Hi Till,
Keys are unbounded (a group of events have same key but that key doesnt repeat
after it is fired other than some odd delayed events). So basically there 1 key
that will be aligned to a window. When you say key space of active windows,
does that include keys for windows that have already fired and could be in
memory footprint? If so, that is basically the problem I would get into and
looking for a solution to clean-up. Like I said earlier overriding tigger to
FIRE_AND_PURGE did not help. If I take the same stream and key and refactor it
to how Chang is doing it with Process Function, issue goes away.
If you mean only currently processing key space of active windows (not the ones
that have already fired) then I would say, that cannot be the case. We are
getting the data from period poll of same number of devices and uniqueness of
key is simply a time identifier prefixed to device identifier. Even though
there could be a little delayed data, the chances of number of unique keys
growing constantly for days is probably none as device list is constant.
Thanks, Ashish
- Ashish
On Tuesday, July 31, 2018, 4:05 AM, Till Rohrmann <trohrm...@apache.org> wrote:
Hi Ashish,
the processing time session windows need to store state in the StateBackends
and I assume that your key space of active windows is constantly growing. That
could explain why you are seeing an ever increasing memory footprint. But
without knowing the input stream and what the UDFs do this is only a guess.
Cheers,Till
On Mon, Jul 30, 2018 at 1:43 PM Fabian Hueske <fhue...@gmail.com> wrote:
Hi Chang,
The state handle objects are not created per key but just once per function
instance.
Instead they route state accesses to the backend (JVM heap or RocksDB) for the
currently active key.
Best, Fabian
2018-07-30 12:19 GMT+02:00 Chang Liu <fluency...@gmail.com>:
Hi Andrey,
Thanks for your reply. My question might be silly, but there is still one part
I would like to fully understand. For example, in the following example:
class MyFunction extends KeyedProcessFunction[String, Click, Click] { // keyed
by Session ID
lazy val userId: ValueState[String] = getRuntimeContext.getState(
new ValueStateDescriptor[String]("userId", BasicTypeInfo.STRING_TYPE_INFO))
lazy val clicks: ListState[Click] = getRuntimeContext.getListState(
new ListStateDescriptor[Click]("clicks", createTypeInformation[Click]))
override def processElement(
click: Click,
context: KeyedProcessFunction[String, Click, Click]#Context,
out: Collector[Click])
: Unit = {
// process, output, clear state if necessary
}
override def onTimer(
timestamp: Long,
ctx: KeyedProcessFunction[String, Click, Click]#OnTimerContext,
out: Collector[Click])
: Unit = {
// output and clear state
}
}
Even though I am regularly clearing the two states, userId and clicks (which
means I am cleaning up the values stored in the States), my question is: then
what about the two State objects themselves: userId and clicks? These States
objects are also created per Session ID right? If the number of Session IDs are
unbounded, than the number of these State objects are also unbounded.
That means, there are userId-state-1 and clicks-state-1 for session-id-1,
userId-state-2 and clicks-state-2 for session-id-2, userId-state-3 and
clicks-state-3 for session-id-3, …, which are handled by different (or same if
two from different range, as you call it, are assigned to the same one) keyed
operator instance.
I am not concerning the actual value in the State (which will be managed
carefully, if I am clearing them carefully). I am thinking about the State
objects themselves, which I have no idea what is happening to them and what
will happen to them.
Many thanks :)
Best regards/祝好,
Chang Liu 刘畅
On 26 Jul 2018, at 10:55, Andrey Zagrebin <and...@data-artisans.com> wrote:
Hi Chang Liu,
The unbounded nature of the stream keyed or not should not lead to out of
memory.
Flink parallel keyed operator instances have fixed number (parallelism) and
just process some range of keyed elements, in your example it is a subrange of
session ids.
The keyed processed elements (http requests) are objects created when they
enter the pipeline and garage collected after having been processed in
streaming fashion.
If they arrive very rapidly it can lead to high back pressure from upstream to
downstream operators, buffers can become full and pipeline stops/slows down
processing external inputs, it usually means that your pipeline is under
provisioned.
The only accumulated data comes from state (windows, user state etc), so if you
control its memory consumption, as Till described, there should be no other
source of out of memory.
Cheers,Andrey
On 25 Jul 2018, at 19:06, Chang Liu <fluency...@gmail.com> wrote:
Hi Till,
Thanks for your reply. But I think maybe I did not make my question clear. My
question is not about whether the States within each keyed operator instances
will run out of memory. My question is about, whether the unlimited keyed
operator instances themselves will run out of memory.
So to reply to your answers, no matter using different State backends or
regularly cleaning up the States (which is exactly what I am doing), it does
not concern the number of keyed operator instances.
I would like to know:
- Will the number of keyed operator instances (Java objects?) grow
unbounded?
- If so, will they run out of memory? This is not actually related to the
memory used by the keyed Stated inside.
- If not, then how Flink is managing this multiple keyed operator instances?
I think this needs more knowledge about how Flink works internally to
understand how keyed operator instances are created, maintained and destroyed.
That’s why I would like your help understanding this.
Many Thanks.
Best regards/祝好,
Chang Liu 刘畅
On 24 Jul 2018, at 14:31, Till Rohrmann <trohrm...@apache.org> wrote:
Hi Chang Liu,
if you are dealing with an unlimited number of keys and keep state around for
every key, then your state size will keep growing with the number of keys. If
you are using the FileStateBackend which keeps state in memory, you will
eventually run into an OutOfMemoryException. One way to solve/mitigate this
problem is to use the RocksDBStateBackend which can go out of core.
Alternatively, you would need to clean up your state before you run out of
memory. One way to do this is to register for every key a timer which clears
the state. But this only works if you don't amass too much state data before
the timer is triggered. If you wish this solution is some kind of a poor man's
state TTL. The Flink community is currently developing a proper implementation
of it which does not rely on additional timers (which increases the state
footprint) [1].
[1] https://issues.apache.org/jira/browse/FLINK-9510
Cheers,Till
On Tue, Jul 24, 2018 at 10:11 AM Chang Liu <fluency...@gmail.com> wrote:
Dear All,
I have questions regarding the keys. In general, the questions are:
- what happens if I am doing keyBy based on unlimited number of keys? How
Flink is managing each KeyedStream under the hood? Will I get memory overflow,
for example, if every KeyStream associated with a specific key is taking
certain amount of memory?
- BTW, I think it is fare to say that, I have to clear my KeyedState so that
the memory used by these State are cleaned up regularly. But still, I am
wondering, even though I am regularly cleaning up State memory, what happened
to memory used by the KeyedStream itself, if there is? And will they be
exploding?
Let me give an example for understanding it clearly. Let’s say we have a
val requestStream: DataStream[HttpRequest]
which is a stream of HTTP requests. And by using the session ID as the key, we
can obtain a KeyedStream per single session, as following:
val streamPerSession: KeyedStream[HttpRequest] =
requestStream.keyBy(_.sessionId)
However, the session IDs are actually a hashcode generated randomly by the Web
service/application, so that means, the number of sessions are unlimited (which
is reasonable, because every time a user open the application or login, he/she
will get a new unique session).
Then, the question is: will Flink eventually run out of memory because the
number of sessions are unlimited (and because we are keying by the session ID)?
- If so, how can we properly manage this situation?
- If not, could you help me understand WHY?
- Let’s also assume that, we are regularly clearing the KeyedState, so the
memory used by the State will not explode.
Many Thanks and Looking forward to your reply :)
Best regards/祝好,
Chang Liu 刘畅
