This sounds like it should work fine. LinkedIn keeps the majority of
things for 7 days. Performance is linear in data size and we have
validated performance up to many TB of data per machine.
The registry you describe sounds like it could potentially be useful.
You would probably have to describe it in more detail for others to
understand all the use cases.
On Thu, Feb 21, 2013 at 6:47 PM, graham sanderson <[EMAIL PROTECTED]> wrote:
> Apologies for asking another question as a newbie without having really tried stuff out, but actually one of our main reasons for wanting to use kafka (not the linkedin use case) is exactly the fact that the "buffer" is not just for buffering. We want to keep data for days to weeks, and be able to add ad-hoc consumers after the fact (obviously we could do that based on downstream systems in HDFS), however lets say we have N machines gathering approximate runtime statistics to use real time in live web applications; it is easy for them to listen to the stream destined for HDFS and keep such stats. If we have to add a new machine, or one dies etc. it totally makes sense to use the same code and just have it replay the last H hours of events to get back up to speed.
> So I'm curious if as this thread suggests that there are problems with keeping days to weeks of data around them and accessing them.
> Note also we are considering using kafka for (continuous/on demand) high performance instrumentation at which point we may not actually have any consumers until we need them (we would want a back-window to produce debug logs from the event stream after the fact, or replay events into other systems), but equally the real time feed may be used for alerting and graphite. Also we might eventually allow ad-hoc queries against data in the event stream, which may require us to turn event generation on/off in the producers, but nonetheless we would efficiently filter the kafka event stream based on arbitrary data - something that can't be done with topic today (even the suggested hierarchical topics) - if we do it right, we can use a schema/producer registry to figure out a small subset of topics that might contain the data we need, then use the schema registry to pick the AVRO schema used to efficiently filter that subset of topics based on any arbitrary set of attributes in the data.
> If the latter sounds useful to anyone then we'll of course contribute back - I'm also curious on the current state of camel etc, since we were already considering building something similar, but it seems like it isn't currently (as of recent open source) zookeeper based which seems odd, but also we are certainly considering allowing for mixing in more dynamic registration where value isn't just schema, but schema + other contextual information common to all events from a producer (e.g. source machine, application, app version etc).
> On Feb 21, 2013, at 7:26 PM, Jay Kreps <[EMAIL PROTECTED]> wrote:
>> You can do this and it should work fine. You would have to keep adding
>> machines to get disk capacity, of course, since your data set would
>> only grow.
>> We will keep an open file descriptor per file, but I think that is
>> okay. Just set the segment size to 1GB, then with 10TB of storage that
>> is only 10k files which should be fine. Adjust the OS open FD limit up
>> a bit if needed. File descriptors don't use too much memory so this
>> should not hurt anything.
>> On Thu, Feb 21, 2013 at 4:00 PM, Anthony Grimes <[EMAIL PROTECTED]> wrote:
>>> Our use case is that we'd like to log data we don't need away and
>>> potentially replay it at some point. We don't want to delete old logs. I
>>> googled around a bit and I only discovered this particular post:
>>> http://mail-archives.apache.org/mod_mbox/incubator-kafka-users/201210.mbox/%[EMAIL PROTECTED]%3E
>>> In summary, it appears the primary issue is that Kafka keeps file handles of