Makes sense especially if you have a cloud with “infinite” resources / nodes which allows you to double, triple etc in the background/parallel the resources of the currently running cluster
I was thinking more about the scenario where you have e.g. 100 boxes and want to / can add e.g. 20 more From: Dmitry Goldenberg [mailto:dgoldenberg...@gmail.com] Sent: Wednesday, June 3, 2015 4:46 PM To: Evo Eftimov Cc: Cody Koeninger; Andrew Or; Gerard Maas; spark users Subject: Re: FW: Re: Autoscaling Spark cluster based on topic sizes/rate of growth in Kafka or Spark's metrics? Evo, One of the ideas is to shadow the current cluster. This way there's no extra latency incurred due to shutting down of the consumers. If two sets of consumers are running, potentially processing the same data, that is OK. We phase out the older cluster and gradually flip over to the new one, insuring no downtime or extra latency. Thoughts? On Wed, Jun 3, 2015 at 11:27 AM, Evo Eftimov <evo.efti...@isecc.com> wrote: You should monitor vital performance / job clogging stats of the Spark Streaming Runtime not “kafka topics” You should be able to bring new worker nodes online and make them contact and register with the Master without bringing down the Master (or any of the currently running worker nodes) Then just shutdown your currently running spark streaming job/app and restart it with new params to take advantage of the larger cluster From: Dmitry Goldenberg [mailto:dgoldenberg...@gmail.com] Sent: Wednesday, June 3, 2015 4:14 PM To: Cody Koeninger Cc: Andrew Or; Evo Eftimov; Gerard Maas; spark users Subject: Re: FW: Re: Autoscaling Spark cluster based on topic sizes/rate of growth in Kafka or Spark's metrics? Would it be possible to implement Spark autoscaling somewhat along these lines? -- 1. If we sense that a new machine is needed, by watching the data load in Kafka topic(s), then 2. Provision a new machine via a Provisioner interface (e.g. talk to AWS and get a machine); 3. Create a "shadow"/mirror Spark master running alongside the initial version which talks to N machines. The new mirror version is aware of N+1 machines (or N+M if we had decided we needed M new boxes). 4. The previous version of the Spark runtime is acquiesced/decommissioned. We possibly get both clusters working on the same data which may actually be OK (at least for our specific use-cases). 5. Now the new Spark cluster is running. Similarly, the decommissioning of M unused boxes would happen, via this notion of a mirror Spark runtime. How feasible would it be for such a mirrorlike setup to be created, especially created programmatically? Especially point #3. The other idea we'd entertained was to bring in a new machine, acquiesce down all currently running workers by telling them to process their current batch then shut down, then restart the consumers now that Spark is aware of a modified cluster. This has the drawback of a downtime that may not be tolerable in terms of latency, by the system's clients waiting for their responses in a synchronous fashion. Thanks. On Thu, May 28, 2015 at 5:15 PM, Cody Koeninger <c...@koeninger.org> wrote: I'm not sure that points 1 and 2 really apply to the kafka direct stream. There are no receivers, and you know at the driver how big each of your batches is. On Thu, May 28, 2015 at 2:21 PM, Andrew Or <and...@databricks.com> wrote: Hi all, As the author of the dynamic allocation feature I can offer a few insights here. Gerard's explanation was both correct and concise: dynamic allocation is not intended to be used in Spark streaming at the moment (1.4 or before). This is because of two things: (1) Number of receivers is necessarily fixed, and these are started in executors. Since we need a receiver for each InputDStream, if we kill these receivers we essentially stop the stream, which is not what we want. It makes little sense to close and restart a stream the same way we kill and relaunch executors. (2) Records come in every batch, and when there is data to process your executors are not idle. If your idle timeout is less than the batch duration, then you'll end up having to constantly kill and restart executors. If your idle timeout is greater than the batch duration, then you'll never kill executors. Long answer short, with Spark streaming there is currently no straightforward way to scale the size of your cluster. I had a long discussion with TD (Spark streaming lead) about what needs to be done to provide some semblance of dynamic scaling to streaming applications, e.g. take into account the batch queue instead. We came up with a few ideas that I will not detail here, but we are looking into this and do intend to support it in the near future. -Andrew 2015-05-28 8:02 GMT-07:00 Evo Eftimov <evo.efti...@isecc.com>: Probably you should ALWAYS keep the RDD storage policy to MEMORY AND DISK – it will be your insurance policy against sys crashes due to memory leaks. Until there is free RAM, spark streaming (spark) will NOT resort to disk – and of course resorting to disk from time to time (ie when there is no free RAM ) and taking a performance hit from that, BUT only until there is no free RAM From: Dmitry Goldenberg [mailto:dgoldenberg...@gmail.com] Sent: Thursday, May 28, 2015 2:34 PM To: Evo Eftimov Cc: Gerard Maas; spark users Subject: Re: FW: Re: Autoscaling Spark cluster based on topic sizes/rate of growth in Kafka or Spark's metrics? Evo, good points. On the dynamic resource allocation, I'm surmising this only works within a particular cluster setup. So it improves the usage of current cluster resources but it doesn't make the cluster itself elastic. At least, that's my understanding. Memory + disk would be good and hopefully it'd take *huge* load on the system to start exhausting the disk space too. I'd guess that falling onto disk will make things significantly slower due to the extra I/O. Perhaps we'll really want all of these elements eventually. I think we'd want to start with memory only, keeping maxRate low enough not to overwhelm the consumers; implement the cluster autoscaling. We might experiment with dynamic resource allocation before we get to implement the cluster autoscale. On Thu, May 28, 2015 at 9:05 AM, Evo Eftimov <evo.efti...@isecc.com> wrote: You can also try Dynamic Resource Allocation https://spark.apache.org/docs/1.3.1/job-scheduling.html#dynamic-resource-allocation Also re the Feedback Loop for automatic message consumption rate adjustment – there is a “dumb” solution option – simply set the storage policy for the DStream RDDs to MEMORY AND DISK – when the memory gets exhausted spark streaming will resort to keeping new RDDs on disk which will prevent it from crashing and hence loosing them. Then some memory will get freed and it will resort back to RAM and so on and so forth Sent from Samsung Mobile -------- Original message -------- From: Evo Eftimov Date:2015/05/28 13:22 (GMT+00:00) To: Dmitry Goldenberg Cc: Gerard Maas ,spark users Subject: Re: Autoscaling Spark cluster based on topic sizes/rate of growth in Kafka or Spark's metrics? You can always spin new boxes in the background and bring them into the cluster fold when fully operational and time that with job relaunch and param change Kafka offsets are mabaged automatically for you by the kafka clients which keep them in zoomeeper dont worry about that ad long as you shut down your job gracefuly. Besides msnaging the offsets explicitly is not a big deal if necessary Sent from Samsung Mobile -------- Original message -------- From: Dmitry Goldenberg Date:2015/05/28 13:16 (GMT+00:00) To: Evo Eftimov Cc: Gerard Maas ,spark users Subject: Re: Autoscaling Spark cluster based on topic sizes/rate of growth in Kafka or Spark's metrics? Thanks, Evo. Per the last part of your comment, it sounds like we will need to implement a job manager which will be in control of starting the jobs, monitoring the status of the Kafka topic(s), shutting jobs down and marking them as ones to relaunch, scaling the cluster up/down by adding/removing machines, and relaunching the 'suspended' (shut down) jobs. I suspect that relaunching the jobs may be tricky since that means keeping track of the starter offsets in Kafka topic(s) from which the jobs started working on. Ideally, we'd want to avoid a re-launch. The 'suspension' and relaunching of jobs, coupled with the wait for the new machines to come online may turn out quite time-consuming which will make for lengthy request times, and our requests are not asynchronous. Ideally, the currently running jobs would continue to run on the machines currently available in the cluster. In the scale-down case, the job manager would want to signal to Spark's job scheduler not to send work to the node being taken out, find out when the last job has finished running on the node, then take the node out. This is somewhat like changing the number of cylinders in a car engine while the car is running... Sounds like a great candidate for a set of enhancements in Spark... On Thu, May 28, 2015 at 7:52 AM, Evo Eftimov <evo.efti...@isecc.com> wrote: @DG; The key metrics should be - Scheduling delay – its ideal state is to remain constant over time and ideally be less than the time of the microbatch window - The average job processing time should remain less than the micro-batch window - Number of Lost Jobs – even if there is a single Job lost that means that you have lost all messages for the DStream RDD processed by that job due to the previously described spark streaming memory leak condition and subsequent crash – described in previous postings submitted by me You can even go one step further and periodically issue “get/check free memory” to see whether it is decreasing relentlessly at a constant rate – if it touches a predetermined RAM threshold that should be your third metric Re the “back pressure” mechanism – this is a Feedback Loop mechanism and you can implement one on your own without waiting for Jiras and new features whenever they might be implemented by the Spark dev team – moreover you can avoid using slow mechanisms such as ZooKeeper and even incorporate some Machine Learning in your Feedback Loop to make it handle the message consumption rate more intelligently and benefit from ongoing online learning – BUT this is STILL about voluntarily sacrificing your performance in the name of keeping your system stable – it is not about scaling your system/solution In terms of how to scale the Spark Framework Dynamically – even though this is not supported at the moment out of the box I guess you can have a sys management framework spin dynamically a few more boxes (spark worker nodes), stop dynamically your currently running Spark Streaming Job, relaunch it with new params e.g. more Receivers, larger number of Partitions (hence tasks), more RAM per executor etc. Obviously this will cause some temporary delay in fact interruption in your processing but if the business use case can tolerate that then go for it From: Gerard Maas [mailto:gerard.m...@gmail.com] Sent: Thursday, May 28, 2015 12:36 PM To: dgoldenberg Cc: spark users Subject: Re: Autoscaling Spark cluster based on topic sizes/rate of growth in Kafka or Spark's metrics? Hi, tl;dr At the moment (with a BIG disclaimer *) elastic scaling of spark streaming processes is not supported. Longer version. I assume that you are talking about Spark Streaming as the discussion is about handing Kafka streaming data. Then you have two things to consider: the Streaming receivers and the Spark processing cluster. Currently, the receiving topology is static. One receiver is allocated with each DStream instantiated and it will use 1 core in the cluster. Once the StreamingContext is started, this topology cannot be changed, therefore the number of Kafka receivers is fixed for the lifetime of your DStream. What we do is to calculate the cluster capacity and use that as a fixed upper bound (with a margin) for the receiver throughput. There's work in progress to add a reactive model to the receiver, where backpressure can be applied to handle overload conditions. See https://issues.apache.org/jira/browse/SPARK-7398 Once the data is received, it will be processed in a 'classical' Spark pipeline, so previous posts on spark resource scheduling might apply. Regarding metrics, the standard metrics subsystem of spark will report streaming job performance. Check the driver's metrics endpoint to peruse the available metrics: <driver>:<ui-port>/metrics/json -kr, Gerard. (*) Spark is a project that moves so fast that statements might be invalidated by new work every minute. On Thu, May 28, 2015 at 1:21 AM, dgoldenberg <dgoldenberg...@gmail.com> wrote: Hi, I'm trying to understand if there are design patterns for autoscaling Spark (add/remove slave machines to the cluster) based on the throughput. Assuming we can throttle Spark consumers, the respective Kafka topics we stream data from would start growing. What are some of the ways to generate the metrics on the number of new messages and the rate they are piling up? This perhaps is more of a Kafka question; I see a pretty sparse javadoc with the Metric interface and not much else... What are some of the ways to expand/contract the Spark cluster? Someone has mentioned Mesos... I see some info on Spark metrics in the Spark monitoring guide <https://spark.apache.org/docs/latest/monitoring.html> . Do we want to perhaps implement a custom sink that would help us autoscale up or down based on the throughput? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Autoscaling-Spark-cluster-based-on-topic-sizes-rate-of-growth-in-Kafka-or-Spark-s-metrics-tp23062.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
