Boost Rate Limiting Test CRD Detection In Scripts

Dec 10, 2025 by Admin 50 views

Introduction

Hey everyone, let's chat about something super important for our Gateway API testing efforts: improving the rate-limiting test CRD detection in our run-gateway-poc-17tests.sh script. Specifically, we're talking about Test 8/17. Right now, this test needs a significant tune-up to truly reflect the actual Rate Limiting support status across different Gateway implementations, as outlined in our README. We want our scripts to be smart enough to accurately identify how each gateway — like Envoy, NGINX, Istio, and Cilium — handles rate limiting, and then log it properly. This isn't just about passing or failing tests; it's about getting richer insights into our infrastructure's behavior and ensuring our documentation and test suite are perfectly in sync. Think of it as giving our test script a much-needed pair of glasses to see things more clearly, making our debugging lives way easier down the road. We're aiming for a seamless, understandable, and highly informative testing experience for everyone involved in maintaining and evolving our Kubernetes Gateway ecosystem. This enhancement will significantly boost the reliability and observability of our entire test suite, helping us catch nuances that might otherwise slip through the cracks. It's all about making our tests not just functional but truly intelligent and insightful, especially when dealing with critical traffic management features like rate limiting. This meticulous approach ensures that as the Gateway API evolves, our testing apparatus keeps pace, providing robust validation across all tested environments. This improvement directly contributes to a stronger, more reliable foundation for future Gateway API development and operations.

Current Status: What Our Script Sees (and Doesn't See)

Alright, let's dive into the current status of our run-gateway-poc-17tests.sh script, specifically looking at lines 868-938, where our rate-limiting test CRD detection magic (or lack thereof) happens. This is where the script tries to figure out how each Gateway implementation has configured its rate limiting policies. It's a crucial part of the test because it helps us understand the underlying mechanics, not just the final HTTP 429 status code. Knowing which specific CRD (Custom Resource Definition) is in play gives us a much clearer picture of how a particular gateway manages traffic shaping. Without accurate detection, we're essentially flying blind, missing out on valuable contextual information. The goal here is to ensure our script is acutely aware of the configuration patterns across all the Gateway API implementations we test. This clarity is indispensable for both developers and operators who need to quickly diagnose issues or verify compliance with specific rate limiting policies. The existing script does a decent job for some gateways, but there are notable blind spots that need immediate attention to elevate the quality of our Gateway API conformance suite.

✅ Currently Detected: Our Script's Sharp Eyes

First off, let's talk about what our current rate-limiting test script is successfully detecting. This is where the script shines, correctly identifying the specific Custom Resource Definitions (CRDs) that various Gateway implementations use to configure rate limiting. Knowing these details is super important for debugging and understanding the architectural choices made by each project. For instance, when we see BackendTrafficPolicy for Envoy Gateway, we immediately know we're dealing with a native, declarative approach to traffic management. This clarity helps us validate not just the outcome (like a 429 response) but also the method of implementation. The precision in these detections builds confidence in our test framework's ability to interpret diverse Gateway API configurations accurately, providing a solid foundation for further analysis. These successful detections are a testament to the initial design of our test script, highlighting its capabilities where integration is well-defined and recognized.

Envoy Gateway: Our script is doing a great job here, correctly identifying BackendTrafficPolicy on line 878. This is how Envoy Gateway natively handles rate limiting, providing a declarative and integrated way to manage traffic flows directly within its ecosystem. It’s a clean, Gateway API-aligned approach, and our script accurately flags it, giving us confidence in our test's understanding of Envoy's rate limiting setup. This consistency in detection is vital for understanding how Envoy Gateway enforces its policies and provides crucial insights for those working with its specific configuration. The BackendTrafficPolicy offers a robust mechanism for controlling traffic to backend services, making its accurate detection a key indicator of proper Gateway API functionality for Envoy. This also helps in verifying that Envoy Gateway adheres to the expected declarative model for rate limiting capabilities.
Kong: For Kong, our script intelligently looks for KongPlugin specifically configured for rate-limiting (check line 885). This makes perfect sense, as Kong often extends its functionality through plugins. Detecting this KongPlugin tells us that Kong is using its established plugin architecture to enforce rate limiting, which is a powerful and flexible method. This distinct detection helps us differentiate Kong's approach from others and is a testament to the script's ability to adapt to vendor-specific implementations. The flexibility of Kong's plugin system allows for a wide array of traffic management features, and the script's ability to pinpoint the KongPlugin for rate limiting confirms our understanding of its extensible nature within the Gateway API context. This detail is invaluable for diagnosing Kong-specific configurations and behaviors.
Istio: When it comes to Istio, our script correctly sniffs out EnvoyFilter for rate limiting on line 892. While EnvoyFilter is generally considered a lower-level configuration primitive in Istio, often used for advanced or custom scenarios, its detection here is crucial. It shows that Istio, leveraging its underlying Envoy proxy, is implementing rate limiting through direct Envoy configuration. This is key for understanding how Istio provides granular control over traffic, even if it means diving a bit deeper into the Envoy configuration itself. The use of EnvoyFilter highlights Istio's powerful, albeit sometimes complex, method of directly manipulating the underlying data plane, which our rate-limiting test must acknowledge. This detection confirms Istio's capability to enforce intricate rate limiting rules via its flexible Envoy integration.
Traefik: Finally, for Traefik, the script looks for Middleware with rateLimit (line 899). This is Traefik's native way of injecting capabilities into its routing chain. Identifying this Middleware confirms that Traefik is using its custom resource to apply rate limiting directly to incoming requests. This consistency across different Gateway API implementations in detecting their specific configuration methods highlights the strength of our current testing framework, even if some parts still need improvement. We love seeing our script correctly identify these configurations because it gives us context beyond just a simple pass/fail, which is invaluable for debugging and development. Traefik's Middleware concept is central to its traffic management, and its accurate detection for rate limiting provides clear insight into how Traefik handles such policies. This confirms that Traefik effectively uses its native resources within the Gateway API framework for this purpose.

❌ Not Detected: Where Our Script Needs a Head Start

Now, let's shift our focus to the areas where our rate-limiting test CRD detection could really use some love. This section highlights the gaps, the places where our script isn't quite catching the full picture, which can lead to confusion and less effective debugging. The absence of detection doesn't always mean the feature isn't working; it often means our script isn't aware of how it's working. This is precisely what we aim to fix to ensure our test script provides comprehensive insights across all tested Gateway API implementations. A robust test script should be able to identify the specific mechanisms each gateway employs, not just observe the final outcome. Bridging these detection gaps will significantly enhance the diagnostic power of our test suite, making it a truly invaluable tool for maintaining and developing Gateway API integrations. We're talking about transforming an implicit understanding into an explicit, logged fact, which is critical for operational excellence and developer productivity.

NGINX Gateway Fabric: This is a big one, guys! Currently, our script is missing any detection for NGINX Gateway Fabric's rate-limiting configuration. This is a significant oversight because NGINX does support rate limiting, albeit through its SnippetsFilter or direct snippet injection, which is a low-level config mechanism. The test might pass because NGINX successfully returns a 429 Too Many Requests status, but our script doesn't log the method NGINX used. This means we're losing valuable information about the NGINX Gateway Fabric's implementation details. It's like knowing a car arrived at its destination but not knowing if it drove, flew, or sailed! We need our script to explicitly say, "Hey, NGINX used SnippetsFilter here!" This clarity is crucial for anyone debugging an NGINX Gateway Fabric setup. Without it, the fallback logic incorrectly assumes no specific configuration was found, which is misleading. This lack of specific detection creates a blind spot that hinders our ability to fully understand and validate NGINX Gateway Fabric's adherence to rate limiting policies, making it harder to pinpoint configuration issues unique to NGINX's approach. This gap must be addressed to ensure comprehensive Gateway API testing.
Cilium: When it comes to Cilium, our script currently shows "No detection." Now, this one is partially correct because, as of now, Cilium does not support HTTP Rate Limiting in the way this test requires. However, the lack of explicit handling or a clear comment means that if someone unfamiliar with Cilium's capabilities looks at the test results, they might misinterpret the "no detection" as an issue with the script rather than a documented limitation of Cilium. We want our script to be crystal clear about why Cilium behaves the way it does in this specific test. This isn't a failure of Cilium, but rather an absence of a feature that the test targets. Adding a note or explicit handling will make our test results much more digestible and prevent unnecessary head-scratching. We want to avoid any ambiguity, ensuring that every result from our rate-limiting test is easily understandable and directly correlates with the expected behavior or known limitations of each Gateway API implementation. This explicit documentation will save significant time and effort for anyone analyzing test results, preventing misinterpretations about Cilium's capabilities regarding HTTP rate limiting within the Gateway API framework.

Issues Identified: Unpacking the Problems

Alright, team, let's break down the specific issues we've pinpointed regarding our rate-limiting test CRD detection. Understanding these problems in detail is the first step towards crafting robust and accurate solutions. It's not just about fixing code; it's about enhancing our overall Gateway API testing strategy and ensuring our scripts truly reflect the nuances of each implementation. We want to eliminate any ambiguity and provide a transparent view into how rate limiting is configured and behaves across different gateways. This clarity is paramount for effective debugging, development, and maintaining trust in our testing framework. Our goal here is to transform our current script into a highly intelligent and informative tool that not only reports what happened but also how it happened, especially concerning critical features like rate limiting. This means addressing both explicit omissions and areas where greater clarity is needed in our reporting and documentation. Each of these issues represents an opportunity to make our test script more comprehensive and user-friendly, pushing us closer to a fully optimized Gateway API conformance suite. By systematically tackling these identified problems, we aim to elevate the quality and reliability of our entire testing apparatus, ensuring it provides maximum value to all stakeholders.

1. Missing NGINX Detection ⚠️ – The Elephant in the Room

Problem: So, here's the deal, guys: we have a glaring hole in our rate-limiting test CRD detection when it comes to NGINX Gateway Fabric. There's no specific CRD check in place for how NGINX configures its rate limiting. This is a classic case of the test passing for the right outcome (429 response) but for the wrong reasons in terms of observability. While NGINX does successfully apply rate limiting and correctly returns a 429 Too Many Requests status, our script fails to acknowledge how NGINX achieved this. It's like seeing a beautifully painted house but having no idea what kind of paint or technique was used. We're missing critical contextual information that could be vital for debugging, understanding, and validating NGINX Gateway Fabric's specific implementation of rate limiting within the Gateway API ecosystem. This oversight not only makes our test logs less informative but also means our automated system isn't fully aware of NGINX's distinct configuration methods, which can lead to confusion during analysis or when new team members are onboarding. We need our script to be more discerning, able to identify SnippetsFilter or similar NGINX-specific configurations, thereby providing a complete picture of its rate limiting strategy. This gap reduces the diagnostic utility of our test script for NGINX deployments.
Expected Behavior: What we should see, my friends, is our script explicitly detecting NGINX's rate limiting configuration. From our research, we know that NGINX primarily uses SnippetsFilter for this purpose. This SnippetsFilter allows for low-level configuration injection directly into the NGINX configuration, which is how NGINX provides powerful and flexible control over traffic management. Therefore, our script must be able to detect the presence of SnippetsFilter resources. A simple kubectl get snippetsfilter -n "$NAMESPACE" command should tell us if this CRD is in use. If SnippetsFilter isn't a standalone CRD, then we need to check for "snippets" configuration within the NginxProxy resource itself. The key is to identify and log the specific NGINX mechanism. This way, when the test passes, we don't just know that rate limiting worked; we know how NGINX made it work. This level of detail elevates our test from a simple pass/fail indicator to a valuable diagnostic tool, providing insights into the specific Gateway API implementation and its nuances. This expected behavior ensures that our test script provides actionable intelligence, not just binary results, particularly for complex and highly configurable gateways like NGINX, which rely on granular control over their proxy settings.
Current Impact: The current situation, while not breaking the test (it still passes due to the 429 response), has a few significant drawbacks. Firstly, as mentioned, NGINX's rate limiting works and returns 429 Too Many Requests, leading to a PASS status. Great! But here's the catch: the CRD detection doesn't log what method NGINX actually used. This means that if you're trying to debug an NGINX Gateway Fabric setup, your test logs won't tell you how rate limiting was configured, making your job much harder. You'd have to manually inspect the cluster, which is a waste of precious time. Secondly, our fallback logic in the script doesn't recognize NGINX's specific implementation method, which is a missed opportunity for comprehensive logging. This lack of explicit recognition makes our test less informative and potentially obscures important details about how rate limiting is achieved in an NGINX Gateway Fabric environment. It's all about enriching our diagnostic capabilities, and currently, we're falling short for NGINX. This also impacts the ability to automate further analysis based on detected configuration types, limiting the potential for advanced reporting within our Gateway API testing framework.
Suggested Fix: To fix this, we need to inject a specific check for NGINX's configuration into our script. We propose adding an if block that looks for SnippetsFilter. If that's found, bingo, we mark HAS_RATE_LIMIT_CONFIG as true and RATE_LIMIT_TYPE as "nginx-snippetsfilter". If SnippetsFilter isn't a separate CRD, we can fall back to checking NginxProxy resources for any "snippets" configuration within their YAML output. This ensures that no matter how NGINX is configured, our script will correctly identify and log its rate-limiting mechanism. This seemingly small change will make a huge difference in the clarity and diagnostic value of our rate-limiting test results for NGINX Gateway Fabric. By implementing this, we close a significant gap in our Gateway API test coverage, providing clearer, more actionable insights into NGINX's traffic management capabilities and specific configuration patterns. This will greatly aid in both development and operational debugging scenarios.

2. Cilium Handling 📝 – Explicitly Acknowledging Limitations

Problem: Alright, let's talk about Cilium. Currently, our rate-limiting test script doesn't have any explicit handling or dedicated logic for Cilium, which is known not to support HTTP Rate Limiting as required by this test. While the current logic technically results in a SKIP for Cilium (because no config is detected and requests succeed without 429s, indicating the feature isn't active), this implicit behavior isn't ideal. It lacks clarity. Someone reviewing the logs might see "no config detected" and then "SKIP" and wonder why. Is it a bug in our detection? Is Cilium just failing silently? This ambiguity can lead to confusion and unnecessary investigation. We want our Gateway API test suite to be as transparent as possible, especially when dealing with known feature limitations in specific implementations. Explicitly stating why Cilium is skipped for rate limiting prevents misinterpretations and reinforces the accuracy of our overall testing framework. This means that while the test result is technically correct, the explanation for that result is lacking, leading to potential miscommunication about Cilium's current capabilities within the Gateway API context.
Expected Behavior: For Cilium, the expected and desired behavior is a clear and unambiguous SKIP status for this HTTP Rate Limiting test. It's crucial to understand that Cilium does offer CiliumClusterwideNetworkPolicy, which provides network-level rate limiting. However, this test specifically targets HTTP-level rate limiting, which is a different beast and currently not supported by Cilium. Therefore, the test should not fail for Cilium; it should gracefully SKIP because the feature isn't implemented. More importantly, our script should communicate why it's skipping. Adding a comment or a log entry explaining Cilium's lack of support for HTTP rate limiting (and perhaps referencing the relevant GitHub issue) would provide immense value. This helps anyone reading the test results understand the context immediately, without needing prior knowledge of Cilium's feature set. It clarifies that the SKIP isn't a flaw in our test or a silent failure by Cilium, but rather an accurate reflection of its current capabilities within the Gateway API context. This explicit handling ensures our test script serves as an educational tool as well as a validator of functionality, preventing misunderstandings about Cilium's traffic management features.
Suggested Fix: The fix here is straightforward yet impactful. We need to add a clear, concise comment directly within the rate-limiting test section of our script, specifically mentioning Cilium. This comment should explain that Cilium Gateway does not support HTTP Rate Limiting and reference the relevant GitHub issue (like #33500). This small addition transforms an implicit SKIP into an explicitly documented SKIP, making the test results far more understandable and eliminating any potential for confusion. It's about adding a layer of transparency that acknowledges the specific capabilities and limitations of each Gateway API implementation we're testing. This ensures that our rate-limiting test provides accurate and context-rich information for all gateways, contributing to a more robust and self-documenting Gateway API conformance suite. This simple documentation change will significantly improve the user experience for anyone interpreting the test outcomes related to Cilium, reducing friction and improving overall clarity.

3. Documentation Alignment 📚 – Syncing Up Our Story

The third critical issue revolves around documentation alignment. Guys, it's super important that our test script comments and the main README.md (and README_ko.md) are telling the same story about rate-limiting support across different Gateway API implementations. Right now, there's a bit of a mismatch, which can lead to confusion. Our README is our source of truth for the project's capabilities, so the script should perfectly echo that truth, not introduce new narratives or omit existing ones. When the script's internal comments don't line up with the external documentation, it creates friction and makes it harder for anyone to get a consistent picture of our rate-limiting test coverage and status. The goal is to ensure a unified message, where our documentation directly informs and validates the behavior seen in our test script. This consistency is vital for maintaining user trust and ensuring that all project stakeholders have access to accurate, up-to-date information regarding Gateway API features.
README States: As of late 2025, our README clearly outlines the rate-limiting support status for various gateways, categorizing them by "Support" and "Method."
- Envoy Gateway: Marked as "O (Native)" with BackendTrafficPolicy. This is our gold standard for native support, indicating a fully integrated and declarative approach.
- NGINX Gateway Fabric: Listed as "△ (Limited)" using SnippetsFilter. This acknowledges its capability but highlights it as a low-level injection, requiring direct configuration manipulation.
- Istio: Also "△ (Limited)" with EnvoyFilter, similarly noting its reliance on low-level Envoy configuration for advanced traffic management.
- Cilium: Explicitly "X (Not supported)," with a clear reference to feature request #33500. This provides a definitive statement on its current lack of HTTP-level rate limiting. This table is a fantastic summary, and our rate-limiting test script should reflect these distinctions perfectly, ensuring that what our documentation promises, our tests acknowledge and explain.
Script Should: To achieve true alignment, our test script needs to do a few things. First, it must detect all three supported methods – Envoy's BackendTrafficPolicy, NGINX's SnippetsFilter (or similar snippets config), and Istio's EnvoyFilter. Currently, NGINX's detection is missing, as we discussed. Second, the script needs to log the detected method type clearly for debugging purposes. This helps us quickly identify how rate limiting was applied. Third, it needs to explicitly document that Cilium is not supported for HTTP rate limiting, ideally with a reference to the GitHub issue, just like the README. Finally, if possible, it would be great if the logging could distinguish between "native" and "limited" support, reflecting the nuances captured in our README. This comprehensive approach ensures our rate-limiting test not only performs the check but also provides rich, consistent, and well-documented context for every Gateway API implementation. It's all about making our tests smart, transparent, and aligned with our project's public-facing information, reducing any potential for conflicting narratives about traffic management capabilities.

Proposed Changes: Leveling Up Our Test Script

Alright, let's get down to business, folks! Now that we've clearly identified the issues with our rate-limiting test CRD detection, it's time to talk about the proposed changes that will make our run-gateway-poc-17tests.sh script much more intelligent, informative, and aligned with our Gateway API documentation. These aren't just minor tweaks; these are targeted enhancements designed to significantly improve the diagnostic capabilities and overall clarity of our testing framework. Our goal here is to ensure that when you run these tests, you don't just get a pass or fail, but a rich understanding of how each Gateway implementation is handling rate limiting. We're focusing on making the script not only functional but also educational and transparent for everyone involved, from new contributors to seasoned maintainers. Each proposed change tackles a specific problem identified earlier, and together, they will create a more robust and insightful rate-limiting test. This systematic approach to improvement ensures that our Gateway API conformance suite remains cutting-edge and provides maximum value. By integrating these specific modifications, we aim to eliminate current ambiguities and provide a crystal-clear picture of traffic management behaviors across the entire Gateway API ecosystem.

Priority 1: Add NGINX Detection 🔴 – Filling the Critical Gap

This is our highest priority change, guys, and it's all about making our rate-limiting test script smarter when dealing with NGINX Gateway Fabric. As we discussed, the current script completely misses NGINX's configuration for rate limiting, which is a significant oversight for a comprehensive Gateway API test suite.
Location: We'll be targeting lines 878-903, right within the core rate limiting CRD check section of run-gateway-poc-17tests.sh. This is where the script currently sniffs out BackendTrafficPolicy for Envoy, KongPlugin for Kong, EnvoyFilter for Istio, and Middleware for Traefik. Our new NGINX detection logic needs to be integrated seamlessly into this existing structure. Placing it here ensures that NGINX's rate limiting configuration is checked alongside other major Gateway API implementations, providing a consistent and logical flow for our test script's detection mechanism.
Action: The main action here is to add robust detection for NGINX's rate limiting configuration. Based on our knowledge, this primarily involves looking for SnippetsFilter or direct snippet configurations within NginxProxy resources. This addition will ensure that when NGINX applies rate limiting, our script recognizes and logs the specific mechanism it used, moving beyond just observing the 429 HTTP status code. This will significantly improve the diagnostic value of our rate-limiting test results for NGINX Gateway Fabric. By explicitly identifying the configuration, we empower users to understand the underlying traffic management principles NGINX employs, which is crucial for advanced debugging and verification of Gateway API compliance. This move transforms a passive observation into an active, intelligent detection.
Code: Here's the specific bash code we'll introduce. It first checks for SnippetsFilter as a standalone CRD, which is the preferred method for configuring snippets. If SnippetsFilter is found, we immediately mark HAS_RATE_LIMIT_CONFIG as true and set RATE_LIMIT_TYPE to "nginx-snippetsfilter." This provides clear, explicit identification. However, we also need a fallback, just in case. If SnippetsFilter isn't found as a distinct CRD, our script will then check the NginxProxy resource directly for any "snippets" configuration within its YAML output. This ensures that no matter how NGINX's low-level configuration is managed, our test script will catch it. This two-pronged approach guarantees comprehensive detection for NGINX Gateway Fabric's rate limiting. This change will make our Gateway API testing for NGINX much more transparent and informative.

# NGINX Gateway Fabric: SnippetsFilter (low-level config for rate limiting)
if kubectl get snippetsfilter -n "$NAMESPACE" 2>/dev/null | grep -q .; then
 HAS_RATE_LIMIT_CONFIG=true
 RATE_LIMIT_TYPE="nginx-snippetsfilter"
 log_debug "Found NGINX SnippetsFilter for rate limiting"
# Fallback: Check NginxProxy for snippets configuration
elif kubectl get nginxproxy -n "$NAMESPACE" -o yaml 2>/dev/null | grep -q "snippets"; then
 HAS_RATE_LIMIT_CONFIG=true
 RATE_LIMIT_TYPE="nginx-snippets"
 log_debug "Found NGINX snippets configuration for rate limiting"
fi

This snippet will be strategically placed after the other gateway-specific CRD checks but before any generic fallback logic, ensuring proper prioritization. It's a game-changer for NGINX Gateway Fabric insights in our rate-limiting test, solidifying our Gateway API conformance checks.

Priority 2: Add Cilium Documentation 🟡 – Clarity for Known Limitations

Next up, we've got a really important change for clarity, especially concerning Cilium. This isn't about fixing a broken test but about making our rate-limiting test script more transparent and user-friendly by explicitly documenting known limitations, rather than leaving them as implicit behaviors. Clear communication of Gateway API feature support, or lack thereof, is vital for managing expectations and providing accurate diagnostic information.
Location: We'll be adding this explanatory comment right at the beginning of the rate-limiting test section, roughly around lines 868-875, before the test actually kicks off. This ensures that anyone looking at the script or the test output will immediately understand the context for Cilium's behavior. It’s like putting a helpful note at the top of a form! Placing this context upfront helps to prevent confusion and unnecessary investigation, making our test script more self-explanatory and aligned with the principles of clear Gateway API documentation. This proactive approach improves the user experience significantly.
Action: The core action is to add a clear, concise comment that explains why Cilium behaves the way it does in this particular rate-limiting test. We want to avoid any confusion or misinterpretation of a SKIP result. This comment will serve as a quick reference for anyone reviewing the test, preventing them from having to dig through external documentation to understand the nuance. This aligns our script comments with the broader Gateway API documentation, reinforcing a consistent message across all project resources. By explicitly stating the reasons for a SKIP, we enhance the transparency and educational value of our test script, making it a more reliable source of truth regarding rate-limiting capabilities.
Code: Here’s the proposed block of comments we'll insert. It clearly states the expected results for various implementations, including an explicit reason for Cilium’s SKIP. This provides immediate context for the rate-limiting test.

# Test 8: Rate limiting [FIX v2: faster sequential requests + v1: CRD check]
#
# Hey folks, let's talk about the expected outcomes for this crucial rate-limiting test!
# It's vital to know what to anticipate from each Gateway implementation:
#   - Envoy Gateway: We expect a solid PASS here! It uses `BackendTrafficPolicy` for native, declarative rate limiting.
#   - NGINX Gateway Fabric: Another PASS! While it uses `SnippetsFilter` for low-level config, it successfully rate limits.
#   - Istio: A PASS is expected! Istio leverages `EnvoyFilter` for its limited, low-level rate limiting capabilities.
#   - Cilium: This one's a SKIP, and here's why: Cilium Gateway currently *does not support HTTP-level rate limiting*.
#             We're tracking this as a feature request on GitHub, issue #33500.
#             Please note that while Cilium offers `CiliumClusterwideNetworkPolicy` for network-level rate limiting,
#             that's different from the HTTP-level rate limiting this specific test requires.
#             So, a SKIP here isn't a failure, it's just acknowledging the feature isn't yet available for this test.
#   - Kong: We're currently seeing a FAIL due to some ongoing Gateway API compatibility issues with Kong.
#   - Traefik: Also currently a FAIL, primarily due to existing configuration challenges.
#
echo -n "Test 8/17: rate-limiting... "
start_test_timer

This detailed comment block clearly sets expectations for the rate-limiting test and specifically clarifies Cilium's position, ensuring that the SKIP is understood as a design decision rather than a test failure. It greatly enhances the human readability and diagnostic utility of our test script within the broader Gateway API testing landscape, providing a transparent view of traffic management feature support.

Priority 3: Improve Logging 🟢 – Richer Insights, Easier Debugging

Our final proposed change, though seemingly small, is all about making our rate-limiting test logs much more useful and easier to digest. We want our logs to be a goldmine of information, not just a sparse summary. Better logging means faster debugging, clearer understanding of test runs, and ultimately, a more efficient development cycle for our Gateway API implementations. Comprehensive logging is the bedrock of effective observability, turning raw data into actionable insights for traffic management analysis. This improvement directly addresses the need for detailed, context-rich feedback from our automated tests, ensuring that every run contributes meaningfully to our understanding of system behavior.
Location: We're going to enhance the debug output specifically at line 920, right where the log_debug statements summarize the rate-limiting test results. This is the perfect spot to inject more context into our test reports, as it's the point where all the collected data from the test run is aggregated and presented. By focusing our improvements here, we ensure that the summary output is as informative as possible, serving as a quick yet comprehensive overview of the rate-limiting test's performance and configuration details. This strategic placement maximizes the visibility of the new, enriched data points.
Action: The main action is to enhance the debug output by including more specific and relevant details about the rate-limiting test. This includes not just the HTTP response counts but also the detected configuration type, the duration of the test, and the specific implementation being tested. This comprehensive approach ensures that every piece of relevant information is right there in your logs. By providing these additional data points, we transform a basic log entry into a powerful diagnostic tool, offering immediate insights into the operational characteristics and configuration methods of each Gateway API implementation during rate limiting operations. This greatly streamlines the process of identifying and resolving issues.
Code: Here's how we'll supercharge our debug logging for the rate-limiting test:

log_debug "Rate limiting test summary:"
log_debug "  - 429 responses: $RATE_429 (Requests hit rate limit!)"
log_debug "  - 200 responses: $RATE_OK (Requests passed through)"
log_debug "  - Other responses: $RATE_OTHER (Any unexpected HTTP codes)"
log_debug "  - Detected Config Type: ${RATE_LIMIT_TYPE:-none} (How rate limiting was set up)"
log_debug "  - Test Duration: ${DURATION_MS}ms (How long the test took)"
log_debug "  - Gateway Implementation: $IMPL (Which Gateway API implementation was tested)"

This revised logging block provides a much richer set of data points. For instance, Detected Config Type will now show "nginx-snippetsfilter" or "envoy-backendtrafficpolicy," giving immediate insight into the underlying mechanism. The Test Duration can help identify performance bottlenecks, and explicitly stating the Gateway Implementation always keeps context front and center. This makes our rate-limiting test logs not just records, but powerful diagnostic tools for Gateway API development. It's about providing all the necessary pieces of information to quickly understand what happened during the test run, significantly improving the efficiency of traffic management debugging efforts.

Test Case Validation: What We Expect to See

After we implement all these fantastic changes, guys, the next critical step is validation. We need to run through our test suite and make sure everything behaves exactly as we expect. This test case validation is crucial to confirm that our rate-limiting test CRD detection is now working flawlessly across all Gateway API implementations. It’s about ensuring that our efforts translate into accurate, predictable, and transparent test results, aligning perfectly with our updated documentation and enhanced logging. This isn't just about the tests passing or failing; it's about making sure the reasons for those outcomes are clearly identified and reported. A robust validation phase guarantees the integrity and reliability of our updated test script, confirming its ability to accurately assess rate limiting behaviors.

Here’s a detailed table outlining the expected results for each Gateway API implementation once our changes are in place. This table will be our go-to reference during the validation phase, helping us confirm that our script correctly identifies the rate-limiting configurations and reports the appropriate status. We want to see a consistent and logical output that reflects the underlying support status and detected configurations. This is where all our hard work on rate-limiting test improvements comes to fruition, providing a clear and verifiable outcome for each gateway, and solidifying our Gateway API conformance suite.

Implementation	Expected Result	Detected Config (after changes)	Actual 429s (from gateway)	Final Status (from script)
Envoy Gateway	PASS	`BackendTrafficPolicy`	Yes (meaning Envoy successfully rate-limited)	✅ PASS
NGINX Gateway Fabric	PASS	`SnippetsFilter` or `nginx-snippets`	Yes (meaning NGINX successfully rate-limited)	✅ PASS
Istio	PASS	`EnvoyFilter`	Yes (meaning Istio/Envoy successfully rate-limited)	✅ PASS
Cilium	SKIP	None (as HTTP rate limiting is not supported)	No (as no HTTP rate limiting occurred)	✅ SKIP
Kong	FAIL	N/A (or a generic plugin if configured, but test fails due to other issues)	No (meaning Kong didn't rate limit as expected in test context)	❌ FAIL
Traefik	FAIL	N/A (or a generic middleware if configured, but test fails due to other issues)	No (meaning Traefik didn't rate limit as expected in test context)	❌ FAIL

This clear mapping will allow us to quickly verify that our enhanced rate-limiting test logic is correctly identifying the CRDs, handling unsupported features gracefully, and providing accurate status reports for every Gateway API implementation. It's the final crucial step to ensure our test script is operating as intended and delivering maximum value, making our traffic management testing unequivocally reliable.

Implementation Checklist: Our Action Plan

Alright team, to make sure we hit all our targets for these rate-limiting test CRD detection improvements, we've put together a clear implementation checklist. This isn't just a list; it's our roadmap to successfully enhancing our run-gateway-poc-17tests.sh script and making our Gateway API testing even more robust. Following these steps meticulously will ensure that we cover all the bases, from initial research to final verification. Each item on this list is a crucial step towards a more transparent, informative, and effective test script, especially when dealing with critical traffic management features like rate limiting. We want to leave no stone unturned in our quest for a top-tier Gateway API conformance suite. This systematic approach will ensure that every aspect of the rate-limiting test enhancement is thoroughly addressed, leading to a high-quality outcome.

[ ] Research NGINX SnippetsFilter CRD Structure: Before we write any code, we need to do our homework. This means thoroughly investigating the NGINX SnippetsFilter CRD. Is it a standalone custom resource that kubectl get snippetsfilter would directly report? Or are snippets typically configured within the NginxProxy resource itself, perhaps as a field in its YAML definition? Understanding this distinction is absolutely key to crafting the correct kubectl query for our rate-limiting test. This initial research prevents wasted effort and ensures our detection logic is precisely targeted for NGINX Gateway Fabric's unique traffic management configurations.
- [ ] Check if SnippetsFilter is a separate CRD: This is the first thing to confirm. If it is, our detection becomes simpler and more direct, streamlining the test script logic.
- [ ] Or if snippets are part of NginxProxy resource: If it's not a separate CRD, then our strategy shifts to parsing the NginxProxy YAML output to find snippet configurations, which requires a slightly different approach for the rate-limiting test.
- [ ] Determine correct kubectl query: Based on the above research, we'll finalize the most accurate and efficient kubectl command to detect NGINX rate limiting configurations within the test script. This ensures reliable Gateway API testing for NGINX Gateway Fabric.
[ ] Add NGINX detection code: Once the research is complete, we'll integrate the new bash code snippet (as proposed in Priority 1) into the run-gateway-poc-17tests.sh script, specifically in the rate-limiting CRD check section. This will give our rate-limiting test the ability to correctly identify NGINX's setup, thereby closing a critical gap in our Gateway API test coverage.
[ ] Add Cilium documentation comment: We'll insert the detailed explanatory comment (from Priority 2) at the beginning of the rate-limiting test block in the script. This ensures clear context for Cilium's SKIP status for HTTP rate limiting, improving the transparency of our Gateway API test results.
[ ] Enhance debug logging: Update the log_debug statements (as shown in Priority 3) to provide more comprehensive information about the rate-limiting test results, including the detected config type, duration, and implementation. This will significantly boost the diagnostic capabilities of our test script for all Gateway API implementations.
[ ] Test script with all 7 implementations: This is crucial. We must run the modified script against all seven Gateway API implementations that we support in our test suite. This comprehensive testing ensures our changes haven't introduced regressions and are working as expected across the board, providing confidence in the robustness of our rate-limiting test enhancements.
[ ] Verify test results match README expectations: Compare the actual output from our test runs against the "Expected Result" table in our README.md and the validation table we just outlined. Every PASS, FAIL, and SKIP, along with detected configurations, must align, solidifying the accuracy of our Gateway API test reporting.
[ ] Update script version/changelog comments: Finally, make sure to update any internal script versioning or changelog comments to reflect these significant improvements to the rate-limiting test logic. This helps maintain a clear history of changes and document the evolution of our Gateway API conformance suite.

Timeline: When We'll Make This Happen

Alright, let's talk timeline for these fantastic rate-limiting test CRD detection improvements. We understand the importance of planning and setting realistic expectations, especially for enhancements that touch core Gateway API testing infrastructure. While these are non-breaking improvements, they add significant value, so we're aiming for a focused effort to get them rolled out. Our goal is to maintain momentum without rushing, ensuring quality and thoroughness at every step. This timeline reflects our commitment to continuously improving our test script and overall Gateway API conformance suite. We want to ensure that all changes related to rate limiting detection and reporting are implemented and validated meticulously, providing a stable and enhanced testing experience. This structured approach helps in managing resources and dependencies effectively across the team, ensuring a smooth transition to the updated test script with minimal disruption to ongoing development efforts.

Review: We're looking at late December 2025 through early January 2026 for the initial review phase. This is where we'll gather feedback on these proposed changes, ensure everyone is on board, and iron out any last-minute details before diving into implementation. This collaborative review process is crucial for a smooth rollout of our enhanced rate-limiting test, allowing stakeholders to provide input and ensure alignment with broader project goals for Gateway API compliance. It ensures that the proposed solutions are robust and universally accepted.
Implementation: The actual coding and integration work for these changes will primarily happen in Q1 2026. This gives us ample time to carefully implement the NGINX detection, add the Cilium documentation, and refine the logging, all while ensuring no regressions are introduced. A dedicated quarter for implementation allows for thorough development, code reviews, and initial testing, which are essential for maintaining the high quality of our test script and ensuring reliable rate-limiting verification for Gateway API implementations.
Testing: Immediately following implementation, a thorough testing phase will also take place within Q1 2026. This includes running the full suite of Gateway API tests across all implementations, as outlined in our validation plan, to confirm that all rate-limiting test enhancements are working perfectly and as expected. This dedicated testing window is essential to guarantee the stability and accuracy of our updated test script, ensuring that the enhanced CRD detection provides consistent and correct results across the diverse Gateway API landscape.

Related Files: Where the Magic Happens and Documentation Lives

For those of you looking to get hands-on or just curious about where these crucial rate-limiting test CRD detection changes will live, here are the key files involved in this enhancement effort. Understanding which files are impacted is absolutely crucial for anyone contributing to, reviewing, or simply comprehending these improvements to our Gateway API testing framework. These files represent the core components of our automated testing environment and our public-facing documentation. We want to ensure that anyone navigating our repository can easily find and understand the context around our rate-limiting test logic and its corresponding status across various gateway implementations. Knowing these file locations is fundamental for both developers implementing the changes and users seeking to verify traffic management capabilities.

/run-gateway-poc-17tests.sh (specifically lines 868-938) - This is the heart of our operation, the main test script itself. All the proposed bash code changes for the NGINX detection logic, the clarifying comments for Cilium, and the enhanced debug logging will be implemented directly within this script. This is the operational brain for our rate-limiting test, defining how we detect configurations, how we execute the test, and how we report the results. Any and all modifications that directly influence the execution and reporting of this specific test will reside here, making it the primary focus of our implementation efforts. It's the engine that drives our Gateway API conformance suite, especially for validating traffic management policies.
/README.md (specifically Section 5.2) - This is our primary, English-language project documentation, serving as the definitive source of truth for our project's capabilities. The "Rate Limiting Support Status" section within this README provides an authoritative overview of which Gateway API implementations natively support rate limiting, which have limited support, and which currently do not. Our entire script update initiative is designed to perfectly align our test script's observed behaviors and reported statuses with the information presented in this critical document. It ensures that our internal testing mechanisms and external project descriptions are always in perfect sync, giving users a consistent and reliable view of traffic management features.
/README_ko.md (specifically Section 5.2) - Just as important as our English README, this is the Korean localized version of our main project documentation. Ensuring consistency across all language versions of our documentation is a best practice. Therefore, this file will also be reviewed and updated to reflect any changes or clarifications related to Rate Limiting support status, guaranteeing that our global audience receives accurate and up-to-date information regarding our Gateway API implementations and their rate-limiting test outcomes. This commitment to multilingual accuracy underlines our dedication to a broad and inclusive user base for our traffic management solutions.

References: Dive Deeper!

Hey everyone, for those of you who really want to dive deeper into the technical details behind these rate-limiting test CRD detection improvements, we've compiled a list of key references. These links will take you directly to the official documentation and relevant GitHub issues that inform our understanding of rate limiting across various Gateway API implementations. Whether you're a developer, an operator, or just curious, these resources are invaluable for understanding the context and rationale behind our proposed changes. We believe in providing full transparency and access to the foundational knowledge that underpins our test script enhancements. It's all about empowering you with the information needed to fully grasp the nuances of Gateway API functionality and our robust rate-limiting test coverage. These references are critical for anyone looking to gain a comprehensive understanding of traffic management in the Kubernetes ecosystem.

Official Documentation

These are the canonical sources, directly from the projects themselves, detailing how they implement rate limiting within their respective Gateway API contexts. They are essential reads for anyone looking to understand the core mechanisms behind traffic management features and how they integrate with the broader Kubernetes environment.

Envoy Gateway BackendTrafficPolicy: This link will take you to the official Envoy Gateway documentation, explaining how their BackendTrafficPolicy CRD is used to configure native rate limiting. It's a great example of declarative traffic management within the Envoy ecosystem, and understanding this is key to appreciating our rate-limiting test for Envoy. This documentation provides crucial insights into Envoy Gateway's native capabilities and its approach to Gateway API conformance for rate limiting.
NGINX Gateway Fabric SnippetsFilter: Dive into the NGINX Gateway Fabric documentation here to learn about SnippetsFilter. This low-level configuration injection mechanism is what NGINX uses for advanced features like rate limiting, and it's precisely what our updated test script aims to detect. This resource illuminates NGINX's powerful, yet granular, approach to traffic management and its integration within the Gateway API framework.
Istio EnvoyFilter Rate Limiting: This Istio documentation details how EnvoyFilter can be used for rate limiting. While more advanced, it showcases Istio's flexibility in leveraging Envoy's capabilities, which our rate-limiting test accurately reflects. Understanding EnvoyFilter is key to grasping Istio's intricate control over traffic management policies, including rate limiting, within its service mesh architecture.

GitHub Issues

Sometimes, understanding why a feature isn't supported or is still under development is just as important as knowing what is supported. GitHub issues provide that crucial context and future outlook, giving you a glimpse into the roadmap and challenges of Gateway API implementations.

Cilium: HTTP Rate Limiting Feature Request #33500: This is the specific GitHub issue for Cilium that tracks the feature request for HTTP Rate Limiting. Understanding this issue is vital for appreciating why Cilium currently SKIPs our rate-limiting test and why our documentation explicitly calls this out. This context is essential for anyone interested in Cilium's evolving traffic management capabilities within the Gateway API landscape.

Gateway API Spec

And of course, it's always good to refer back to the source! The Gateway API Specification is the foundation upon which all these implementations are built, providing the overarching standard for traffic management in Kubernetes.

Gateway API v1.2.0: This link directs you to the official documentation for Gateway API v1.2.0. This specification defines the standardized interfaces for service networking in Kubernetes, including concepts that underpin rate limiting and other traffic management features tested by our script. It provides the overarching context for all Gateway API implementations and helps ensure a consistent understanding of how rate limiting should behave across diverse platforms.

Notes: Final Thoughts and Key Takeaways

Before we wrap this up, guys, let's just hammer home a few final notes and key takeaways about these rate-limiting test CRD detection improvements. It’s important to reinforce the impact and context of these changes for our run-gateway-poc-17tests.sh script and the broader Gateway API testing landscape. We want to ensure that everyone understands the spirit behind these enhancements, which are designed to make our traffic management testing more robust and insightful. These points summarize why these updates are not just good, but essential, for the continued evolution of our Gateway API conformance suite.

This is a non-breaking improvement: First and foremost, let's be clear: these changes are designed to be non-breaking. Your existing rate-limiting tests will still pass or fail correctly based on the actual behavior of the gateways. We're not altering the fundamental pass/fail logic; rather, we're enhancing the diagnostic information surrounding those outcomes. So, no need to panic about immediate test failures or regressions! This is an upgrade, not a disruption, for our Gateway API conformance suite, specifically improving the observability of rate limiting features.
Primary benefit: Better debugging and logging: The main advantage of all this work is significantly better debugging and logging. When you run the rate-limiting test after these changes, you'll get richer, more detailed output in your logs. This means faster troubleshooting when things go wrong and clearer understanding of why things work (or don't work) across different Gateway API implementations. Imagine how much time this will save when you're trying to figure out a subtle configuration issue related to traffic management! This directly translates to increased developer productivity and operational efficiency.
Secondary benefit: Alignment with documentation: A crucial secondary benefit is achieving better alignment with our documentation. Our README.md provides the authoritative word on rate-limiting support across various gateways, and these script updates ensure our internal test reports perfectly reflect that external documentation. This consistency reduces confusion and makes our entire project more coherent and reliable for anyone using our Gateway API components, fostering a unified understanding of traffic management capabilities.
No urgency - can be implemented when convenient in Q1 2026: While these are valuable improvements, there's no immediate urgency for their implementation. We've set a target for Q1 2026, which provides ample time to integrate these changes thoughtfully and thoroughly without disrupting other high-priority work. This flexibility allows us to prioritize other critical tasks while still ensuring these enhancements get the attention they deserve when the time is right, ensuring a smooth and controlled evolution of our rate-limiting test suite.
Focus on value: Ultimately, every single change here is focused on adding value to our Gateway API testing efforts. From explicit NGINX detection to clear Cilium documentation and verbose logging, each enhancement contributes to a more intelligent, transparent, and user-friendly test script. It's about making our tests not just pass or fail, but tell a comprehensive and understandable story about rate limiting behavior across the Kubernetes Gateway ecosystem, empowering users with better traffic management insights.