Updates - 1. Increasing the buffer sizes and adjusting the max topology spout pending helped a bit. But the problem that I see from logs is that the time it takes for the various threads (executor) to move messages from one queue to other takes abnormal amounts of time. Not sure how to reduce that.
Thanks Kashyap On Wed, Jul 22, 2015 at 3:36 PM, Kashyap Mhaisekar <[email protected]> wrote: > I kind of believe that the MainThread which picks the data from incoming > queue is taking a longish time.Did anyone face this? > > Execute and Process latencies are under 3-8 ms but the overall time taken > for the message to get processed is close to a 300ms. This is where I dont > understand what is happening. The case of missing 290ms > > How is the overall time taken for a message computed? Is it the process > latency at the Spout or a sum of process latencies at all the bolts? > > Thanks > Kashyap > > On Sun, Jul 19, 2015 at 1:30 PM, Kashyap Mhaisekar <[email protected]> > wrote: > >> I changed it to debug to find out the reason behind increased times as I >> suspected buffer overflow. It was info level. >> >> Regards >> Kashyap >> On Jul 19, 2015 1:18 PM, "Nathan Leung" <[email protected]> wrote: >> >> Are your logs on debug level? Try changing to info. >> On Jul 19, 2015 1:32 PM, "Kashyap Mhaisekar" <[email protected]> wrote: >> >>> Thanks Nathan. >>> The reason for the increased time taken between bolts could be due to - >>> 1. Buffer overflow >>> 2. GC activity. >>> 3. Low parallelism >>> 4. Latency between machines (around 0.3 ms) >>> >>> Debug logs indicate queue capacity and population of queues in limits, >>> so probably that is not the cause. >>> >>> For GC, I see GC/MarkSweepCompact and GC/Copy hovering at 500 ms. Am not >>> sure if this number is good or bad. Still figuring out... >>> >>> Parallelism does not seem to be a problem as capacity is under 0.3-0.5 >>> for all bolts. >>> >>> Do you know of any other reasons based on experience? >>> >>> Thanks for the time >>> >>> Thanks >>> Kashyap >>> On Jul 19, 2015 02:29, "Nathan Leung" <[email protected]> wrote: >>> >>>> Generally, binary search combined with observation of the system >>>> (whether it meets throughput/latency targets) is a good approach. >>>> On Jul 17, 2015 6:28 PM, "Kashyap Mhaisekar" <[email protected]> >>>> wrote: >>>> >>>>> Nathan, >>>>> The bolts are extending BaseBasicBolt and also, the in the spout am >>>>> explicitly emitting a msgId hence tuples should be tagged and anchored. >>>>> What I see is this - >>>>> 1. The logic exection in the bolt takes not more than 1 ms (start of >>>>> execute() and end of execute()) >>>>> 2. The time is being spent *between* the bolts >>>>> 3. The thread dumps all show LMAX disruptor at - >>>>> com.lmax.disruptor.blockingwaitstrategy.*waitfor() *where the maximum >>>>> CPU time is being spent. >>>>> >>>>> Is there a pattern with which the buffer sizes need to be tuned? :( >>>>> >>>>> Thanks >>>>> Kashyap >>>>> >>>>> On Thu, Jul 16, 2015 at 6:29 PM, Andrew Xor < >>>>> [email protected]> wrote: >>>>> >>>>>> Thanks for the clarification regarding Task ID's Nathan, I was under >>>>>> that false impression as the site docs are a bit misleading. Thanks for >>>>>> pointing that out! >>>>>> >>>>>> Regards. >>>>>> >>>>>> Kindly yours, >>>>>> >>>>>> Andrew Grammenos >>>>>> >>>>>> -- PGP PKey -- >>>>>> <https://www.dropbox.com/s/2kcxe59zsi9nrdt/pgpsig.txt> >>>>>> https://www.dropbox.com/s/ei2nqsen641daei/pgpsig.txt >>>>>> >>>>>> On Fri, Jul 17, 2015 at 2:12 AM, Nathan Leung <[email protected]> >>>>>> wrote: >>>>>> >>>>>>> If your tuples are reliable (spout emit with message id) and >>>>>>> anchored (emit from bolt anchored to input tuple), then you have to >>>>>>> answer >>>>>>> that yourself. If not, then your output queue size is not constrained by >>>>>>> the framework and you may still have high latency. >>>>>>> On Jul 16, 2015 7:05 PM, "Kashyap Mhaisekar" <[email protected]> >>>>>>> wrote: >>>>>>> >>>>>>>> Nathan, >>>>>>>> My max spout pending is set to 1. Now is my problem with latency or >>>>>>>> with throughput. >>>>>>>> >>>>>>>> Thank you! >>>>>>>> Kashyap >>>>>>>> On Jul 16, 2015 5:46 PM, "Nathan Leung" <[email protected]> wrote: >>>>>>>> >>>>>>>>> If your tuples are anchored max spout pending indirectly affects >>>>>>>>> how many tuples are generated ;). >>>>>>>>> On Jul 16, 2015 6:18 PM, "Kashyap Mhaisekar" <[email protected]> >>>>>>>>> wrote: >>>>>>>>> >>>>>>>>>> Thanks Nathan. One question though - Are there any best practices >>>>>>>>>> when tuples are getting generated in the topology and not really >>>>>>>>>> controllable via Max Spout Pending? >>>>>>>>>> >>>>>>>>>> Thanks >>>>>>>>>> Kashyap >>>>>>>>>> >>>>>>>>>> On Thu, Jul 16, 2015 at 5:07 PM, Nathan Leung <[email protected]> >>>>>>>>>> wrote: >>>>>>>>>> >>>>>>>>>>> Also I would argue that this is not important unless your >>>>>>>>>>> application is especially latency sensitive or your queue is so >>>>>>>>>>> long that >>>>>>>>>>> it is causing in flight tuples to timeout. >>>>>>>>>>> On Jul 16, 2015 6:05 PM, "Nathan Leung" <[email protected]> >>>>>>>>>>> wrote: >>>>>>>>>>> >>>>>>>>>>>> Sorry for a brief response.. The number of tuples in flight >>>>>>>>>>>> absolutely affects your max latency. You need to tune your >>>>>>>>>>>> topology max >>>>>>>>>>>> spout pending. Lower value will reduce your end to end latency, >>>>>>>>>>>> but if >>>>>>>>>>>> it's too low it may affect throughput. I've posted to the group >>>>>>>>>>>> about this >>>>>>>>>>>> before; if you do a search you may find some posts where I've >>>>>>>>>>>> discussed >>>>>>>>>>>> this in more detail. >>>>>>>>>>>> On Jul 16, 2015 5:56 PM, "Kashyap Mhaisekar" < >>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>> >>>>>>>>>>>>> Nathan, >>>>>>>>>>>>> Thanks. Have been running on a bare bones topology as >>>>>>>>>>>>> suggested. I am inclined to believe that the no. of messages in >>>>>>>>>>>>> the >>>>>>>>>>>>> topology at that point in time is affecting the "latency". >>>>>>>>>>>>> >>>>>>>>>>>>> Am trying to now figure out how should the topology be >>>>>>>>>>>>> structured when the no. of transient tupples in the topology is >>>>>>>>>>>>> very high. >>>>>>>>>>>>> >>>>>>>>>>>>> Topology is structured as follows - >>>>>>>>>>>>> Consumer (A java program sends a message to storm cluster) -> >>>>>>>>>>>>> A (Spout) ->(Emits a number say 100) -> B (bolt) [Emits 100 >>>>>>>>>>>>> messages]) -> >>>>>>>>>>>>> C (bolt) [Passes along the message to next bolt) -> D (bolt) >>>>>>>>>>>>> [Passes along >>>>>>>>>>>>> the message to next bolt] -> E (bolt) [Aggregates all the data >>>>>>>>>>>>> and confirms >>>>>>>>>>>>> if all the 100 messages are processed) >>>>>>>>>>>>> >>>>>>>>>>>>> What I observed is as follows - >>>>>>>>>>>>> 1. The time taken for an end to end processing of the message >>>>>>>>>>>>> (Sending the message to Storm cluster and till the time the >>>>>>>>>>>>> aggregation is >>>>>>>>>>>>> complete) is directly dependent on the volume of messages that is >>>>>>>>>>>>> entering >>>>>>>>>>>>> into storm and also the no. of emits done by the spout A. >>>>>>>>>>>>> *Test 1: 100 sequential messages to storm with B emitting 100 >>>>>>>>>>>>> tuples per message (100X100=10000) there are 10000 tuples emitted >>>>>>>>>>>>> and the >>>>>>>>>>>>> time taken to aggregate the 100 is 15 ms to 100 ms* >>>>>>>>>>>>> *Test 2: 100 sequential messages to storm with B emitting 1000 >>>>>>>>>>>>> tuples per message (100X1000=100000) **there are 100000 >>>>>>>>>>>>> tuples emitted and the time taken to aggregate the 100 is 4 >>>>>>>>>>>>> seconds to 10 >>>>>>>>>>>>> seconds* >>>>>>>>>>>>> 2.Strange thing is - the more parallelism i add, the times are >>>>>>>>>>>>> so much more bad. Am trying to figure out if the memory per >>>>>>>>>>>>> worker is a >>>>>>>>>>>>> constraint, but this is the firs time am seeing this. >>>>>>>>>>>>> >>>>>>>>>>>>> Question - How should the use case be handled where in the no. >>>>>>>>>>>>> of tuples in the topology could increase exponentially.., >>>>>>>>>>>>> >>>>>>>>>>>>> Thanks >>>>>>>>>>>>> Kashyap >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> On Thu, Jul 16, 2015 at 3:52 PM, Nick R. Katsipoulakis < >>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>>> Thank you all for the valuable info. >>>>>>>>>>>>>> >>>>>>>>>>>>>> Unfortunately, I have to use it for my (research) prototype >>>>>>>>>>>>>> therefore I have to go along with it. >>>>>>>>>>>>>> >>>>>>>>>>>>>> Thank you again, >>>>>>>>>>>>>> Nick >>>>>>>>>>>>>> >>>>>>>>>>>>>> 2015-07-16 16:33 GMT-04:00 Nathan Leung <[email protected]>: >>>>>>>>>>>>>> >>>>>>>>>>>>>>> Storm task ids don't change: >>>>>>>>>>>>>>> https://groups.google.com/forum/#!topic/storm-user/7P23beQIL4c >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> On Thu, Jul 16, 2015 at 4:28 PM, Andrew Xor < >>>>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Direct grouping as it is shown in storm docs, means that >>>>>>>>>>>>>>>> you have to have a specific task id and use "direct streams" >>>>>>>>>>>>>>>> which is error >>>>>>>>>>>>>>>> prone, probably increase latency and might introduce >>>>>>>>>>>>>>>> redundancy problems as >>>>>>>>>>>>>>>> the producer of tuple needs to know the id of the task the >>>>>>>>>>>>>>>> tuple will have >>>>>>>>>>>>>>>> to go; so imagine a scenario where the receiving task fails >>>>>>>>>>>>>>>> for some reason >>>>>>>>>>>>>>>> and the producer can't relay the tuples unless it received the >>>>>>>>>>>>>>>> re-spawned >>>>>>>>>>>>>>>> task's id. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Hope this helps. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Kindly yours, >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Andrew Grammenos >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> -- PGP PKey -- >>>>>>>>>>>>>>>> <https://www.dropbox.com/s/2kcxe59zsi9nrdt/pgpsig.txt> >>>>>>>>>>>>>>>> https://www.dropbox.com/s/ei2nqsen641daei/pgpsig.txt >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> On Thu, Jul 16, 2015 at 11:24 PM, Nick R. Katsipoulakis < >>>>>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Hello again, >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Nathan, I am using direct-grouping because the application >>>>>>>>>>>>>>>>> I am working on has to be able to send tuples directly to >>>>>>>>>>>>>>>>> specific tasks. >>>>>>>>>>>>>>>>> In general control the data flow. Can you please explain to >>>>>>>>>>>>>>>>> me why you >>>>>>>>>>>>>>>>> would not recommend direct grouping? Is there any particular >>>>>>>>>>>>>>>>> reason in the >>>>>>>>>>>>>>>>> architecture of Storm? >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Thanks, >>>>>>>>>>>>>>>>> Nick >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> 2015-07-16 16:20 GMT-04:00 Nathan Leung <[email protected] >>>>>>>>>>>>>>>>> >: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> I would not recommend direct grouping unless you have a >>>>>>>>>>>>>>>>>> good reason for it. Shuffle grouping is essentially random >>>>>>>>>>>>>>>>>> with even >>>>>>>>>>>>>>>>>> distribution which makes it easier to characterize its >>>>>>>>>>>>>>>>>> performance. Local >>>>>>>>>>>>>>>>>> or shuffle grouping stays in process so generally it will be >>>>>>>>>>>>>>>>>> faster. >>>>>>>>>>>>>>>>>> However you have to be careful in certain cases to avoid >>>>>>>>>>>>>>>>>> task starvation >>>>>>>>>>>>>>>>>> (e.g. you have kafka spout with 1 partition on the topic and >>>>>>>>>>>>>>>>>> 1 spout task, >>>>>>>>>>>>>>>>>> feeding 10 bolt "A" tasks in 10 worker processes). Direct >>>>>>>>>>>>>>>>>> grouping depends >>>>>>>>>>>>>>>>>> on your code (i.e. you can create hotspots), fields grouping >>>>>>>>>>>>>>>>>> depends on >>>>>>>>>>>>>>>>>> your key distribution, etc. >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> On Thu, Jul 16, 2015 at 3:50 PM, Nick R. Katsipoulakis < >>>>>>>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> Hello all, >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> I have two questions: >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> 1) How do you exactly measure latency? I am doing the >>>>>>>>>>>>>>>>>>> same thing and I have a problem getting the exact >>>>>>>>>>>>>>>>>>> milliseconds of latency >>>>>>>>>>>>>>>>>>> (mainly because of clock drifting). >>>>>>>>>>>>>>>>>>> 2) (to Nathan) Is there a difference in speeds among >>>>>>>>>>>>>>>>>>> different groupings? For instance, is shuffle faster than >>>>>>>>>>>>>>>>>>> direct grouping? >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> Thanks, >>>>>>>>>>>>>>>>>>> Nick >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> 2015-07-15 17:37 GMT-04:00 Nathan Leung < >>>>>>>>>>>>>>>>>>> [email protected]>: >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> Two things. Your math may be off depending on >>>>>>>>>>>>>>>>>>>> parallelism. One emit from A becomes 100 emitted from C, >>>>>>>>>>>>>>>>>>>> and you are >>>>>>>>>>>>>>>>>>>> joining all of them. >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> Second, try the default number of ackers (one per >>>>>>>>>>>>>>>>>>>> worker). All your ack traffic is going to a single task. >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> Also you can try local or shuffle grouping if possible >>>>>>>>>>>>>>>>>>>> to reduce network transfers. >>>>>>>>>>>>>>>>>>>> On Jul 15, 2015 12:45 PM, "Kashyap Mhaisekar" < >>>>>>>>>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Hi, >>>>>>>>>>>>>>>>>>>>> We are attempting a real-time distributed computing >>>>>>>>>>>>>>>>>>>>> using storm and the solution has only one problem - >>>>>>>>>>>>>>>>>>>>> inter bolt latency on same machine or across machines >>>>>>>>>>>>>>>>>>>>> ranges between 2 - 250 ms. I am not able to figure out >>>>>>>>>>>>>>>>>>>>> why. Network >>>>>>>>>>>>>>>>>>>>> latency is under 0.5 ms. By latency, I mean the time >>>>>>>>>>>>>>>>>>>>> between an emit of one bolt/spout to getting the message >>>>>>>>>>>>>>>>>>>>> in execute() of >>>>>>>>>>>>>>>>>>>>> next bolt. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> I have a topology like the below - >>>>>>>>>>>>>>>>>>>>> A (Spout) ->(Emits a number say 1000) -> B (bolt) >>>>>>>>>>>>>>>>>>>>> [Receives this number and divides this into 10 emits of >>>>>>>>>>>>>>>>>>>>> 100 each) -> C >>>>>>>>>>>>>>>>>>>>> (bolt) [Recieves these emits and divides this to 10 emits >>>>>>>>>>>>>>>>>>>>> of 10 numbers) -> >>>>>>>>>>>>>>>>>>>>> D (bolt) [Does some computation on the number and emits >>>>>>>>>>>>>>>>>>>>> one message] -> E >>>>>>>>>>>>>>>>>>>>> (bolt) [Aggregates all the data and confirms if all the >>>>>>>>>>>>>>>>>>>>> 1000 messages are >>>>>>>>>>>>>>>>>>>>> processed) >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Every bolt takes under 3 msec to complete and as a >>>>>>>>>>>>>>>>>>>>> result, I estimated that the end to end processing for >>>>>>>>>>>>>>>>>>>>> 1000 takes not more >>>>>>>>>>>>>>>>>>>>> than 50 msec including any latencies. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> *Observations* >>>>>>>>>>>>>>>>>>>>> 1. The end to end time from Spout A to Bolt E takes >>>>>>>>>>>>>>>>>>>>> 200 msec to 3 seconds. My estimate was under 50 msec >>>>>>>>>>>>>>>>>>>>> given that each bolt >>>>>>>>>>>>>>>>>>>>> and spout take under 3 msec to execute including any >>>>>>>>>>>>>>>>>>>>> latencies. >>>>>>>>>>>>>>>>>>>>> 2. I noticed that the most of the time is spent >>>>>>>>>>>>>>>>>>>>> between Emit from a Spout/Bolt and execute() of the >>>>>>>>>>>>>>>>>>>>> consuming bolt. >>>>>>>>>>>>>>>>>>>>> 3. Network latency is under 0.5 msec. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> I am not able to figure out why it takes so much time >>>>>>>>>>>>>>>>>>>>> between a spout/bolt to next bolt. I understand that the >>>>>>>>>>>>>>>>>>>>> spout/bolt buffers >>>>>>>>>>>>>>>>>>>>> the data into a queue and then the subsequent bolt >>>>>>>>>>>>>>>>>>>>> consumes from there. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> *Infrastructure* >>>>>>>>>>>>>>>>>>>>> 1. 5 VMs with 4 CPU and 8 GB ram. Workers are with >>>>>>>>>>>>>>>>>>>>> 1024 MB and there are 20 workers overall. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> *Test* >>>>>>>>>>>>>>>>>>>>> 1. The test was done with 25 messages to the spout => >>>>>>>>>>>>>>>>>>>>> 25 messages are sent to spout in a span of 5 seconds. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> *Config values* >>>>>>>>>>>>>>>>>>>>> Config config = new Config(); >>>>>>>>>>>>>>>>>>>>> config.put(Config.TOPOLOGY_WORKERS, >>>>>>>>>>>>>>>>>>>>> Integer.parseInt(20)); >>>>>>>>>>>>>>>>>>>>> config.put(Config.TOPOLOGY_EXECUTOR_RECEIVE_BUFFER_SIZE, >>>>>>>>>>>>>>>>>>>>> 16384); >>>>>>>>>>>>>>>>>>>>> config.put(Config.TOPOLOGY_EXECUTOR_SEND_BUFFER_SIZE, >>>>>>>>>>>>>>>>>>>>> 16384); >>>>>>>>>>>>>>>>>>>>> config.put(Config.TOPOLOGY_ACKER_EXECUTORS, 1); >>>>>>>>>>>>>>>>>>>>> config.put(Config.TOPOLOGY_RECEIVER_BUFFER_SIZE, 8); >>>>>>>>>>>>>>>>>>>>> config.put(Config.TOPOLOGY_TRANSFER_BUFFER_SIZE, 64); >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Please let me know if you have encountered similar >>>>>>>>>>>>>>>>>>>>> issues and any steps you have taken to mitigate the time >>>>>>>>>>>>>>>>>>>>> taken between >>>>>>>>>>>>>>>>>>>>> spout/bolt and another bolt. >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Thanks >>>>>>>>>>>>>>>>>>>>> Kashyap >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> -- >>>>>>>>>>>>>>>>>>> Nikolaos Romanos Katsipoulakis, >>>>>>>>>>>>>>>>>>> University of Pittsburgh, PhD candidate >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> -- >>>>>>>>>>>>>>>>> Nikolaos Romanos Katsipoulakis, >>>>>>>>>>>>>>>>> University of Pittsburgh, PhD candidate >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> -- >>>>>>>>>>>>>> Nikolaos Romanos Katsipoulakis, >>>>>>>>>>>>>> University of Pittsburgh, PhD candidate >>>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>> >>>>>> >>>>> >
