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

F
Full Disclosure
Recorded Future
Recorded Future
T
Tenable Blog
S
Securelist
C
CERT Recently Published Vulnerability Notes
T
Threatpost
S
Schneier on Security
A
Arctic Wolf
The Hacker News
The Hacker News
C
CXSECURITY Database RSS Feed - CXSecurity.com
Know Your Adversary
Know Your Adversary
P
Privacy International News Feed
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
The Register - Security
The Register - Security
Cisco Talos Blog
Cisco Talos Blog
AWS News Blog
AWS News Blog
K
Kaspersky official blog
T
True Tiger Recordings
T
Threat Research - Cisco Blogs
V
Vulnerabilities – Threatpost
P
Palo Alto Networks Blog
T
The Exploit Database - CXSecurity.com
小众软件
小众软件
B
Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Azure Blog
Microsoft Azure Blog
Cyberwarzone
Cyberwarzone
C
Cybersecurity and Infrastructure Security Agency CISA
T
Tor Project blog
Spread Privacy
Spread Privacy
Malwarebytes
Malwarebytes
P
Proofpoint News Feed
F
Fox-IT International blog
F
Fortinet All Blogs
P
Privacy & Cybersecurity Law Blog
G
GRAHAM CLULEY
量子位
Latest news
Latest news
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 叶小钗
Project Zero
Project Zero
T
Tailwind CSS Blog
N
Netflix TechBlog - Medium
Martin Fowler
Martin Fowler
IntelliJ IDEA : IntelliJ IDEA – the Leading IDE for Professional Development in Java and Kotlin | The JetBrains Blog
IntelliJ IDEA : IntelliJ IDEA – the Leading IDE for Professional Development in Java and Kotlin | The JetBrains Blog
I
Intezer
博客园_首页
腾讯CDC
H
Hackread – Cybersecurity News, Data Breaches, AI and More
D
Darknet – Hacking Tools, Hacker News & Cyber Security

Datadog | The Monitor blog

Introducing this year’s new Datadog Ambassadors and the new Datadog Champions program Measure the real impact of AI coding tools on software delivery with Datadog AI Impact How to measure developer experience (DevEx) in the AI era Improve API authentication detection with Datadog Securing AI agents: Why guardrail placement is a key design decision Project and manage cloud spend with Datadog budget forecasting How to audit and clean up monitors effectively How we made a SQL query optimization agent 59% more accurate using autoresearch and LLM Observability Reduce CVE noise with OpenVEX assessments in Datadog Diagnose slow PostgreSQL queries faster with explain plan correlation Explore Datadog metrics with Natural Language Queries Attribute AI costs across providers with Datadog Cloud Cost Management Simplify micro-frontend observability with Datadog RUM Toto 2.0: Time series forecasting enters the scaling era Diagnose and resolve database performance issues faster with Database Investigator Datadog for Government achieves FedRAMP® High certification Analyze cloud costs with flexible spreadsheets in Datadog Sheets Inside Datadog’s AI Research Lab: Meet two PhD candidates behind Toto Connect triage and investigation in a single workflow with Datadog Cloud SIEM Monitor and optimize Supabase query performance with Datadog Database Monitoring This Month in Datadog - April 2026 Add dynamically updating context to logs with Reference Tables and Observability Pipelines Introducing ARFBench: A time series question-answering benchmark based on real incidents The product signal latency gap slowing your growth Turn developer feedback into operational insight with Datadog Forms and Sheets How to investigate cloud credential compromise with Bits AI Security Analyst Evaluate, optimize, and secure your Google Cloud AI stack with Datadog Bringing observability data hosting to the UK on AWS Identify and fix code issues faster with Datadog’s Azure DevOps Source Code integration Steganography at scale: Embedding share URLs in Datadog widget screenshots Every team should be A/B testing Centralize observability management with Datadog Governance Console Spotting CI/CD misconfigurations before the bots do: Securing GitHub Actions with Datadog IaC Security Route OTel data from AI apps to ClickHouse and Datadog using Observability Pipelines Manage service tracing across hosts with Single Step Instrumentation rules Offline evaluation for AI agents: Best practices Detect runtime threats in Python Lambda functions with Datadog AAP 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 How we built a real-world evaluation platform for autonomous SRE agents at scale 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 When upserts don't update but still write: Debugging Postgres performance at scale 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 Closing the verification loop, Part 2: Fully autonomous optimization Closing the verification loop: Observability-driven harnesses for building with agents When an AI agent came knocking: Catching malicious contributions in Datadog’s open source repos 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 Designing MCP tools for agents: Lessons from building Datadog's MCP server 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 Fine-tune Toto for turbocharged forecasts 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 How we reduced the size of our Agent Go binaries by up to 77%
Test network paths with TCP, UDP, and ICMP in Datadog
2026-04-23 · via Datadog | The Monitor blog

When developers and SREs design application tests, they often prioritize user workflows and API availability. Extending that suite with network tests that match your app’s traffic protocols can reveal whether issues originate in the network or application layer.

In this post, we’ll explore how you can design effective network tests using the Transmission Control Protocol (TCP), User Datagram Protocol (UDP), or Internet Control Message Protocol (ICMP), including:

How to choose the right network test protocol

Traceroute-based tests are a common way to measure network health. By showing you the amount of time it takes for devices to respond to incoming requests, traceroute queries enable you to measure performance along every step of the network path, from the intermediary hosts to the destination server.

Depending on your testing tool, you may be able to run these queries over different protocols, usually ICMP, TCP, or UDP. Choosing a protocol that matches your app’s traffic—such as UDP for media streaming or TCP for web services—helps ensure your performance metrics reflect actual user experience. It can also help you determine how your app may interact with network components that can restrict traffic, such as firewalls.

Traceroute queries include two parts:

  • The request sent to each device in a network path

  • The reply sent from these devices, which is used to measure latency and availability

Requests and replies can be communicated using different protocols. For example, traceroutes use ICMP to send replies from intermediary hosts. Depending on your testing tool, however, you may be able to send the initial request over a different protocol like UDP or TCP.

Which protocol to test with: ICMP, TCP, or UDP?

Different protocols produce different kinds of results. ICMP is used for network diagnostics, as it has no transport layer and doesn’t support packet sequencing or retransmission. By contrast, TCP and UDP include more advanced functionality for handling packet transmission and are designed to carry app data, which enables them to reveal more about how your app behaves under real traffic conditions. Therefore, implementing traceroute queries with TCP or UDP enables you to gain additional insight into retransmission behavior and congestion response. Compared to ICMP, routers and firewalls are also less likely to rate limit or block TCP and UDP traffic.

TCP is useful for tracking the rate of successful data delivery, which can help you evaluate network throughput. By contrast, UDP helps you determine how well your system performs under heavy sustained traffic and measure characteristics like jitter.

TCP tracks connection state to guarantee reliable delivery. By treating data as a stream of sequenced bytes, TCP ensures that information is received in the order it was sent. Note that in high-jitter environments, this can lead to head-of-line (HoL) blocking, where the stack waits for a missing or delayed segment before passing subsequent data to the app, potentially causing significant latency.

Additionally, if a TCP packet is not acknowledged in a set timeframe, the sending device will automatically attempt to retransmit the data. If too many acknowledgements are missed, the sending device may trigger a retransmission timeout (RTO) mechanism to limit network congestion and help stabilize the connection. Depending on your operating system and network configuration, RTOs may last anywhere from 200 milliseconds to upwards of 120 seconds. Like HoL blocking, this can result in noticeable latency. These performance impacts mean that TCP is generally used for apps where data accuracy and reliability is more important than speed.

By contrast, UDP doesn’t attempt to sequence or retransmit packets. While these features make UDP less reliable than TCP, they also help UDP perform faster. Therefore, UDP is often used in situations where high-speed transmission is critical, like live video streaming, voice calls, or fast-paced games.

When to use SYN vs. SACK for TCP traceroute tests

Besides offering different protocol options to test with, testing tools may allow you to customize the requests themselves. For example, Network Path tests in Datadog Synthetic Monitoring let you choose between SYN and SACK strategies for TCP-based tests.

Network Path testing in Datadog Synthetic Monitoring with TCP protocol selected and SYN and SACK strategy options, enabling protocol-accurate traceroute testing.

TCP uses three-way handshaking (SYN, SYN-ACK, ACK) to establish sessions, helping ensure that the destination port is available and data is transmitted reliably. Handshaking identifies “zombified” or “half-open” connections where a port may be open but the app is unresponsive. When sending test requests over TCP in Datadog synthetic tests, you can decide whether you want to send a full handshake (SACK) or a one-way request (SYN).

SYN is the standard option for most tests, as the partial connection means that traceroute queries will run even if the destination port is closed. However, firewalls may misinterpret SYN requests as SYN floods or port scans. In these situations, you can use TCP SACK instead, which simulates a new session opening with each traceroute query. Keep in mind that not all targets support TCP SACK, which may result in test failures.

How to simulate realistic network traffic using Datadog Synthetic Monitoring

Not every operating system makes it easy to use different protocols within your tests. For example, to send traceroute requests over TCP, you may need to install third-party tools like tracetcp for Windows or tcptraceroute for Linux and macOS. And once you’ve installed these tools, you may need to manually set up cron jobs to schedule each of your traceroute tests.

Datadog Synthetic Monitoring enables you to create and schedule Network Path tests that use either ICMP, UDP, or TCP to send requests. As your test runs finish, the results are visualized as diagrams showing the flow of requests through every host in your network. You can view these results alongside the rest of your synthetic test findings, such as those from your API, browser, and mobile tests.

Network Path test results in Datadog Synthetic Monitoring showing latency along multiple hops, isolating a network-level performance issue.

Let’s say that you’re an SRE. A few updates to your video streaming app have led to complaints of increased pixelation and frozen frames from users. To rule out potential network issues as the cause, you create a Network Path test that uses UDP requests to accurately replicate your traffic.

Datadog enables you to specify factors such as:

  • The maximum time to live (TTL) for these requests

  • The number of end-to-end queries sent to the destination server

  • The number of traceroute queries executed per test run

You can also create an assertion that defines success for your test. In this case, you set the success condition as an average packet loss below 0.5%. You also decide to schedule these tests to run every five minutes, which helps you quickly catch issues without being overwhelmed by brief changes in network capacity or performance. Finally, you set your test to alert you if any two consecutive runs fail.

Soon you’re notified of a potential issue related to a new app release. While ruling out potential causes, you notice an alert from your Network Path test. In the test results, you see that one of the hops along the traceroute path resulted in errors, suggesting that the issue originates in your network configuration. This means you don’t need to loop your app team in right away. Instead, you forward this information to your network team for further troubleshooting.

Visualize end-to-end network health with Datadaog Synthetic Network Path tests

Sending traceroute requests over the protocol of your choice can give you a more accurate understanding of your network performance. By correlating protocol-specific test results with hop-by-hop visualizations, you can determine if a latency spike is a universal network issue (ICMP/UDP) or an application-layer negotiation failure (TCP). You can even use a combination of tests running on various protocols to evaluate different aspects of your network health. UDP provides visibility into real-time performance and packet loss, while TCP enables you to better measure overall reliability and firewall traversal.

While you may be able to use some of these protocols with native traceroute tests, creating your network tests in Datadog Synthetic Monitoring gives you access to additional protocols, greater control over configuration parameters like TTL and query count, and automated scheduling.

You can use our documentation to get started with Synthetic Monitoring and Network Path tests. Or, if you’re new to Datadog, you can sign up for a 14-day free trial.