Hi Cody, From my understanding rate control is an optional configuration in Spark Streaming and is disabled by default, so user can reach maximum throughput without any configuration.
The reason why rate control is so important in streaming processing is that Spark Streaming and other streaming frameworks are easily prone to unexpected behavior and failure situation due to network boost and other un-controllable inject rate. Especially for Spark Streaming, the large amount of processed data will delay the processing time, which will further delay the ongoing job, and finally lead to failure. Thanks Jerry From: Cody Koeninger [mailto:c...@koeninger.org] Sent: Tuesday, December 30, 2014 6:50 AM To: Tathagata Das Cc: Hari Shreedharan; Shao, Saisai; Sean McNamara; Patrick Wendell; Luis Ángel Vicente Sánchez; Dibyendu Bhattacharya; dev@spark.apache.org; Koert Kuipers Subject: Re: Which committers care about Kafka? Can you give a little more clarification on exactly what is meant by 1. Data rate control If someone wants to clamp the maximum number of messages per RDD partition in my solution, it would be very straightforward to do so. Regarding the holy grail, I'm pretty certain you can't have end-to-end transactional semantics without the client code being in charge of offset state. That means the client code is going to also need to be in charge of setting up an initial state for updateStateByKey that makes sense; as long as they can do that, the job should be safe to restart from arbitrary failures. On Mon, Dec 29, 2014 at 4:33 PM, Tathagata Das <tathagata.das1...@gmail.com<mailto:tathagata.das1...@gmail.com>> wrote: Hey all, Some wrap up thoughts on this thread. Let me first reiterate what Patrick said, that Kafka is super super important as it forms the largest fraction of Spark Streaming user base. So we really want to improve the Kafka + Spark Streaming integration. To this end, some of the things that needs to be considered can be broadly classified into the following to sort facilitate the discussion. 1. Data rate control 2. Receiver failure semantics - partially achieving this gives at-least once, completely achieving this gives exactly-once 3. Driver failure semantics - partially achieving this gives at-least once, completely achieving this gives exactly-once Here is a run down of what is achieved by different implementations (based on what I think). 1. Prior to WAL in Spark 1.2, the KafkaReceiver could handle 3, could handle 1 partially (some duplicate data), and could NOT handle 2 (all previously received data lost). 2. In Spark 1.2 with WAL enabled, the Saisai's ReliableKafkaReceiver can handle 3, can almost completely handle 1 and 2 (except few corner cases which prevents it from completely guaranteeing exactly-once). 3. I believe Dibyendu's solution (correct me if i am wrong) can handle 1 and 2 perfectly. And 3 can be partially solved with WAL, or possibly completely solved by extending the solution further. 4. Cody's solution (again, correct me if I am wrong) does not use receivers at all (so eliminates 2). It can handle 3 completely for simple operations like map and filter, but not sure if it works completely for stateful ops like windows and updateStateByKey. Also it does not handle 1. The real challenge for Kafka is in achieving 3 completely for stateful operations while also handling 1. (i.e., use receivers, but still get driver failure guarantees). Solving this will give us our holy grail solution, and this is what I want to achieve. On that note, Cody submitted a PR on his style of achieving exactly-once semantics - https://github.com/apache/spark/pull/3798 . I am reviewing it. Please follow the PR if you are interested. TD On Wed, Dec 24, 2014 at 11:59 PM, Cody Koeninger <c...@koeninger.org<mailto:c...@koeninger.org>> wrote: > The conversation was mostly getting TD up to speed on this thread since he > had just gotten back from his trip and hadn't seen it. > > The jira has a summary of the requirements we discussed, I'm sure TD or > Patrick can add to the ticket if I missed something. > On Dec 25, 2014 1:54 AM, "Hari Shreedharan" > <hshreedha...@cloudera.com<mailto:hshreedha...@cloudera.com>> > wrote: > >> In general such discussions happen or is posted on the dev lists. Could >> you please post a summary? Thanks. >> >> Thanks, >> Hari >> >> >> On Wed, Dec 24, 2014 at 11:46 PM, Cody Koeninger >> <c...@koeninger.org<mailto:c...@koeninger.org>> >> wrote: >> >>> After a long talk with Patrick and TD (thanks guys), I opened the >>> following jira >>> >>> https://issues.apache.org/jira/browse/SPARK-4964 >>> >>> Sample PR has an impementation for the batch and the dstream case, and a >>> link to a project with example usage. >>> >>> On Fri, Dec 19, 2014 at 4:36 PM, Koert Kuipers >>> <ko...@tresata.com<mailto:ko...@tresata.com>> wrote: >>> >>>> yup, we at tresata do the idempotent store the same way. very simple >>>> approach. >>>> >>>> On Fri, Dec 19, 2014 at 5:32 PM, Cody Koeninger >>>> <c...@koeninger.org<mailto:c...@koeninger.org>> >>>> wrote: >>>>> >>>>> That KafkaRDD code is dead simple. >>>>> >>>>> Given a user specified map >>>>> >>>>> (topic1, partition0) -> (startingOffset, endingOffset) >>>>> (topic1, partition1) -> (startingOffset, endingOffset) >>>>> ... >>>>> turn each one of those entries into a partition of an rdd, using the >>>>> simple >>>>> consumer. >>>>> That's it. No recovery logic, no state, nothing - for any failures, >>>>> bail >>>>> on the rdd and let it retry. >>>>> Spark stays out of the business of being a distributed database. >>>>> >>>>> The client code does any transformation it wants, then stores the data >>>>> and >>>>> offsets. There are two ways of doing this, either based on idempotence >>>>> or >>>>> a transactional data store. >>>>> >>>>> For idempotent stores: >>>>> >>>>> 1.manipulate data >>>>> 2.save data to store >>>>> 3.save ending offsets to the same store >>>>> >>>>> If you fail between 2 and 3, the offsets haven't been stored, you start >>>>> again at the same beginning offsets, do the same calculations in the >>>>> same >>>>> order, overwrite the same data, all is good. >>>>> >>>>> >>>>> For transactional stores: >>>>> >>>>> 1. manipulate data >>>>> 2. begin transaction >>>>> 3. save data to the store >>>>> 4. save offsets >>>>> 5. commit transaction >>>>> >>>>> If you fail before 5, the transaction rolls back. To make this less >>>>> heavyweight, you can write the data outside the transaction and then >>>>> update >>>>> a pointer to the current data inside the transaction. >>>>> >>>>> >>>>> Again, spark has nothing much to do with guaranteeing exactly once. In >>>>> fact, the current streaming api actively impedes my ability to do the >>>>> above. I'm just suggesting providing an api that doesn't get in the >>>>> way of >>>>> exactly-once. >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> On Fri, Dec 19, 2014 at 3:57 PM, Hari Shreedharan < >>>>> hshreedha...@cloudera.com<mailto:hshreedha...@cloudera.com> >>>>> > wrote: >>>>> >>>>> > Can you explain your basic algorithm for the once-only-delivery? It is >>>>> > quite a bit of very Kafka-specific code, that would take more time to >>>>> read >>>>> > than I can currently afford? If you can explain your algorithm a bit, >>>>> it >>>>> > might help. >>>>> > >>>>> > Thanks, >>>>> > Hari >>>>> > >>>>> > >>>>> > On Fri, Dec 19, 2014 at 1:48 PM, Cody Koeninger >>>>> > <c...@koeninger.org<mailto:c...@koeninger.org>> >>>>> > wrote: >>>>> > >>>>> >> >>>>> >> The problems you guys are discussing come from trying to store state >>>>> in >>>>> >> spark, so don't do that. Spark isn't a distributed database. >>>>> >> >>>>> >> Just map kafka partitions directly to rdds, llet user code specify >>>>> the >>>>> >> range of offsets explicitly, and let them be in charge of committing >>>>> >> offsets. >>>>> >> >>>>> >> Using the simple consumer isn't that bad, I'm already using this in >>>>> >> production with the code I linked to, and tresata apparently has >>>>> been as >>>>> >> well. Again, for everyone saying this is impossible, have you read >>>>> either >>>>> >> of those implementations and looked at the approach? >>>>> >> >>>>> >> >>>>> >> >>>>> >> On Fri, Dec 19, 2014 at 2:27 PM, Sean McNamara < >>>>> >> sean.mcnam...@webtrends.com<mailto:sean.mcnam...@webtrends.com>> wrote: >>>>> >> >>>>> >>> Please feel free to correct me if I’m wrong, but I think the exactly >>>>> >>> once spark streaming semantics can easily be solved using >>>>> updateStateByKey. >>>>> >>> Make the key going into updateStateByKey be a hash of the event, or >>>>> pluck >>>>> >>> off some uuid from the message. The updateFunc would only emit the >>>>> message >>>>> >>> if the key did not exist, and the user has complete control over >>>>> the window >>>>> >>> of time / state lifecycle for detecting duplicates. It also makes >>>>> it >>>>> >>> really easy to detect and take action (alert?) when you DO see a >>>>> duplicate, >>>>> >>> or make memory tradeoffs within an error bound using a sketch >>>>> algorithm. >>>>> >>> The kafka simple consumer is insanely complex, if possible I think >>>>> it would >>>>> >>> be better (and vastly more flexible) to get reliability using the >>>>> >>> primitives that spark so elegantly provides. >>>>> >>> >>>>> >>> Cheers, >>>>> >>> >>>>> >>> Sean >>>>> >>> >>>>> >>> >>>>> >>> > On Dec 19, 2014, at 12:06 PM, Hari Shreedharan < >>>>> >>> hshreedha...@cloudera.com<mailto:hshreedha...@cloudera.com>> wrote: >>>>> >>> > >>>>> >>> > Hi Dibyendu, >>>>> >>> > >>>>> >>> > Thanks for the details on the implementation. But I still do not >>>>> >>> believe >>>>> >>> > that it is no duplicates - what they achieve is that the same >>>>> batch is >>>>> >>> > processed exactly the same way every time (but see it may be >>>>> processed >>>>> >>> more >>>>> >>> > than once) - so it depends on the operation being idempotent. I >>>>> believe >>>>> >>> > Trident uses ZK to keep track of the transactions - a batch can be >>>>> >>> > processed multiple times in failure scenarios (for example, the >>>>> >>> transaction >>>>> >>> > is processed but before ZK is updated the machine fails, causing a >>>>> >>> "new" >>>>> >>> > node to process it again). >>>>> >>> > >>>>> >>> > I don't think it is impossible to do this in Spark Streaming as >>>>> well >>>>> >>> and >>>>> >>> > I'd be really interested in working on it at some point in the >>>>> near >>>>> >>> future. >>>>> >>> > >>>>> >>> > On Fri, Dec 19, 2014 at 1:44 AM, Dibyendu Bhattacharya < >>>>> >>> > dibyendu.bhattach...@gmail.com<mailto:dibyendu.bhattach...@gmail.com>> >>>>> >>> > wrote: >>>>> >>> > >>>>> >>> >> Hi, >>>>> >>> >> >>>>> >>> >> Thanks to Jerry for mentioning the Kafka Spout for Trident. The >>>>> Storm >>>>> >>> >> Trident has done the exact-once guarantee by processing the >>>>> tuple in a >>>>> >>> >> batch and assigning same transaction-id for a given batch . The >>>>> >>> replay for >>>>> >>> >> a given batch with a transaction-id will have exact same set of >>>>> >>> tuples and >>>>> >>> >> replay of batches happen in exact same order before the failure. >>>>> >>> >> >>>>> >>> >> Having this paradigm, if downstream system process data for a >>>>> given >>>>> >>> batch >>>>> >>> >> for having a given transaction-id , and if during failure if same >>>>> >>> batch is >>>>> >>> >> again emitted , you can check if same transaction-id is already >>>>> >>> processed >>>>> >>> >> or not and hence can guarantee exact once semantics. >>>>> >>> >> >>>>> >>> >> And this can only be achieved in Spark if we use Low Level Kafka >>>>> >>> consumer >>>>> >>> >> API to process the offsets. This low level Kafka Consumer ( >>>>> >>> >> https://github.com/dibbhatt/kafka-spark-consumer) has >>>>> implemented the >>>>> >>> >> Spark Kafka consumer which uses Kafka Low Level APIs . All of the >>>>> >>> Kafka >>>>> >>> >> related logic has been taken from Storm-Kafka spout and which >>>>> manages >>>>> >>> all >>>>> >>> >> Kafka re-balance and fault tolerant aspects and Kafka metadata >>>>> >>> managements. >>>>> >>> >> >>>>> >>> >> Presently this Consumer maintains that during Receiver failure, >>>>> it >>>>> >>> will >>>>> >>> >> re-emit the exact same Block with same set of messages . Every >>>>> >>> message have >>>>> >>> >> the details of its partition, offset and topic related details >>>>> which >>>>> >>> can >>>>> >>> >> tackle the SPARK-3146. >>>>> >>> >> >>>>> >>> >> As this Low Level consumer has complete control over the Kafka >>>>> >>> Offsets , >>>>> >>> >> we can implement Trident like feature on top of it like having >>>>> >>> implement a >>>>> >>> >> transaction-id for a given block , and re-emit the same block >>>>> with >>>>> >>> same set >>>>> >>> >> of message during Driver failure. >>>>> >>> >> >>>>> >>> >> Regards, >>>>> >>> >> Dibyendu >>>>> >>> >> >>>>> >>> >> >>>>> >>> >> On Fri, Dec 19, 2014 at 7:33 AM, Shao, Saisai < >>>>> saisai.s...@intel.com<mailto:saisai.s...@intel.com>> >>>>> >>> >> wrote: >>>>> >>> >>> >>>>> >>> >>> Hi all, >>>>> >>> >>> >>>>> >>> >>> I agree with Hari that Strong exact-once semantics is very hard >>>>> to >>>>> >>> >>> guarantee, especially in the failure situation. From my >>>>> >>> understanding even >>>>> >>> >>> current implementation of ReliableKafkaReceiver cannot fully >>>>> >>> guarantee the >>>>> >>> >>> exact once semantics once failed, first is the ordering of data >>>>> >>> replaying >>>>> >>> >>> from last checkpoint, this is hard to guarantee when multiple >>>>> >>> partitions >>>>> >>> >>> are injected in; second is the design complexity of achieving >>>>> this, >>>>> >>> you can >>>>> >>> >>> refer to the Kafka Spout in Trident, we have to dig into the >>>>> very >>>>> >>> details >>>>> >>> >>> of Kafka metadata management system to achieve this, not to say >>>>> >>> rebalance >>>>> >>> >>> and fault-tolerance. >>>>> >>> >>> >>>>> >>> >>> Thanks >>>>> >>> >>> Jerry >>>>> >>> >>> >>>>> >>> >>> -----Original Message----- >>>>> >>> >>> From: Luis Ángel Vicente Sánchez [mailto: >>>>> langel.gro...@gmail.com<mailto:langel.gro...@gmail.com>] >>>>> >>> >>> Sent: Friday, December 19, 2014 5:57 AM >>>>> >>> >>> To: Cody Koeninger >>>>> >>> >>> Cc: Hari Shreedharan; Patrick Wendell; >>>>> >>> >>> dev@spark.apache.org<mailto:dev@spark.apache.org> >>>>> >>> >>> Subject: Re: Which committers care about Kafka? >>>>> >>> >>> >>>>> >>> >>> But idempotency is not that easy t achieve sometimes. A strong >>>>> only >>>>> >>> once >>>>> >>> >>> semantic through a proper API would be superuseful; but I'm not >>>>> >>> implying >>>>> >>> >>> this is easy to achieve. >>>>> >>> >>> On 18 Dec 2014 21:52, "Cody Koeninger" >>>>> >>> >>> <c...@koeninger.org<mailto:c...@koeninger.org>> >>>>> wrote: >>>>> >>> >>> >>>>> >>> >>>> If the downstream store for the output data is idempotent or >>>>> >>> >>>> transactional, and that downstream store also is the system of >>>>> >>> record >>>>> >>> >>>> for kafka offsets, then you have exactly-once semantics. >>>>> Commit >>>>> >>> >>>> offsets with / after the data is stored. On any failure, >>>>> restart >>>>> >>> from >>>>> >>> >>> the last committed offsets. >>>>> >>> >>>> >>>>> >>> >>>> Yes, this approach is biased towards the etl-like use cases >>>>> rather >>>>> >>> >>>> than near-realtime-analytics use cases. >>>>> >>> >>>> >>>>> >>> >>>> On Thu, Dec 18, 2014 at 3:27 PM, Hari Shreedharan < >>>>> >>> >>>> hshreedha...@cloudera.com<mailto:hshreedha...@cloudera.com> >>>>> >>> >>>>> wrote: >>>>> >>> >>>>> >>>>> >>> >>>>> I get what you are saying. But getting exactly once right is >>>>> an >>>>> >>> >>>>> extremely hard problem - especially in presence of failure. >>>>> The >>>>> >>> >>>>> issue is failures >>>>> >>> >>>> can >>>>> >>> >>>>> happen in a bunch of places. For example, before the >>>>> notification >>>>> >>> of >>>>> >>> >>>>> downstream store being successful reaches the receiver that >>>>> updates >>>>> >>> >>>>> the offsets, the node fails. The store was successful, but >>>>> >>> >>>>> duplicates came in either way. This is something worth >>>>> discussing >>>>> >>> by >>>>> >>> >>>>> itself - but without uuids etc this might not really be >>>>> solved even >>>>> >>> >>> when you think it is. >>>>> >>> >>>>> >>>>> >>> >>>>> Anyway, I will look at the links. Even I am interested in all >>>>> of >>>>> >>> the >>>>> >>> >>>>> features you mentioned - no HDFS WAL for Kafka and once-only >>>>> >>> >>>>> delivery, >>>>> >>> >>>> but >>>>> >>> >>>>> I doubt the latter is really possible to guarantee - though I >>>>> >>> really >>>>> >>> >>>> would >>>>> >>> >>>>> love to have that! >>>>> >>> >>>>> >>>>> >>> >>>>> Thanks, >>>>> >>> >>>>> Hari >>>>> >>> >>>>> >>>>> >>> >>>>> >>>>> >>> >>>>> On Thu, Dec 18, 2014 at 12:26 PM, Cody Koeninger >>>>> >>> >>>>> <c...@koeninger.org<mailto:c...@koeninger.org>> >>>>> >>> >>>>> wrote: >>>>> >>> >>>>> >>>>> >>> >>>>>> Thanks for the replies. >>>>> >>> >>>>>> >>>>> >>> >>>>>> Regarding skipping WAL, it's not just about optimization. >>>>> If you >>>>> >>> >>>>>> actually want exactly-once semantics, you need control of >>>>> kafka >>>>> >>> >>>>>> offsets >>>>> >>> >>>> as >>>>> >>> >>>>>> well, including the ability to not use zookeeper as the >>>>> system of >>>>> >>> >>>>>> record for offsets. Kafka already is a reliable system that >>>>> has >>>>> >>> >>>>>> strong >>>>> >>> >>>> ordering >>>>> >>> >>>>>> guarantees (within a partition) and does not mandate the use >>>>> of >>>>> >>> >>>> zookeeper >>>>> >>> >>>>>> to store offsets. I think there should be a spark api that >>>>> acts >>>>> >>> as >>>>> >>> >>>>>> a >>>>> >>> >>>> very >>>>> >>> >>>>>> simple intermediary between Kafka and the user's choice of >>>>> >>> >>>>>> downstream >>>>> >>> >>>> store. >>>>> >>> >>>>>> >>>>> >>> >>>>>> Take a look at the links I posted - if there's already been 2 >>>>> >>> >>>> independent >>>>> >>> >>>>>> implementations of the idea, chances are it's something >>>>> people >>>>> >>> need. >>>>> >>> >>>>>> >>>>> >>> >>>>>> On Thu, Dec 18, 2014 at 1:44 PM, Hari Shreedharan < >>>>> >>> >>>>>> hshreedha...@cloudera.com<mailto:hshreedha...@cloudera.com>> >>>>> >>> >>>>>> wrote: >>>>> >>> >>>>>>> >>>>> >>> >>>>>>> Hi Cody, >>>>> >>> >>>>>>> >>>>> >>> >>>>>>> I am an absolute +1 on SPARK-3146. I think we can implement >>>>> >>> >>>>>>> something pretty simple and lightweight for that one. >>>>> >>> >>>>>>> >>>>> >>> >>>>>>> For the Kafka DStream skipping the WAL implementation - >>>>> this is >>>>> >>> >>>>>>> something I discussed with TD a few weeks ago. Though it is >>>>> a >>>>> >>> good >>>>> >>> >>>> idea to >>>>> >>> >>>>>>> implement this to avoid unnecessary HDFS writes, it is an >>>>> >>> >>>> optimization. For >>>>> >>> >>>>>>> that reason, we must be careful in implementation. There >>>>> are a >>>>> >>> >>>>>>> couple >>>>> >>> >>>> of >>>>> >>> >>>>>>> issues that we need to ensure works properly - specifically >>>>> >>> >>> ordering. >>>>> >>> >>>> To >>>>> >>> >>>>>>> ensure we pull messages from different topics and >>>>> partitions in >>>>> >>> >>>>>>> the >>>>> >>> >>>> same >>>>> >>> >>>>>>> order after failure, we’d still have to persist the >>>>> metadata to >>>>> >>> >>>>>>> HDFS >>>>> >>> >>>> (or >>>>> >>> >>>>>>> some other system) - this metadata must contain the order of >>>>> >>> >>>>>>> messages consumed, so we know how to re-read the messages. >>>>> I am >>>>> >>> >>>>>>> planning to >>>>> >>> >>>> explore >>>>> >>> >>>>>>> this once I have some time (probably in Jan). In addition, >>>>> we >>>>> >>> must >>>>> >>> >>>>>>> also ensure bucketing functions work fine as well. I will >>>>> file a >>>>> >>> >>>>>>> placeholder jira for this one. >>>>> >>> >>>>>>> >>>>> >>> >>>>>>> I also wrote an API to write data back to Kafka a while >>>>> back - >>>>> >>> >>>>>>> https://github.com/apache/spark/pull/2994 . I am hoping >>>>> that >>>>> >>> this >>>>> >>> >>>>>>> will get pulled in soon, as this is something I know people >>>>> want. >>>>> >>> >>>>>>> I am open >>>>> >>> >>>> to >>>>> >>> >>>>>>> feedback on that - anything that I can do to make it better. >>>>> >>> >>>>>>> >>>>> >>> >>>>>>> Thanks, >>>>> >>> >>>>>>> Hari >>>>> >>> >>>>>>> >>>>> >>> >>>>>>> >>>>> >>> >>>>>>> On Thu, Dec 18, 2014 at 11:14 AM, Patrick Wendell >>>>> >>> >>>>>>> <pwend...@gmail.com<mailto:pwend...@gmail.com>> >>>>> >>> >>>>>>> wrote: >>>>> >>> >>>>>>> >>>>> >>> >>>>>>>> Hey Cody, >>>>> >>> >>>>>>>> >>>>> >>> >>>>>>>> Thanks for reaching out with this. The lead on streaming >>>>> is TD - >>>>> >>> >>>>>>>> he is traveling this week though so I can respond a bit. >>>>> To the >>>>> >>> >>>>>>>> high level point of whether Kafka is important - it >>>>> definitely >>>>> >>> >>>>>>>> is. Something like 80% of Spark Streaming deployments >>>>> >>> >>>>>>>> (anecdotally) ingest data from Kafka. Also, good support >>>>> for >>>>> >>> >>>>>>>> Kafka is something we generally want in Spark and not a >>>>> library. >>>>> >>> >>>>>>>> In some cases IIRC there were user libraries that used >>>>> unstable >>>>> >>> >>>>>>>> Kafka API's and we were somewhat waiting on Kafka to >>>>> stabilize >>>>> >>> >>>>>>>> them to merge things upstream. Otherwise users wouldn't be >>>>> able >>>>> >>> >>>>>>>> to use newer Kakfa versions. This is a high level >>>>> impression >>>>> >>> only >>>>> >>> >>>>>>>> though, I haven't talked to TD about this recently so it's >>>>> worth >>>>> >>> >>> revisiting given the developments in Kafka. >>>>> >>> >>>>>>>> >>>>> >>> >>>>>>>> Please do bring things up like this on the dev list if >>>>> there are >>>>> >>> >>>>>>>> blockers for your usage - thanks for pinging it. >>>>> >>> >>>>>>>> >>>>> >>> >>>>>>>> - Patrick >>>>> >>> >>>>>>>> >>>>> >>> >>>>>>>> On Thu, Dec 18, 2014 at 7:07 AM, Cody Koeninger >>>>> >>> >>>>>>>> <c...@koeninger.org<mailto:c...@koeninger.org>> >>>>> >>> >>>>>>>> wrote: >>>>> >>> >>>>>>>>> Now that 1.2 is finalized... who are the go-to people to >>>>> get >>>>> >>> >>>>>>>>> some long-standing Kafka related issues resolved? >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> The existing api is not sufficiently safe nor flexible >>>>> for our >>>>> >>> >>>>>>>> production >>>>> >>> >>>>>>>>> use. I don't think we're alone in this viewpoint, because >>>>> I've >>>>> >>> >>>>>>>>> seen several different patches and libraries to fix the >>>>> same >>>>> >>> >>>>>>>>> things we've >>>>> >>> >>>>>>>> been >>>>> >>> >>>>>>>>> running into. >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> Regarding flexibility >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> https://issues.apache.org/jira/browse/SPARK-3146 >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> has been outstanding since August, and IMHO an equivalent >>>>> of >>>>> >>> >>>>>>>>> this is absolutely necessary. We wrote a similar patch >>>>> >>> >>>>>>>>> ourselves, then found >>>>> >>> >>>>>>>> that >>>>> >>> >>>>>>>>> PR and have been running it in production. We wouldn't be >>>>> able >>>>> >>> >>>>>>>>> to >>>>> >>> >>>> get >>>>> >>> >>>>>>>> our >>>>> >>> >>>>>>>>> jobs done without it. It also allows users to solve a >>>>> whole >>>>> >>> >>>>>>>>> class of problems for themselves (e.g. SPARK-2388, >>>>> arbitrary >>>>> >>> >>>>>>>>> delay of >>>>> >>> >>>>>>>> messages, etc). >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> Regarding safety, I understand the motivation behind >>>>> >>> >>>>>>>>> WriteAheadLog >>>>> >>> >>>> as >>>>> >>> >>>>>>>> a >>>>> >>> >>>>>>>>> general solution for streaming unreliable sources, but >>>>> Kafka >>>>> >>> >>>>>>>>> already >>>>> >>> >>>>>>>> is a >>>>> >>> >>>>>>>>> reliable source. I think there's a need for an api that >>>>> treats >>>>> >>> >>>>>>>>> it as such. Even aside from the performance issues of >>>>> >>> >>>>>>>>> duplicating the write-ahead log in kafka into another >>>>> >>> >>>>>>>>> write-ahead log in hdfs, I >>>>> >>> >>>> need >>>>> >>> >>>>>>>>> exactly-once semantics in the face of failure (I've had >>>>> >>> >>>>>>>>> failures >>>>> >>> >>>> that >>>>> >>> >>>>>>>>> prevented reloading a spark streaming checkpoint, for >>>>> >>> instance). >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> I've got an implementation i've been using >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> >>>>> >>> https://github.com/koeninger/spark-1/tree/kafkaRdd/external/kaf >>>>> >>> >>>>>>>>> ka /src/main/scala/org/apache/spark/rdd/kafka >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> Tresata has something similar at >>>>> >>> >>>>>>>> https://github.com/tresata/spark-kafka, >>>>> >>> >>>>>>>>> and I know there were earlier attempts based on Storm >>>>> code. >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> Trying to distribute these kinds of fixes as libraries >>>>> rather >>>>> >>> >>>>>>>>> than >>>>> >>> >>>>>>>> patches >>>>> >>> >>>>>>>>> to Spark is problematic, because large portions of the >>>>> >>> >>>> implementation >>>>> >>> >>>>>>>> are >>>>> >>> >>>>>>>>> private[spark]. >>>>> >>> >>>>>>>>> >>>>> >>> >>>>>>>>> I'd like to help, but i need to know whose attention to >>>>> get. >>>>> >>> >>>>>>>> >>>>> >>> >>>>>>>> >>>>> >>> ----------------------------------------------------------------- >>>>> >>> >>>>>>>> ---- To unsubscribe, e-mail: >>>>> dev-unsubscr...@spark.apache.org<mailto:dev-unsubscr...@spark.apache.org> >>>>> >>> For >>>>> >>> >>>>>>>> additional commands, e-mail: >>>>> >>> >>>>>>>> dev-h...@spark.apache.org<mailto:dev-h...@spark.apache.org> >>>>> >>> >>>>>>>> >>>>> >>> >>>>>>>> >>>>> >>> >>>>>>> >>>>> >>> >>>>> >>>>> >>> >>>> >>>>> >>> >>> >>>>> >>> >> >>>>> >>> >>>>> >>> >>>>> >> >>>>> > >>>>> >>>> >>> >>
