Hi, I am sharing the last doc, which is about progress monitor (a little deferred sharing by my vacation): https://docs.google.com/document/d/1Ov9A7V2tMs4uVimcSeHL5eftRJ3MCJBiVSFNdz8rmjU/edit?usp=sharing
This last one seems like pretty independent from the first two (execution tracking and exception classifier), so it could be completely decoupled. We may want to leave this proposal, apart from the first two, more discussed here in dev list rather than diving deeper into implementation. (there are obviously more several challenging things) Thanks, Hwanju 2019년 5월 28일 (화) 오후 11:41, Hwanju Kim <hwand...@gmail.com>님이 작성: > (Somehow my email has failed to be sent multiple times, so I am using my > personal email account) > > Hi, > > Piotrek - Thanks for the feedback! I revised the doc as commented. > > Here's the second part about exception classification - > https://docs.google.com/document/d/1pcHg9F3GoDDeVD5GIIo2wO67Hmjgy0-hRDeuFnrMgT4/edit?usp=sharing > I put cross-links between the first and the second. > > Thanks, > Hwanju > > 2019년 5월 24일 (금) 오전 3:57, Piotr Nowojski <pi...@ververica.com>님이 작성: > >> Hi Hwanju, >> >> I looked through the document, however I’m not the best person to >> review/judge/discuss about implementation details here. I hope that Chesney >> will be able to help in this regard. >> >> Piotrek >> >> > On 24 May 2019, at 09:09, Kim, Hwanju <hwanj...@amazon.com.INVALID> >> wrote: >> > >> > Hi, >> > >> > As suggested by Piotrek, the first part, execution state tracking, is >> now split to a separate doc: >> > >> https://docs.google.com/document/d/1oLF3w1wYyr8vqoFoQZhw1QxTofmAtlD8IF694oPLjNI/edit?usp=sharing >> > >> > We'd appreciate any feedback. I am still using the same email thread to >> provide a full context, but please let me know if it's better to have a >> separate email thread as well. We will be sharing the remaining parts once >> ready. >> > >> > Thanks, >> > Hwanju >> > >> > On 5/17/19, 12:59 AM, "Piotr Nowojski" <pi...@ververica.com> wrote: >> > >> > 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 >> >>> >> >> >> >> >> >> >> > >> > >> > >> >>
