I have a Kafka stream that receives records and I want to concatenate messages based on particular field.
A message in a stream looks like following:
Key: 2099
Payload{
email: tom#emample.com
eventCode: 2099
}
Expected Output:
key: 2099
Payload{
emails: tom#example, bill#acme.com, jane#example.com
}
I can get the stream to run fine, I just am not sure what the lamda should contain.
This is what I have done so far. I am not sure whether I should use map, aggregate or reduce or combination of those operations.
final StreamsBuilder builder = new StreamsBuilder();
KStream<String, Payload> inputStream = builder.stream(INPUT_TOPIC);
inputStream
.groupByKey()
.windowedBy(TimeWindows.of(TimeUnit.MINUTES.toMillis(300000)))
// Not sure what to do here …..
}).to (OUTPUT_TOPIC );
It could be something like this
inputStream.groupByKey().windowedBy(TimeWindows.of(TimeUnit.MINUTES.toMillis(300000)))
.aggregate(PayloadAggr::new, new Aggregator<String, Payload, PayloadAggr>() {
#Override
public PayloadAggr apply(String key, Payload newValue, PayloadAggr result) {
result.setKey(key);
if(result.getEmails()==null){
result.setEmails(newValue.getEmail());
}else{
result.setEmails(result.getEmails() + "," + newValue.getEmail());
}
return result;
}
}, .../* You serdes and store */}).toStream().to(OUTPUT_TOPIC);
Related
I have two DataStreams, the first one called DataStream<String> source which receive records from a message broker, and the second one is a SingleOutputOperator<Event> events, which is the result of mapping the source into Event.class.
I have a uses cases that needs to use SingleOutputOperator<Event> events and other that uses DataStream<String> source. In one of the use cases that use DataStream<String> source, I need to join the SingleOutputOperator<String> result after apply some filters and to avoid to map the source again into Event.class as I already have that operation done and that Stream, I need to search each record into the SingleOutputOperator<String> result into the SingleOutputOperator<Event> events and the apply another map to export a SingleOutputOperator<EventOutDto> out.
This is the idea as example:
DataStream<String> source = env.readFrom(source);
SingleOutputOperator<Event> events = source.map(s -> mapper.readValue(s, Event.class));
public void filterAndJoin(DataStream<String> source, SingleOutputOperator<Event> events){
SingleOutputOperator<String> filtered = source.filter(s -> new FilterFunction());
SingleOutputOperator<EventOutDto> result = (this will be the result of search each record
based on id in the filtered stream into the events stream where the id must match and return the event if found)
.map(event -> new EventOutDto(event)).addSink(new RichSinkFunction());
}
I have this code:
filtered.join(events)
.where(k -> {
JsonNode tree = mapper.readTree(k);
String id = "";
if (tree.get("Id") != null) {
id = tree.get("Id").asText();
}
return id;
})
.equalTo(e -> {
return e.Id;
})
.window(TumblingEventTimeWindows.of(Time.seconds(1)))
.apply(new JoinFunction<String, Event, BehSingleEventTriggerDTO>() {
#Override
public EventOutDto join(String s, Event event) throws Exception {
return new EventOutDto(event);
}
})
.addSink(new SinkFunction());
In the above code all works fine, the ids are the same, so basically the where(id).equalTo(id) should work, but the process never reaches the apply function.
Observation: Watermark are assigned with the same timestamp
Questions:
Any idea why?
Am I explained myself fine?
I solved the join by doing this:
SingleOutputStreamOperator<ObjectDTO> triggers = candidates
.keyBy(new KeySelector())
.intervalJoin(keyedStream.keyBy(e -> e.Id))
.between(Time.milliseconds(-2), Time.milliseconds(1))
.process(new new ProcessFunctionOne())
.keyBy(k -> k.otherId)
.process(new ProcessFunctionTwo());
I have a simple java lambda function which has the following code
public String handleRequest(Map<String, Object> input, Context context) {
Map<String, String> result = new HashMap<String, String>() {{
put("status", "success");
}};
String resultStr = new GsonBuilder().create().toJson(result, HashMap.class);
logger.info("ended function successfully " + resultStr);
return resultStr;
}
I can see in the cloudwatch the following lines
2020-07-10T17:52:26.198-07:00
START RequestId: 1b0ff049-3a61-4874-9172-9bee142dc076 Version: $LATEST
2020-07-10T17:52:26.203-07:00
2020-07-11 00:52:26 INFO KVSTriggerLamda:53 - ended function successfully {"result":"Success"}
2020-07-10T17:52:26.204-07:00
END RequestId: 1b0ff049-3a61-4874-9172-9bee142dc076
My Amazon connects call triggers this function and plays a simple prompt of "Success" or "Error" depending on the state. I always get "error"
What should the correct return value? I have followed aws documentation which specifies that I need to provide a simple flat JSON return value.
I finally got it working by just returning the Map itself.
Map<String, String> result = new HashMap<String, String>() {{
put("status", "success");
}};
return result;
Thanks to #tgdavies comments -
The output type can be an object or void.
https://docs.aws.amazon.com/lambda/latest/dg/java-handler.html#java-handler-types
Context:
We have some schema files in Cloud Storage. In our Dataflow job, we need to refer to these schema files to transform our data. These schema files change on a daily/weekly basis. Our data source is PubSub and we window PubSub messages into a fixed window of 1 minutes. The schema files we need fit well into memory, they are about 90 MB.
What I have tried:
Referring to this doc from Apache Beam, we created a side input that writes into a global window with a GenerateSequence like so:
// Creates a side input that refreshes the schema every minute
PCollectionView<Map<String, byte[]>> dataBlobView =
pipeline.apply(GenerateSequence.from(0).withRate(1, Duration.standardDays(1L)))
.apply(Window.<Long>into(new GlobalWindows()).triggering(
Repeatedly.forever(AfterProcessingTime.pastFirstElementInPane()))
.discardingFiredPanes())
.apply(ParDo.of(new DoFn<Long, Map<String, byte[]>>() {
#ProcessElement
public void processElement(ProcessContext ctx) throws Exception {
byte[] avroSchemaBlob = getAvroSchema();
byte[] fileDescriptorSetBlob = getFileDescriptorSet();
byte[] depsBlob = getFileDescriptorDeps();
Map<String, byte[]> dataBlobs = ImmutableMap.of(
"version", Longs.toByteArray(ctx.element().byteValue()),
"avroSchemaBlob", avroSchemaBlob,
"fileDescriptorSetBlob", fileDescriptorSetBlob,
"depsBlob", depsBlob);
ctx.output(dataBlobs);
}
}))
.apply(View.asSingleton());
"getAvroSchema", "getFileDescriptorSet" and "getFileDescriptorDeps" read files as byte[] from Cloud Storage.
However, this approach failed from the exception:
org.apache.beam.vendor.guava.v26_0_jre.com.google.common.util.concurrent.UncheckedExecutionException: java.lang.IllegalArgumentException: PCollection with more than one element accessed as a singleton view.
I then tried writing my own Combine Globally function like so:
static class GetLatestVersion implements SerializableFunction<Iterable<Map<String, byte[]>>, Map<String, byte[]>> {
#Override
public Map<String, byte[]> apply(Iterable<Map<String, byte[]>> versions) {
Map<String, byte[]> result = Maps.newHashMap();
Long maxVersion = Long.MIN_VALUE;
for (Map<String, byte[]> version: versions){
Long currentVersion = Longs.fromByteArray(version.get("version"));
logger.info("Side input version: " + currentVersion);
if (currentVersion > maxVersion) {
result = version;
maxVersion = currentVersion;
}
}
return result;
}
}
But it still triggers the same exception........
I then came across this and this Beam email archives and it seems like what's suggested in the Beam doc does not work. And I have to use a MultiMap to avoid the exception I ran into above. With a MultiMap, I will also have to iterate through the values and have my own logic to pick my desired value (latest).
My questions:
Why do I still get the exception "PCollection with more than one element accessed as a singleton view" even after I globally combine everything into 1 result?
If I go with the MultiMap approach, wouldn't the job eventually run out of memory? Because everyday we are basically increasing the MultiMap by 90 MB (the size of our data blob), unless Dataflow has some smart MultiMap implementation behind the scene.
What is the recommended way to do this?
Thanks
Use .apply(View.asMap()) instead of .apply(View.asSingleton());
This is the full example:
PCollectionView<Map<String, byte[]>> dataBlobView =
pipeline.apply(GenerateSequence.from(0).withRate(1, Duration.standardDays(1L)))
.apply(Window.<Long>into(new GlobalWindows()).triggering(
Repeatedly.forever(AfterProcessingTime.pastFirstElementInPane()))
.discardingFiredPanes())
.apply(ParDo.of(new DoFn<Long, KV<String, byte[]>>() {
#ProcessElement
public void processElement(ProcessContext ctx) throws Exception {
byte[] avroSchemaBlob = getAvroSchema();
byte[] fileDescriptorSetBlob = getFileDescriptorSet();
byte[] depsBlob = getFileDescriptorDeps();
ctx.output(KV.of("version", Longs.toByteArray(ctx.element().byteValue())));
ctx.output(KV.of("avroSchemaBlob", avroSchemaBlob));
ctx.output(KV.of("fileDescriptorSetBlob", fileDescriptorSetBlob));
ctx.output(KV.of("depsBlob", depsBlob));
}
}))
.apply(View.asMap());
You can use the map from the side inputs as described in documentation.
Apache Beam version 2.34.0
I really need help!
I can't extract the timestamp for a message sent by a producer. In my project I work with Json, I have a class in which I define the keys and one in which I define the values of the message that I will send via a producer on a "Raw" topic. I have 2 other classes that do the same thing for the output message that my consumer will read on the topic called "Tdt". In the main class KafkaStreams.java I define the stream and map the keys and values. Starting Kafka locally, I start a producer who writes a message on the "raw" topic with keys and values, then on another shell the consumer starts reading the exit message on the "tdt" topic. How do I get the event timestamp? I need to know the timestamp in which the message was sent by the producer. Do I need a TimestampExtractor?
Here is my main class kafkastreams (my application works great, I just need the timestamp)
#Bean("app1StreamTopology")
public KStream<LibAssIbanRawKey, LibAssIbanRawValue> kStream() throws ParseException {
JsonSerde<Dwsitspr4JoinValue> Dwsitspr4JoinValueSerde = new JsonSerde<>(Dwsitspr4JoinValue.class);
KStream<LibAssIbanRawKey, LibAssIbanRawValue> stream = defaultKafkaStreamsBuilder.stream(inputTopic);
stream.peek((k,v) -> logger.info("Debug3 Chiave descrizione -> ({})",v.getCATRAPP()));
GlobalKTable<Integer, Dwsitspr4JoinValue> categoriaRapporto = defaultKafkaStreamsBuilder
.globalTable(temptiptopicname,
Consumed.with(Serdes.Integer(), Dwsitspr4JoinValueSerde)
// .withOffsetResetPolicy(Topology.AutoOffsetReset.EARLIEST)
);
logger.info("Debug3 Chiave descrizione -> ({})",categoriaRapporto.toString()) ;
stream.peek((k,v) -> logger.info("Debug4 Chiave descrizione -> ({})",v.getCATRAPP()) );
stream
.join(categoriaRapporto, (k, v) -> v.getCATRAPP(), (valueStream, valueGlobalKtable) -> {
// Value mapping
LibAssIbanTdtValue newValue = new LibAssIbanTdtValue();
newValue.setDescrizioneRidottaCodiceCategoriaDelRapporto(valueGlobalKtable.getDescrizioneRidotta());
newValue.setDescrizioneEstesaCodiceCategoriaDelRapporto(valueGlobalKtable.getDescrizioneEstesa());
newValue.setIdentificativo(valueStream.getAUD_CCID());
.
.
.//Other Value Mapped
.
.
.map((key, value) -> {
// Key mapping
LibAssIbanTdtKey newKey = new LibAssIbanTdtKey();
newKey.setData(dtf.format(localDate));
newKey.setIdentificatoreUnivocoDellaRigaDiTabella(key.getTABROWID());
return KeyValue.pair(newKey, value);
}).to(outputTopic, Produced.with(new JsonSerde<>(LibAssIbanTdtKey.class), new JsonSerde<>(LibAssIbanTdtValue.class)));
return stream;
}
}
Yes you need a TimestampExtractor.
public class YourTimestampExtractor implements TimestampExtractor {
#Override
public long extract(ConsumerRecord<Object, Object> consumerRecord, long l) {
// do whatever you want with the timestamp available with consumerRecord.timestamp()
...
// return here the timestamp you want to use (here default)
return consumerRecord.timestamp();
}
}
You'll need to tell kafka stream what extractor to use under the key StreamsConfig.DEFAULT_TIMESTAMP_EXTRACTOR_CLASS_CONFIG
I have a simple program because I'm trying to receive data using kafka. When I start a kafka producer and I send data, for example: "Hello", I get this when I print the message: (null, Hello). And I don't know why this null appears. Is there any way to avoid this null? I think it's due to Tuple2<String, String>, the first parameter, but I only want to print the second parameter. And another thing, when I print that using System.out.println("inside map "+ message); it does not appear any message, does someone know why? Thanks.
public static void main(String[] args){
SparkConf sparkConf = new SparkConf().setAppName("org.kakfa.spark.ConsumerData").setMaster("local[4]");
// Substitute 127.0.0.1 with the actual address of your Spark Master (or use "local" to run in local mode
sparkConf.set("spark.cassandra.connection.host", "127.0.0.1");
// Create the context with 2 seconds batch size
JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
Map<String, Integer> topicMap = new HashMap<>();
String[] topics = KafkaProperties.TOPIC.split(",");
for (String topic: topics) {
topicMap.put(topic, KafkaProperties.NUM_THREADS);
}
/* connection to cassandra */
CassandraConnector connector = CassandraConnector.apply(sparkConf);
System.out.println("+++++++++++ cassandra connector created ++++++++++++++++++++++++++++");
/* Receive kafka inputs */
JavaPairReceiverInputDStream<String, String> messages =
KafkaUtils.createStream(jssc, KafkaProperties.ZOOKEEPER, KafkaProperties.GROUP_CONSUMER, topicMap);
System.out.println("+++++++++++++ streaming-kafka connection done +++++++++++++++++++++++++++");
JavaDStream<String> lines = messages.map(
new Function<Tuple2<String, String>, String>() {
public String call(Tuple2<String, String> message) {
System.out.println("inside map "+ message);
return message._2();
}
}
);
messages.print();
jssc.start();
jssc.awaitTermination();
}
Q1) Null values:
Messages in Kafka are Keyed, that means they all have a (Key, Value) structure.
When you see (null, Hello) is because the producer published a (null,"Hello") value in a topic.
If you want to omit the key in your process, map the original Dtream to remove the key: kafkaDStream.map( new Function<String,String>() {...})
Q2) System.out.println("inside map "+ message); does not print. A couple of classical reasons:
Transformations are applied in the executors, so when running in a cluster, that output will appear in the executors and not on the master.
Operations are lazy and DStreams need to be materialized for operations to be applied.
In this specific case, the JavaDStream<String> lines is never materialized i.e. not used for an output operation. Therefore the map is never executed.