Thanks for the heads up mate.
Ooow – that is essentially the custom feedback loop mentioned in my previous emails in generic Architecture Terms and what you have done is only one of the possible implementations moreover based on Zookeeper – there are other possible designs not using things like zookeeper at all and hence achieving much lower latency and responsiveness
Can I also give you a friendly advice – there is a looooong way FROM “we=Sigmoid and our custom sigmoid solution”, TO your earlier statement that Spark Streaming does “NOT” crash UNCEREMNOUSLY – please maintain responsible and objective communication and facts
we = Sigmoid
back-pressuring mechanism = Stoping the receiver from receiving more messages when its about to exhaust the worker memory. Here's a similar kind of proposal if you haven't seen already.
On Mon, May 18, 2015 at 6:53 PM, Evo Eftimov <firstname.lastname@example.org> wrote:
Who are “we” and what is the mysterious “back-pressuring mechanism” and is it part of the Spark Distribution (are you talking about implementation of the custom feedback loop mentioned in my previous emails below)- asking these because I can assure you that at least as of Spark Streaming 1.2.0, as Evo says Spark Streaming DOES crash in “unceremonious way” when the free RAM available for In Memory Cashed RDDs gets exhausted
We fix the receivers rate at which it should consume at any given point of time. Also we have a back-pressuring mechanism attached to the receivers so it won't simply crashes in the "unceremonious way" like Evo said. Mesos has some sort of auto-scaling (read it somewhere), may be you can look into that also.
On Mon, May 18, 2015 at 5:20 PM, Evo Eftimov <email@example.com> wrote:
And if you want to genuinely “reduce the latency” (still within the boundaries of the micro-batch) THEN you need to design and finely tune the Parallel Programming / Execution Model of your application. The objective/metric here is:
a) Consume all data within your selected micro-batch window WITHOUT any artificial message rate limits
b) The above will result in a certain size of Dstream RDD per micro-batch.
c) The objective now is to Process that RDD WITHIN the time of the micro-batch (and also account for temporary message rate spike etc which may further increase the size of the RDD) – this will avoid any clogging up of the app and will process your messages at the lowest latency possible in a micro-batch architecture
d) You achieve the objective stated in c by designing, varying and experimenting with various aspects of the Spark Streaming Parallel Programming and Execution Model – e.g. number of receivers, number of threads per receiver, number of executors, number of cores, RAM allocated to executors, number of RDD partitions which correspond to the number of parallel threads operating on the RDD etc etc
Re the “unceremonious removal of DStream RDDs” from RAM by Spark Streaming when the available RAM is exhausted due to high message rate and which crashes your (hence clogged up) application the name of the condition is:
Loss was due to java.lang.Exception
java.lang.Exception: Could not compute split, block
input-4-1410542878200 not found
You can use
Maximum rate (number of records per second) at which each receiver will receive data. Effectively, each stream will consume at most this number of records per second. Setting this configuration to 0 or a negative number will put no limit on the rate. See the deployment guide in the Spark Streaming programing guide for mode details.
Another way is to implement a feedback loop in your receivers monitoring the performance metrics of your application/job and based on that adjusting automatically the receiving rate – BUT all these have nothing to do with “reducing the latency” – they simply prevent your application/job from clogging up – the nastier effect of which is when S[ark Streaming starts removing In Memory RDDs from RAM before they are processed by the job – that works fine in Spark Batch (ie removing RDDs from RAM based on LRU) but in Spark Streaming when done in this “unceremonious way” it simply Crashes the application
Thanks, Akhil. So what do folks typically do to increase/contract the capacity? Do you plug in some cluster auto-scaling solution to make this elastic?
Does Spark have any hooks for instrumenting auto-scaling?
In other words, how do you avoid overwheling the receivers in a scenario when your system's input can be unpredictable, based on users' activity?
On May 17, 2015, at 11:04 AM, Akhil Das <firstname.lastname@example.org> wrote:
With receiver based streaming, you can actually specify spark.streaming.blockInterval which is the interval at which the receiver will fetch data from the source. Default value is 200ms and hence if your batch duration is 1 second, it will produce 5 blocks of data. And yes, with sparkstreaming when your processing time goes beyond your batch duration and you are having a higher data consumption then you will overwhelm the receiver's memory and hence will throw up block not found exceptions.
On Sun, May 17, 2015 at 7:21 PM, dgoldenberg <email@example.com> wrote:
I keep hearing the argument that the way Discretized Streams work with Spark
Streaming is a lot more of a batch processing algorithm than true streaming.
For streaming, one would expect a new item, e.g. in a Kafka topic, to be
available to the streaming consumer immediately.
With the discretized streams, streaming is done with batch intervals i.e.
the consumer has to wait the interval to be able to get at the new items. If
one wants to reduce latency it seems the only way to do this would be by
reducing the batch interval window. However, that may lead to a great deal
of churn, with many requests going into Kafka out of the consumers,
potentially with no results whatsoever as there's nothing new in the topic
at the moment.
Is there a counter-argument to this reasoning? What are some of the general
approaches to reduce latency folks might recommend? Or, perhaps there are
ways of dealing with this at the streaming API level?
If latency is of great concern, is it better to look into streaming from
something like Flume where data is pushed to consumers rather than pulled by
them? Are there techniques, in that case, to ensure the consumers don't get
overwhelmed with new data?
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