I have a requirement to read items from an external queue, and persist them to a JDBC store. The items must be processed one-by-one, and the next item must only be read from the external queue once the previous item has been successfully persisted. At any given time there may or may not be an item available to read, and if not the application must block until the next item is available.
In order to enforce the one-by-one semantics, I decided to use a cold Observable using the generate method:
return Observable.generate(emitter -> {
final Future<Message> receivedFuture = ...;
final Message message = receivedFuture.get();
emitter.onNext(message);
});
This seems to work as expected for the receiving side.
In order to persist the data to the database, I decided to make use of the Vertx JDBCPool library.
messageObservable
.flatMapSingle(message ->
jdbcPool.prepareQuery("...")
.rxExecute(Tuple.of(...)) // produces a hot observable
)
According to the Vertx docs, the JDBCPool RX methods all produce hot observables.
The problem here seems to be that the flatmap to the JDBCPool method causes the entire chain to become hot. This has the undesirable consequence that messages are read from the queue before the previous message was persisted.
In other words, instead of
Read message 1
Write message 1
Read message 2
Write message 2
I now get
Read message 1
Read message 2
Read message 3
Write message 1
Read message 4
Write message 2
The only solution I have at the moment is to do a very undesirable thing and put the JDBCPool query in its own chain:
messageObservable
.flatMapSingle(message ->
Single.just(
jdbcPool.prepareQuery("...")
.rxExecute(Tuple.of(...))
.blockingWait()
)
I want to know what if there is a way I can combine both the one-by-one semantics of a cold observable stream in combination with a hot observable operation, while keeping the chain intact.
Example my topology code like this:
builder.setSpout("spout", new KafkaSpout);
builder.setBolt("bolt1", new Bolt1).shuffleGrouping("spout");
builder.setBolt("bolt2", new Bolt2).shuffleGrouping("bolt1");
builder.setBolt("bolt3", new Bolt3).shuffleGrouping("bolt2");
When bolt1 emitted, the message will be auto acked. But when bolt2 or bolt3 has exception occured, this message can't be resend, How can I retrieve failed message?
Storm has the concept of tuple trees at the helm of it. Let me try to explain using your example provided in the question.
When your spout calls the collector.emit method, the newly emitted tuple, let's call it tuple1, is added to tuple tree. This tuple reaches bolt1 as it has subscribed to it and will receive data emitted from the spout. Once it receives tuple1 as input in the execute method, after processing the input a new value is emitted as tuple2 which is added in the tuple tree after tuple1. Before exiting the execute method the tuple is acknowledged by calling collector.ack implicitly which tells storm that tuple1 has been processed please remove it from the tuple tree and now remains tuple2 which is passed on to bolt2 for processing.
Now the question arises what happens if bolt1 is unable to acknowledge due to some reason. Storm will see that after a certain period of time, which is the topology timeout time (defaults to 30s), the tuple tree hasn't exhausted thus it will replay the tuple from the start and the same above process will follow.
Hope i am able to explain what happens on failure. For more detail please read this or watch this
Any idea how to make kafka-to-kafka mirroring but with a sampling (for example only 10% of the messages)?
You could use MirrorMakerMessageHandler (which is configured by message.handler parameter):
https://github.com/apache/kafka/blob/1.0/core/src/main/scala/kafka/tools/MirrorMaker.scala#L430
The handler itself would need to make a decision whether to forward a message. A simple implementation would be just a counter of messages received, and forwarding if 0 == counter % 10.
However this handler is invoked for every message received, so it means that you'd be receiving all of messages & throwing away 90% of them.
The alternative is to modify main loop, where the mirror maker consumer receives the message, and forwards it to producers (that send the message to mirror cluster) is here
https://github.com/apache/kafka/blob/1.0/core/src/main/scala/kafka/tools/MirrorMaker.scala#L428
You would need to modify the consumer part to either-or:
forward only N-th (10th) message/offset
seek to only N-th message in log
I prefer the former idea, as in case of multiple MM instances in the same consumer group, you would still get reasonable behaviour. Second choice would demand more work from you to handle reassignments.
Also, telling which message is from 10% is non-trivial, I just assumed that it's every 10th message received.
We’re using Storm with the Kafka Spout. When we fail messages, we’d like to replay them, but in some cases bad data or code errors will cause messages to always fail a Bolt, so we’ll get into an infinite replay cycle. Obviously we’re fixing errors when we find them, but would like our topology to be generally fault tolerant. How can we ack() a tuple after it’s been replayed more than N times?
Looking through the code for the Kafka Spout, I see that it was designed to retry with an exponential backoff timer and the comments on the PR state:
"The spout does not terminate the retry cycle (it is my conviction that it should not do so, because it cannot report context about the failure that happened to abort the reqeust), it only handles delaying the retries. A bolt in the topology is still expected to eventually call ack() instead of fail() to stop the cycle."
I've seen StackOverflow responses that recommend writing a custom spout, but I'd rather not be stuck maintaining a custom patch of the internals of the Kafka Spout if there's a recommended way to do this in a Bolt.
What’s the right way to do this in a Bolt? I don’t see any state in the tuple that exposes how many times it’s been replayed.
Storm itself does not provide any support for your problem. Thus, a customized solution is the only way to go. Even if you do not want to patch KafkaSpout, I think, introducing a counter and breaking the replay cycle in it, would be the best approach. As an alternative, you could also inherit from KafkaSpout and put a counter in your subclass. This is of course somewhat similar to a patch, but might be less intrusive and easier to implement.
If you want to use a Bolt, you could do the following (which also requires some changes to the KafkaSpout or a subclass of it).
Assign an unique IDs as an additional attribute to each tuple (maybe, there is already a unique ID available; otherwise, you could introduce a "counter-ID" or just the whole tuple, ie, all attributes, to identify each tuple).
Insert a bolt after KafkaSpout via fieldsGrouping on the ID (to ensure that a tuple that is replayed is streamed to the same bolt instance).
Within your bolt, use a HashMap<ID,Counter> that buffers all tuples and counts the number of (re-)tries. If the counter is smaller than your threshold value, forward the input tuple so it gets processed by the actual topology that follows (of course, you need to anchor the tuple appropriately). If the count is larger than your threshold, ack the tuple to break the cycle and remove its entry from the HashMap (you might also want to LOG all failed tuples).
In order to remove successfully processed tuples from the HashMap, each time a tuple is acked in KafkaSpout you need to forward the tuple ID to the bolt so that it can remove the tuple from the HashMap. Just declare a second output stream for your KafkaSpout subclass and overwrite Spout.ack(...) (of course you need to call super.ack(...) to ensure KafkaSpout gets the ack, too).
This approach might consume a lot of memory though. As an alternative to have an entry for each tuple in the HashMap you could also use a third stream (that is connected to the bolt as the other two), and forward a tuple ID if a tuple fails (ie, in Spout.fail(...)). Each time, the bolt receives a "fail" message from this third stream, the counter is increase. As long as no entry is in the HashMap (or the threshold is not reached), the bolt simply forwards the tuple for processing. This should reduce the used memory but requires some more logic to be implemented in your spout and bolt.
Both approaches have the disadvantage, that each acked tuple results in an additional message to your newly introduces bolt (thus, increasing network traffic). For the second approach, it might seem that you only need to send a "ack" message to the bolt for tuples that failed before. However, you do not know which tuples did fail and which not. If you want to get rid of this network overhead, you could introduce a second HashMap in KafkaSpout that buffers the IDs of failed messages. Thus, you can only send an "ack" message if a failed tuple was replayed successfully. Of course, this third approach makes the logic to be implemented even more complex.
Without modifying KafkaSpout to some extend, I see no solution for your problem. I personally would patch KafkaSpout or would use the third approach with a HashMap in KafkaSpout subclass and the bolt (because it consumed little memory and does not put a lot of additional load on the network compared to the first two solutions).
Basically it works like this:
If you deploy topologies they should be production grade (this is, a certain level of quality is expected, and the number of tuples low).
If a tuple fails, check if the tuple is actually valid.
If a tuple is valid (for example failed to be inserted because it's not possible to connect to an external database, or something like this) reply it.
If a tuple is miss-formed and can never be handled (for example an database id which is text and the database is expecting an integer) it should be ack, you will never be able to fix such thing or insert it into the database.
New kinds of exceptions, should be logged (as well as the tuple contents itself). You should check these logs and generate the rule to validate tuples in the future. And eventually add code to correctly process them (ETL) in the future.
Don't log everything, otherwise your log files will be huge, be very selective on what do you log. The contents of the log files should be useful and not a pile of rubbish.
Keep doing this, and eventually you will only cover all cases.
We also face the similar data where we have bad data coming in causing the bolt to fail infinitely.
In order to resolve this on runtime, we have introduced one more bolt naming it as "DebugBolt" for reference. So the spout sends the message to this bolt first and then this bolts does the required data fix for the bad messages and then emits them to the required bolt. This way one can fix the data errors on the fly.
Also, if you need to delete some messages, you can actually pass an ignoreFlag from your DebugBolt to your original Bolt and your original bolt should just send an ack to spout without processing if the ignoreFlag is True.
We simply had our bolt emit the bad tuple on an error stream and acked it. Another bolt handled the error by writing it back to a Kafka topic specifically for errors. This allows us to easily direct normal vs. error data flow through the topology.
The only case where we fail a tuple is because some required resource is offline, such as a network connection, DB, ... These are retriable errors. Anything else is directed to the error stream to be fixed or handled as is appropriate.
This all assumes of course, that you don't want to incur any data loss. If you only want to attempt a best effort and ignore after a few retries, then I would look at other options.
As per my knowledge Storm doesn't provide built-in support for this.
I have applied below-mentioned implementation:
public class AuditMessageWriter extends BaseBolt {
private static final long serialVersionUID = 1L;
Map<Object, Integer> failedTuple = new HashMap<>();
public AuditMessageWriter() {
}
/**
* {#inheritDoc}
*/
#Override
public void prepare(Map stormConf, TopologyContext context, OutputCollector collector) {
this.collector = collector;
//any initialization if u want
}
/**
* {#inheritDoc}
*/
#Override
public void execute(Tuple input) {
try {
//Write your processing logic
collector.ack(input);
} catch (Exception e2) {
//In case of any exception save the tuple in failedTuple map with a count 1
//Before adding the tuple in failedTuple map check the count and increase it and fail the tuple
//if failure count reaches the limit (message reprocess limit) log that and remove from map and acknowledge the tuple
log(input);
ExceptionHandler.LogError(e2, "Message IO Exception");
}
}
void log(Tuple input) {
try {
//Here u can pass result to dead queue or log that
//And ack the tuple
} catch (Exception e) {
ExceptionHandler.LogError(e, "Exception while logging");
}
}
#Override
public void cleanup() {
// To declare output fields.Not required in this alert.
}
#Override
public void declareOutputFields(OutputFieldsDeclarer declarer) {
// To declare output fields.Not required in this alert.
}
#Override
public Map<String, Object> getComponentConfiguration() {
return null;
}
}
My use-case is to identify entities from which expected events have not been received after X amount of time in real-time rather than using batch jobs. For Example:
If we have received PaymentInitiated event at time T but didn't receive either of PaymentFailed / PaymentAborted / PaymentSucedded by T+X, then raise a trigger saying PaymentStuck along with details of PaymentIntitiated event.
How can I model such use-cases in Apache Storm as it is rolling time period X on each event, rather than fixed time interval.
Thanks,
Harish
For Storm, would need to put all your logic into your UDF code using low level Java API (I doubt that Trindent is helpful). I never worked with Samza and cannot provide any help for it (or judge which system would be the better fit for your problem).
In Storm for example, you could assign a timestamp to each tuple in Spout.nextTuple(), and buffer all tuples of an incomplete payment within a Bolt in descending order of the timestamp. Each time Bolt.execute() is called, you can compare the timestamp of the new tuple with the head (ie, oldest tuple) of your queue. If the input tuple has a larger timestamep than head-T plus X, you know that your head tuple times out and you can raise your trigger for it.
Of course, you need to do fieldsGrouping() to ensure that all tuples belonging to the same payment are processed by the same Bolt instance. You might also need to somewhat order the incoming bolt tuples by timestamp or use more advance time-out logic to deal with out-of-order tuples (with regard to increasing timestamps).
Depending on you latency requirement and input stream rate you might also use "tick tuples" to trigger the comparison of the head tuple with this dummy tick tuple. Or as an ever stricter implementation, do all this logic directly in Spout.next() (if you know that all tuples of a payment go through the same Spout instance).