domain-list-community: Generate customizable domain groups for V2Ray
domain-list-community is a community-maintained domain grouping repository that compiles structured domain rules into a V2Ray-compatible geosite/dlc.dat, suitable for scenarios requiring fine-grained domain-based traffic split and policy management.
GitHub v2fly/domain-list-community Updated 2025-12-21 Branch main Stars 7.1K Forks 1.2K
Go tool Domain lists Routing / Split-tunneling Community-maintained

💡 Deep Analysis

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What exact problem does this project solve? How does it convert human-readable domain lists into V2Ray-consumable geosite data?

Core Analysis

Project Positioning: The project bridges community-maintained, human-readable domain lists and V2Ray-consumable geosite binary (dlc.dat), enabling routing rules to reference semantic file-based subsets directly.

Technical Analysis

  • Textual data model: Domains are organized under data/ files and support domain/full/keyword/regexp/include/@attribute, which aids auditing and versioning.
  • Build tool (Go): go run ./ expands include directives, strips comments/empty lines, converts rule types to the expected V2Ray entries, and packages everything into dlc.dat for efficient runtime loading.
  • Attributes & modularity: Filenames map to geosite sections; @attribute lets you tag subsets within a file so routing can reference geosite:name@attr precisely.

Practical Recommendations

  1. Use the repository’s released dlc.dat or generate locally with go run ./ and verify dlc.dat.sha256sum.
  2. Design semantic filenames and attributes to match routing needs (e.g., geosite:category-media@streaming).
  3. Prefer domain/full and avoid overusing regexp/keyword as advised in the README.

Notes

Build artifacts are not one-to-one equivalent to raw source lines: the README warns against copy-pasting raw text into runtime configs — always run the build or use published dlc.dat.

Summary: The project converts auditable, human-friendly domain lists into a machine-friendly binary format, providing a reproducible and verifiable pipeline for fine-grained domain-based routing.

85.0%
Why does the project use a text-based directory+syntax and a Go build tool? What are the clear advantages and trade-offs of this architecture?

Core Analysis

Core Question: Why a text-based data model plus a Go build process? How does this design balance collaboration, auditability, and runtime efficiency?

Technical Analysis

  • Advantages:
  • Auditable & collaborative: Text files in Git facilitate PR review, rollback, and diff-based accountability; small per-file granularity reduces merge conflicts.
  • Attributes & modularity: File-level naming and @attribute enable semantic references for fine-grained routing.
  • Build & performance: A Go-based builder runs cleanly in CI, producing dlc.dat that V2Ray can load efficiently, avoiding runtime parsing overhead.
  • Trade-offs / Limitations:
  • Build dependency: Requires Go and a build step; users must trust/verify dlc.dat (sha256 mitigates risk).
  • Static data nature: Suited for maintainable lists; less appropriate for highly dynamic threat feeds or rapidly changing domains.
  • Misuse risk: regexp/keyword support exists but carries correctness/performance risks; the README discourages frequent use.

Practical Recommendations

  1. Lock builder versions in CI and publish signed/sha256 dlc.dat to avoid pulling unverified binaries into production.
  2. Define a clear naming/attribute convention (e.g., category-ads-*, geosite:cn@mobile).
  3. Combine this static dataset with dynamic IP/ASN or threat feeds when runtime recency is required.

Notes

The architecture favors auditability and runtime efficiency but mandates a build step; enterprises should formalize build-and-verify processes for compliance.

Summary: The text+Go approach delivers strong collaboration and runtime benefits while requiring disciplined build/versioning and supplementary dynamic data sources for time-sensitive use cases.

85.0%
How should one design geosite files and attributes to support complex routing policies? What practical organization patterns exist?

Core Analysis

Core Question: How to organize files and @attribute under data/ to make routing policies maintainable, reusable, and composable?

Technical Analysis

  • Organization patterns:
  • File-by-semantics: Split files by service type (category-media), owner (google), or region (geolocation-!cn).
  • Cross-cutting via @attribute: Tag subsets inside files with @ads, @streaming, @cdn so rules can reference geosite:name@ads precisely.
  • Reuse via include: Put common entries into shared/ or base-*.txt and include: them to avoid duplication.
  • Small-grain + composition: Keep files single-responsibility; compose rules using multiple geosite: refs instead of one monolithic list.

Practical Recommendations

  1. Adopt a naming convention, e.g., category-<type>-<scope> (like category-media-global, category-ads-all) and document it.
  2. Put risky regexp/keyword entries into separate @experimental files and enforce stricter CI review.
  3. Define include rules to avoid cycles; have CI detect include graphs for loops.
  4. Prefer geosite:filename@attr in routing rules for traceability rather than broad keyword matches.

Notes

Keeping files small and semantically clear improves maintainability and reduces debugging effort; avoid stuffing many unrelated rules into one file.

Summary: Semantic filenames, attribute-based subgroups, and hierarchical include reuse let you break complex routing policies into composable, auditable modules, lowering maintenance cost and increasing transparency.

85.0%
In which scenarios is this project not the best choice? What supplementary data sources or alternatives are needed?

Core Analysis

Core Question: In which scenarios is this project not ideal, and what should be supplemented or used as alternatives?

Technical Analysis

  • Unsuitable scenarios:
  • Real-time threat response: Rapidly changing malicious domains require low-latency updates; a static dlc.dat struggles here.
  • IP/ASN-based policies: The project manages domains only and cannot supply IP or ASN lists (e.g., country IP blocks).
  • High-performance regex needs: regexp usage can be inefficient and is discouraged in the README.

  • Enterprise redistribution/compliance: License is unspecified (meta shows Unknown); enterprises must confirm licensing before redistribution.

  • Supplementary/alternative options:

  • Dynamic sources: Merge dlc.dat with threat intel APIs, dynamic DNS monitoring, or commercial blacklists.
  • IP/ASN datasets: Use MaxMind, RIR data, or BGP-based ASN lists for IP-level policies.
  • Managed services: For SLA/compliance and faster updates, consider commercial or hosted list providers.

Practical Recommendations

  1. Use domain-based rules from this project in parallel with IP/ASN rules from other sources.
  2. For rapidly changing threats, rely on API-driven dynamic rules with short TTLs in your proxy/router.
  3. Enterprises should verify licensing or contact maintainers before integrating/redistributing.

Notes

The project is a structured, auditable domain set foundation but is not a one-stop solution for time-sensitive or IP-layer control—supplement with dynamic feeds or commercial services as needed.

Summary: Treat the project as a stable domain grouping backbone and augment it with real-time threat feeds and IP/ASN data or choose enterprise services for stronger timeliness and compliance guarantees.

85.0%
How to integrate this project into CI/CD and ensure consistency and traceability for production usage?

Core Analysis

Core Question: How to integrate the domain-list build process into CI/CD and ensure production uses consistent and traceable dlc.dat artifacts?

Technical Analysis

  • Key stages: build (go run ./), verify (sha256), test (rule regressions), release (artifact with metadata), deploy (pull specific release & verify).
  • CI job essentials:
  • Run format validation, include-cycle checks, and a build attempt on PRs to prevent merging broken changes.
  • In the release pipeline, build dlc.dat, compute dlc.dat.sha256sum, and attach commit/build metadata to the release artifact.
  • Execute regression tests that validate expected matches for sample domains against the target proxy/router.

Practical Recommendations (Steps)

  1. CI (PR)
    - Steps: lint -> include-loop-check -> go run ./ --datapath (build attempt). Failures block merge.
  2. CI (Release)
    - Steps: checkout commit -> build dlc.dat -> compute sha256 -> attach metadata & publish release artifacts.
  3. Deployment
    - Production fetches a pinned release dlc.dat, verifies sha256 before installation, and logs version/metadata.
  4. Regression tests
    - Include key routing match-cases in automated tests to detect regressions in matching behavior.

Notes

Do not deploy artifacts built from main directly into production. Always use releases with sha256 and recorded build metadata for auditability.

Summary: CI-based build & validation, release artifacts with sha256 and metadata, and regression testing ensure consistency and traceability for production use.

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How to evaluate and reduce mis-matching and performance issues introduced by `regexp` and `keyword` rules?

Core Analysis

Core Question: How to balance flexibility vs. danger of regexp and keyword rules? How to evaluate and mitigate mismatch and performance impacts?

Technical Analysis

  • Risk points:
  • Correctness: Regex or keyword can be overly broad, causing unintended routing matches.

  • Performance: Processing many or complex regexes increases matching cost; proxies may struggle to handle them efficiently.

  • Mitigation strategies:

  • Prefer alternatives: Use domain or full whenever possible; reserve regex/keyword for unavoidable cases.
  • Isolation: Put these rules in separate files and tag them (e.g., @experimental, @regex).
  • Stricter reviews: Require higher review standards for PRs that add regex/keyword rules, including rationales and sample matches.
  • Automated tests: Run matching regression tests in CI using sample whitelists/blacklists to detect regressions.
  • Complexity limits: Measure regex complexity (backtracking risk, capturing groups) in CI and block merges that exceed thresholds.

Practical Recommendations

  1. Require example inputs/expected outputs for each regexp/keyword and run them in CI.
  2. Replace complex regex with precise full/domain when possible.
  3. Monitor runtime metrics (match latency, CPU) on proxy endpoints and include them in regression checks.

Notes

The README discourages frequent use; minimizing such rules and enforcing CI validation is key to risk control.

Summary: Combine policy (isolation & review) with engineering (automated tests & complexity checks) to retain expressiveness while controlling mismatch and performance risks.

85.0%

✨ Highlights

  • Broad community attention and adoption (stars and forks)
  • Data-directory based grouping; generates dlc.dat consumable by V2Ray
  • License and metadata incomplete (license unknown; releases/commits info missing)
  • regexp/keyword rules are error-prone and inefficient for matching; use cautiously in production

🔧 Engineering

  • Community-maintained collection of domain sublists, split by files and compiled into geosite sections for routing
  • Supports concise syntax (domain/keyword/regexp/full/include) and can compile into a unified binary data file
  • Provides a Go-based generator, with usage examples and contribution workflow documented in README

⚠️ Risks

  • License unknown, preventing assessment of legal compliance for commercial redistribution
  • Repository metadata shows no contributors or releases, posing a risk of incomplete maintenance or sync issues
  • Using regexp/keyword rules can degrade matching performance and cause routing misclassification

👥 For who?

  • Network engineers and operators who need fine-grained domain-based routing and grouping
  • Advanced users and community contributors who use or customize V2Ray/geosite
  • Projects or services that want to quickly generate reusable routing rules from community-maintained data