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GitHub keeps repository traffic in a rolling 14-day window. The REST API returns clone and page-view totals and dated breakdowns only for the most recent 14 days. If you need trends older than that, you must collect the responses yourself before each day drops out of the window. GitHub does not document a recovery backfill after an observation expires.
What “14 days” means in GitHub traffic
The limit is a rolling lookback, not a permanent archive. At any request time, GitHub’s repository traffic endpoints expose the latest 14 days of data. The response contains aggregate totals and rows for individual time buckets, but a bucket disappears when it moves beyond that window. Waiting until the end of a month and then asking GitHub for the month’s history will leave the first half of the month unavailable.
This applies to the traffic API’s clone and view statistics. GitHub’s reference describes the result as the total number of clones and the breakdown per day or week for the last 14 days. The web interface follows the same short horizon for its traffic graph.
The figures are aggregate, not an audit log
Traffic statistics do not identify visitors or clone events. A clone count represents full repository clones; fetches are not counted as clones in the traffic graph. Visitor values are aggregate unique estimates supplied by GitHub. Referral sources, referral paths and popular content are ranked lists, not a complete record of every visit or URL request.
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What the repository traffic view contains
Anyone with push access to a repository can view its traffic. The interface separates several measures that are easy to lump together under the word “traffic.”
- Full clones: repository clones, excluding ordinary fetches.
- Visitors: GitHub’s aggregate visitor and unique-visitor measures for the recent window.
- Referring sites: ranked source domains or sites sending traffic.
- Popular content: ranked repository pages or paths that received attention.
Full-clone and visitor information updates hourly. Referring sites and popular content update daily. That difference affects the timing of an archive: a snapshot taken while a day is still open can change on the next run even when the date itself has not changed.
Time zones and bucket boundaries
GitHub assigns traffic-graph data to UTC+0. The daily buckets are aligned to midnight UTC regardless of the repository owner’s location. If your team operates in London, New York or Tokyo, a “day” in your local dashboard may not match GitHub’s day. Store the timestamp GitHub returns, and convert it for presentation only after retaining the original UTC value.
When you request weekly data, weeks begin on Monday. Do not group daily rows by local calendar week and then compare them directly with GitHub’s weekly response; the boundaries can differ around time-zone transitions and week starts.
Traffic API endpoints worth archiving
The collector can request only the measures needed for a report, but preserving the raw responses makes later analysis and schema changes safer. The principal repository traffic paths are:
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| Endpoint path | What it returns | Supported period | Update behavior |
|---|---|---|---|
traffic/views |
Page-view totals, daily or weekly rows, and unique estimates | per=day or per=week |
Visitor and view information updates hourly |
traffic/clones |
Full-clone totals, daily or weekly rows, and unique estimates | per=day or per=week |
Clone information updates hourly |
traffic/popular/referrers |
Top referring sites | Endpoint-defined recent ranking | Updates daily |
traffic/popular/paths |
Top referring paths or popular content paths | Endpoint-defined recent ranking | Updates daily |
For the first two endpoints, request the period explicitly. A daily request is usually easiest to normalize, while a weekly request can support a second validation or reporting view. Keep the aggregate fields as well as every dated row; totals and rows are both part of the response contract and can be useful when checking a collector.
Build an archive before data expires
A dependable archive is a small data pipeline: authenticate, request each endpoint, record when the request happened, preserve the original JSON, and upsert dated rows. The following sequence avoids the most common gaps.
1. Create a least-privilege credential
Use either a fine-grained personal access token or a GitHub App credential. GitHub documents the repository Administration permission at read level for these traffic requests. Grant access only to the repositories that need reporting, keep the secret in a secret manager or repository secret, and rotate it according to your organization’s policy.
Do not put a token in a committed script, a generated report, or a log line. A collector should fail closed when the credential is absent or rejected, and its alert should identify the repository and endpoint without printing the secret.
2. Schedule at least one run each day
Run the collector daily, preferably at a consistent UTC time. Daily collection is sufficient to preserve every day’s bucket because the lookback is 14 days, but it does not freeze an in-progress day. If hourly freshness matters, poll more often and retain each raw response; that increases API calls and operational work.
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GitHub Actions can run a scheduled workflow, but GitHub documents an important continuity risk: scheduled workflows in public repositories are automatically disabled after 60 days without repository activity. Add a health check and notification for missed runs, or use an external scheduler when the archive must continue independently of repository activity.
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3. Request the endpoints explicitly
For each repository, issue authenticated requests equivalent to:
GET /repos/OWNER/REPO/traffic/views?per=day
GET /repos/OWNER/REPO/traffic/clones?per=day
GET /repos/OWNER/REPO/traffic/popular/referrers
GET /repos/OWNER/REPO/traffic/popular/pathsUse the API version header required by your GitHub client or organization, and record that version with the response. Request weekly variants when your reports need GitHub’s Monday-based weekly buckets. Treat non-success responses as collection failures rather than replacing the day with zeros.
4. Preserve raw and normalized data
Write the unmodified JSON response to durable storage with these fields alongside it:
- repository identifier (owner and repository name, or an immutable internal ID);
- endpoint name and requested period;
- retrieval timestamp in UTC;
- API version header used;
- collector schema or code version;
- HTTP status and, where useful, response headers that describe rate limits.
Then normalize each dated row into a table keyed by (repository, metric, bucket_timestamp). That composite key makes retries idempotent: rerunning a failed job updates the same bucket instead of creating duplicates. Keep the raw object even after normalization so a later parser can recover fields your first schema did not use.
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5. Keep the current bucket mutable
Do not permanently seal today’s row after the first successful request. GitHub says full-clone and visitor information update hourly, so a later run can revise the value for the same UTC date. Referring-site and popular-content rankings update daily and can also change between retrievals. Upsert the open bucket and record the latest retrieval time; close it only after your own policy says the day is complete.
6. Aggregate only after collection
Calculate weekly and monthly totals in your own database after the daily rows are stored. Summing ordinary counts such as views or full clones is appropriate when the buckets do not overlap. Do not sum daily uniques values and label the result “unique people for the month.” Each daily unique value is GitHub’s estimate for that period; the same person can appear on multiple days, and the API does not provide an identity-level deduplication key.
Choose the operating model
There is no single best archive architecture. The right choice depends on how critical continuity is, how many repositories you monitor and how often you query the results.
| Decision | Option | Strength | Trade-off |
|---|---|---|---|
| Scheduler | GitHub Actions cron | Quick to deploy beside repository code | Public-repository schedules can be disabled after 60 inactive days; workflow ownership remains inside GitHub |
| Scheduler | External cron or service | Independent health checks and continuity | Requires another host, secret store and operational owner |
| Credential | Fine-grained personal access token | Simple for an individual or small team; repository-scoped permissions | Rotation and ownership follow a person’s account |
| Credential | GitHub App | Installation-scoped access and service identity | More setup, private-key handling and installation management |
| Storage | Repository files | Transparent history and easy review for a small archive | Large JSON histories are awkward to query and can expose data to every repository reader |
| Storage | Object storage | Cheap immutable raw retention and straightforward partitioning | Requires a separate query layer for reports |
| Storage | Database | Fast filtering, upserts and dashboards | Needs schema maintenance, backups and access controls |
| Collection | Daily snapshots | Low call volume and enough to prevent 14-day loss | Less detail about hourly changes |
| Collection | Several polls per day | Captures more of the hourly update cycle | More API calls, storage and reconciliation work |
Common failure modes and fixes
A scheduled workflow stopped running
Check the workflow’s run history and the repository’s recent activity. For a public repository, the 60-day automatic-disable rule is a likely cause. Re-enable the workflow, add a notification when no successful run arrives within your expected interval, and consider moving the schedule to an external service.
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A request returns an authorization error
Verify that the token or App installation can access the target repository and has the documented Administration read permission. Check that the collector is using the intended owner and repository, and replace an expired or revoked credential. Never “fix” an authorization error by broadening permissions to unrelated repositories.
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Daily totals appear to change after collection
This is expected while the UTC day is open. Retain the retrieval timestamp and overwrite the same normalized key on the next run. If a value changes after your closeout period, preserve both raw responses and investigate the API response or collector version rather than silently discarding the difference.
Reports do not match local calendar dates
Display the original UTC bucket and label any converted local date clearly. A local-day report that crosses midnight UTC must be generated from your own time-zone transformation, not by renaming GitHub’s bucket.
Referral or popular-content numbers look incomplete
Those endpoints provide ranked lists, not an event-level export. A missing page or domain in the ranking does not prove it generated zero visits. Store the list as a snapshot with its retrieval time and avoid treating rank order as a complete denominator.
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A month’s “unique visitors” total seems inflated
Do not add daily unique estimates. Report each day, use GitHub’s aggregate values as supplied, or describe a monthly figure as a sum of daily estimates rather than a count of distinct people.
GitHub Enterprise Server data-residency limitation
GitHub’s documentation states that repository traffic statistics are unavailable through both the REST API and the web UI on GitHub Enterprise Server data-residency sites. An archiver built for GitHub.com therefore cannot be assumed to work for those installations. Confirm the deployment type before designing storage, credentials or reporting around these endpoints.
A practical retention and reporting design
Keep two layers. The raw layer stores one object per retrieval, partitioned by repository, endpoint and UTC date, with the response and metadata untouched. The normalized layer stores dated metrics keyed by repository, metric and bucket timestamp, plus the latest retrieval time and schema version. This supports both reproducible audits and simple dashboards.
For a weekly report, group daily count rows using UTC Monday-to-Sunday boundaries or use the API’s weekly response as a comparison. For a monthly report, sum non-unique count metrics from the daily normalized rows and show unique estimates separately. Include a coverage indicator that identifies missing collection days; a zero is a measurement, while a missing row is a collection problem.
Finally, test the archive with a deliberately repeated run. The second run should update the same bucket, preserve the raw first response, and produce no duplicate normalized row. Test an expired or invalid credential in a non-production repository so the alert path is known before a real 14-day gap occurs.
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