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From iain wright <iainw...@gmail.com>
Subject Re: Is it a good idea to use Flume Interceptor to process data?
Date Thu, 28 Jul 2016 05:40:24 GMT
You likely want to pose the ZK questions on the zookeeper list. I know I've seen folks have
problems when receiving >1MB of data in a response, and definitely problems with > 200k
children of a znode

That said I've used it with hbase 0.94-98 with ~20k regions without issue, I believe region
severs use watchers vs polling 

How often do the formulas change? Below doc states there is a potential race condition or
gap in events with watchers, in that you need to set an additional watcher after receiving
an event

Maybe it would be possible to use on heap cache, pub sub queue, and DB as a source of truth?
It's a pattern that has worked for us , although not in the context of flume

If you don't have the formula in cache go to DB (then cache it).
If you do have the formula in cache use it.
If something changes the formula, it writes to the DB and publishes a message to a topic that
all agents listen on, and agents change their formula based on the published message. 

The caveat being if an agent ever disconnects from the pubsub topic, to either self murder
or go to the DB every time


Sent from my iPhone

> On Jul 27, 2016, at 8:57 PM, Thanh Hong Dai <hdthanh@tma.com.vn> wrote:
> Hi,
> We actually attach the Interceptor to the source, as you have said. Sorry for the confusion.
> (I also found out that I wrote “other streaming processing frameworks such as Spark
of Kafka”, which should be read as “other streaming processing frameworks such as Spark
or Storm”)
> Thanks for the suggestion about Zookeeper. We are aware of the configuration storage
functionality of Zookeeper, but we don’t have much experience using it. Would storing around
5000 formula (usually simple ones, less than 100 bytes) affect the overall performance of
Zookeeper? To detect update, there are 2 approaches: poll all the formulas, or use watcher.
Which approach would be better?
> The monitoring data is not latency sensitive – the process that put the data of the
last hour into Kafka only runs at 5th or 10th minute of the hour. We are allowed to take one
more hour to process the data (which means that we can see the 8AM data at 10AM at the latest).
> Best regards,
> Thanh Hong.
> From: Chris Horrocks [mailto:chris@hor.rocks] 
> Sent: Wednesday, 27 July, 2016 7:28 PM
> To: user@flume.apache.org
> Subject: Re: Is it a good idea to use Flume Interceptor to process data?
> Some rough initial thoughts:
> This is interesting but you might need to elaborate on how you've achieved attaching
an interceptor to a channel (and why, in lieu of attaching it to the source):
> we attach the Interceptor to the channel
> Personally I'd have done this by feeding data into Spark Streaming and keeping flume
as low overhead as possible, particularily if it's monitoring data that's latency sensitive.
For storing the calculations variables for consumption by the interceptor I'd go with something
like ZooKeeper. 
> --
> Chris Horrocks
> On Wed, Jul 27, 2016 at 12:39 pm, Thanh Hong Dai <'hdthanh@tma.com.vn'> wrote:
> Hi,
> To give some background: We are currently buffering monitoring data into Kafka, where
each message in Kafka records several metrics at a point in time.
> For each of the record, we need to perform some calculation based on the metrics in the
record, append the results (multiple of them) to the record and send the resulting record
into a data store (let’s call it DS1). All data required for the calculation are encapsulated
in the record, essentially making this an embarrassingly parallel problem.
> The formula for the calculation is stored in a different data store (let’s call it
DS2), and can be changed (add/delete/modified by user). We are not required to react to the
change immediately, but we should do so in reasonable time (e.g. 5 minutes).
> Currently, we have prototyped an implementation which implements the data processing
as described above in an Interceptor. We define the source as Kafka, the Sink as the sink
for DS2, and we attach the Interceptor to the channel. As described above, the Interceptor
will be reading the formula from DS1 regularly for any change, and will be responsible for
processing the data as they come in from Kafka.
> We are aware of other streaming processing frameworks such as Spark of Kafka. However,
the implementation above is motivated by the fact that Flume has provided reliable streaming,
and we want to reuse as much code as possible.
> Is this usage of Flume a good idea in term of performance and scalability?
> Best regards,
> Hong Dai Thanh.

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