惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

大猫的无限游戏
大猫的无限游戏
博客园 - 【当耐特】
Cloudbric
Cloudbric
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Attack and Defense Labs
Attack and Defense Labs
爱范儿
爱范儿
The Cloudflare Blog
腾讯CDC
Security Archives - TechRepublic
Security Archives - TechRepublic
TaoSecurity Blog
TaoSecurity Blog
云风的 BLOG
云风的 BLOG
Recent Announcements
Recent Announcements
C
Check Point Blog
Schneier on Security
Schneier on Security
S
Schneier on Security
J
Java Code Geeks
B
Blog RSS Feed
Cisco Talos Blog
Cisco Talos Blog
Vercel News
Vercel News
Stack Overflow Blog
Stack Overflow Blog
博客园_首页
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
A
About on SuperTechFans
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Google DeepMind News
Google DeepMind News
阮一峰的网络日志
阮一峰的网络日志
罗磊的独立博客
A
Arctic Wolf
S
Secure Thoughts
P
Palo Alto Networks Blog
The Last Watchdog
The Last Watchdog
SecWiki News
SecWiki News
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
博客园 - 三生石上(FineUI控件)
D
Darknet – Hacking Tools, Hacker News & Cyber Security
量子位
U
Unit 42
I
InfoQ
D
DataBreaches.Net
P
Privacy International News Feed
T
Troy Hunt's Blog
博客园 - 叶小钗
T
Threatpost
博客园 - Franky
K
Kaspersky official blog
Hugging Face - Blog
Hugging Face - Blog
IT之家
IT之家
www.infosecurity-magazine.com
www.infosecurity-magazine.com
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
C
Cisco Blogs

Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Detecting AWS EBS performance issues with Datadog
2013-07-30 · via Datadog | The Monitor blog

Elastic Block Storage (EBS) is a storage service offered by Amazon Web Services (AWS) that is backed by network block storage. EBS is critical for traditional database systems since it combines large storage capacity with reasonable throughput and latency. However, because it relies on the network, EBS can cause performance issues in your systems running on EC2.

When performance issues occur, it is important to determine if these issues are caused by EBS or some other part of your infrastructure. Datadog collects and aggregates various AWS metrics and offers features which can identify if performance issues are originating in EBS. The necessary steps to enable these capabilities are detailed below.

Measuring Storage Performance in EBS

Storage performance is usually measured in input/output operations per second (IOPS). It is not a perfect measure but acts as a useful yardstick to compare different storage systems.

EBS performance is thus expressed in IOPS. A careful read of the EBS documentation indicates that IOPS refer to operations on blocks that are up to 16KB in size.1

EBS volumes come in 2 flavors: standard and Provisioned IOPS:

Standard EBS - Standard EBS volumes deliver 100 IOPS on average (on blocks of 16KB or less). This is roughly the number of IOPS a single desktop-class 7200rpm SATA hard drive can deliver. In comparison, a similar desktop-class SSD drive can deliver between 5,000 and 100,000 IOPS.

**Provisioned IOPS - **Provisioned IOPS can deliver up to 4,000 IOPS per volume if you have purchased that throughput. If you strictly adhere to a number conditions, you can expect 99.9% of the time in a given year that the volume will deliver between 90% and 100% of its provisioned IOPS.

For more on how to achieve optimal Provisioned IOPS performance, see our blog post: Getting optimal performance with AWS EBS Provisioned IOPS.

Why AWS EBS performance issues occur

AWS EBS performance issues occur for two fundamental reasons:

  1. Standard EBS volumes are slow – Because EBS data traffic must use the network, it will always be an order of magnitude slower (as measured by its latency) than using local storage. Typical latency for network storage is 50-100 ms, versus 10ms for local storage.
  2. The actual storage and network hardware is shared – The network that exists between your instances and your EBS volumes is shared with other customers. When other customers begin to use a higher volume of the network or storage volume, your performance may be affected. This is not the case when you use local storage.

How to detect AWS EBS performance issues with Datadog

To detect AWS EBS performance issues you need to track the CloudWatch metric “VolumeQueueLength”. This is quick and easy to do in Datadog. The graphs shown below are available after signing up for a free trial of Datadog and enabling the AWS integration.

AWS CloudWatch’s VolumeQueueLength metric measures the number of I/O requests that are pending at a given time. By comparing VolumeQueueLength for each EBS volume attached to a slow application you can narrow down the cause of the slowness to an EBS issue. To see VolumeQueueLength in Datadog, go to the metrics explorer by hovering over the “Metrics” tab and selecting “Explorer” from the dropdown menu.

Metric explorer dropdown

On the left of the Metrics Explorer screen, begin typing “aws.ebs.volume_queue_length” in the “Graph:” text box and select it from the dropdown options.

Metric Explorer Detail

By default the Metrics Explorer will track all the hosts you’re monitoring with Datadog. You want to track just hosts with EBS volumes connected to you slow applications. In the “Over:” text box, enter the hostname for an EBS volume related to slow applications.

Metric Explorer Scope Detail

Finally, select the time period to analyze. For this example, we will look at the past 24 hours by choosing “The Past Day” from the “Show” menu.

Metric Explorer Detail Timeframe

A sustained increase of VolumeQueueLength way above 1 on a standard EBS volume should be treated as exhausting the throughput of that EBS volume. A sustained increase of the same metric way above the number of provisioned IOPS/100 should be treated as maxing out the throughput of that EBS volume. In the image below there are two distinct cases where queue length is significantly above 1 for extended periods of time. Both indicate periods when the EBS volume’s throughput was maxed out.

EBS Metric Explorer Timeframe

How to fix AWS EBS performance issues

There are a number of ways to resolve EBS performance issues once they’re detected, or to try to avoid these altogether. These steps include:

  • Selecting the right storage and instance types

  • Priming your EBS volumes

  • Using Instance Store volumes instead of EBS

  • Purchasing Provisioned IOPS

  • Replacing a degraded EBS volume if needed

More information on how to implement these resolutions or how to avoid issues in the first place is available in our free eBook - The Top 5 Ways to Improve Your AWS EC2 Performance.