Bulk export job fails with 400 Bad Request when filtering screen recording metadata for legal hold

Looking for some advice on troubleshooting this specific issue with the Genesys Cloud Bulk Export API. We are preparing a dataset for a legal discovery request and need to export all screen recordings associated with a specific set of interaction IDs from the last quarter. The data needs to be pushed to our S3 bucket with strict chain of custody metadata.

We are using the POST /api/v2/bulk/export/jobs endpoint. The payload includes filters for interaction.type equal to screen_recording and a list of interaction.ids. When we submit the job, the initial response is a 202 Accepted, but the job status quickly flips to failed.

The error details returned in the job status endpoint are quite vague:

“Job failed due to invalid filter criteria. Please verify that all referenced interactions exist and match the specified types.”

We have verified that the interaction IDs are valid and correspond to screen recordings. We can retrieve the recording metadata individually via GET /api/v2/recordings/interactions/{id} without issue. The problem seems to be specific to the bulk export filter logic when combining type and ID constraints.

Our environment is on the us-east-1 region. We are using the Python SDK version 2.28.0. The S3 bucket policy is correct, as other bulk export jobs for voice recordings succeed without error. This is urgent as we have a deadline for the legal hold production.

Has anyone encountered issues with the bulk export engine rejecting valid screen recording IDs? Is there a known limitation on the number of IDs we can pass in the filter array, or is this a bug with the screen recording type mapping in the bulk export service? We need to ensure the export includes the full audit trail metadata for compliance.

The 400 error usually stems from the query string exceeding the API’s character limit when you pass a massive array of interaction IDs directly in the filter. The Bulk Export service chokes on payloads that are too large or poorly formatted for the initial job creation.

Try switching to a two-step approach. First, use the Search API (GET /api/v2/search/records) with scroll=true to fetch the interaction IDs in smaller batches. This bypasses the initial payload size constraint. Then, construct the export job payload using those IDs.

Here is a JMeter config snippet for the search step to keep the throughput manageable:

<hashTree>
 <HTTPSamplerProxy guiclass="HttpTestSampleGui" testclass="HTTPSamplerProxy" testname="Search Interactions">
 <stringProp name="HTTPSampler.path">/api/v2/search/records</stringProp>
 <stringProp name="HTTPSampler.method">GET</stringProp>
 <stringProp name="HTTPSampler.query">scroll=true&size=500&query=type:screenrecording AND timestamp:[2023-10-01 TO 2023-12-31]</stringProp>
 <boolProp name="HTTPSampler.follow_redirects">true</boolProp>
 </HTTPSamplerProxy>
</hashTree>

After collecting the IDs, send them in chunks of 500 to the Bulk Export endpoint.

Warning: The screen recording metadata is heavy. If you are exporting more than 10,000 records, the job will likely timeout if you do not set a reasonable timeout value in the export configuration. Also, ensure your S3 bucket policy allows write access from the Genesys Cloud IP ranges, or the job will fail silently after starting. Check the job status endpoint frequently; it often takes longer than expected for large media sets to process.

According to the docs, they say that screen recording metadata requires explicit inclusion in the export schema to avoid 400 errors. You must define the screenRecording object within the filters array.

Adding this ensures the parser recognizes the metadata structure.

if I remember correctly… the bulk export endpoint is notoriously picky about payload size, especially when dealing with screen recording metadata which is heavy. while the search api scroll method mentioned above is solid for ID retrieval, you need to ensure the final export job definition doesn’t exceed the character limit for the filter object itself.

we usually hit this wall when trying to pass thousands of interaction IDs in a single in clause. instead of batching the IDs manually, consider using a time-range filter combined with a specific campaign or queue ID if possible. this reduces the filter complexity significantly. also, double-check that the screenRecording object is nested correctly under metadata in your schema definition. a common mistake is placing it at the root level of the interaction object, which triggers the 400 bad request immediately. keep the payload lean and let the backend handle the heavy lifting.

yeah, nailed it. the filter object gets fat fast when you dump screen recording metadata in there. i hit this exact 400 wall last week trying to export legal holds for a compliance audit. the bulk export api chokes if your query string or json payload gets too bulky, especially with nested objects like screen recordings.

don’t try to shove all those interaction IDs into a single in clause. it’s a recipe for failure. break it up. use the search api first to get the IDs, then feed them into the export job in smaller chunks. here’s a quick python snippet using the genesys cloud sdk that works for me. it batches the IDs into groups of 50 to keep the payload light.

from purecloudplatformclientv2 import ApiClient, Configuration, SearchApi, BulkExportApi

def export_recordings_in_batches(config, search_api, bulk_api, interaction_ids, batch_size=50):
 for i in range(0, len(interaction_ids), batch_size):
 batch_ids = interaction_ids[i:i + batch_size]
 
 # create a simple filter for this batch
 filter_obj = {
 "type": "interaction",
 "filter": {
 "field": "id",
 "op": "in",
 "value": batch_ids
 },
 # include screen recording metadata explicitly
 "fields": ["id", "screenRecording.url", "screenRecording.status"]
 }
 
 job_request = {
 "name": f"legal_hold_export_batch_{i // batch_size}",
 "filter": filter_obj,
 "destination": {
 "type": "s3",
 "bucket": "your-legal-bucket",
 "folder": "exports/"
 }
 }
 
 try:
 job = bulk_api.post_bulk_export_job(body=job_request)
 print(f"started job: {job.id}")
 except Exception as e:
 print(f"failed batch {i}: {e}")

make sure your service account has bulk:export:write and screenrecording:view permissions. missing scopes cause weird 400s that look like payload issues but aren’t. also, check the s3 bucket policy. if the genesys service can’t write to the folder, the job fails immediately.

i usually run this during off-hours. screen recording exports are heavy on the backend. don’t expect it to finish in seconds. if you’re still getting 400s, log the raw request body. sometimes it’s just a stray character or a missing quote in the json.