ingesting, processing, filtering, aggregating, and querying the entirety of their log data — anywhere from billions to trillions of lines daily. Each minute, Salesforce ingests 200 million metrics, while each day, Salesforce processes five billion daily events globally. In total, Salesforce accumulates dozens of petabytes of data in their transactional store, five petabytes of logs in their data centers, and almost 200 petabytes in their Hadoop storage.

    Salesforce teams use Druid to unlock real-time insights into product performance and user experiences, diving into large datasets instantly. Anyone from engineers to account executives can query a wide variety of dimensions, filters and aggregations to better understand trends, troubleshoot any issues that arise and set strategies for the future.

    By using Druid’s compaction abilities, Salesforce also decreased the number of Druid rows by 82%, leading to an overall reduction of their storage footprint by 47% and accelerating their query response times by 30%.