While looking into NIFI-6151, I commented that record processing can be done by scripting processor(s), but the most appropriate approach is probably to use InvokeScriptedProcessor, as you can add more complex properties (specifying Controller Services, e.g.), versus user-defined properties for ExecuteScript.
Having said that, there is a decent amount of setup code you'd need for any record-based scripted processor, and there is Best Practice around how to handle the records, namely that you process the first record before creating a RecordSetWriter, in case your custom processor code needs to update the schema that the RecordSetWriter will use. The Groovy example below is adapted from AbstractRecordProcessor, the common base class for all record processors in the standard NAR. Note the two comment sections for handling the first and rest of the incoming records, these are where you'd put your custom code to handle records. It's probably best to add a private method to your scripted processor and just call that once for the first record and then once again in the loop (that's what AbstractRecordProcessor does):
import org.apache.nifi.flowfile.attributes.CoreAttributes
import org.apache.nifi.processor.AbstractProcessor
import org.apache.nifi.processor.ProcessContext
import org.apache.nifi.processor.ProcessSession
import org.apache.nifi.processor.Relationship
import org.apache.nifi.processor.io.StreamCallback
import org.apache.nifi.serialization.*
import org.apache.nifi.serialization.record.*
import org.apache.nifi.schema.access.SchemaNotFoundException
import java.util.concurrent.atomic.AtomicInteger
class MyRecordProcessor extends AbstractProcessor {
// Properties
static final PropertyDescriptor RECORD_READER = new PropertyDescriptor.Builder()
.name("record-reader")
.displayName("Record Reader")
.description("Specifies the Controller Service to use for reading incoming data")
.identifiesControllerService(RecordReaderFactory.class)
.required(true)
.build()
static final PropertyDescriptor RECORD_WRITER = new PropertyDescriptor.Builder()
.name("record-writer")
.displayName("Record Writer")
.description("Specifies the Controller Service to use for writing out the records")
.identifiesControllerService(RecordSetWriterFactory.class)
.required(true)
.build()
def REL_SUCCESS = new Relationship.Builder().name("success").description('FlowFiles that were successfully processed are routed here').build()
def REL_FAILURE = new Relationship.Builder().name("failure").description('FlowFiles are routed here if an error occurs during processing').build()
@Override
protected List<PropertyDescriptor> getSupportedPropertyDescriptors() {
def properties = [] as ArrayList
properties.add(RECORD_READER)
properties.add(RECORD_WRITER)
properties
}
@Override
Set<Relationship> getRelationships() {
[REL_SUCCESS, REL_FAILURE] as Set<Relationship>
}
@Override
void onTrigger(ProcessContext context, ProcessSession session) {
def flowFile = session.get()
if (!flowFile) return
def readerFactory = context.getProperty(RECORD_READER).asControllerService(RecordReaderFactory)
def writerFactory = context.getProperty(RECORD_WRITER).asControllerService(RecordSetWriterFactory)
final Map<String, String> attributes = new HashMap<>()
final AtomicInteger recordCount = new AtomicInteger()
final FlowFile original = flowFile
final Map<String, String> originalAttributes = flowFile.attributes
try {
flowFile = session.write(flowFile, { inStream, outStream ->
def reader = readerFactory.createRecordReader(originalAttributes, inStream, getLogger())
try {
// Get the first record and process it before we create the Record Writer.
// We do this so that if the Processor updates the Record's schema, we can provide
// an updated schema to the Record Writer. If there are no records, then we can
// simply create the Writer with the Reader's schema and begin & end the RecordSet
def firstRecord = reader.nextRecord()
if (!firstRecord) {
def writeSchema = writerFactory.getSchema(originalAttributes, reader.schema)
def writer = writerFactory.createWriter(getLogger(), writeSchema, outStream)
try {
writer.beginRecordSet()
def writeResult = writer.finishRecordSet()
attributes['record.count'] = String.valueOf(writeResult.recordCount)
attributes[CoreAttributes.MIME_TYPE.key()] = writer.mimeType
attributes.putAll(writeResult.attributes)
recordCount.set(writeResult.recordCount)
} finally {
writer.close()
}
return
}
/////////////////////////////////////////
// TODO process first record
/////////////////////////////////////////
def writeSchema = writerFactory.getSchema(originalAttributes, firstRecord.schema)
def writer = writerFactory.createWriter(getLogger(), writeSchema, outStream)
try {
writer.beginRecordSet()
writer.write(firstRecord)
def record
while (record = reader.nextRecord()) {
//////////////////////////////////////////
// TODO process next record
//////////////////////////////////////////
writer.write(processed)
}
def writeResult = writer.finishRecordSet()
attributes.put('record.count', String.valueOf(writeResult.recordCount))
attributes.put(CoreAttributes.MIME_TYPE.key(), writer.mimeType)
attributes.putAll(writeResult.attributes)
recordCount.set(writeResult.recordCount)
} finally {
writer.close()
}
} catch (final SchemaNotFoundException e) {
throw new ProcessException(e.localizedMessage, e)
} catch (final MalformedRecordException e) {
throw new ProcessException('Could not parse incoming data', e)
} finally {
reader.close()
}
} as StreamCallback)
} catch (final Exception e) {
getLogger().error('Failed to process {}; will route to failure', [flowFile, e] as Object[])
session.transfer(flowFile, REL_FAILURE);
return;
}
flowFile = session.putAllAttributes(flowFile, attributes)
recordCount.get() ? session.transfer(flowFile, REL_SUCCESS) : session.remove(flowFile)
def count = recordCount.get()
session.adjustCounter('Records Processed', count, false)
getLogger().info('Successfully converted {} records for {}', [count, flowFile] as Object[])
}
}
processor = new MyRecordProcessor()
After that, it's time to process the first record separately from the rest. This is because the reader and/or the custom processor code may change the writer's schema from what the reader's schema. This happens, for example, during schema inference, a capability of RecordReaders since NiFi 1.9.0.
Then the first record is written, and the process continues for the rest of the records. At the end, the standard record-based attributes are populated, then the flow file is updated and transferred. The script above also includes error-handling for when things go wrong.
As always, if you try this out please let me know how/if it works for you, and I welcome all comments, questions, and suggestions. Cheers!
