I implemented a connection to a kafka stream as described here. Now I attempt to write the data into a postgres database using a Jdbc sink.
Now the source with Kafka seems to have no type. So when writing statements for SQL it all looks like type Nothing.
How can I use fromSource that I have actually a typed source for Kafka?
What I so far tried is the following:
object Main {
def main(args: Array[String]) {
val builder = KafkaSource.builder
builder.setBootstrapServers("localhost:29092")
builder.setProperty("partition.discovery.interval.ms", "10000")
builder.setTopics("created")
builder.setBounded(OffsetsInitializer.latest)
builder.setStartingOffsets(OffsetsInitializer.earliest)
builder.setDeserializer(KafkaRecordDeserializationSchema.of(new CreatedEventSchema))
val source = builder.build()
val env = StreamExecutionEnvironment.getExecutionEnvironment
val streamSource = env
.fromSource(source, WatermarkStrategy.noWatermarks, "Kafka Source")
streamSource.addSink(JdbcSink.sink(
"INSERT INTO conversations (timestamp, active_conversations, total_conversations) VALUES (?,?,?)",
(statement, event) => {
statement.setTime(1, event.date)
statement.setInt(1, event.a)
statement.setInt(3, event.b)
},JdbcExecutionOptions.builder()
.withBatchSize(1000)
.withBatchIntervalMs(200)
.withMaxRetries(5)
.build(),
new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
.withUrl("jdbc:postgresql://localhost:5432/reporting")
.withDriverName("org.postgresql.Driver")
.withUsername("postgres")
.withPassword("veryverysecret:-)")
.build()
))
env.execute()
}
}
Which does not compile because event is of type Nothing. But I think it must not be that because with CreatedEventSchema Flink should be able to deserialise.
Maybe it also important to note that actually I just want to process the values of the kafka messages.
In Java you might do something like this:
KafkaSource<Event> source =
KafkaSource.<Event>builder()
.setBootstrapServers("localhost:9092")
.setTopics(TOPIC)
.setStartingOffsets(OffsetsInitializer.earliest())
.setValueOnlyDeserializer(new EventDeserializationSchema())
.build();
with a value deserializer along these lines:
public class EventDeserializationSchema extends AbstractDeserializationSchema<Event> {
private static final long serialVersionUID = 1L;
private transient ObjectMapper objectMapper;
#Override
public void open(InitializationContext context) {
objectMapper = JsonMapper.builder().build().registerModule(new JavaTimeModule());
}
#Override
public Event deserialize(byte[] message) throws IOException {
return objectMapper.readValue(message, Event.class);
}
#Override
public TypeInformation<Event> getProducedType() {
return TypeInformation.of(Event.class);
}
}
Sorry I don't have a Scala example handy, but hopefully this will point you in the right direction.
Related
I am Junit testing my flink code where I am reading message from Kafka and using class X extends DeserializationSchema to convert into a POJO . For junit testing I am reading from a file that would return a DataStream <String> but now I want to convert into DataStream<Y> by using the same class X to deserialize the message . Can someone help me how can this be done?
Class X extends DeserializationSchema <Y> {
ObjectMapper om = ......
#Override
public void open(InitializationContext context){
//some flink metric intialization
}
#Override
public Y deserialize(byte[] inputData) {
// some custom logic that I really interested in testing
}
}
KafkaSourceBuilder<Y> sourceBuilder = KafkaSource.<Y>builder()
.setTopics(inputTopic)
.setGroupId(kafkaInputGroupId)
.setProperties(kafkaInputParameters)
.setStartingOffsets(getOffset(jobConfig, inputTopic))
.setValueOnlyDeserializer(new X());
I tried the below
DataStream<String> text =
env.readTextFile("src/test/resources/mobile_integration_test.txt");
text.map(new MapFunction<String, Y>() {
#Override
public Y map(String value) throws Exception {
return new X().deserialize(value.getBytes());
}
}).print();
but this gives me NPE beause of the metric being initialized in the open method (explained above).
Any better ways to do this?
I'm trying to join two streams, one from the data collection, one consumes from Kafka.
code snippet
public static void main(String[] args) {
KafkaSource<JsonNode> kafkaSource = ...
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// Kafka messages : {"name": "John"}
final DataStream<JsonNode> dataStream1 = env.fromSource(kafkaSource, waterMark(), "Kafka").rebalance()
.assignTimestampsAndWatermarks(waterMark());
final DataStream<String> dataStream2 = env.fromElements("John", "Zbe", "Abe")
.assignTimestampsAndWatermarks(waterMark());
dataStream1
.join(dataStream2)
.where(new KeySelector<JsonNode, String>() {
#Override
public String getKey(JsonNode value) throws Exception {
return value.get("name").asText();
}
})
.equalTo(new KeySelector<String, String>() {
#Override
public String getKey(String value) throws Exception {
return value;
}
})
.window(SlidingEventTimeWindows.of(Time.minutes(50) /* size */, Time.minutes(10) /* slide */))
.apply(new JoinFunction<JsonNode, String, String>() {
#Override
public String join(JsonNode first, String second) throws Exception {
return first+" "+second;
}
}).print();
env.execute();
}
watermark
private static <T> WatermarkStrategy<T> waterMark() {
return new WatermarkStrategy<T>() {
#Override
public WatermarkGenerator<T> createWatermarkGenerator(
org.apache.flink.api.common.eventtime.WatermarkGeneratorSupplier.Context context) {
return new AscendingTimestampsWatermarks<>();
}
#Override
public TimestampAssigner<T> createTimestampAssigner(TimestampAssignerSupplier.Context context) {
return (event, timestamp) -> System.currentTimeMillis();
}
};
}
After running snippet code, it doesn't have any merged data in the output. Am I going wrong somewhere?
Apache flink version: 1.13.2
The problem is probably related to watermarking. Since you're not using event-time-based timestamps, try changing SlidingEventTimeWindows to SlidingProcessingTimeWindows and see if it then produces results.
The underlying problem is probably a lack of data. The rebalance() on the Kafka stream guarantees that idle partitions won't stall the watermarks unless all partitions are idle. But if this is an unbounded streaming job, unless you have some data that falls after the first window, the watermark won't advance far enough to trigger the first window.
Options:
Send some data with larger timestamps
Configure the Kafka source as a bounded stream by using the .setBounded(...) option on the KakfaSource builder
Stop the job using the --drain option (docs)
The fact that dataStream2 is bounded is also a problem, but I'm not sure how much of one. At best this will prevent any windows after the first one from producing any results (since datastream joins are inner joins).
I am using Kafka Connector in Apache Flink for access to streams served by Confluent Kafka.
Apart from schema registry url ConfluentRegistryAvroDeserializationSchema.forGeneric(...) expecting 'reader' schema.
Instead of providing read schema I want to use same writer's schema(lookup in registry) for reading the message too because Consumer will not have latest schema.
FlinkKafkaConsumer010<GenericRecord> myConsumer =
new FlinkKafkaConsumer010<>("topic-name", ConfluentRegistryAvroDeserializationSchema.forGeneric(<reader schema goes here>, "http://host:port"), properties);
myConsumer.setStartFromLatest();
https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/connectors/kafka.html
"Using these deserialization schema record will be read with the schema that was retrieved from Schema Registry and transformed to a statically provided"
Since I do not want to keep schema definition at consumer side how do I deserialize Avro message from Kafka using writer's schema?
Appreciate your help!
I don't think it is possible to use directly ConfluentRegistryAvroDeserializationSchema.forGeneric. It is intended to be used with a reader schema and they have preconditions checking for this.
You have to implement your own. Two import things:
Set specific.avro.reader to false (other wise you'll get specific records)
The KafkaAvroDeserializer has to be lazily initialized (because it isn't serializable it self, as it holds a reference to the schema registry client)
import io.confluent.kafka.schemaregistry.client.CachedSchemaRegistryClient;
import io.confluent.kafka.schemaregistry.client.SchemaRegistryClient;
import io.confluent.kafka.serializers.AbstractKafkaAvroSerDeConfig;
import io.confluent.kafka.serializers.KafkaAvroDeserializer;
import io.confluent.kafka.serializers.KafkaAvroDeserializerConfig;
import java.util.HashMap;
import java.util.Map;
import org.apache.avro.generic.GenericRecord;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.typeutils.TypeExtractor;
import org.apache.flink.streaming.util.serialization.KeyedDeserializationSchema;
public class KafkaGenericAvroDeserializationSchema
implements KeyedDeserializationSchema<GenericRecord> {
private final String registryUrl;
private transient KafkaAvroDeserializer inner;
public KafkaGenericAvroDeserializationSchema(String registryUrl) {
this.registryUrl = registryUrl;
}
#Override
public GenericRecord deserialize(
byte[] messageKey, byte[] message, String topic, int partition, long offset) {
checkInitialized();
return (GenericRecord) inner.deserialize(topic, message);
}
#Override
public boolean isEndOfStream(GenericRecord nextElement) {
return false;
}
#Override
public TypeInformation<GenericRecord> getProducedType() {
return TypeExtractor.getForClass(GenericRecord.class);
}
private void checkInitialized() {
if (inner == null) {
Map<String, Object> props = new HashMap<>();
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, registryUrl);
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, false);
SchemaRegistryClient client =
new CachedSchemaRegistryClient(
registryUrl, AbstractKafkaAvroSerDeConfig.MAX_SCHEMAS_PER_SUBJECT_DEFAULT);
inner = new KafkaAvroDeserializer(client, props);
}
}
}
env.addSource(
new FlinkKafkaConsumer<>(
topic,
new KafkaGenericAvroDeserializationSchema(schemaReigstryUrl),
kafkaProperties));
I have a particular requirement for invoking an Interactive Query from inside a Stream . This is because I need to create a new Stream which should have data contained inside the State Store. Truncated code below:
tempModifiedDataStream.to(topic.getTransformedTopic(), Produced.with(Serdes.String(), Serdes.String()));
GlobalKTable<String, String> myMetricsTable = builder.globalTable(
topic.getTransformedTopic(),
Materialized.<String, String, KeyValueStore<Bytes, byte[]>>as(
topic.getTransformedStoreName() /* table/store name */)
.withKeySerde(Serdes.String()) /* key serde */
.withValueSerde(Serdes.String()) /* value serde */
);
KafkaStreams streams = new KafkaStreams(builder.build(), kStreamsConfigs());
KStream<String, String> tempAggrDataStream = tempModifiedDataStream
.flatMap((key, value) -> {
try {
List<KeyValue<String, String>> result = new ArrayList<>();
ReadOnlyKeyValueStore<String, String> keyValueStore =
streams .store(
topic.getTransformedStoreName(),
QueryableStoreTypes.keyValueStore());
In the last line, To access the State Store I need to have the KafkaStreams object and the Topology is finalized when I create the KafkaStreams object. The problem with this approach is that the 'tempAggrDataStream' is hence not part of the Topology and that part of the code does not get executed. And I cant move the KafkaStreams definition below as otherwise I can't call the Interactive Query.
I am a bit new to Kafka Streams ; so is this something silly from my side?
If you want to achieve sending all content of the topic content after each data modification, I think you should rather use Processor API.
You could create org.apache.kafka.streams.kstream.Transformer with state store.
For each processing message it will update state store and send all content to downstream.
It is not very efficient, because it will be forwarding for each processing message the whole content of the topic/state store (that can be thousands, millions of records).
If you need only latest value it is enough to set your topic cleanup.policy to compact. And from other site use KTable, which give abstraction of Table (Snapshot of stream)
Sample Transformer code for forwarding whole content of state store is as follow. The whole work is done in transform(String key, String value) method.
public class SampleTransformer
implements Transformer<String, String, KeyValue<String, String>> {
private String stateStoreName;
private KeyValueStore<String, String> stateStore;
private ProcessorContext context;
public SampleTransformer(String stateStoreName) {
this.stateStoreName = stateStoreName;
}
#Override
#SuppressWarnings("unchecked")
public void init(ProcessorContext context) {
this.context = context;
stateStore = (KeyValueStore) context.getStateStore(stateStoreName);
}
#Override
public KeyValue<String, String> transform(String key, String value) {
stateStore.put(key, value);
stateStore.all().forEachRemaining(keyValue -> context.forward(keyValue.key, keyValue.value));
return null;
}
#Override
public void close() {
}
}
More information about Processor APi can be found:
https://docs.confluent.io/current/streams/developer-guide/processor-api.html
https://kafka.apache.org/documentation/streams/developer-guide/processor-api.html
How to combine Processor API with Stream DSL can be found:
https://kafka.apache.org/documentation/streams/developer-guide/dsl-api.html#applying-processors-and-transformers-processor-api-integration
I am working on processor node in kafka streams. For a simple code, I have written like below just to filter UserID , is this the correct way of doing processor node in kafka streams ?
But, the below code doesn't compile, throws an error with : The method filter(Predicate<? super Object,? super Object>) in the type KStream<Object,Object> is not applicable for the arguments (new Predicate<String,String>(){})
KStreamBuilder builder = new KStreamBuilder();
builder.stream(topic)
.filter(new Predicate <String, String>() {
//#Override
public boolean test(String key, String value) {
Hashtable<Object, Object> message;
// put you processor logic here
return message.get("UserID").equals("1");
}
})
.to(streamouttopic);
final KafkaStreams streams = new KafkaStreams(builder, props);
final CountDownLatch latch = new CountDownLatch(1);
// attach shutdown handler to catch control-c
Runtime.getRuntime().addShutdownHook(new Thread("streams-shutdown-hook") {
#Override
public void run() {
streams.close();
latch.countDown();
}
});
try {
streams.start();
latch.await();
} catch (Throwable e) {
System.exit(1);
}
System.exit(0);
Can someone guide me please?
builder.stream(topic) returns KStream<Object,Object> type because you don't specify the generic types. And <Object,Object> is not compatible with <String,String>.
If you know, that the actual type is KStream<String,String> you can specify the type as follows:
builder.<Sting,String>stream(topic)
.filter(...)
To answer you question about "processor nodes": yes, adding a filter() will add a processor node internally. Note, that at DSL level, you don't need to think in term of processors usually.
If you want to use processors explicitly, you can use Processor API instead of the DSL. Check out the WordCount example: https://github.com/apache/kafka/blob/trunk/streams/examples/src/main/java/org/apache/kafka/streams/examples/wordcount/WordCountProcessorDemo.java
Note, that using the DSL, internally the code will be translated into a processor topology which is the runtime model of Kafka Streams.
Probably you are using Predicate class from another package. You need to use
import org.apache.kafka.streams.kstream.Predicate;