FileSink in Apache Flink not generating logs in output folder - apache-kafka

Question

I am using Apache Flink to read data from kafka topic and to store it in files on server. I am using FileSink to store files, it creates the directory structure date and time wise but no logs files are getting created.
When i run the program it creates directory structure as below but log files are not getting stored here.
/flink/testlogs/2021-12-08--07
/flink/testlogs/2021-12-08--06
I want the log files should be written every 15 mins to a new log file.
Below is the code.
DataStream <String> kafkaTopicData = env.addSource(new FlinkKafkaConsumer<String>("MyTopic",new SimpleStringSchema(),p));
OutputFileConfig config = OutputFileConfig
.builder()
.withPartPrefix("prefix")
.withPartSuffix(".ext")
.build();
DataStream <Tuple6 < String,String,String ,String, String ,Integer >> newStream=kafkaTopicData.map(new LogParser());
final FileSink<Tuple6<String, String, String, String, String, Integer>> sink = FileSink.forRowFormat(new Path("/flink/testlogs"),
new SimpleStringEncoder < Tuple6 < String,String,String ,String, String ,Integer >> ("UTF-8"))
.withRollingPolicy(DefaultRollingPolicy.builder()
.withRolloverInterval(TimeUnit.MINUTES.toMillis(15))
.withInactivityInterval(TimeUnit.MINUTES.toMillis(5))
.withMaxPartSize(1024 * 1024 * 1024)
.build())
.withOutputFileConfig(config)
.build();
newStream.sinkTo(sink);
env.execute("DataReader");
LogParser returns Tuple6.

Answer 1

When used in streaming mode, Flink's FileSink requires that checkpointing be enabled. To do this, you need to specify where you want checkpoints to be stored, and at what interval you want them to occur.
To configure this in flink-conf.yaml, you would do something like this:
state.checkpoints.dir: s3://checkpoint-bucket
execution.checkpointing.interval: 10s
Or in your application code you can do this:
env.getCheckpointConfig().setCheckpointStorage("s3://checkpoint-bucket");
env.enableCheckpointing(10000L);
Another important detail from the docs:
Given that Flink sinks and UDFs in general do not differentiate between normal job termination (e.g. finite input stream) and termination due to failure, upon normal termination of a job, the last in-progress files will not be transitioned to the “finished” state.