urbandan commented on a change in pull request #4730: URL: https://github.com/apache/nifi/pull/4730#discussion_r561648596
########## File path: nifi-external/nifi-kafka-connect/nifi-kafka-connector/src/main/java/org/apache/nifi/kafka/connect/StatelessNiFiSourceConnector.java ########## @@ -0,0 +1,109 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.nifi.kafka.connect; + +import org.apache.kafka.common.config.ConfigDef; +import org.apache.kafka.connect.connector.Task; +import org.apache.kafka.connect.source.SourceConnector; +import org.apache.nifi.kafka.connect.validators.ConnectRegularExpressionValidator; +import org.apache.nifi.stateless.flow.StatelessDataflow; + +import java.util.ArrayList; +import java.util.HashMap; +import java.util.List; +import java.util.Map; + +public class StatelessNiFiSourceConnector extends SourceConnector { + static final String OUTPUT_PORT_NAME = "output.port"; + static final String TOPIC_NAME = "topics"; + + static final String TOPIC_NAME_ATTRIBUTE = "topic.name.attribute"; + static final String KEY_ATTRIBUTE = "key.attribute"; + static final String HEADER_REGEX = "header.attribute.regex"; + + private Map<String, String> properties; + private boolean primaryNodeOnly; + + @Override + public void start(final Map<String, String> properties) { + this.properties = new HashMap<>(properties); + + final StatelessDataflow dataflow = StatelessKafkaConnectorUtil.createDataflow(properties); + primaryNodeOnly = dataflow.isSourcePrimaryNodeOnly(); + dataflow.shutdown(); + } + + @Override + public void reconfigure(final Map<String, String> properties) { Review comment: I wouldn't override reconfigure - the default implementation calls stop and start on the connector. If you do override it, you need to apply the new configurations - so everything done in start needs to be repeated here. ########## File path: nifi-external/nifi-kafka-connect/nifi-kafka-connector/src/main/java/org/apache/nifi/kafka/connect/StatelessNiFiSourceTask.java ########## @@ -0,0 +1,298 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.nifi.kafka.connect; + +import org.apache.kafka.clients.producer.RecordMetadata; +import org.apache.kafka.common.config.ConfigException; +import org.apache.kafka.connect.data.Schema; +import org.apache.kafka.connect.errors.RetriableException; +import org.apache.kafka.connect.header.ConnectHeaders; +import org.apache.kafka.connect.source.SourceRecord; +import org.apache.kafka.connect.source.SourceTask; +import org.apache.nifi.components.state.Scope; +import org.apache.nifi.flowfile.FlowFile; +import org.apache.nifi.stateless.flow.DataflowTrigger; +import org.apache.nifi.stateless.flow.StatelessDataflow; +import org.apache.nifi.stateless.flow.TriggerResult; +import org.apache.nifi.util.FormatUtils; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import java.util.ArrayList; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.Optional; +import java.util.Set; +import java.util.concurrent.TimeUnit; +import java.util.concurrent.atomic.AtomicLong; +import java.util.regex.Pattern; + +public class StatelessNiFiSourceTask extends SourceTask { + public static final String STATE_MAP_KEY = "task.index"; + private static final Logger logger = LoggerFactory.getLogger(StatelessNiFiSourceTask.class); + + private StatelessDataflow dataflow; + private String outputPortName; + private String topicName; + private String topicNameAttribute; + private TriggerResult triggerResult; + private String keyAttributeName; + private Pattern headerAttributeNamePattern; + private long timeoutMillis; + private String dataflowName; + private long failureYieldExpiration = 0L; + + private final Map<String, String> clusterStatePartitionMap = Collections.singletonMap(STATE_MAP_KEY, "CLUSTER"); + private Map<String, String> localStatePartitionMap = new HashMap<>(); + private boolean primaryNodeOnly; + private boolean primaryNodeTask; + + private final AtomicLong unacknowledgedRecords = new AtomicLong(0L); + + @Override + public String version() { + return StatelessKafkaConnectorUtil.getVersion(); + } + + @Override + public void start(final Map<String, String> properties) { + logger.info("Starting Source Task with properties {}", StatelessKafkaConnectorUtil.getLoggableProperties(properties)); + + final String timeout = properties.getOrDefault(StatelessKafkaConnectorUtil.DATAFLOW_TIMEOUT, StatelessKafkaConnectorUtil.DEFAULT_DATAFLOW_TIMEOUT); + timeoutMillis = (long) FormatUtils.getPreciseTimeDuration(timeout, TimeUnit.MILLISECONDS); + + topicName = properties.get(StatelessNiFiSourceConnector.TOPIC_NAME); + topicNameAttribute = properties.get(StatelessNiFiSourceConnector.TOPIC_NAME_ATTRIBUTE); + keyAttributeName = properties.get(StatelessNiFiSourceConnector.KEY_ATTRIBUTE); + + if (topicName == null && topicNameAttribute == null) { + throw new ConfigException("Either the topic.name or topic.name.attribute configuration must be specified"); + } + + final String headerRegex = properties.get(StatelessNiFiSourceConnector.HEADER_REGEX); + headerAttributeNamePattern = headerRegex == null ? null : Pattern.compile(headerRegex); + + dataflow = StatelessKafkaConnectorUtil.createDataflow(properties); + primaryNodeOnly = dataflow.isSourcePrimaryNodeOnly(); + + // Determine the name of the Output Port to retrieve data from + dataflowName = properties.get(StatelessKafkaConnectorUtil.DATAFLOW_NAME); + outputPortName = properties.get(StatelessNiFiSourceConnector.OUTPUT_PORT_NAME); + if (outputPortName == null) { + final Set<String> outputPorts = dataflow.getOutputPortNames(); + if (outputPorts.isEmpty()) { + throw new ConfigException("The dataflow specified for <" + dataflowName + "> does not have an Output Port at the root level. Dataflows used for a Kafka Connect Source Task " + + "must have at least one Output Port at the root level."); + } + + if (outputPorts.size() > 1) { + throw new ConfigException("The dataflow specified for <" + dataflowName + "> has multiple Output Ports at the root level (" + outputPorts.toString() + + "). The " + StatelessNiFiSourceConnector.OUTPUT_PORT_NAME + " property must be set to indicate which of these Ports Kafka records should be retrieved from."); + } + + outputPortName = outputPorts.iterator().next(); + } + + final String taskIndex = properties.get(STATE_MAP_KEY); + localStatePartitionMap.put(STATE_MAP_KEY, taskIndex); + primaryNodeTask = "0".equals(taskIndex); + + if (primaryNodeOnly && !primaryNodeTask) { + logger.warn("Configured Dataflow ({}) requires that the source be run only on the Primary Node, but the Connector is configured with more than one task. The dataflow will only be run by" + + " one of the tasks.", dataflowName); + } + + final Map<String, String> localStateMap = (Map<String, String>) (Map) context.offsetStorageReader().offset(localStatePartitionMap); + final Map<String, String> clusterStateMap = (Map<String, String>) (Map) context.offsetStorageReader().offset(clusterStatePartitionMap); + + dataflow.setComponentStates(localStateMap, Scope.LOCAL); + dataflow.setComponentStates(clusterStateMap, Scope.CLUSTER); + } + + @Override + public List<SourceRecord> poll() throws InterruptedException { + final long yieldExpiration = Math.max(failureYieldExpiration, dataflow.getSourceYieldExpiration()); + final long now = System.currentTimeMillis(); + final long yieldMillis = yieldExpiration - now; + if (yieldMillis > 0) { + // If source component has yielded, we don't want to trigger it again until the yield expiration expires, in order to avoid + // overloading the source system. + logger.debug("Source of NiFi flow has opted to yield for {} milliseconds. Will pause dataflow until that time period has elapsed.", yieldMillis); + Thread.sleep(yieldMillis); + return null; + } + + // If the source of the dataflow requires that the task be run on Primary Node Only, and this is not Task 0, then + // we do not want to run the task. + if (primaryNodeOnly && !primaryNodeTask) { + logger.debug("Source of dataflow {} is to be run on Primary Node only, and this task is not the Primary Node task. Will not trigger dataflow.", dataflow); + return null; + } + + if (unacknowledgedRecords.get() > 0) { + // If we have records that haven't yet been acknowledged, we want to return null instead of running. + // We need to wait for the last results to complete before triggering the dataflow again. + return null; + } + + logger.debug("Triggering dataflow"); + final long start = System.nanoTime(); + + final DataflowTrigger trigger = dataflow.trigger(); + final Optional<TriggerResult> resultOptional = trigger.getResult(timeoutMillis, TimeUnit.MILLISECONDS); + if (!resultOptional.isPresent()) { + logger.warn("Dataflow timed out after waiting {} milliseconds. Will cancel the execution.", timeoutMillis); + trigger.cancel(); + return null; + } + + triggerResult = resultOptional.get(); + + if (!triggerResult.isSuccessful()) { + logger.error("Dataflow {} failed to execute properly", dataflowName, triggerResult.getFailureCause().orElse(null)); + trigger.cancel(); + failureYieldExpiration = System.currentTimeMillis() + 1000L; // delay next execution for 1 second to avoid constnatly failing and utilization huge amounts of resources + return null; + } + + // Verify that data was only transferred to the expected Output Port + verifyFlowFilesTransferredToProperPort(triggerResult, outputPortName, trigger); + + final long nanos = System.nanoTime() - start; + + final List<FlowFile> outputFlowFiles = triggerResult.getOutputFlowFiles(outputPortName); + final List<SourceRecord> sourceRecords = new ArrayList<>(outputFlowFiles.size()); + + Map<String, ?> componentState = dataflow.getComponentStates(Scope.CLUSTER); + final Map<String, ?> partitionMap; + if (componentState == null || componentState.isEmpty()) { + componentState = dataflow.getComponentStates(Scope.LOCAL); + partitionMap = localStatePartitionMap; + } else { + partitionMap = clusterStatePartitionMap; + } + + for (final FlowFile flowFile : outputFlowFiles) { + final byte[] contents = triggerResult.readContent(flowFile); + final SourceRecord sourceRecord = createSourceRecord(flowFile, contents, componentState, partitionMap); Review comment: @markap14, I misunderstood the offset commit flow of the source tasks. No need for this change, all of the records can have the same component state in their header. The framework will wait for all messages in the batch to be ack'd by kafka before committing the offsets. ########## File path: nifi-external/nifi-kafka-connect/nifi-kafka-connector/src/main/java/org/apache/nifi/kafka/connect/StatelessNiFiSourceTask.java ########## @@ -0,0 +1,298 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.nifi.kafka.connect; + +import org.apache.kafka.clients.producer.RecordMetadata; +import org.apache.kafka.common.config.ConfigException; +import org.apache.kafka.connect.data.Schema; +import org.apache.kafka.connect.errors.RetriableException; +import org.apache.kafka.connect.header.ConnectHeaders; +import org.apache.kafka.connect.source.SourceRecord; +import org.apache.kafka.connect.source.SourceTask; +import org.apache.nifi.components.state.Scope; +import org.apache.nifi.flowfile.FlowFile; +import org.apache.nifi.stateless.flow.DataflowTrigger; +import org.apache.nifi.stateless.flow.StatelessDataflow; +import org.apache.nifi.stateless.flow.TriggerResult; +import org.apache.nifi.util.FormatUtils; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import java.util.ArrayList; +import java.util.Collections; +import java.util.HashMap; +import java.util.List; +import java.util.Map; +import java.util.Optional; +import java.util.Set; +import java.util.concurrent.TimeUnit; +import java.util.concurrent.atomic.AtomicLong; +import java.util.regex.Pattern; + +public class StatelessNiFiSourceTask extends SourceTask { + public static final String STATE_MAP_KEY = "task.index"; + private static final Logger logger = LoggerFactory.getLogger(StatelessNiFiSourceTask.class); + + private StatelessDataflow dataflow; + private String outputPortName; + private String topicName; + private String topicNameAttribute; + private TriggerResult triggerResult; + private String keyAttributeName; + private Pattern headerAttributeNamePattern; + private long timeoutMillis; + private String dataflowName; + private long failureYieldExpiration = 0L; + + private final Map<String, String> clusterStatePartitionMap = Collections.singletonMap(STATE_MAP_KEY, "CLUSTER"); + private Map<String, String> localStatePartitionMap = new HashMap<>(); + private boolean primaryNodeOnly; + private boolean primaryNodeTask; + + private final AtomicLong unacknowledgedRecords = new AtomicLong(0L); + + @Override + public String version() { + return StatelessKafkaConnectorUtil.getVersion(); + } + + @Override + public void start(final Map<String, String> properties) { + logger.info("Starting Source Task with properties {}", StatelessKafkaConnectorUtil.getLoggableProperties(properties)); + + final String timeout = properties.getOrDefault(StatelessKafkaConnectorUtil.DATAFLOW_TIMEOUT, StatelessKafkaConnectorUtil.DEFAULT_DATAFLOW_TIMEOUT); + timeoutMillis = (long) FormatUtils.getPreciseTimeDuration(timeout, TimeUnit.MILLISECONDS); + + topicName = properties.get(StatelessNiFiSourceConnector.TOPIC_NAME); + topicNameAttribute = properties.get(StatelessNiFiSourceConnector.TOPIC_NAME_ATTRIBUTE); + keyAttributeName = properties.get(StatelessNiFiSourceConnector.KEY_ATTRIBUTE); + + if (topicName == null && topicNameAttribute == null) { + throw new ConfigException("Either the topic.name or topic.name.attribute configuration must be specified"); + } + + final String headerRegex = properties.get(StatelessNiFiSourceConnector.HEADER_REGEX); + headerAttributeNamePattern = headerRegex == null ? null : Pattern.compile(headerRegex); + + dataflow = StatelessKafkaConnectorUtil.createDataflow(properties); + primaryNodeOnly = dataflow.isSourcePrimaryNodeOnly(); + + // Determine the name of the Output Port to retrieve data from + dataflowName = properties.get(StatelessKafkaConnectorUtil.DATAFLOW_NAME); + outputPortName = properties.get(StatelessNiFiSourceConnector.OUTPUT_PORT_NAME); + if (outputPortName == null) { + final Set<String> outputPorts = dataflow.getOutputPortNames(); + if (outputPorts.isEmpty()) { + throw new ConfigException("The dataflow specified for <" + dataflowName + "> does not have an Output Port at the root level. Dataflows used for a Kafka Connect Source Task " + + "must have at least one Output Port at the root level."); + } + + if (outputPorts.size() > 1) { + throw new ConfigException("The dataflow specified for <" + dataflowName + "> has multiple Output Ports at the root level (" + outputPorts.toString() + + "). The " + StatelessNiFiSourceConnector.OUTPUT_PORT_NAME + " property must be set to indicate which of these Ports Kafka records should be retrieved from."); + } + + outputPortName = outputPorts.iterator().next(); + } + + final String taskIndex = properties.get(STATE_MAP_KEY); + localStatePartitionMap.put(STATE_MAP_KEY, taskIndex); + primaryNodeTask = "0".equals(taskIndex); + + if (primaryNodeOnly && !primaryNodeTask) { + logger.warn("Configured Dataflow ({}) requires that the source be run only on the Primary Node, but the Connector is configured with more than one task. The dataflow will only be run by" + + " one of the tasks.", dataflowName); + } + + final Map<String, String> localStateMap = (Map<String, String>) (Map) context.offsetStorageReader().offset(localStatePartitionMap); + final Map<String, String> clusterStateMap = (Map<String, String>) (Map) context.offsetStorageReader().offset(clusterStatePartitionMap); + + dataflow.setComponentStates(localStateMap, Scope.LOCAL); + dataflow.setComponentStates(clusterStateMap, Scope.CLUSTER); + } + + @Override + public List<SourceRecord> poll() throws InterruptedException { + final long yieldExpiration = Math.max(failureYieldExpiration, dataflow.getSourceYieldExpiration()); + final long now = System.currentTimeMillis(); + final long yieldMillis = yieldExpiration - now; + if (yieldMillis > 0) { + // If source component has yielded, we don't want to trigger it again until the yield expiration expires, in order to avoid + // overloading the source system. + logger.debug("Source of NiFi flow has opted to yield for {} milliseconds. Will pause dataflow until that time period has elapsed.", yieldMillis); + Thread.sleep(yieldMillis); + return null; + } + + // If the source of the dataflow requires that the task be run on Primary Node Only, and this is not Task 0, then + // we do not want to run the task. + if (primaryNodeOnly && !primaryNodeTask) { + logger.debug("Source of dataflow {} is to be run on Primary Node only, and this task is not the Primary Node task. Will not trigger dataflow.", dataflow); + return null; + } + + if (unacknowledgedRecords.get() > 0) { + // If we have records that haven't yet been acknowledged, we want to return null instead of running. + // We need to wait for the last results to complete before triggering the dataflow again. + return null; + } + + logger.debug("Triggering dataflow"); + final long start = System.nanoTime(); + + final DataflowTrigger trigger = dataflow.trigger(); + final Optional<TriggerResult> resultOptional = trigger.getResult(timeoutMillis, TimeUnit.MILLISECONDS); + if (!resultOptional.isPresent()) { + logger.warn("Dataflow timed out after waiting {} milliseconds. Will cancel the execution.", timeoutMillis); + trigger.cancel(); + return null; + } + + triggerResult = resultOptional.get(); + + if (!triggerResult.isSuccessful()) { + logger.error("Dataflow {} failed to execute properly", dataflowName, triggerResult.getFailureCause().orElse(null)); + trigger.cancel(); + failureYieldExpiration = System.currentTimeMillis() + 1000L; // delay next execution for 1 second to avoid constnatly failing and utilization huge amounts of resources + return null; + } + + // Verify that data was only transferred to the expected Output Port + verifyFlowFilesTransferredToProperPort(triggerResult, outputPortName, trigger); + + final long nanos = System.nanoTime() - start; + + final List<FlowFile> outputFlowFiles = triggerResult.getOutputFlowFiles(outputPortName); + final List<SourceRecord> sourceRecords = new ArrayList<>(outputFlowFiles.size()); + + Map<String, ?> componentState = dataflow.getComponentStates(Scope.CLUSTER); + final Map<String, ?> partitionMap; + if (componentState == null || componentState.isEmpty()) { + componentState = dataflow.getComponentStates(Scope.LOCAL); + partitionMap = localStatePartitionMap; + } else { + partitionMap = clusterStatePartitionMap; + } + + for (final FlowFile flowFile : outputFlowFiles) { + final byte[] contents = triggerResult.readContent(flowFile); + final SourceRecord sourceRecord = createSourceRecord(flowFile, contents, componentState, partitionMap); + sourceRecords.add(sourceRecord); + } + + logger.debug("Returning {} records from poll() method (took {} nanos to run dataflow)", sourceRecords.size(), nanos); + + // If there is at least one record, we don't want to acknowledge the trigger result until Kafka has committed the Record. + // This is handled by incrementing the unacknkowledgedRecords count. Then, Kafka Connect will call this.commitRecords(). + // The commitRecords() call will then decrement the number of unacknowledgedRecords, and when all unacknowledged Records have been + // acknowledged, it will acknowledge the trigger result. + // + // However, if there are no records, this.commitRecords() will never be called. As a result, we need toe nsure that we acknowledge the trigger result here. + if (sourceRecords.size() > 0) { + unacknowledgedRecords.addAndGet(sourceRecords.size()); + } else { + triggerResult.acknowledge(); + } + + return sourceRecords; + } + + private void verifyFlowFilesTransferredToProperPort(final TriggerResult triggerResult, final String expectedPortName, final DataflowTrigger trigger) { + final Map<String, List<FlowFile>> flowFileOutputMap = triggerResult.getOutputFlowFiles(); + + for (final Map.Entry<String, List<FlowFile>> entry : flowFileOutputMap.entrySet()) { + final String portName = entry.getKey(); + final List<FlowFile> flowFiles = entry.getValue(); + + if (!flowFiles.isEmpty() && !expectedPortName.equals(portName)) { + logger.error("Dataflow transferred FlowFiles to Port {} but was expecting data to be transferred to {}. Rolling back session.", portName, expectedPortName); + trigger.cancel(); + throw new RetriableException("Data was transferred to unexpected port. Expected: " + expectedPortName + ". Actual: " + portName); + } + } + } + + + private SourceRecord createSourceRecord(final FlowFile flowFile, final byte[] contents, final Map<String, ?> componentState, final Map<String, ?> partitionMap) { + final Schema valueSchema = (contents == null || contents.length == 0) ? null : Schema.BYTES_SCHEMA; + + // Kafka Connect currently gives us no way to determine the number of partitions that a given topic has. + // Therefore, we have no way to partition based on an attribute or anything like that, unless we left it up to + // the dataflow developer to know how many partitions exist a priori and explicitly set an attribute in the range of 0..max, + // but that is not a great solution. Kafka does support using a Simple Message Transform to change the partition of a given + // record, so that may be the best solution. + final Integer topicPartition = null; + + final String topic; + if (topicNameAttribute == null) { + topic = topicName; + } else { + final String attributeValue = flowFile.getAttribute(topicNameAttribute); + topic = attributeValue == null ? topicName : attributeValue; + } + + final ConnectHeaders headers = new ConnectHeaders(); + if (headerAttributeNamePattern != null) { + // TODO: When we download/create the dataflow, create a hash of it. Then save that state. When we do it next time, + // compare the hash to the last one. If changed, need to trigger connect framework to tell it that the config has changed. + // Would be done via Source/Sink Context. + // Or perhaps we should include the flow JSON itself in the configuration... would require that we string-ify the JSON though. This would be the cleanest, though. Would be optional. + // We can just document that you either include it inline, or you don't make changes to the dataflow; instead, save as a separate dataflow and update task to point to the new one. + + for (final Map.Entry<String, String> entry : flowFile.getAttributes().entrySet()) { + if (headerAttributeNamePattern.matcher(entry.getKey()).matches()) { + final String headerName = entry.getKey(); + final String headerValue = entry.getValue(); + headers.add(headerName, headerValue, Schema.STRING_SCHEMA); + } + } + } + + final Object key = keyAttributeName == null ? null : flowFile.getAttribute(keyAttributeName); + final Schema keySchema = key == null ? null : Schema.STRING_SCHEMA; + final Long timestamp = System.currentTimeMillis(); + + return new SourceRecord(partitionMap, componentState, topic, topicPartition, keySchema, key, valueSchema, contents, timestamp, headers); + } + + @Override + public void commitRecord(final SourceRecord record, final RecordMetadata metadata) throws InterruptedException { + super.commitRecord(record, metadata); + + final long unacked = unacknowledgedRecords.decrementAndGet(); + logger.debug("SourceRecord {} committed; number of unacknowledged FlowFiles is now {}", record, unacked); + + if (unacked < 1) { Review comment: Thanks for the explanation, makes sense ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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