markap14 commented on a change in pull request #4730: URL: https://github.com/apache/nifi/pull/4730#discussion_r555183585
########## File path: nifi-external/nifi-kafka-connect/nifi-kafka-connector/src/main/java/org/apache/nifi/kafka/connect/StatelessNiFiSourceTask.java ########## @@ -0,0 +1,275 @@ +/* + * 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.Header; +import org.apache.kafka.connect.source.SourceRecord; +import org.apache.kafka.connect.source.SourceTask; +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.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 { + 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 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); + + // 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(); + } + } + + @Override + public List<SourceRecord> poll() throws InterruptedException { + 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(); + 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()); + for (final FlowFile flowFile : outputFlowFiles) { + final byte[] contents = triggerResult.readContent(flowFile); + final SourceRecord sourceRecord = createSourceRecord(flowFile, contents); + 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"); + } + } + } + + + private SourceRecord createSourceRecord(final FlowFile flowFile, final byte[] contents) { + final Map<String, ?> partition = Collections.emptyMap(); + final Map<String, ?> sourceOffset = Collections.emptyMap(); + 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 List<Header> headers; Review comment: I looked for this but somehow i couldn't find the ConnectHeader class in my IDE. Was able to find it now, since I know what to look for. Will use this. Thanks! ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
