My topology reads from Kafka and makes a HTTP call to an external system. The ingestion rate in Kafka is about 200 messages per second. The external system only supports 20 HTTP calls per second. How can i introduce throttling so that the bolt that makes HTTP calls processes only 20 messages per second?
You can use the topology.max.spout.pending setting to throttle the spout based on how many tuples are in flight in the topology. The setting is per spout instance, so if you have e.g. 10 spout executors and you set max 100 tuples, you will get a max of 1000 tuples in the topology.
You can use the resetTimeout method on the OutputCollector to keep tuples you want to postpone from failing due to timeout.
This being said, you probably need to batch up your messages into larger bundles. If you can only process 20 messages per second, and you have an input of 200 per second, you will start falling behind and never catch up.
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My topology reads from RabbitMQ, and it's processing rate is about 2500 messages per seconds, but Complete latency is about 7ms. Shouldn't it be equal 1000 / 2500 = 0.4ms?
Topology summary:
Please, help me to understand, what does mean parameter Complete latency in my case.
Topology process messages from RabbitMQ queue with rate about 2500/sec
RabbitMQ screenshot:
According to Storm docs The complete latency is just for spouts. It is the average amount of time it took for ack or fail to be called for a tuple after it was emitted.
So, it is the time between your rabbitmq-spout emitted tuple and the last bolt acked it.
Storm has an internal queue to make pressure, the maximum size of this queue is defined in topology.max.spout.pending variable in configs. If you set it to a high value your rabbit consumer would read messages from the rabbit to fulfil this queue ahead of real processing with bolts in topology, causing the wrong measure of real latency of your topology.
In the RabbitMQ panel, you see how fast messages are consumed from it, not how they are processed, you compare hot and round.
To measure latency I would recommend running your topology for a couple of days, 202 seconds according to your screenshot is too tight.
In my topology I see around 1 - 2 ms latency when transferring tuples from spouts to bolts or from bolts to bolts. I am calculating latency using nanosecond timestamps because the whole topology runs inside a single worker.
Topology is run in a cluster which runs in a production capable hardware.
To my understanding, tuples need not be serialized/de-serialized in this case as everything is inside single JVM. I have set parallelism hint for most spouts and bolts to 5 and spouts only produce events at a rate of 100 per second. I dont think high latency is due to queuing of events because I dont see any increase of latency with time. No memory increase either. log levels are set to ERROR. CPU usage is in the range of 200 to 300 %.
what could be causing this latency? I was expecting only few us's for tuple transfer.
I'm going to assume you're using one of the released Storm versions, and not 2.0.0-SNAPSHOT, since the queueing implementation has changed in that version.
I think it's likely that the delay is because Storm batches up tuples before delivering them to the consumer. Take a look at https://github.com/apache/storm/blob/v1.2.1/storm-core/src/jvm/org/apache/storm/utils/DisruptorQueue.java#L247, and also look at the Flusher class in that file. When a spout/bolt publishes a tuple, it is put into the _currentBatch list. It stays there until either enough tuples have been received so the batch is "big enough" (you can look at the _inputBatchSize variable to figure out when this is), or until the Flusher is triggered (happens by default once per millisecond).
I was using kafka-storm to connect kafka and storm. I have 3 servers running zookeeper, kafka and storm. There is a topic 'test' in kafka that has 9 partitions.
In the storm topology, the number of KafkaSpout executor is 9 and by default, the number of tasks should be 9 as well. And the 'extract' bolt is the only bolt connected to KafkaSpout, the 'log' spout.
From the UI, there is a huge rate of failure in the spout. However, he number of executed message in bolt = the number of emitted message - the number of failed mesage in bolt. This equation is almost matched when the failed message is empty at the beginning.
Based on my understanding, this means that the bolt did receive the message from spout but the ack signals are suspended in flight. That's the reason why the number of acks in spout are so small.
This problem might be solved by increase the timeout seconds and spout pending message number. But this will cause more memory usage and I cannot increase it to infinite.
I was wandering if there is a way to force storm ignore the ack in some spout/bolt, so that it will not waiting for that signal until time out. This should increase the throughout significantly but not guarantee for message processing.
if you set the number of ackers to 0 then storm will automatically ack every sample.
config.setNumAckers(0);
please note that the UI only measures and shows 5% of the data flow.
unless you set
config.setStatsSampleRate(1.0d);
try increasing the bolt's timeout and reducing the amount of topology.max.spout.pending.
also, make sure the spout's nextTuple() method is non blocking and optimized.
i would also recommend profiling the code, maybe your storm Queues are being filled and you need to increase their sizes.
config.put(Config.TOPOLOGY_TRANSFER_BUFFER_SIZE,32);
config.put(Config.TOPOLOGY_EXECUTOR_RECEIVE_BUFFER_SIZE,16384);
config.put(Config.TOPOLOGY_EXECUTOR_SEND_BUFFER_SIZE,16384);
Your capacity numbers are a bit high, leading me to believe that you're really maximizing the use of system resources (CPU, memory). In other words, the system seems to be bogged down a bit and that's probably why tuples are timing out. You might try using the topology.max.spout.pending config property to limit the number of inflight tuples from the spout. If you can reduce the number just enough, the topology should be able to efficiently handle the load without tuples timing out.
I'm using JmsSpout and BaseBasicBolt.
All bolts acked, but a lot(about half) of spout fail.
What's the possible reason?
or how can I log why the message fail? the JmsSpout just print which message fail, without error info.
If you do not actively fail() tuples in bolts, the only reason for failing tuples is a timeout. The default timeout in Storm is 30 seconds (you can configure it via TOPOLOGY_MESSAGE_TIMEOUT_SECS). If a tuples get emitted, Storm wait for the timeout duration to receive an ack. If not ack is received within this duration, Strom fails the tuple.
Increasing the timeout can fix the problem (you should set the timeout to a larger value than your expected processing latency).
If your expected processing latency is already lower then the timeout value, it indicates that you have a bottleneck in you topology. Thus, one (or multiple) operators cannot process incoming tuples fast enough. Therefore, incoming tuples get buffered in input queues increasing there latency as the queue is growing over time. You need to increase the parallelism for those bottleneck operators to resolve the issue.
If it's linked to another bolt, but no instances of the next bolt are available for a while. How long will it hang around? Indefinitely? Long enough?
How about if many tuples are waiting, because there is a line or queue for the next available bolt. Will they merge? Will bad things happen if too many get backed up?
By default tuples will timeout after 30 seconds after being emitted; You can change this value, but unless you know what you are doing don't do it (topology.message.timeout.secs)
Failed and timeout out tuples will be replayed by the spout, if the spout is reading from a reliable data source (eg. kafka); this is, storm has guaranteed message processing. If you are codding your own spouts, you might want to dig deep into this.
You can see if you are having timeout tuples on storm UI, when tuples are failing on the spout but not on the bolts.
You don't want tuples to timeout inside your topology (for example there is a performance penalty on kafka for not reading sequential). You should adjust the capacity of your topology process tuples (this is, tweak the bolt parallelism by changing the number of executors) and by setting the parameter topology.max.spout.pending to a reasonable conservative value.
increase the topology.message.timeout.secs parameter is no real solution, because soon or late if the capacity of your topology is not enough the tuples will start to fail.
topology.max.spout.pending is the max number of tuples that can be waiting. The spout will emit more tuples as long the number of tuples not fully processed is less than the given value. Note that the parameter topology.max.spout.pending is per spout (each spout has it's internal counter and keeps track of the tuples which are not fully processed).
There is a deserialize-queue for buffering the coming tuples, if it hangs long enough, the queue will be full,and tuples will be lost if you don't use the ack function to make sure it will be resent.
Storm just drops them if the tuples are not consumed until timeout. (default is 30 seconds)
After that, Storm calls fail(Object msgId) method of Spout. If you want to replay the failed tuples, you should implement this function. You need to keep the tuples in memory, or other reliable storage systems, such as Kafka, to replay them.
If you do not implement the fail(Object msgId) method, Storm just drops them.
Reference: https://storm.apache.org/documentation/Guaranteeing-message-processing.html