flink kafkaproducer send duplicate message in exactly once mode when checkpoint restore - streaming

Question

I am writing a case to test flink two step commit, below is overview.
sink kafka is exactly once kafka producer. sink step is mysql sink extend two step commit. sink compare is mysql sink extend two step commit, and this sink will occasionally throw a exeption to simulate checkpoint failed.
When checkpoint is failed and restore, I find mysql two step commit will work fine, but kafka consumer will read offset from last success and kafka producer produce messages even he was done it before this checkpoint failed.
How to avoid duplicate message in this case?
Thanks for help.
env:
flink 1.9.1
java 1.8
kafka 2.11
kafka producer code:
dataStreamReduce.addSink(new FlinkKafkaProducer<>(
"flink_output",
new KafkaSerializationSchema<Tuple4<String, String, String, Long>>() {
#Override
public ProducerRecord<byte[], byte[]> serialize(Tuple4<String, String, String, Long> element, #Nullable Long timestamp) {
UUID uuid = UUID.randomUUID();
JSONObject jsonObject = new JSONObject();
jsonObject.put("uuid", uuid.toString());
jsonObject.put("key1", element.f0);
jsonObject.put("key2", element.f1);
jsonObject.put("key3", element.f2);
jsonObject.put("indicate", element.f3);
return new ProducerRecord<>("flink_output", jsonObject.toJSONString().getBytes(StandardCharsets.UTF_8));
}
},
kafkaProps,
FlinkKafkaProducer.Semantic.EXACTLY_ONCE
)).name("sink kafka");
checkpoint settings:
StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();
executionEnvironment.enableCheckpointing(10000);
executionEnvironment.getCheckpointConfig().setTolerableCheckpointFailureNumber(0);
executionEnvironment.getCheckpointConfig().setPreferCheckpointForRecovery(true);
mysql sink:
dataStreamReduce.addSink(
new TwoPhaseCommitSinkFunction<Tuple4<String, String, String, Long>,
Connection, Void>
(new KryoSerializer<>(Connection.class, new ExecutionConfig()), VoidSerializer.INSTANCE) {
int count = 0;
Connection connection;
#Override
protected void invoke(Connection transaction, Tuple4<String, String, String, Long> value, Context context) throws Exception {
if (count > 10) {
throw new Exception("compare test exception.");
}
PreparedStatement ps = transaction.prepareStatement(
" insert into test_two_step_compare(slot_time, key1, key2, key3, indicate) " +
" values(?, ?, ?, ?, ?) " +
" ON DUPLICATE KEY UPDATE indicate = indicate + values(indicate) "
);
ps.setString(1, context.timestamp().toString());
ps.setString(2, value.f0);
ps.setString(3, value.f1);
ps.setString(4, value.f1);
ps.setLong(5, value.f3);
ps.execute();
ps.close();
count += 1;
}
#Override
protected Connection beginTransaction() throws Exception {
LOGGER.error("compare in begin transaction");
try {
if (connection.isClosed()) {
throw new Exception("mysql connection closed");
}
}catch (Exception e) {
LOGGER.error("mysql connection is error: " + e.toString());
LOGGER.error("reconnect mysql connection");
String jdbcURI = "jdbc:mysql://";
Class.forName("com.mysql.jdbc.Driver");
Connection connection = DriverManager.getConnection(jdbcURI);
connection.setAutoCommit(false);
this.connection = connection;
}
return this.connection;
}
#Override
protected void preCommit(Connection transaction) throws Exception {
LOGGER.error("compare in pre Commit");
}
#Override
protected void commit(Connection transaction) {
LOGGER.error("compare in commit");
try {
transaction.commit();
} catch (Exception e) {
LOGGER.error("compare Commit error: " + e.toString());
}
}
#Override
protected void abort(Connection transaction) {
LOGGER.error("compare in abort");
try {
transaction.rollback();
} catch (Exception e) {
LOGGER.error("compare abort error." + e.toString());
}
}
#Override
protected void recoverAndCommit(Connection transaction) {
super.recoverAndCommit(transaction);
LOGGER.error("compare in recover And Commit");
}
#Override
protected void recoverAndAbort(Connection transaction) {
super.recoverAndAbort(transaction);
LOGGER.error("compare in recover And Abort");
}
})
.setParallelism(1).name("sink compare");

Answer 1

I'm not quite sure I understand the question correctly:
When checkpoint is failed and restore, I find mysql two step commit will work fine, but kafka producer will read offset from last success and produce message even he was done it before this checkpoint failed.
Kafka producer is not reading any data. So, I'm assuming your whole pipeline rereads old offsets and produces duplicates. If so, you need to understand how Flink ensures exactly once.
Periodic checkpoints are created to have a consistent state in case of failure.
These checkpoints contain the offset of the last successfully read record at the time of the checkpoint.
Upon recovery Flink will reread all records from the offset stored in the last successful checkpoint. Thus, the same records will be replayed as have been generated in between last checkpoint and failure.
The replayed records will restore the state right before the failure.
It will produce duplicate outputs originating from the replayed input records.
It is the responsibility of the sinks to ensure that no duplicates are effectively written to the target system.
For the last point, there are two options:
only output data, when a checkpoint has been written, such that no effective duplicates can ever appear in the target. This naive approach is very universal (independent of the sink) but adds the checkpointing interval to the latency.
let the sink deduplicate the output.
The latter option is used for the Kafka sink. It uses Kafka transactions for letting it deduplicate data. To avoid duplicates on consumer side, you need to ensure it's not reading uncommitted data as mentioned in the documentation. Also make sure your transaction timeout is large enough that it doesn't discard data between failure and recovery.