Kubernetes

144 posts

datadogNov 2, 20202 min readCurated summary

Introducing Glommio, a thread-per-core crate for Rust and Linux | Datadog

Datadog has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms. The announcement positions Datadog as a broad observability provider spanning infrastructure, applications, logs, security, digital experience, software delivery, service management, and AI. The supplied content contains mostly site navigation rather than the article’s supporting details or Gartner’s evaluation rationale. ## Datadog’s Observability Scope - Infrastructure monitoring, metrics, containers, Kubernetes, networks, serverless systems, cloud costs, GPUs, and storage. - Application performance monitoring, service monitoring, profiling, dynamic instrumentation, and agent observability. - Log management, sensitive-data scanning, audit trails, and observability pipelines. - Database, data-streams, data-quality, and jobs monitoring. ## Broader Platform Capabilities - Security features including cloud security, SIEM, workload protection, code security, vulnerability management, and compliance. - Digital-experience tools such as real-user monitoring, session replay, synthetic monitoring, error tracking, and product analytics. - Software-delivery capabilities covering CI visibility, test optimization, continuous testing, code coverage, and feature flags. - Service-management tools for incidents, events, SLOs, workflows, case management, and software catalogs. - AI offerings including Bits AI agents, investigation tools, agent observability, GPU monitoring, and MCP integrations. Overall, the announcement emphasizes Datadog’s unified and expansive observability platform. A complete assessment of Gartner’s specific strengths, cautions, and evaluation criteria would require the full blog post or linked Gartner report.

Kubernetes Datadog Observability Application Performance Monitoring+3

datadogOct 7, 20202 min readCurated summary

How we wrote a Python profiler | Datadog

The post explains how Datadog built a low-overhead statistical profiler for Python. Rather than tracing every function call, the profiler periodically samples running threads and reconstructs their Python and native call stacks. The main challenge is collecting accurate stack data without pausing applications for too long or introducing unsafe behavior inside CPython. ### Why Traditional Profiling Is Expensive - Deterministic profilers instrument every function call and return. - This provides detailed data but can significantly slow production workloads. - A statistical profiler reduces overhead by sampling execution at regular intervals instead of observing every event. ### Sampling Python Threads - The profiler interrupts running threads to capture their current execution state. - Python’s signal-handling model complicates this because signals are generally processed by the main thread. - The implementation must coordinate native threads, operating-system signals, and the Python interpreter to sample worker threads reliably. - Sampling must avoid interfering with application locks or triggering unsafe operations in signal handlers. ### Reconstructing Call Stacks - A useful profile needs both Python-level frames and native stack information. - The profiler walks Python frames to identify functions, files, and line numbers. - It also handles time spent in native extensions and the interpreter itself. - Collected samples are aggregated into call stacks, allowing Datadog to show CPU usage and hotspots across the application. ### Balancing Accuracy and Overhead - Sampling frequency affects the trade-off between detail and runtime cost. - More frequent samples improve visibility into short-lived work but consume more resources. - The profiler is designed to operate continuously in production, so it prioritizes low overhead, safe memory handling, and resilience across Python versions and deployment environments. The central recommendation is to use statistical sampling for always-on production profiling. It provides actionable performance data with far less impact than call-by-call instrumentation, provided the implementation carefully accounts for CPython’s threading, signal, and native-extension behavior.

Kubernetes Datadog Observability Application Performance Monitoring+3

datadogSep 23, 20201 min readCurated summary

The Old Datadog and the Sea | Datadog

The provided content does not include the tech blog post itself. It mainly contains Datadog’s navigation menu and a promotional link announcing its Gartner Magic Quadrant leadership, so there is not enough article content to summarize accurately. Please provide the blog post’s full text or the relevant sections.

Kubernetes Datadog Observability Application Performance Monitoring+3

datadogSep 23, 20194 min readCurated summary

Computing accurate percentiles with DDSketch | Datadog

Datadog’s post explains why accurately computing percentiles is difficult when monitoring large-scale, distributed systems. Traditional approaches either require retaining every observation or sacrifice accuracy through fixed-size summaries, especially for long-tailed data such as request latency. DDSketch addresses this by providing mergeable percentile estimates with a guaranteed relative-error bound and memory usage that remains effectively constant. ## Why Percentiles Matter - Averages can hide slow requests and do not describe the tail of a distribution. - Percentiles such as p95, p99, and p99.9 are more useful for measuring latency and reliability. - Monitoring systems must calculate these values from enormous numbers of observations across many hosts and services. - Storing every measurement is too expensive, while calculating percentiles independently on each machine and averaging the results is mathematically incorrect. ## Limitations of Common Approaches - Exact percentile calculation requires sorting or retaining all values, which is impractical for high-volume metrics. - Histograms use predefined buckets, making their accuracy dependent on bucket boundaries. - Fixed-width buckets are inefficient for distributions spanning several orders of magnitude: - Small values may require fine-grained buckets. - Large values may require a huge number of buckets. - Many quantile sketches optimize for rank accuracy, but a small rank error can still produce a large value error in heavy-tailed distributions. - Summaries must also be mergeable so that data collected from multiple agents can be combined without losing their accuracy guarantees. ## DDSketch’s Logarithmic Mapping - DDSketch groups values into logarithmically spaced bins rather than equally sized intervals. - Values close together near zero receive finer absolute resolution, while larger values receive wider buckets. - Each value is mapped to a key based on its logarithm: - Positive and negative values are handled separately. - Zero and values sufficiently close to zero use a dedicated zero bucket. - A representative value is chosen for each bucket, typically using the bucket’s geometric center. - Because adjacent buckets have a fixed ratio, the estimated value is bounded by a predictable relative error rather than a fixed absolute error. ## Relative-Error Guarantees - DDSketch is configured with a target relative accuracy, such as 1%. - Its logarithmic base is selected so that the returned quantile is within that relative-error bound of the true value. - Relative error is particularly appropriate for latency data: - An error of a few milliseconds matters greatly for a 10 ms request. - The same absolute error is much less significant for a 10-second request. - The sketch preserves accuracy across a wide range of values without requiring a proportional increase in the number of buckets. ## Distributed Aggregation and Memory Use - DDSketches can be merged by adding the bucket counts from separate sketches. - This allows agents, hosts, containers, and regional services to aggregate measurements into a global percentile. - Merging does not require access to the original observations. - The sketch stores counts rather than individual values, substantially reducing memory and network costs. - Datadog also describes bounded-memory variants that collapse older or less significant bins when necessary, allowing sketches to maintain a fixed storage limit while retaining useful tail information. ## Practical Trade-offs - Higher accuracy requires more buckets and therefore more memory. - Lower accuracy reduces resource usage but produces wider estimates. - The choice of relative accuracy should reflect the metric’s operational needs rather than defaulting to the smallest possible error. - Implementations must account for negative values, zeros, very small values, and values outside the normal range. - Accurate percentile reporting depends not only on the sketch algorithm but also on correct aggregation and consistent configuration across producers. DDSketch is therefore a practical choice for observability systems that need scalable, mergeable, and predictable percentile calculations. Its logarithmic buckets and relative-error guarantees make it especially well suited to latency and other long-tailed measurements where fixed-width histograms or rank-based approximations can be misleading.

Kubernetes Datadog Observability Application Performance Monitoring+3

datadogApr 2, 20191 min readCurated summary

Building highly reliable data pipelines at Datadog | Datadog

The provided text does not include the blog post’s actual article content. It contains Datadog’s navigation menu and a link titled “Highly Reliable Data Pipelines,” so the post’s argument, architecture, and technical conclusions cannot be summarized reliably. ## Available Information - The page appears to be a Datadog engineering blog post about building highly reliable data pipelines. - The surrounding content is primarily Datadog product navigation. - It also promotes Datadog’s recognition as a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms. Please provide the article body or a complete page extract for a detailed technical summary.

Kubernetes Datadog Observability Application Performance Monitoring+3

datadogJan 22, 20192 min readCurated summary

Rethinking UX for AI-driven alerting | Datadog

Datadog’s page announces that the company was named a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms. The supplied content, however, primarily contains site navigation rather than the referenced blog post, so it does not provide details about the article’s argument concerning AI-driven alerting. ## Gartner Recognition - Datadog highlights its recognition as a Leader in Gartner’s Magic Quadrant for Observability Platforms. - The announcement is presented as a promotional resource linked from the Datadog website. ## Datadog’s Product Portfolio The navigation emphasizes Datadog’s broad observability and security platform, including: - **Infrastructure:** infrastructure, container, network, serverless, GPU, storage, and cloud-cost monitoring. - **Applications:** APM, service monitoring, profiling, dynamic instrumentation, and agent observability. - **Data and logs:** database, data-stream, job, quality, log, sensitive-data, and pipeline monitoring. - **Security:** code, cloud, vulnerability, workload, application, API, and SIEM security tools. - **Digital experience:** browser and mobile RUM, session replay, synthetic monitoring, product analytics, and error tracking. - **Software delivery:** CI visibility, test optimization, code coverage, feature flags, and developer portals. - **Service management:** incident response, SLOs, event management, workflows, and case management. - **AI:** Bits AI agents, investigations, chat, security analysis, agent observability, and MCP integrations. The provided text does not include enough of the actual “Rethinking UX for AI-Driven Alerting” article to summarize its technical concepts or conclusions.

Kubernetes AI Agents Datadog Observability+4

datadogNov 1, 20181 min readCurated summary

Improving trust with Datadog Log Management | Datadog

The provided content does not include the blog post’s main article text. It contains Datadog navigation links and a promotional banner announcing Datadog as a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms, plus a link suggesting the article concerns improving trust with Datadog Log Management. ## Datadog’s Observability Platform - Datadog promotes a broad observability platform covering: - Infrastructure and cloud monitoring - Application performance monitoring - Logs and sensitive-data protection - Security monitoring - Digital experience monitoring - Software delivery and service management - AI-powered investigation and automation - The banner highlights Datadog’s recognition as a Gartner Magic Quadrant Leader. ## Log Management and Trust - The linked article appears to focus on improving trust through Datadog Log Management. - The supplied text does not provide details about the specific problems, technologies, or recommendations discussed in the post. A complete summary requires the article’s actual body text rather than the surrounding website navigation.

Kubernetes Datadog Observability Application Performance Monitoring+3

datadogAug 13, 20182 min readCurated summary

Introducing Kafka-Kit: Tools for scaling Kafka | Datadog

Datadog’s “Kafka Kit” is a collection of operational tools designed to make Apache Kafka easier to scale and manage. The post argues that Kafka’s built-in administrative mechanisms become difficult to use safely as clusters grow, particularly when rebalancing partitions or adding and removing brokers. Kafka Kit automates these workflows while emphasizing balanced assignments, controlled changes, and operational visibility. ## Why Kafka Scaling Becomes Difficult - Growing Kafka clusters require frequent partition movement and broker rebalancing. - Native Kafka reassignment workflows can involve large, complex JSON configurations. - Poorly planned changes can create: - Uneven storage and traffic distribution - Excessive network and disk I/O - Overloaded brokers - Extended recovery times - Operational changes must account for replication, leadership, broker capacity, and rack or availability-zone placement. ## Kafka Kit’s Approach - Kafka Kit provides reusable tooling for common Kafka administration tasks. - The tools generate and apply partition assignments instead of requiring operators to construct them manually. - Assignments can be optimized for more even distribution of: - Partitions - Replicas - Leaders - Storage and traffic - The tooling is intended to support both routine balancing and larger cluster changes, such as adding or decommissioning brokers. ## Safer Partition Reassignment - Reassignments can be performed incrementally rather than moving all partitions at once. - Changes can be throttled to limit their effect on production workloads. - Operators can inspect proposed assignments before applying them. - Controlled movement reduces the risk of saturating Kafka brokers, disks, or network links. - The approach makes long-running migrations easier to monitor and interrupt if necessary. ## Operating Kafka at Scale - Datadog built the tools from its experience running Kafka as a critical part of its data infrastructure. - At large scale, Kafka administration needs to be repeatable and automatable rather than dependent on manual intervention. - Separating planning from execution allows teams to validate capacity and placement before changing the cluster. - Standardized tooling also helps reduce the chance of configuration errors during high-risk maintenance operations. Kafka Kit is most useful for teams operating Kafka clusters large enough that manual partition management is unreliable or disruptive. Automating assignment generation, throttling, validation, and broker lifecycle changes can make scaling more predictable and safer.

Kubernetes Datadog Observability Application Performance Monitoring+4

datadogApr 24, 20181 min readCurated summary

Using Datadog APM to improve the performance of Homebrew | Datadog

Datadog announces that Gartner named it a Leader in the 2026 Magic Quadrant for Observability Platforms. The provided content is primarily the website’s navigation menu and does not include the blog article’s substantive discussion, methodology, or supporting evidence. ## Gartner Recognition - Datadog highlights its position as a Leader in Gartner’s Magic Quadrant for Observability Platforms. - The linked resource appears to support or explain the recognition. ## Datadog’s Observability Portfolio The navigation indicates that Datadog’s platform spans: - **Infrastructure:** infrastructure, container, network, serverless, GPU, storage, and cloud-cost monitoring. - **Applications:** APM, profiling, dynamic instrumentation, and service monitoring. - **Logs and data:** log management, database monitoring, data-stream monitoring, and observability pipelines. - **Security:** cloud security, SIEM, vulnerability management, workload protection, and code security. - **Digital experience:** real-user monitoring, session replay, synthetic monitoring, product analytics, and error tracking. - **Software delivery and service management:** CI visibility, testing, incident response, SLOs, workflow automation, and internal developer portals. - **AI capabilities:** agent observability, AI integrations, investigation agents, and GPU monitoring. The supplied excerpt does not provide enough article text to summarize Datadog’s specific strengths, Gartner’s evaluation criteria, or the company’s evidence for being named a Leader.

Kubernetes Datadog Observability Application Performance Monitoring+4

datadogApr 16, 20181 min readCurated summary

Cgo and Python | Datadog

The provided content does not include the blog post itself. It contains Datadog’s navigation menu and a link to an engineering article titled “CGO and Python,” but no article text from which to produce a reliable summary. Please provide the post’s body or a readable URL extract, and I can summarize it in the requested format.

Kubernetes Datadog Observability Application Performance Monitoring+3

datadogSep 20, 20173 min readCurated summary

Secure (and usable) multi-AWS account IAM setup | Datadog

The post presents a defense-in-depth approach to securing AWS accounts with IAM. Its central recommendation is to minimize long-lived credentials and broad permissions by combining strong authentication, role-based access, least privilege, and continuous auditing. Secure account governance is treated as an ongoing operational process rather than a one-time configuration. ## Protect the Root User - Use the root user only for tasks that cannot be performed through IAM. - Enable multi-factor authentication (MFA), preferably with a hardware security key. - Avoid creating root access keys. - Store root credentials securely and monitor for any root-user activity. ## Use Federated, Role-Based Access - Prefer AWS IAM Identity Center or an external identity provider for human access. - Grant users access through groups and roles instead of individual permissions. - Use short-lived role credentials rather than permanent IAM user access keys. - Require separate roles for administrative, development, production, and read-only work. ## Apply Least Privilege - Start with narrowly scoped permissions and expand them only when necessary. - Restrict actions by resource, account, region, and relevant condition keys. - Avoid wildcard permissions such as `Action: "*"` and `Resource: "*"`. - Use IAM Access Analyzer and CloudTrail activity to identify unused or excessive permissions. - Add permission boundaries or organization-level Service Control Policies when teams need guardrails around delegated administration. ## Secure Workloads and Automation - Assign IAM roles directly to EC2 instances, Lambda functions, containers, and other workloads. - Do not embed access keys in source code, configuration files, or deployment artifacts. - Store unavoidable secrets in services such as AWS Secrets Manager or Systems Manager Parameter Store. - Rotate and revoke credentials promptly when they are exposed or no longer required. ## Monitor and Audit IAM - Enable CloudTrail across accounts and regions, with logs protected from modification. - Alert on suspicious activity, including root-user use, policy changes, disabled logging, and unusual access-key behavior. - Regularly review users, groups, roles, policies, and unused credentials. - Use AWS Config, Security Hub, or equivalent controls to check compliance with account-security requirements. ## Centralize Governance - Manage multiple AWS accounts through AWS Organizations. - Keep production and sensitive workloads isolated from development accounts. - Apply Service Control Policies to prevent high-risk actions, even for administrators. - Establish a controlled emergency or “break-glass” access process with strong monitoring. The practical recommendation is to combine MFA, centralized identity, temporary role credentials, narrowly scoped permissions, and continuous auditing. No individual IAM setting is sufficient on its own; security comes from layering preventive controls with detection and response.

Kubernetes Datadog Observability Application Performance Monitoring+2

datadogSep 6, 20171 min readCurated summary

Robust statistical distances for machine learning | Datadog

The supplied text does not include the blog post itself; it is largely Datadog’s navigation menu. The only identifiable article is **“Robust Statistical Distances for Machine Learning,”** so a detailed, source-grounded summary is not possible without the article body. ## Article Focus - The post appears to address statistical distances used to compare probability distributions in machine-learning systems. - Its focus is likely making these comparisons more **robust to outliers, noisy observations, and distribution shifts**. - Such distances can support tasks including anomaly detection, model monitoring, data-drift detection, and evaluating generated data. ## Why Robustness Matters - Conventional distance measures may be disproportionately influenced by extreme values. - Outliers can make two otherwise similar datasets appear substantially different. - A robust distance should distinguish meaningful distribution changes from isolated or corrupted observations. ## Practical Implication The article’s central recommendation is presumably to choose statistical-distance methods based not only on mathematical properties, but also on their resistance to noise and outliers. Please provide the actual article text for a complete, section-by-section summary with the specific techniques and conclusions.

Kubernetes Datadog Observability Application Performance Monitoring+4

datadogAug 23, 20171 min readCurated summary

Scaling support with Vagrant and Terraform | Datadog

The provided content does not include the blog post itself. It consists primarily of Datadog’s navigation menu and a promotional banner announcing its Gartner recognition, while the linked page suggests an article about scaling support with Vagrant and Terraform. ### Visible Content - Datadog announces that it was named a **Leader in the Gartner Magic Quadrant for Observability Platforms**. - The page navigation lists products across: - Infrastructure and application monitoring - Logs, security, and digital experience - CI/CD, service management, and AI - The URL references an engineering post titled **“Scaling Support With Vagrant and Terraform,”** but no article text is present. A meaningful technical summary requires the article body or a complete extract of the post.

Kubernetes Datadog Observability Infrastructure Monitoring+2

datadogAug 15, 20172 min readCurated summary

Improving cloud security visibility with ChatOps | Datadog

Datadog announces that it was named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms. The provided content does not include the blog post’s body or Gartner’s evaluation details; it mainly contains Datadog’s navigation menu and product links. ## Announcement - Datadog highlights its recognition as a Leader in Gartner’s 2026 Observability Platforms Magic Quadrant. - The linked resource appears to be a Gartner-related announcement rather than a technical deep dive. ## Datadog’s Product Scope The navigation reflects a broad observability and operations platform covering: - Infrastructure monitoring, metrics, containers, Kubernetes, networks, serverless systems, and cloud costs - Application performance monitoring, profiling, dynamic instrumentation, and agent observability - Database, data-stream, jobs, and quality monitoring - Log management, sensitive-data scanning, audit trails, and observability pipelines - Security capabilities including cloud security, SIEM, workload protection, code security, and vulnerability management - Digital experience tools such as real-user monitoring, session replay, synthetic monitoring, and error tracking - Software delivery, CI visibility, testing, feature flags, and code coverage - Incident response, service catalogs, SLOs, workflow automation, and case management - AI agents, GPU monitoring, AI integrations, and investigation tools The supplied excerpt does not provide enough information to summarize Gartner’s criteria, Datadog’s strengths or weaknesses, or the report’s comparative findings.

Kubernetes Datadog Observability CI/CD+4

datadogJul 11, 20173 min readCurated summary

Piecewise regression: When one line simply isn’t enough | Datadog

Piecewise regression offers a practical way to model time series whose trends change over time, something a single straight-line regression cannot represent well. The technique divides data into segments and fits a separate regression line to each one, allowing systems such as observability platforms to detect trend shifts more accurately. Its usefulness depends on selecting meaningful breakpoints without overfitting noise. ## Why a Single Regression Line Falls Short - Ordinary linear regression assumes one constant relationship between time and the measured value. - Real-world operational metrics often contain: - Sudden changes in growth rate - Traffic or usage shifts - Deployment-related behavior changes - Periods of increase followed by stabilization or decline - A single line averages these different behaviors, producing inaccurate trend estimates and potentially misleading forecasts. ## How Piecewise Regression Works - The time series is divided into multiple regions by one or more breakpoints. - Each region receives its own regression equation, such as: - Before the breakpoint: one intercept and slope - After the breakpoint: a different intercept and slope - The fitting process searches for the breakpoint that minimizes the combined prediction error across all segments. - Models may require the lines to connect at the breakpoint, preventing unrealistic discontinuities, or allow independent segments when abrupt jumps are meaningful. ## Finding Useful Breakpoints - Candidate breakpoints are evaluated by comparing the residual error produced by different segmentations. - A breakpoint is valuable when it significantly improves the fit rather than merely explaining random fluctuations. - More segments can capture complex behavior, but they also increase the risk of overfitting. - Practical implementations therefore need safeguards such as minimum segment sizes, limits on the number of breakpoints, and validation against noisy data. ## Applications in Observability - Piecewise models can improve the interpretation of infrastructure and application metrics. - They are particularly useful for identifying: - Changes in request volume - Altered resource-consumption patterns - Performance regressions - Long-term growth phases - Recovery or stabilization after an incident - By distinguishing genuine trend changes from normal variation, the method can support better anomaly detection and forecasting. ## Limitations and Tradeoffs - Noisy or sparse data can make breakpoint selection unstable. - A model with too many segments may describe historical noise instead of general behavior. - Sudden outliers can distort regression parameters unless they are handled separately. - Piecewise regression captures trend changes, but it does not automatically explain their causes; engineers still need deployment, traffic, and infrastructure context. Piecewise regression is therefore best treated as a lightweight, interpretable tool for detecting changes in metric behavior. It provides more realistic trend modeling than a single regression line while remaining simpler and easier to operate than highly complex forecasting models.

Kubernetes Datadog Observability Application Performance Monitoring+3