You can also add another HDFS sink on the collector. Make sure to give it a different file prefix and bind it to the same channel as the existing sink. You won’t need a sink group for this as both sinks will pull from the same channel. I don’t need 2 HDFS writers on my collectors under normal use but it helps when there’s been a backlog for some reason.
There're two parts in our deployment(flume-agent, flume-collector), we have quite a lot of flume-agents collect logs and send to several flume-collectors. There is no problem with the flume-agent as it can send as fast as the logs generated. But when the collector receive the logs, they always stick in the channel as the hdfs-sink can not write fast enough. So the problem we face now is how to increase the writing speed to hdfs. The attachment is our configuration of flume-collector. Thanks
several tips we've tried:
increase flume-collector amount
increase channel size and and transaction size
increase hdfs batch-size
On Tue, Nov 5, 2013 at 6:27 AM, Paul Chavez <email@example.com> wrote:
What do you mean by ‘log jam’? Do you mean events are stuck in the channel and all processing stops, or just that events are moving slower than you’d like?
If it’s just going slowly I would start by graphing channel sizes, and event put/take rates for your sinks. This will show you which sink might need to be sped up, either by having multiple sinks drain the same channel, tweaking batch sizes or moving any filechannels to dedicated disks.
If it’s events getting stuck in the channel due to missing headers or corrupt data, I would use interceptors to ensure the necessary headers are applied. For instance, I use a couple of ‘category’ headers to route event in downstream agents and on the initial source have a static interceptor that puts in the proper header with the value ‘missing’ if the header doesn’t exist from the app. Then I can ensure delivery and also have a bucket in HDFS that I can monitor to ensure no events are getting lost.
As for your nightly processing, if you use Oozie to trigger workflows you can set dataset dependencies to prevent things from running until the data is ready. I have hourly workflows that run this way, they don’t trigger until the current partition exists and then they process the previous partition.
From: chenchun [mailto:firstname.lastname@example.org]
Sent: Monday, November 04, 2013 3:35 AM
Subject: logs jams in flume collector
Hi, we are using flume to transfer logs to hdfs. We find lots of logs jams in flume collector. if the generated logs can't write into hdfs by middle night, our daily report will not be calculated in time. Any suggestions to identify the bottlenecks written hdfs?
have a good day!