I have a Java application to lunch a flink job to process Kafka streaming.
The application is pending here at the job submission at flinkEnv.execute("flink job name") since the job is running forever for streamings incoming from kafka.
In this case, how can I get job id returned from the execution? I see the jobid is printing in the console. Just wonder, how to get jobid is this case without flinkEnv.execute returning yet.
How I can cancel a flink job given job name from remote server in Java?
As far as I know there is currently no nice programmatic way to control Flink. But since Flink is written in Java everything you can do with the console can also be done with internal class org.apache.flink.client.CliFrontend which is invoked by the console scripts.
An alternative would be using the REST API of the Flink JobManager.
you can use rest api to consume flink job process.
check below link: https://ci.apache.org/projects/flink/flink-docs-master/monitoring/rest_api.html.
maybe you can try to request http://host:port/jobs/overview to get all job's message that contains job's name and job's id. Such as
{"jobs":[{"jid":"d6e7b76f728d6d3715bd1b95883f8465","name":"Flink Streaming Job","state":"RUNNING","start-time":1628502261163,"end-time":-1,"duration":494208,"last-modification":1628502353963,"tasks":{"total":6,"created":0,"scheduled":0,"deploying":0,"running":6,"finished":0,"canceling":0,"canceled":0,"failed":0,"reconciling":0,"initializing":0}}]}
I really hope this will help you.
Related
A newcomer to both Beam/Flink. So not sure if this question is related to Beam or to Flink. We are setting up to run Beam application using Flink runner.
I have a fairly stateless streaming application without any aggregation/states. I am basically reading from Pubsublite and do some simple transformation of data, generate a ProducerRecord of it and submit it to be two separate Kafka topics. All my experiments has been successful so far and I even got it to work locally using Minikube/Flink K8s operator etc.
Unfortunately, I am stuck in a stage where I am unable to figure out the right docs/topics to read to understand the issue. If there is any error while saving to Kafka or if Kafka is available, it seems the Pubsublite message is acked before being successfully saved into Kafka. If I restart my app after failure or anything, the original pubsublite message is not reprocessed or resent to Kafka. I am losing data in that case as it seems the message has already been acked in the previous step (I can also see there is no backlog from Google cloud console).
Ideally, my goal is that the message is only acked after we have save it to both the Kafka of if it is acked before, then the state is save locally and after restart Beam/Flink will retry just sending it to Kafka.
I initially though the way to do this is to use some form of checkpoints/savepoints but looks like they are more for stateless streaming application. Am I misunderstanding the concept?
My current code is simply:
msgs.apply("Map pubsubmessage to producerrecord", MapElements.via(new FormatPubSubMessage(options.getTopic())))
.setCoder(ProducerRecordCoder.of(VoidCoder.of(), ByteArrayCoder.of()))
.apply("Write to primary kafka topic", KafkaIO.<Void, byte[]>writeRecords()
.withBootstrapServers(options.getBootstrapServers())
.withTopic(options.getTopic())
.withKeySerializer(VoidSerializer.class)
.withValueSerializer(ByteArraySerializer.class)
);
Any pointers to docs/concepts on how one would go about achieving it?
I did configure the standalone Debezium and tested the streaming. After that I created a pipeline as follows
pipeline.apply("Read from DebeziumIO",
DebeziumIO.<String>read()
.withConnectorConfiguration(
DebeziumIO.ConnectorConfiguration.create()
.withUsername("user")
.withPassword("password")
.withHostName("hostname")
.withPort("1433")
.withConnectorClass(SqlServerConnector.class)
.withConnectionProperty("database.server.name", "customer")
.withConnectionProperty("database.dbname", "test001")
.withConnectionProperty("database.include.list", "test002")
.withConnectionProperty("include.schema.changes", "true")
.withConnectionProperty("database.history.kafka.bootstrap.servers", "kafka:9092")
.withConnectionProperty("database.history.kafka.topic", "schema-changes.inventory")
.withConnectionProperty("connect.keep.alive", "false")
.withConnectionProperty("connect.keep.alive.interval.ms", "200")
).withFormatFunction(new SourceRecordJson.SourceRecordJsonMapper()).withCoder(StringUtf8Coder.of())
)
When I start the pipeline using DirectRunner, datastream is not captured by the pipeline. In my pipeline code I just added code to dump the data into console for the time being.
Also from the log I observe that the Debezium is being started and stopped frequently. Is that by design?
Also when there is a change made into the DB (INSERT/DELETE/UPDATE), I dont find it being reflected in the logs.
So my question is,
Configuration what I provided is that sufficient?
Why is the pipeline not being triggered when there is a change?
What additional steps I need to perform to get it working?
Restarting debezium multiple times can it cause performance impacts. Since it creates a jdbc connection.
The FLink version is 1.12, I follow the step(https://ci.apache.org/projects/flink/flink-docs-release-1.12/deployment/metric_reporters.html#prometheuspushgateway-orgapacheflinkmetricsprometheusprometheuspushgatewayreporter), fill my config, run my job in Flink cluster. but after a few hours, I find cannot see metric data on grafana, so i loigin server and see pushgateway log, find like "Out of memory" error log.
i dont understand, actually i set deleteOnShutdown=true and some of my jobs is closed. why pushgateway will OOM?
This problem has always existed, However, it was not described in the previous v1.13 documents. you can see the pull request to get more info.
If you want to use push model in your Flink cluster, i recommend use influxdb.
I have a Flink application that reads from a single Kafka topic.
I am trying to stop FlinkKafkaConsumer to stop pulling messages.
My final goal is to build a method to deploy my Flink application from time to time without downtime at all - how to deploy a new job without downtime.
I have tried to use "kafkaConsumer.close()" but that does not work. I am trying to stop the consumer from pulling new messages without killing the entire Job, at the same time I will upload a new Job with the updated code that reads from the same topic.
How do I do that ?
Would it be possible to send a special 'switch' message on all partitions of the kafka topic? Then you could override isEndOfStream(T nextElement) in your Kafka DeserializationSchema, and have your new job instance start working after the last switch message
I wanna run flink programs on demand submit them went one conditions happens. How run flink jobs from java code in flink 1.3.0 version?
You can use Flink's REST API to submit a job from another running Flink job. For more details see the REST API documentation.