Organic Traffic Stats

Organic Traffic Stats accepts a list of domains or URLs and returns structured traffic metrics in the Actor Output tab.

Purpose

This Actor is built for repeatable collection of core search metrics across one or many targets. It normalizes incoming values, processes them in stable batches, and publishes clean records suitable for reporting, monitoring, and downstream workflows.

Core Capabilities

• Accepts both plain domains and full URLs.
• Handles single and large input lists.
• Produces structured, machine-friendly dataset output.
• Removes duplicate targets from final output.
• Publishes results directly to default Apify dataset (visible in Output).

Supported Input Format

The Actor uses one input field:

• Domains or URLs

Accepted examples:

• example.com
• www.example.com
• https://example.com
• https://example.com/
• https://example.com/path

Input normalization behavior:

• Empty values are ignored.
• Values are normalized before processing.
• Duplicate entries are merged in final output.

Programmatic input can also use mixed separators (newline/comma/semicolon) when required by your integration.

Output Fields

Each record can include:

• checked_domain
• table_number
• timestamp
• Domain
• Rank
• Organic Keywords
• Organic Traffic
• Organic Cost
• Adwords Keywords
• Adwords Traffic
• Adwords Cost

Output Tab and Dataset Behavior

• Results are pushed to the default dataset during the run.
• Output tab displays dataset records after run completion.
• Actor output schema includes the overview dataset view.
• Final stored output is unique by normalized domain/URL.

Batch Processing Behavior

• Large input sets are split into fixed-size request batches.
• Current request batch size is 25 targets.
• Example: 50 input values are processed as 2 request batches.

This keeps processing stable for both small and large runs.

Recommended Input Practices

• Prefer canonical domains or stable URLs.
• Avoid malformed strings and unsupported protocols.
• Keep each input entry to one target value.
• For large lists, run with realistic operational expectations for runtime.

Local Development

Run directly:

$python main.py

Run with Apify CLI local environment:

$apify run

Deployment

Push latest code:

$apify push

Trigger a remote run and print output dataset:

$apify call -o

Verification Checklist

After a remote run:

1. Run status is SUCCEEDED.
2. Dataset has items.
3. Output tab shows records.
4. Fields contain expected values.
5. Duplicate targets are not repeated in final output.

Troubleshooting

If output is empty:

• Confirm input is provided and non-empty.
• Confirm values are valid domains/URLs.
• Re-run with a small known-good subset first.

If run fails early:

• Confirm dependencies in requirements.txt.
• Confirm Actor build succeeded.
• Review run logs for startup/runtime errors.

If some targets do not return data:

• Re-run those targets only.
• Validate input formatting.
• Compare raw input list and dataset output for normalization effects.

Project Structure

• main.py - actor runtime logic
• .actor/actor.json - actor metadata and file wiring
• .actor/input_schema.json - input UI schema
• .actor/output_schema.json - output schema
• .actor/dataset_schema.json - dataset view configuration
• Dockerfile - container build definition
• requirements.txt - Python dependencies

Security and Operations

• Keep private project files private.
• Share only intended run or dataset links.
• Treat any sensitive operational files as restricted.

Changelog Summary

• Simplified input to domains/URLs only.
• Output written to Apify dataset for Output tab visibility.
• Duplicate targets removed from final dataset.
• Log messages cleaned to avoid exposing account identifiers.