是的，Flink CDC可以通过快照ID去消费。在Flink CDC中，我们可以使用enableSnapshotMode()方法来开启快照模式，这样就可以通过指定的快照ID来消费数据。
以下是一个简单的示例：
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
DataStreamSource<Row> source = env.addSource(new FlinkKafkaConsumer09("topic", new RowSchema(), props));
source.print();
// 开启快照模式
source.enableCheckpointing(1000); // 设置checkpoint间隔为1秒
source.disableChaining(); // 禁用数据流链式执行
source.uid("MyJob"); // 为数据流分配唯一的job ID
// 提交任务
env.execute("Flink CDC Example");
在这个示例中，我们将数据源添加到Flink应用程序中，并为其分配了一个唯一的job ID。然后，我们使用enableCheckpointing()方法来开启checkpoint功能，并将checkpoint间隔设置为1秒。最后，我们使用uid()方法为数据流分配一个唯一的ID，以便我们在需要回滚到某个快照时能够唯一标识该数据流。
当我们想要回滚到某个特定的快照时，我们可以使用setRestorePoint()方法来设置我们要回滚到的快照ID。例如：
```java
// 设置回滚点
final long checkpointId = 4L;
final long timestamp = 1587433595000L;
final String savepointPath = "/path/to/savepoint";
final List allVertices = getAllVertices();
final Execution execution = getCurrentExecutionAttempt();
execution.getJobClient().get().requestCheckpoint(checkpointId, timestamp);
execution.getJobClient().get().notifyCheckpointComplete(checkpointId, timestamp);
List keyGroupsToRestore = KeyGroupRangeAssignment.assignKeyGroupsToOperators(allVertices, jobConfiguration.getMaxParallelism());
Set stateHandles = StatefulOperatorStateBackend.loadSavedState(keyGroupsToRestore, operatorStates, fileSystemsProvider, savedpointDirectoryServiceLoader::createSavedpointDirectoryForKeygroupMappingFileAndReturnTheAbsolutePathOnSuccess, null, false, true);
for (Map.Entry> e : partitionsPerChannel.entrySet()) {
int channelIndex = e.getKey();
if ((stateHandles == null || stateHandles.isEmpty()) && isLocalRecoverySupportedByKeygroupsInChannel(channelIndex)) {
throw new IllegalStateException("No states found for channels " + Arrays.toString(channels[channelIndex]));
}
statesManager.recoverFromLocalState(e.getValue(), localRecoveryDelegateFactoryFunction.apply(operator), recoveryTarget -> handleRecoveredInputChannel(inputChannel, inputSerializer, outputSerializer, serializationSchema, deserializationSchema, target, context, streamMetrics, metricsListenerRegistry, timeCharacteristicMonitor, taskName, tupleTimestampWatermarks, watermarkGeneratorSupplier, windowAssigner, processingTimeService, clock, maxOutstandingRequestSize, outgoingEdgesOfThisSubtask, globalModifiedOffsets, modifiedOffsetsBeforeFirstElement, subtaskIndex, numNonInterruptibleActionsBeforeCutback, cutbackTrigger, sideOutputTagExtractors, internalTimerService, userCodeClassLoader, customMetricUpdates, metricQueryService, queryableStateUtil, resultIterationPointerResolver, iterationPointerCompareResultComparator, restartStrategy, restorePoint, restorationTarget, executor, loggerContext, errorReporter, defaultRuntimeContext, sqlMetricContainer, dataViewMetricsProducer, rowtimeAttributeDescriptor, lateDataOutputTag, eventTimeExtractor, currentProcessingTime, progressBarrierHandler, barrierCleanerTask, cleanShutdownBehavior, resourceManagerEndpoint, applicationStatus, slot
