How do I troubleshoot duplicate records from a dataset API?

Here’s a practical way to troubleshoot duplicate records coming from a dataset API.

1) Confirm where the duplicates are happening

First determine whether the duplicates are:

• Already in the source data
• Added by the API response
• Introduced by your client code when fetching, merging, or storing results

A quick test:

• Call the API directly with a tool like curl, Postman, or browser.
• Compare the raw response with what your app/database receives.

2) Identify the unique key

Figure out what should make a record unique:

• id
• uuid
• combination of fields like (date, user_id, event_type)

Then check duplicates using that key.

Example logic:

• If two rows have the same id, the API may be repeating records.
• If id is missing or unstable, the source may not guarantee uniqueness.

3) Check pagination behavior

Duplicate records often come from paging issues:

• Overlapping pages
• Incorrect offset/limit logic
• Records changing between requests while paginating

Things to verify:

• Are you using page, offset, cursor, or next_token correctly?
• Does the API recommend cursor-based pagination instead of offset pagination?
• Are you reusing the same page token accidentally?

If records are inserted/updated while you’re paging, offset-based pagination can repeat or skip rows.

4) Inspect filtering and sorting

Duplicates can appear if:

• Sorting is not deterministic
• Filters are inconsistent between requests
• Results are grouped or joined in a way that repeats rows

Best practice:

• Always use a stable sort order, ideally by a unique field.
• Avoid changing query parameters across paged requests.

5) Look for retry logic that reprocesses the same page

Sometimes duplicates are caused by your client:

• Retrying a request after a timeout
• Re-fetching the last successful page
• Appending results instead of replacing/upserting them

Check whether your code:

• Appends each response blindly
• Retries without deduplication
• Processes the same response multiple times

6) Compare timestamps and sync windows

If the API is incremental:

• Make sure your “since” or “updated_after” timestamp is correct
• Account for late-arriving updates
• Use a small overlap window only if you dedupe afterward

Example:

• Query data from last_sync_time - 5 minutes
• Deduplicate by record ID during ingestion

7) Check joins or expanded nested data

If the API returns related objects, duplicates may come from:

• One-to-many relationships flattened into repeated rows
• Expand/include parameters
• Backend joins without proper aggregation

In that case, repeated records may be expected and need grouping on your side.

8) Validate with a dedupe query

If you store the data in SQL, run a quick check:

SELECT id, COUNT(*)
FROM your_table
GROUP BY id
HAVING COUNT(*) > 1;

If you don’t have a single ID:

SELECT field1, field2, COUNT(*)
FROM your_table
GROUP BY field1, field2
HAVING COUNT(*) > 1;

9) Add logging

Log:

• Request URL and parameters
• Page token / offset / cursor
• Record IDs returned per request
• Retry attempts
• Record count per batch

This makes it much easier to see where duplicates first appear.

10) Implement deduplication as a safeguard

Even if you fix the root cause, add a safety net:

• Use a set/map keyed by unique ID in memory
• Use UPSERT in the database
• Deduplicate before downstream processing

Example:

• Insert only if id not already seen
• Or update existing rows on conflict

Common root causes

• Offset pagination on changing data
• Missing stable sort order
• Client retrying pages
• API returning expanded/joined rows
• No unique identifier in the dataset
• ETL job reprocessing the same window

If you want, I can also help you debug this with:

1. your API pagination style,
2. a sample response, or
3. the code you use to fetch/store the data.