Hi Hwanju & Chesney, Regarding various things that both of you mentioned, like accounting of state restoration separately or batch scheduling, we can always acknowledge some limitations of the initial approach and maybe we can address them later if we evaluate it worth the effort.
Generally speaking all that you have written make sense to me, so +1 from my side to split the discussion into separate threads. Piotrek > On 17 May 2019, at 08:57, Kim, Hwanju <hwanj...@amazon.com.INVALID> wrote: > > Hi Piotrek, > > Thanks for insightful feedback and indeed you got most tricky parts and > concerns. > >> 1. Do we currently account state restore as “RUNNING”? If yes, this might be >> incorrect from your perspective. > > As Chesnay said, initializeState is called in StreamTask.invoke after > transitioning to RUNNING. So, task state restore part is currently during > RUNNING. I think accounting state restore as running seems fine, since state > size is user's artifact, as long as we can detect service error during > restore (indeed, DFS issue usually happens at createCheckpointStorage (e.g., > S3 server error) and RocksDB issue happens at initializeState in > StreamTask.invoke). We can discuss about the need to have separate state to > track restore and running separately, but it seems to add too many messages > in common paths just for tracking. > >> 2a. This might be more tricky if various Tasks are in various stages. For >> example in streaming, it should be safe to assume that state of the job, is >> “minimum” of it’s Tasks’ states, so Job should be accounted as RUNNING only >> if all of the Tasks are either RUNNING or COMPLETED. > > Right. For RUNNING, all the tasks in the graph transitions to RUNNING. For > others, when the first task transitions to SCHEDULED, SCHEDULING stage > begins, and when the first task transitions to DEPLOYING, it starts DEPLOYING > stage. This would be fine especially for eager scheduling and full-restart > fail-over strategy. In the individual or partial restart, we may not need to > specifically track SCHEDULING and DEPLOYING states while treating job as > running relying on progress monitor. > >> 2b. However in batch - including DataStream jobs running against bounded >> data streams, like Blink SQL - this might be more tricky, since there are >> ongoing efforts to schedule part of the job graphs in stages. For example do >> not schedule probe side of the join until build side is done/completed. > > Exactly. I have roughly looked at batch side, but not in detail yet and am > aware of ongoing scheduling work. Initial focus of breaking out to multiple > states like scheduling/deploying would be only for streaming with eager > scheduling. Need to give more thought how to deal with batching/lazy > scheduling. > >> What exactly would you like to report here? List of exception with downtime >> caused by it, for example: exception X caused a job to be down for 13 >> minutes, 1 minute in scheduling, 1 minute deploying, 11 minutes state >> restore? > > Basically, initial cause is traced back from each component of downtime, > which is accounted to a certain type like user or system based on the > classification. So you're right. Interesting part here is about secondary > failure. For example, a user error causes a job to restart but then > scheduling is failed by system issue. We need to account failing, restarting > time to user, while scheduling time on restart (e.g,. 5min timeout) is to > system. A further example is that a system error causes a job to be failing, > but one of the user function is not reacting to cancellation (for > full-restart), prolonged failing time (e.g., watchdog timeout 3min) shouldn’t > be accounted to system (of course, the other way around has been seen -- > e.g., FLINK-5463). > >> Why do you think about implementing classifiers? Couldn’t we classify >> exceptions by exception type, like `FlinkUserException`, >> `FlinkNetworkException`, `FlinkStateBackendException` … and make sure that >> we throw correct exception types + handle/wrap exceptions correctly when >> crossing Flink system/user code border? This way we could know exactly >> whether exception occurred in the user code or in Flink code. > > I think classifier here is complementary to exception type approach. In this > context, classifier is "f(exception) -> type". Type is used as metric > dimension to set alert on certain types or have downtime breakdown on each > type (type is not just fixed to "user" or "system" but can be more specific > and customizable like statebackend and network). If we do wrap exceptions > perfectly as you said, f() is simple enough to look at Exception type and > then return its corresponding type. > > Initially we also thought complete wrapping would be ideal. However, even > inside UDF, it can call in Flink framework like state update or call out > dependent services, which service provider may want to classify separately. > In addition, Flink allows user to use lower level API like streamoperator to > make the border a little blurring. Those would make complete wrapping > challenging. Besides, stack-based classification beyond exception type could > still be needed for stuck progress classification. > > Without instrumentation, one of base classifiers that work for our > environment in many cases is user-class-loader classifier, which can detect > if an exception is thrown from the class loaded from user JAR/artifact > (although this may be less desirable in an environment where user's artifacts > can be installed directly in system lib/, but service providers would be > opting in self-contained jar submission keeping system environment for > system-only). > >> One thing that might be tricky is if error in Flink code is caused by >> user’s mistake. > > Right, this is the trickiest part. Based on our analysis with real data, the > most ambiguous ones are custom serialization and out-of-resource errors. The > former is usually seen in Flink runtime code rather than in UDF. The latter > is that Flink stack is just a victim by resource hog/leak of user code (OOM, > too many open files). For the serialization issue, we've been looking at (and > learning) various serialization errors seen in the field to get reasonable > classification. For the out-of-resource, rather than user vs. system > classification, we can tag the type as "resource" relying on dump (e.g., heap > dump) and postmortem analysis as-needed basis. > >> Hmmm, this might be tricky. We can quite easily detect which exact Task is >> causing back pressure in at least couple of different ways. Tricky part >> would be to determine whether this is caused by user or not, but probably >> some simple stack trace probing on back pressured task once every N seconds >> should solve this - similar how sampling profilers work. > > Again you're right and like you said, this part would be mostly reusing the > existing building blocks such as latency marker and backpressure samplings. > If configured only with progress monitoring not latency distribution > tracking, latency marker can be lightweight skipping histogram update part > just updating latest timestamp with longer period not to adversely affect > performance. Once stuck progress is detected, stack sampling can tell us more > about the context that causes backpressure. > >> Luckily it seems like those four issues/proposals could be >> implemented/discussed independently or in stages. > Agreed. Once some level of initial discussion clears things out at least high > level, I can start out more independent threads. > > Best, > Hwanju > > On 5/16/19, 2:44 AM, "Piotr Nowojski" <pi...@ververica.com> wrote: > > Hi Hwanju, > > Thanks for starting the discussion. Definitely any improvement in this > area would be very helpful and valuable. Generally speaking +1 from my side, > as long as we make sure that either such changes do not add performance > overhead (which I think they shouldn’t) or they are optional. > >> Firstly, we need to account time for each stage of task execution such as >> scheduling, deploying, and running, to enable better visibility of how long >> a job takes in which stage while not running user functions. > > Couple of questions/remarks: > 1. Do we currently account state restore as “RUNNING”? If yes, this might > be incorrect from your perspective. > 2a. This might be more tricky if various Tasks are in various stages. For > example in streaming, it should be safe to assume that state of the job, is > “minimum” of it’s Tasks’ states, so Job should be accounted as RUNNING only > if all of the Tasks are either RUNNING or COMPLETED. > 2b. However in batch - including DataStream jobs running against bounded > data streams, like Blink SQL - this might be more tricky, since there are > ongoing efforts to schedule part of the job graphs in stages. For example do > not schedule probe side of the join until build side is done/completed. > >> Secondly, any downtime in each stage can be associated with a failure cause, >> which could be identified by Java exception notified to job manager on task >> failure or unhealthy task manager (Flink already maintains a cause but it >> can be associated with an execution stage for causal tracking) > > What exactly would you like to report here? List of exception with > downtime caused by it, for example: exception X caused a job to be down for > 13 minutes, 1 minute in scheduling, 1 minute deploying, 11 minutes state > restore? > >> Thirdly, downtime reason should be classified into user- or system-induced >> failure. This needs exception classifier by drawing the line between >> user-defined functions (or public API) and Flink runtime — This is >> particularly challenging to have 100% accuracy at one-shot due to empirical >> nature and custom logic injection like serialization, so pluggable >> classifier filters are must-have to enable incremental improvement. > > Why do you think about implementing classifiers? Couldn’t we classify > exceptions by exception type, like `FlinkUserException`, > `FlinkNetworkException`, `FlinkStateBackendException` … and make sure that we > throw correct exception types + handle/wrap exceptions correctly when > crossing Flink system/user code border? This way we could know exactly > whether exception occurred in the user code or in Flink code. > > One thing that might be tricky is if error in Flink code is caused by > user’s mistake. > > >> Fourthly, stuck progress > > Hmmm, this might be tricky. We can quite easily detect which exact Task is > causing back pressure in at least couple of different ways. Tricky part would > be to determine whether this is caused by user or not, but probably some > simple stack trace probing on back pressured task once every N seconds should > solve this - similar how sampling profilers work. > > Luckily it seems like those four issues/proposals could be > implemented/discussed independently or in stages. > > Piotrek > >> On 11 May 2019, at 06:50, Kim, Hwanju <hwanj...@amazon.com.INVALID> wrote: >> >> Hi, >> >> I am Hwanju at AWS Kinesis Analytics. We would like to start a discussion >> thread about a project we consider for Flink operational improvement in >> production. We would like to start conversation early before detailed >> design, so any high-level feedback would welcome. >> >> For service providers who operate Flink in a multi-tenant environment, such >> as AWS Kinesis Data Analytics, it is crucial to measure application health >> and clearly differentiate application unavailability issue caused by Flink >> framework or service environment from the ones caused by application code. >> The current metrics of Flink represent overall job availability in time, it >> still needs to be improved to give Flink operators better insight for the >> detailed application availability. The current availability metrics such as >> uptime and downtime measures the time based on the running state of a job, >> which does not necessarily represent actual running state of a job (after a >> job transitions to running, each task should still be scheduled/deployed in >> order to run user-defined functions). The detailed view should enable >> operators to have visibility on 1) how long each specific stage takes (e.g., >> task scheduling or deployment), 2) what failure is introduced in which stage >> leading to job downtime, 3) whether such failure is classified to user code >> error (e.g., uncaught exception from user-defined function) or >> platform/environmental errors (e.g., checkpointing issue, unhealthy nodes >> hosting job/task managers, Flink bug). The last one is particularly needed >> to allow Flink operators to define SLA where only a small fraction of >> downtime should be introduced by service fault. All of these visibility >> enhancements can help community detect and fix Flink runtime issues quickly, >> whereby Flink can become more robust operating system for hosting data >> analytics applications. >> >> The current proposal is as follows. Firstly, we need to account time for >> each stage of task execution such as scheduling, deploying, and running, to >> enable better visibility of how long a job takes in which stage while not >> running user functions. Secondly, any downtime in each stage can be >> associated with a failure cause, which could be identified by Java exception >> notified to job manager on task failure or unhealthy task manager (Flink >> already maintains a cause but it can be associated with an execution stage >> for causal tracking). Thirdly, downtime reason should be classified into >> user- or system-induced failure. This needs exception classifier by drawing >> the line between user-defined functions (or public API) and Flink runtime — >> This is particularly challenging to have 100% accuracy at one-shot due to >> empirical nature and custom logic injection like serialization, so pluggable >> classifier filters are must-have to enable incremental improvement. >> Fourthly, stuck progress, where task is apparently running but not being >> able to process data generally manifesting itself as long backpressure, can >> be monitored as higher level job availability and the runtime can determine >> whether the reason to be stuck is caused by user (e.g., under-provisioned >> resource, user function bug) or system (deadlock or livelock in Flink >> runtime). Finally, all the detailed tracking information and metrics are >> exposed via REST and Flink metrics, so that Flink dashboard can have >> enhanced information about job execution/availability and operators can set >> alarm appropriately on metrics. >> >> Best, >> Hwanju >> > > >
