Yes Hari, that’s exactly my point: by default, whatever are the channels (Memory or File), one channel filling up will slow down the associated Source, and therefore any other channel associated to it. (It will also impact by extension, any client sending event to this source, as the source will acknowledge the events slower). The higher the keep-alive is (default 3sec), the bigger the global impact will be.
Vincentius might reduce this impact by lowering the keep-alive of his channels to 1 second (lowest possible value). What do you think of a future evolution enabling an optional configuration of the channels, in order to change the keep-alive time unit (default Seconds for non reg), so that some users like Vincentius and I could put the Keep-live to something like 100ms, with channels “Optionnal” for example ? Also could you elaborate on the conséquences of having a keep-alive = 0 ? I understand that in a Channel full situation, the tryAquire will immediately fail, without waiting, but I do not get the Semaphore dieing possiblity. Regards Jeff From: Hari Shreedharan [mailto:[email protected]] Sent: vendredi 14 novembre 2014 01:29 To: [email protected] Cc: [email protected] Subject: RE: All channels in an agent get slower after a channel is full It is expected that if one channel is full, the whole batch is considered failed, and the source will retry. If even one required channel is full, the whole transaction fails. If you don’t want this mark channels are optional. Also, all channels have a keep-alive, that is the period (in seconds) that the put fails with lack of data. You can reduce this via configuration. If you reduce this to to 0, it may cause major concurrency issues (since semaphores will start dieing etc). Things slowing down could be because of this as well. Thanks, Hari On Thu, Nov 13, 2014 at 4:22 PM, [email protected]<mailto:[email protected]> <[email protected]<mailto:[email protected]>> wrote: Hi Hari, I’m jumping in this discussion as I’m facing similar behavior on channel full impacts. I was trying to optimize an HTTPSink that does not sustain the performance it should when I faced same issue than described below, but with MemoryChannels: 1 source (let’s say Avro), with a Replicating Selector duplicating the events in 2 MemoryChannels. When one MemoryChannel is full, the other one is getting down, and even worse, the Source is getting down as well. So I suspected initially my particular Sink to have effect on other threads or on the JVM. So I removed it, and tried a very simple config: a1.sources = r1 a1.channels = c1 a1.sinks = k1 a1.sources.r1.type = avro a1.sources.r1.channels = c1 a1.sources.r1.bind = 0.0.0.0 a1.sources.r1.port = 1234 a1.channels.c1.type = memory a1.channels.c1.capacity = 1000 a1.sinks.k1.type = avro a1.sinks.k1.channel = c1 a1.sinks.k1.hostname = 127.0.0.1 a1.sinks.k1.port = 3456 I put another agent listening on the AVRO events on 3456, and I inject load into the main one, then I stop the listener agent. => The channel c1 is off course filling up… but the source is impacted as well, by the channel. The threaddump is explicit: "New I/O worker #15" prio=6 tid=0x000000000d252000 nid=0x2990 waiting on condition [0x0000000010cee000] java.lang.Thread.State: TIMED_WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <0x00000007818f9c00> (a java.util.concurrent.Semaphore$NonfairSync) at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:226) at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedNanos(AbstractQueuedSynchronizer.java:1033) at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1326) at java.util.concurrent.Semaphore.tryAcquire(Semaphore.java:588) at org.apache.flume.channel.MemoryChannel$MemoryTransaction.doCommit(MemoryChannel.java:128) at org.apache.flume.channel.BasicTransactionSemantics.commit(BasicTransactionSemantics.java:151) at org.apache.flume.channel.ChannelProcessor.processEvent(ChannelProcessor.java:267) at org.apache.flume.source.AvroSource.append(AvroSource.java:348) at sun.reflect.GeneratedMethodAccessor40.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:606) at org.apache.avro.ipc.specific.SpecificResponder.respond(SpecificResponder.java:88) at org.apache.avro.ipc.Responder.respond(Responder.java:149) at org.apache.avro.ipc.NettyServer$NettyServerAvroHandler.messageReceived(NettyServer.java:188) The source gets stuck on these commits, until the “keep-alive” timeout expires. I cannot lower a lot this keep-alive, as the lowest value seems to be 1 second. (Unit is seconds). To put it in a nutshell, I don’t know if this behavior is expected, but if one Channel is filling up (at least a MemoryChannel), as per my understand it will impact any other channel linked to the same source, and will impact the Source itself. Do you see any way to prevent a Source from being impacted by the channel filling up ? In my specific scenario, I would prefer losing some events, or at least keep the other channels working. PS: I’m using Flume 1.5 for these tests. Regards From: Hari Shreedharan [mailto:[email protected]] Sent: jeudi 13 novembre 2014 22:04 To: [email protected]<mailto:[email protected]> Cc: [email protected]<mailto:[email protected]> Subject: Re: All channels in an agent get slower after a channel is full Yeah, when you are sharing disks — that would cause one channel’s behavior affect others since your disk is your bottleneck. Thanks, Hari On Thu, Nov 13, 2014 at 1:02 PM, Vincentius Martin <[email protected]<mailto:[email protected]>> wrote: Right now, I am using FileChannel. Thanks Regards, Vincentius Martin On Fri, Nov 14, 2014 at 4:00 AM, Hari Shreedharan <[email protected]<mailto:[email protected]>> wrote: Are you using MemoryChannel or File Channel? Thanks, Hari On Thu, Nov 13, 2014 at 12:59 PM, Vincentius Martin <[email protected]<mailto:[email protected]>> wrote: Yes, they are sharing the same disk I used to try it with memory channel, it also produced the same impact when a channel in an agent with many channels reaches its channel capacity. It caused ChannelException and made other channels slower. Regards, Vincentius Martin On Fri, Nov 14, 2014 at 3:47 AM, Hari Shreedharan <[email protected]<mailto:[email protected]>> wrote: Are all the channels sharing the same disk(s)? Thanks, Hari On Thu, Nov 13, 2014 at 12:44 PM, Vincentius Martin <[email protected]<mailto:[email protected]>> wrote: it is between agents, I am using avro sinks and file channels while all of those channels write the checkpoint to a disk. For the rest, I am using default configuration. Regards, Vincentius Martin On Fri, Nov 14, 2014 at 1:39 AM, Hari Shreedharan <[email protected]<mailto:[email protected]>> wrote: What does your configuration look like? What sink are you using? On Thu, Nov 13, 2014 at 8:23 AM, Vincentius Martin <[email protected]<mailto:[email protected]>> wrote: Hi, In my cluster, I have an agent with one source connected to multiple channels. Each channel connected to different sink (1 channel paired with 1 sink) which send events to different agents (like one to many relation). Just like the multiplexing flow example in Flume user guide website. However, when a channel reaches its capacity (already full) I see that the agent performance gets slower. What I mean by getting slower is that, all other channel-sink pairs in that agent also get slower when sending events to their destination. I can understand if the overfilled channel-sink pair get slower, but why it affects another channel-sink pairs in that agent? From what I see here, the other pairs should be independent with the overfilled channel except that they use the same source, right? Thanks! Regards, Vincentius Martin ________________________________ This message is for the designated recipient only and may contain privileged, proprietary, or otherwise confidential information. If you have received it in error, please notify the sender immediately and delete the original. Any other use of the e-mail by you is prohibited. 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