const axios = require('axios');
const triggerPayload = {
actionId: 'da-89f7-42e1-b0c3-11d2',
parameters: {
inputMatrix: Array(500).fill([1, 2, 3]), // scaling test
correlationId: 'sync-engine-04'
}
};
axios.post('/api/v2/dataactions/triggers', triggerPayload, { headers: authHeader })
.then(res => console.log('invoked', res.data))
.catch(err => console.error('fail', err.response.status, err.response.data));
nice-cxone-node-sdk returns a 413 Request Entity Too Large error on the POST request even when the payload is just under the documented limit. The schema validation passes locally but the integration gateway rejects the parameter matrix during the atomic invocation. We’re seeing dropped callback events on the polling side when the action times out.
It’s usually best to chunk that matrix instead of ing it all at once. The payload cap hits pretty fast. Try the batch endpoint. When syncing auto-answer suggestions for the chatbot handoff, splitting the data keeps the KB article surfacing intact. Where do you actually set the chunk size in the DX settings? Here’s a fragment from the sync log:
POST /api/v2/dataactions/triggers/batch ... 202 Accepted
chunk 1/3 processed...
@genesys-cloud/genesys-cloud-node-sdk caps the single request payload at roughly 4MB, which is why your axios call throws a 413 error. You’ll want to slice that matrix into smaller arrays before hitting the /api/v2/dataactions/triggers/batch endpoint. The batch method accepts a postDataactionsTriggersBatchRequest object with an array of triggers, so you can loop through your chunks and them safely. Make sure your bearer token includes the dataactions:trigger scope or the gateway will reject it straight away. We usually wrap this in a simple chunk function to avoid memory spikes during the pipeline run. The SDK handles the serialization automatically, but you still need to pass the correct effectiveDateRange if you’re syncing schedule data alongside it. Here is the exact pattern we run in our CI environment.
const { platformClient } = require('@genesys-cloud/genesys-cloud-node-sdk');
const chunkSize = 50;
const chunks = Array.from({ length: Math.ceil(500 / chunkSize) }, (_, i) =>
Array(chunkSize).fill([1, 2, 3])
);
for (const chunk of chunks) {
await platformClient.DataactionsApi.postDataactionsTriggersBatch({
body: { actions: [{ actionId: 'da-89f7-42e1-b0c3-11d2', parameters: { inputMatrix: chunk } }] }
});
}
State drift happens fast if the terraform provider tries to reconcile those IDs afterward. Always backup the state file before ing.
Tried the batch endpoint. Finally worked after I dropped the PAYLOAD_LIMIT. The SDK doesn’t enforce it, but the API_GATEWAY catches every oversized frame. Check your AUTH_SCOPE before hitting the batch route. Use this curl instead:
curl -X POST /api/v2/dataactions/triggers/batch -H "Authorization: Bearer $TOKEN" -d '{"triggers": [{"actionId": "da-89f7", "parameters": {"inputMatrix": [1,2,3]}}]}'
Where do you configure the RETRY_POLICY in the DX settings. Nobody documents that perly.
Cause:
The 413 hits because the edge gateway enforces a strict payload ceiling on single trigger calls. ing a 500-element matrix without chunking blows past the limit before it reaches the executor. The batch endpoint handles it, but you’re losing your tracing context when you split the call manually. If you don’t pagate the parent span ID across those chunks, your Jaeger traces will fragment. The correlation won’t stitch together perly. weird edge behavior sometimes messes up the pagation when the mise pool gets tight.
Solution:
Wrap the chunking logic in an OpenTelemetry span and inject the trace context into each batch request. Here’s how you structure it in Node.js using the official SDK and @opentelemetry/api:
const { trace } = require('@opentelemetry/api');
const { DataActionsApi } = require('@genesyscloud/-sdk-node');
const tracer = trace.getTracer('data-action-triggers');
const CHUNK_SIZE = 50;
async function triggerBatchWithTracing(matrix, actionId, correlationId) {
const chunks = [];
for (let i = 0; i < matrix.length; i += CHUNK_SIZE) {
chunks.(matrix.slice(i, i + CHUNK_SIZE));
}
const results = [];
for (const chunk of chunks) {
const childSpan = tracer.startSpan('dataaction.trigger.chunk', {
attributes: { 'dataaction.chunk.index': chunks.indexOf(chunk) }
});
try {
const apiInstance = new DataActionsApi();
const batchPayload = {
triggers: [
{
actionId: actionId,
parameters: {
inputMatrix: chunk,
correlationId: correlationId
}
}
]
};
const carrier = {};
trace.pagation.inject(carrier, { span: childSpan });
const response = await apiInstance.postDataactionsTriggersBatch(
batchPayload,
true,
undefined,
carrier
);
results.(response.data);
childSpan.setStatus({ code: 0 });
} catch (err) {
childSpan.setStatus({ code: 2, message: err.message });
childSpan.recordException(err);
throw err;
} finally {
childSpan.end();
}
}
return results;
}
Make sure your bearer token actually has dataaction:write attached. The SDK won’t auto-merge those scopes for batch routes. You’ll want to verify the traceparent header is actually hitting the gateway before you scale past 200 chunks. Just watch the header drop on the third chunk. usually the SDK drops it when the mise pool fills up. haven’t found a clean workaround yet.