Know what breaks before you swap actors
ApifyForge Schema Diff is a schema comparison tool that shows exactly what's compatible between two Apify actors: shared fields, type mismatches, fields unique to each, a compatibility score, and specific migration notes — all for $0.15 per comparison. Essential before replacing one actor with another in a data extraction pipeline.
Swapping actors in a pipeline without checking schema compatibility is the leading cause of silent data loss. A field named "phone" as a string in one actor and an array in another breaks your downstream processing. ApifyForge Schema Diff catches these issues before the swap, not after.
Compares every field by name and type across both actors' dataset schemas. Identifies exact matches, near-matches, and complete mismatches systematically.
Organizes all fields into shared (same name + type), type mismatches (same name, different type), unique to actor A, and unique to actor B. Clear at-a-glance compatibility picture.
Percentage of fields that overlap with matching types. 90% means easy migration with minimal changes; 30% means significant rework. One number to assess swap difficulty.
Specific guidance for each incompatibility: 'field phone needs transformation: string -> array' and 'field X has no equivalent'. A concrete checklist for your migration work.
Reads schemas from both actors' latest builds via the Apify API. No actor runs triggered — just schema comparison from build metadata. Results in seconds.
Run ApifyForge Schema Diff before switching actors in a pipeline to know exactly what will break and what will work. Prevents silent data loss from schema incompatibilities.
There are several ways to compare two actors' output schemas before swapping. Each trades off thoroughness, speed, and the risk of missing incompatibilities.
| Method | Coverage | Time | Cost |
|---|---|---|---|
| ApifyForge Schema Diff | All fields + types + compatibility score | Under 15 seconds | $0.15/comparison |
| Manual README comparison | Documented fields only (may be incomplete) | 15-30 minutes | Free (time cost) |
| Run both actors and compare output | Actual output (most accurate) | 30-60 minutes | 2x compute cost |
| Swap and hope | None — discover issues in production | Zero (upfront) | Free until pipeline breaks |
{
"actorA": "website-contact-scraper",
"actorB": "google-maps-email-extractor",
"compatibilityScore": 45,
"sharedFields": [
{ "field": "url", "type": "string" },
{ "field": "emails", "type": "array" }
],
"typeMismatches": [
{ "field": "phone", "typeA": "string", "typeB": "array" }
],
"uniqueToA": ["domain", "socialLinks"],
"uniqueToB": ["businessName", "rating", "address"]
}Enter two actor IDs to compare
ApifyForge Schema Diff reads both dataset schemas and compares every field
Get a compatibility score with migration notes for each incompatibility
Several approaches exist for assessing actor compatibility before swapping, from manual comparison to trial runs.
Open both actors' README pages in separate tabs and manually compare output field lists. Quick but relies on documentation accuracy — many READMEs are incomplete or outdated. Does not check types or calculate compatibility scores.
Best for: a quick initial assessment when both actors have well-documented READMEs.
Execute both actors with similar inputs and manually compare the JSON output. Most accurate method since it uses real data, but costs 2x the compute and takes 30-60 minutes. Does not produce a structured compatibility report.
Best for: validating specific data quality concerns after a Schema Diff shows high compatibility.
Use generic JSON comparison tools to diff two output samples. Shows structural differences but requires running both actors first and does not understand schema semantics. No compatibility score or migration notes.
Best for: developers comfortable with command-line tools who already have output samples.
Replace the actor in your pipeline and run the full pipeline in a staging environment. Catches real integration issues but requires significant setup time and debugging when things break. High risk, high effort.
Best for: final validation after Schema Diff confirms high compatibility.
Automated field-by-field comparison with four-category classification, compatibility score, and migration notes. $0.15 per comparison with no actor runs triggered. Results in under 15 seconds.
Best for: developers who want fast, structured compatibility assessment before any migration work.
Every schema comparison executes on your own Apify account at the standard pay-per-event rate of $0.15 per comparison. ApifyForge has no platform fee or subscription. Cheaper than a single failed pipeline run caused by an incompatible actor swap.
ApifyForge Schema Diff compares two actors' dataset_schema.json files and organizes every field into four categories: shared fields (same name and type in both actors), type mismatches (same field name but different types), fields unique to actor A, and fields unique to actor B. It calculates a compatibility score (percentage of overlapping fields) and generates specific migration notes for each mismatch.
Each ApifyForge Schema Diff run costs $0.15, charged as a pay-per-event (PPE) fee on your own Apify account. The tool reads schema metadata from both actors' latest builds — no actor runs are triggered. Apify's free tier includes $5/month in credits, enough for approximately 33 schema comparisons per month.
Use ApifyForge Schema Diff before swapping one actor for another in a data pipeline. For example, if you're considering replacing your current Google Maps scraper with a cheaper alternative, Schema Diff shows exactly which output fields are compatible, which need transformation, and which are missing entirely. A 90% compatibility score means easy migration; a 30% score means major rework.
The compatibility score is the percentage of total unique field names that appear in both actors with the same type. A score of 90% means 9 out of 10 fields are directly compatible — your downstream pipeline will work with minimal changes. A score of 30% means most fields differ and significant data transformation is needed. ApifyForge Schema Diff also counts type mismatches separately, since those need explicit transformation rather than simple renaming.
ApifyForge Schema Diff generates specific migration guidance for each incompatibility. Examples: 'field phone needs transformation: string -> array', 'field socialLinks has no equivalent in actor B', 'field rating type matches but description differs'. These notes give you a concrete migration checklist before you start changing your pipeline code.
No. ApifyForge Schema Diff reads dataset schemas from each actor's latest build metadata via the Apify API. It does not start, execute, or trigger any actor runs. The only charge is the $0.15 PPE fee for the comparison itself.