Hi Avi,
In the last snippet that you posted, you have not activated checkpoints.
Checkpoints are needed for the StreamingFileSink to produce results,
especially in the case of BulkWriters (like Parquet) where
the part file is rolled upon reception of a checkpoint and the part is
finalised (i.e. "committed") when the checkpoint gets completed
successfully.
Could you please enable checkpointing and make sure that the job runs long
enough for at least some checkpoints to be completed?
Thanks a lot,
Kostas
On Thu, Nov 29, 2018 at 7:03 AM Avi Levi <avi.l...@bluevoyant.com> wrote:
> Checkout this little App. you can see that the file is created but no data
> is written. even for a single record
>
> import io.eels.component.parquet.ParquetWriterConfig
> import org.apache.avro.Schema
> import org.apache.avro.generic.{ GenericData, GenericRecord }
> import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
> import org.apache.hadoop.fs.Path
> import org.apache.parquet.avro.AvroParquetWriter
> import org.apache.parquet.hadoop.{ ParquetFileWriter, ParquetWriter }
> import org.apache.parquet.hadoop.metadata.CompressionCodecName
> import scala.io.Source
> import org.apache.flink.streaming.api.scala._
>
> object Tester extends App {
> val env = StreamExecutionEnvironment.getExecutionEnvironment
> def now = System.currentTimeMillis()
> val path = new Path(s"test-$now.parquet")
> val schemaString =
> Source.fromURL(getClass.getResource("/request_schema.avsc")).mkString
> val schema: Schema = new Schema.Parser().parse(schemaString)
> val compressionCodecName = CompressionCodecName.SNAPPY
> val config = ParquetWriterConfig()
> val genericReocrd: GenericRecord = new GenericData.Record(schema)
> genericReocrd.put("name", "test_b")
> genericReocrd.put("code", "NoError")
> genericReocrd.put("ts", 100L)
> val stream = env.fromElements(genericReocrd)
> val writer: ParquetWriter[GenericRecord] =
> AvroParquetWriter.builder[GenericRecord](path)
> .withSchema(schema)
> .withCompressionCodec(compressionCodecName)
> .withPageSize(config.pageSize)
> .withRowGroupSize(config.blockSize)
> .withDictionaryEncoding(config.enableDictionary)
> .withWriteMode(ParquetFileWriter.Mode.OVERWRITE)
> .withValidation(config.validating)
> .build()
>
> writer.write(genericReocrd)
> stream.addSink { r =>
> println(s"In Sink $r")
> writer.write(r)
> }
> env.execute()
> // writer.close()
> }
>
>
> On Thu, Nov 29, 2018 at 6:57 AM vipul singh <neoea...@gmail.com> wrote:
>
>> Can you try closing the writer?
>>
>> AvroParquetWriter has an internal buffer. Try doing a .close() in
>> snapshot()( since you are checkpointing hence this method will be called)
>>
>> On Wed, Nov 28, 2018 at 7:33 PM Avi Levi <avi.l...@bluevoyant.com> wrote:
>>
>>> Thanks Rafi,
>>> I am actually not using assignTimestampsAndWatermarks , I will try to
>>> add it as you suggested. however it seems that the messages I repeating in
>>> the stream over and over even if I am pushing single message manually to
>>> the queue, that message will repeat infinity
>>>
>>> Cheers
>>> Avi
>>>
>>>
>>> On Wed, Nov 28, 2018 at 10:40 PM Rafi Aroch <rafi.ar...@gmail.com>
>>> wrote:
>>>
>>>> Hi Avi,
>>>>
>>>> I can't see the part where you use assignTimestampsAndWatermarks.
>>>> If this part in not set properly, it's possible that watermarks are not
>>>> sent and nothing will be written to your Sink.
>>>>
>>>> See here for more details:
>>>> https://ci.apache.org/projects/flink/flink-docs-stable/dev/connectors/kafka.html#kafka-consumers-and-timestamp-extractionwatermark-emission
>>>>
>>>> Hope this helps,
>>>> Rafi
>>>>
>>>> On Wed, Nov 28, 2018, 21:22 Avi Levi <avi.l...@bluevoyant.com wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I am trying to implement Parquet Writer as SinkFunction. The pipeline
>>>>> consists of kafka as source and parquet file as a sink however it seems
>>>>> like the stream is repeating itself like endless loop and the parquet file
>>>>> is not written . can someone please help me with this?
>>>>>
>>>>> object ParquetSinkWriter{
>>>>> private val path = new Path("tmp/pfile")
>>>>> private val schemaString =
>>>>> Source.fromURL(getClass.getResource("/dns_request_schema.avsc")).mkString
>>>>> private val avroSchema: Schema = new
>>>>> Schema.Parser().parse(schemaString)
>>>>> private val compressionCodecName = CompressionCodecName.SNAPPY
>>>>> private val config = ParquetWriterConfig()
>>>>> val writer: ParquetWriter[GenericRecord] =
>>>>> AvroParquetWriter.builder[GenericRecord](path)
>>>>> .withSchema(avroSchema)
>>>>> .withCompressionCodec(compressionCodecName)
>>>>> .withPageSize(config.pageSize)
>>>>> .withRowGroupSize(config.blockSize)
>>>>> .withDictionaryEncoding(config.enableDictionary)
>>>>> .withWriteMode(ParquetFileWriter.Mode.OVERWRITE)
>>>>> .withValidation(config.validating)
>>>>> .build()
>>>>> }
>>>>>
>>>>> class ParquetSinkWriter(path: Path, avroSchema: Schema) extends
>>>>> SinkFunction[GenericRecord] {
>>>>> import ParquetSinkWriter._
>>>>> override def invoke(value: GenericRecord): Unit = {
>>>>> println(s"ADDING TO File : $value") // getting this output
>>>>> writer.write(value) //the output is not written to the file
>>>>> }
>>>>> }
>>>>>
>>>>> //main app
>>>>> object StreamingJob extends App {
>>>>> implicit val env: StreamExecutionEnvironment =
>>>>> StreamExecutionEnvironment.getExecutionEnvironment
>>>>> env.enableCheckpointing(500)
>>>>>
>>>>> env.getCheckpointConfig.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
>>>>>
>>>>> env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
>>>>> env.getCheckpointConfig.setMinPauseBetweenCheckpoints(500)
>>>>> env.getCheckpointConfig.setCheckpointTimeout(600)
>>>>> env.getCheckpointConfig.setFailOnCheckpointingErrors(false)
>>>>> env.getCheckpointConfig.setMaxConcurrentCheckpoints(1)
>>>>> env.setRestartStrategy(RestartStrategies.failureRateRestart(2,
>>>>> Time.seconds(3), Time.seconds(3)))
>>>>> val backend: StateBackend = new
>>>>> RocksDBStateBackend("file:///tmp/rocksdb", true)
>>>>> env.setStateBackend(backend)
>>>>> val writer = new ParquetSinkWriter(outputPath, schema)
>>>>> *val stream2: DataStream[DnsRequest] = env.addSource(//consume from
>>>>> kafka)*
>>>>> *stream2.map { r =>*
>>>>> * println(s"MAPPING $r") //this output keeps repeating in a loop*
>>>>> * val genericReocrd: GenericRecord = new GenericData.Record(schema)*
>>>>> * genericReocrd.put("qname", r.qname)*
>>>>> * genericReocrd.put("rcode", r.rcode)*
>>>>> * genericReocrd.put("ts", r.ts)*
>>>>> * genericReocrd*
>>>>> * }.addSink(writer) *
>>>>>
>>>>> Thanks for your help
>>>>> Avi
>>>>>
>>>>>
>>
>> --
>> Thanks,
>> Vipul
>>
>
