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database-monitoring

8 posts

datadog

How we built a real-world evaluation platform for autonomous SRE agents at scale (opens in new tab)

The provided content does not include the blog post itself. It contains Datadog navigation links and a page title announcing that Datadog was named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms, but no substantive discussion of the evaluation platform or its conclusions. ## Available Information - Datadog’s page promotes its recognition as a Gartner Magic Quadrant Leader. - The navigation lists products across: - Infrastructure and application monitoring - Logs, databases, and data observability - Security - Digital experience monitoring - CI/CD and software delivery - Incident and service management - AI capabilities, including Bits AI Agents and Bits Investigation - The referenced URL path suggests the intended article may concern Datadog’s “Bits AI eval platform,” but the article text is not included. ## Conclusion Please provide the full blog post content for a meaningful section-by-section summary.

database-monitoringaik8sobservability+4
datadog

When upserts don't update but still write: Debugging Postgres performance at scale (opens in new tab)

The provided content does not include the tech blog post itself. It consists primarily of Datadog’s website navigation and a promotional link about its Gartner recognition, so there is not enough article content to summarize reliably. ## Available Information - Datadog was named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms. - The page navigation lists Datadog products across: - Infrastructure and application monitoring - Database and log management - Security - Digital experience monitoring - Software delivery - Incident and service management - AI-powered observability tools - The URL path references `debugging-postgres-performance`, suggesting the intended article may concern PostgreSQL performance debugging, but the article text is not included. Please provide the blog post’s body or a working text extraction for a substantive summary.

database-monitoringk8sdatadogobservability+2
datadog

Inside Husky’s query engine: Real-time access to 100 trillion events | Datadog (opens in new tab)

The provided content does not include the blog post itself. It contains Datadog’s navigation menu and a promotional link announcing its recognition as a Leader in Gartner’s Magic Quadrant for Observability Platforms, but no substantive discussion of the linked “Husky Query Architecture” article. ## Available Content ### Datadog’s Observability Platform - Datadog promotes products covering: - Infrastructure and container monitoring - Application performance monitoring - Logs and database monitoring - Security - Digital experience monitoring - CI/CD and software delivery - Incident and service management - AI and agent observability - The navigation emphasizes Datadog’s broad, integrated platform approach. ### Gartner Recognition - The page links to Datadog’s announcement that it was named a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms. - The supplied text does not include the evaluation criteria, cited strengths, limitations, or Gartner’s comparative analysis. No reliable summary of the Husky query architecture can be produced without the article’s body text.

database-monitoringk8sobservabilitylog-management+4
datadog

How we optimized LLM use for cost, quality, and safety to facilitate writing postmortems | Datadog (opens in new tab)

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. As a result, the article’s argument, technical details, and conclusion cannot be reliably summarized. ## Visible Page Content ### Datadog’s Gartner Recognition - Datadog promotes being named a **Leader in the Gartner Magic Quadrant for Observability Platforms**. - The linked resource appears to concern the **2026** observability-platform evaluation. ### Datadog Product Areas - Infrastructure and application monitoring - Logs, metrics, databases, and data pipelines - Security and cloud protection - Real user monitoring and digital experience - CI/CD and software delivery - Incident response and service management - AI capabilities, including Bits AI, agent observability, and GPU monitoring ### Article Reference - The URL path indicates an article titled **“LLMs for Postmortems.”** - However, no article text or sections about large language models, incident analysis, or postmortem generation are included in the supplied content. Please provide the full article body for an accurate summary.

database-monitoringk8sai-agentobservability+4
datadog

Timeseries indexing at scale | Datadog (opens in new tab)

Datadog’s “Time Series Indexing at Scale” explains how an observability platform can index and query enormous numbers of time series without making tag-based searches prohibitively expensive. The central challenge is matching flexible combinations of metric names and tags while keeping ingestion, storage, and query latency predictable. The article presents indexing strategies and architectural trade-offs that allow Datadog to support high-cardinality telemetry at scale. ## The Challenge of Time-Series Indexing - A time series is identified not only by its metric name but also by its complete set of tags. - Modern monitoring systems may contain billions of series generated by containers, hosts, services, and dynamic infrastructure. - Queries often filter on multiple tags, requiring the system to efficiently find the intersection of several large sets of series. - Indexing must support both: - Fast writes as new series appear - Low-latency reads for interactive dashboards and alerts - High-cardinality tags make naïve database indexes expensive in both storage and query processing. ## Inverted Indexes for Tags - Datadog uses an inverted-index model that maps searchable terms—such as metric names and tag values—to the series containing them. - A query can retrieve the posting list for each term and intersect those lists rather than scanning every time series. - Common terms may correspond to very large lists, so the system must optimize how these lists are stored, compressed, and combined. - The index separates metadata used to identify series from the time-series values stored for those series. ## Distributed Indexing - Index data is partitioned across machines so that no single node must hold or process the entire dataset. - Sharding enables horizontal scaling as the number of metrics, tags, and customers grows. - Query coordination gathers results from multiple shards and combines them into a single response. - The design must balance: - Even distribution of index data - Avoidance of hot shards - Efficient fan-out during queries - Resilience when individual nodes fail ## Managing Index Growth and Cardinality - Dynamic environments continuously create and remove series, making index lifecycle management essential. - Datadog must handle churn caused by short-lived containers, deployments, and changing tag values. - Compression and compact data structures reduce the memory and storage required for posting lists. - The system distinguishes between frequently queried data and less-used data to control resource consumption. - Cardinality limits and indexing policies help prevent unusually large tag dimensions from overwhelming the system. ## Query Performance and Trade-offs - Indexing every possible attribute would improve search flexibility but increase write, storage, and maintenance costs. - The platform therefore makes trade-offs between indexing coverage, freshness, and query speed. - Query execution can combine index filtering with additional processing over the remaining candidate series. - Caching and reuse of intermediate results can reduce repeated work for common queries. - The architecture is designed to maintain predictable latency even as data volume and query complexity increase. ## Operational Considerations - Large-scale indexing requires monitoring the index itself, including shard balance, ingestion lag, memory usage, and query fan-out. - Background processes must compact, expire, and rebalance index data without disrupting active queries. - Fault tolerance is important because an index outage can affect dashboards and alerts even when the underlying metric data remains available. - Separating indexing from time-series storage allows each subsystem to scale and evolve independently. Datadog’s approach illustrates that scalable observability depends as much on metadata indexing as on storing metric values. Systems handling high-cardinality telemetry should use distributed inverted indexes, compact representations, careful lifecycle management, and explicit trade-offs between flexibility and operational cost.

database-monitoringk8sobservabilitylog-management+4
datadog

.NET Continuous Profiler: Memory usage | Datadog (opens in new tab)

Datadog is presented as a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms. However, the provided content contains only the announcement headline, link, and website navigation; it does not include the blog post’s analysis, criteria, or supporting details. ## Announcement - Datadog’s headline claim is recognition as a “Leader” in Gartner’s 2026 Magic Quadrant for Observability Platforms. - The linked page appears to be a Datadog resource or announcement page. ## Available Product Scope The navigation indicates that Datadog’s observability platform spans: - Infrastructure monitoring, metrics, containers, Kubernetes, networks, serverless systems, and cloud costs - Application performance monitoring, profiling, and dynamic instrumentation - Logs, databases, data pipelines, and data quality - Security monitoring and cloud security - Real user monitoring, synthetic monitoring, session replay, and error tracking - CI/CD visibility, testing, developer portals, and software delivery - Incident management, service catalogs, SLOs, workflow automation, and AI-powered investigation Because the actual article text is missing, no further claims about Gartner’s evaluation or Datadog’s strengths can be reliably summarized.

database-monitoringk8sdatadogobservability+4
datadog

Introducing Glommio, a thread-per-core crate for Rust and Linux | Datadog (opens in new tab)

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.

database-monitoringk8sdatadogobservability+3
figma

Postmortem: Service disruption on January 21-22, 2020 | Figma Blog (opens in new tab)

Figma’s January 21–22, 2020 outages were caused by separate database failures that compounded one another. A long-running query triggered the first incident and created a vacuuming backlog; the next day, PostgreSQL 9 produced a severely mis-planned query after database statistics changed, while aggressive autovacuuming increased write and lock pressure. Upgrading to PostgreSQL 11 restored stability and addressed both the query-planning and autovacuum performance issues. ## Incident Timeline ### January 21: Long-Running Query - Automated alerts reported elevated error rates at 6:11 AM PST. - Engineers found an expensive, long-running database query driving CPU usage. - Canceling the query at 6:54 AM restored normal performance. - The terminated query left behind a backlog of data requiring vacuuming. ### January 22: Database Saturation - Increased write IOPS and lock contention appeared, despite database CPU being below normal. - Queued API requests eventually made Figma unavailable to some users. - Engineers canceled nonessential queries and increased allocated IOPS, providing only temporary relief. - Performance deteriorated again in the afternoon. - Restarting the database temporarily disabled a suspected background process. - Figma performed an emergency upgrade from PostgreSQL 9 to PostgreSQL 11. - The service returned online at 8:15 PM, with metrics back to normal. ## Aggressive Autovacuuming - The vacuuming backlog crossed the threshold for PostgreSQL’s more aggressive transaction-ID wraparound protection. - This mode generated substantial locking and write activity, particularly in the PostgreSQL version Figma was using. - Canceling autovacuum operations on large tables temporarily improved metrics, but the operations resumed. - Fully suppressing the aggressive behavior required changing `autovacuum_freeze_max_age` and rebooting the database. - Autovacuum was a significant contributor, but disabling it did not eliminate all performance problems. ## PostgreSQL Query Planner Failure - A complex query repeatedly appeared in lock-contention reports. - PostgreSQL estimated that the query would return more than 20 million rows, while the actual result contained only three. - The incorrect plan used full table scans instead of expected indexes. - It also wrote large amounts of data to temporary buffers, matching the observed increases in write IOPS and temporary-byte metrics. - The issue was likely caused by inaccurate statistics or a PostgreSQL planner defect or limitation following a routine statistics change. ## Upgrade and Preventive Measures - PostgreSQL 11 generated a substantially better plan for the problematic query. - Newer PostgreSQL versions improve autovacuum performance and query planning. - PostgreSQL 10+ also provides more advanced performance-analysis tools through Amazon RDS. - Figma had already tested the PostgreSQL 11 upgrade in staging and prepared a detailed production rollout plan, allowing the emergency upgrade to succeed safely. - The company planned to improve monitoring for expensive queries and impose stricter limits on query execution time. Figma concluded that upgrading PostgreSQL, improving query monitoring, and enforcing tighter runtime limits were necessary to prevent similar database-driven outages.

database-monitoringpostgresqlamazon-rdsquery-optimization+2