Hi Stephan & Kostas & Piotrek, thanks for these inputs, Maybe what I expressed is not clear. For the implementation, I want to know what you think, rather than must making another set from scratch. Piotrek you are right, implementation is the part of this FLIP too, because we can not list all detail things in the FLIP, so the implementation do affect user's behaviors. And the maintenance / development costs are also points.
I think you already persuaded me. I am thinking about based on StreamingFileSink. And extending StreamingFileSink can solve "partition commit" requirement, I have tried in my POC. And it is true, Recoverable things and S3 things also important. So I listed "What is missing" for StreamingFileSink in previous mail. (It is not defense for making another set from scratch) Hi Stephan, >> The FLIP is "Filesystem connector in Table", it's about building up Flink Table's capabilities. What I mean is this is not just for Hive, this FLIP is for table. So we don't need do all things for Hive. But Hive is also a "format" (or something else) of Filesystem connector. Its requirements can be considered. I think you are right about the design, and let me take this seriously, a unify way make us stronger, less confuse, less surprise, more rigorous design. And I am pretty sure table things are good for enhancing DataStream api too. Hi Kostas, Yes, Parquet and Orc are the main formats. Good to know your support~ I think streaming file sink and file system connector things are important to Flink, it is good&time to think about these common requirements, think about batch support, it is not just about table, it is for whole Flink too. If there are some requirements that is hard to support or violates existing design. Exclude them. Best, Jingsong Lee On Wed, Mar 18, 2020 at 10:31 PM Piotr Nowojski <pi...@ververica.com> wrote: > Hi Kurt, > > +1 for having some offline discussion on this topic. > > But I think the question about using StreamingFileSink or implementing > subset of it’s feature from scratch is quite fundamental design decision, > with impact on the behaviour of Public API, so I wouldn’t discard it as > technical detail and should be included as part of the FLIP (I know It > could be argued in opposite direction). > > Piotrek > > > On 18 Mar 2020, at 13:55, Kurt Young <ykt...@gmail.com> wrote: > > > > Hi all, > > > > Thanks for the discuss and feedbacks. I think this FLIP doesn't imply the > > implementation > > of such connector yet, it only describes the functionality and expected > > behaviors from user's > > perspective. Reusing current StreamingFileSink is definitely one of the > > possible ways to > > implement it. Since there are lots of details and I would suggest we can > > have an offline meeting > > to discuss the how these could be achieved by extending StremingFileSink, > > and how much > > effort we need to put on it. What do you think? > > > > Best, > > Kurt > > > > > > On Wed, Mar 18, 2020 at 7:21 PM Kostas Kloudas <kklou...@gmail.com> > wrote: > > > >> Hi all, > >> > >> I also agree with Stephan on this! > >> > >> It has been more than a year now that most of our efforts have had the > >> "unify" / "unification"/ etc either on their title or in their core > >> and this has been the focus of all our resources. By deviating from > >> this now, we only put more stress on other teams in the future. When > >> the users start using a given API, with high probability, they will > >> ask (and it is totally reasonable) consistent behaviour from all the > >> other APIs that ship with Flink. This will eventually lead to having > >> to answer the questions that we now deem as difficult in a future > >> release, when we will have to "unify" again. > >> > >> In addition, Hive integration is definitely a "nice to have" feature > >> but it does not mean that we need to push for 100% compatibility if it > >> is not required. > >> @Jingsong Li if you think that Parquet and Orc are the main formats, > >> we can focus on these and provide good support for them (both reading > >> and writing). For maintainability, I think that given the amount of > >> demand for these formats, it is not going to be a huge problem at > >> least for now. > >> > >> Given the above, I am also leaning towards a solution that aims at > >> extending the StreamingFileSink to efficiently support bulk formats > >> like Parquet and Orc, rather than creating a new sink that locks > >> Hive-dependent usecases to a specific API. > >> > >> Cheers, > >> Kostas > >> > >> > >> > >> > >> > >> On Wed, Mar 18, 2020 at 12:03 PM Stephan Ewen <se...@apache.org> wrote: > >>> > >>>>> The FLIP is "Filesystem connector in Table", it's about building up > >>> Flink Table's capabilities. > >>> > >>> That is exactly what worries me. The whole effort is not thinking about > >>> Flink as a whole any more. > >>> This proposal is not trying to build a consistent user experience > across > >>> batch and streaming, across Table API, SQL, and DataStream. > >>> > >>> The proposal is building a separate, disconnected ecosystem for the > Table > >>> API, specific to batch processing and some limited streaming setups. > >>> Specific to one type of environment (Hive and HDFS). It willingly omits > >>> support for other environments and conflicts with efforts in other > >>> components to unify. > >>> > >>> Supporting common use cases is good, but in my opinion not at the price > >> of > >>> creating a "fractured" project where the approaches in different layers > >>> don't fit together any more. > >>> > >>> > >>>> *## StreamingFileSink not support writer with path* > >>>> > >>>> The FLIP is "Filesystem connector in Table", it's about building up > >> Flink > >>>> Table's capabilities. But I think Hive is important, I see that most > >> users > >>>> use Flink and Spark to write data from Kafka to hive. Streaming > >> writing, I > >>>> see that these two engines are convenient and popular. I mean, Flink > >> is not > >>>> only a hive runtime, but also an important part of offline data > >> warehouse. > >>>> The thing is StreamingFileSink not support hadoop record writers. Yes, > >> we > >>>> can support them one by one. I see the community integrating ORC [1]. > >> But > >>>> it's really not an easy thing. And we have to be careful to maintain > >>>> compatibility. After all, users downstream use other computing engines > >> to > >>>> analyze. > >>>> Yes, exposing "RecoverableFsDataOutputStream" to writers is good to > >>>> subsequent optimization [2]. But there are many cases. It is enough > for > >>>> users to generate new files at the checkpoint. They pay more attention > >> to > >>>> whether they can do it and whether there is a risk of compatibility. > >>>> Therefore, RecordWriter is used here. > >>>> > >>>> *## External HDFS access* > >>>> > >>>> Including hadoop configuration and Kerberos related things. > >>>> > >>>> *## Partition commit* > >>>> > >>>> Committing a partition is to notify the downstream application that > the > >>>> partition has finished writing, the partition is ready to be read.The > >>>> common way is to add a success file or update metastore. Of course, > >> there > >>>> are other ways to notify. We need to provide flexible mechanisms. > >>>> As you mentioned, yes, we can extend "StreamingFileSink" for this > part. > >>>> > >>>> *## Batch / Streaming Unification* > >>>> > >>>> Yes, it is about exactly-once and single commit at the end, There are > >> also > >>>> some "bounded" differences. For example, batch can support sorting. In > >> this > >>>> way, you can sort by partition, which can reduce the number of writers > >>>> written at the same time. Dynamic partition writing in batch may > >> produce > >>>> many unordered partitions. > >>>> > >>>> [1] https://issues.apache.org/jira/browse/FLINK-10114 > >>>> [2] https://issues.apache.org/jira/browse/FLINK-11499 > >>>> > >>>> Best, > >>>> Jingsong Lee > >>>> > >>>> On Tue, Mar 17, 2020 at 8:00 PM LakeShen <shenleifight...@gmail.com> > >>>> wrote: > >>>> > >>>>> Hi Jingsong , > >>>>> > >>>>> I am looking forward this feature. Because in some streaming > >>>> application,it > >>>>> need transfer their messages to hdfs , in order to offline analysis. > >>>>> > >>>>> Best wishes, > >>>>> LakeShen > >>>>> > >>>>> Stephan Ewen <se...@apache.org> 于2020年3月17日周二 下午7:42写道： > >>>>> > >>>>>> I would really like to see us converging the stack and the > >>>> functionality > >>>>>> here. > >>>>>> Meaning to try and use the same sinks in the Table API as for the > >>>>>> DataStream API, and using the same sink for batch and streaming. > >>>>>> > >>>>>> The StreamingFileSink has a lot of things that can help with that. > >> If > >>>>>> possible, it would be nice to extend it (which would help move > >> towards > >>>>> the > >>>>>> above goal) rather than build a second sink. Building a second sink > >>>> leads > >>>>>> us further away from unification. > >>>>>> > >>>>>> I am a bit puzzled by the statement that sinks are primarily for > >> Hive. > >>>>> The > >>>>>> Table API should not be coupled to Hive, it should be an > >> independent > >>>>>> batch/streaming API for many use cases, supporting very well for > >> batch > >>>>> and > >>>>>> streaming interplay. Supporting Hive is great, but we should not be > >>>>>> building this towards Hive, as just yet another Hive runtime. Why > >> "yet > >>>>>> another Hive runtime" when what we have a unique streaming engine > >> that > >>>>> can > >>>>>> do much more? We would drop our own strength and reduce ourselves > >> to a > >>>>>> limited subset. > >>>>>> > >>>>>> Let's build a File Sink that can also support Hive, but can do so > >> much > >>>>>> more. For example, efficient streaming file ingestion as > >> materialized > >>>>> views > >>>>>> from changelogs. > >>>>>> > >>>>>> > >>>>>> *## Writing Files in Streaming* > >>>>>> > >>>>>> To write files in streaming, I don't see another way than using the > >>>>>> streaming file sink. If you want to write files across checkpoints, > >>>>> support > >>>>>> exactly-once, and support consistent "stop with savepoint", it is > >> not > >>>>>> trivial. > >>>>>> > >>>>>> A part of the complexity comes from the fact that not all targets > >> are > >>>>>> actually file systems, and not all have simple semantics for > >>>> persistence. > >>>>>> S3 for example does not support renames (only copies, which may > >> take a > >>>>> lot > >>>>>> of time) and it does not support flush/sync of data (the S3 file > >> system > >>>>> in > >>>>>> Hadoop exposes that but it does not work. flush/sync, followed by a > >>>>>> failure, leads to data loss). You need to devise a separate > >> protocol > >>>> for > >>>>>> that, which is exactly what has already been done and abstracted > >> behind > >>>>> the > >>>>>> recoverable writers. > >>>>>> > >>>>>> If you re-engineer that in the, you will end up either missing many > >>>>> things > >>>>>> (intermediate persistence on different file systems, and atomic > >> commit > >>>> in > >>>>>> the absence of renames, etc.), or you end up doing something > >> similar as > >>>>> the > >>>>>> recoverable writers do. > >>>>>> > >>>>>> > >>>>>> *## Atomic Commit in Batch* > >>>>>> > >>>>>> For batch sinks, it is also desirable to write the data first and > >> then > >>>>>> atomically commit it once the job is done. > >>>>>> Hadoop has spent a lot of time making this work, see this doc here, > >>>>>> specifically the section on 'The "Magic" Committer'. [1] > >>>>>> > >>>>>> What Flink has built in the RecoverableWriter is in some way an > >> even > >>>>> better > >>>>>> version of this, because it works without extra files (we pass data > >>>>> through > >>>>>> checkpoint state) and it supports not only committing once at the > >> end, > >>>>> but > >>>>>> committing multiple time intermediate parts during checkpoints. > >>>>>> > >>>>>> Meaning using the recoverable writer mechanism in batch would > >> allow us > >>>> to > >>>>>> immediately get the efficient atomic commit implementations on > >> file:// > >>>>>> hdfs:// and s3://, with a well defined way to implement it also for > >>>> other > >>>>>> file systems. > >>>>>> > >>>>>> > >>>>>> *## Batch / Streaming Unification* > >>>>>> > >>>>>> It would be great to start looking at these things in the same way: > >>>>>> - streaming (exactly-once): commits files (after finished) at the > >>>> next > >>>>>> checkpoint > >>>>>> - batch: single commit at the end of the job > >>>>>> > >>>>>> > >>>>>> *## DataStream / Table API Stack Unification* > >>>>>> > >>>>>> Having the same set of capabilities would make it much easier for > >> users > >>>>> to > >>>>>> understand the system. > >>>>>> Especially when it comes to consistent behavior across external > >>>> systems. > >>>>>> Having a different file sink in Table API and DataStream API means > >> that > >>>>>> DataStream can write correctly to S3 while Table API cannot. > >>>>>> > >>>>>> > >>>>>> *## What is missing?* > >>>>>> > >>>>>> It seems there are some things that get in the way of naturally > >>>>>> Can you make a list of what features are missing in the > >>>> StreamingFileSink > >>>>>> that make it usable for the use cases you have in mind? > >>>>>> > >>>>>> Best, > >>>>>> Stephan > >>>>>> > >>>>>> [1] > >>>>>> > >>>>>> > >>>>> > >>>> > >> > https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/committer_architecture.html > >>>>>> > >>>>>> > >>>>>> On Mon, Mar 16, 2020 at 12:31 PM Jingsong Li < > >> jingsongl...@gmail.com> > >>>>>> wrote: > >>>>>> > >>>>>>> Hi Piotr, > >>>>>>> > >>>>>>> I am very entangled. > >>>>>>> > >>>>>>> Let me re-list the table streaming sink requirements: > >>>>>>> - In table, maybe 90% sinks are for Hive. The parquet and orc > >> are the > >>>>>> most > >>>>>>> important formats. Hive provide RecordWriters, it is easy to > >> support > >>>>> all > >>>>>>> hive formats by using it, and we don't need concern hive version > >>>>>>> compatibility too, but it can not work with FSDataOutputStream. > >>>>>>> - Hive table maybe use external HDFS. It means, hive has its own > >>>> hadoop > >>>>>>> configuration. > >>>>>>> - In table, partition commit is needed, we can not just move > >> files, > >>>> it > >>>>> is > >>>>>>> important to complete table semantics to update catalog. > >>>>>>> > >>>>>>> You are right DataStream and Table streaming sink will not be > >> fully > >>>>>>> compatible, each with its own set of limitations, quirks and > >>>> features. > >>>>>>> But if re-using DataStream, batch and streaming also will not be > >>>> fully > >>>>>>> compatible. Provide a unify experience to batch and streaming is > >> also > >>>>>>> important. > >>>>>>> > >>>>>>> Table and DataStream have different concerns, and they tilt in > >>>>> different > >>>>>>> directions. > >>>>>>> > >>>>>>> Of course, it is very good to see a unify implementation to solve > >>>> batch > >>>>>>> sink and hive things, unify DataStream batch sink and DataStream > >>>>>> streaming > >>>>>>> sink and Table batch sink and Table streaming sink. > >>>>>>> > >>>>>>> Le's see what others think. > >>>>>>> > >>>>>>> Best, > >>>>>>> Jingsong Lee > >>>>>>> > >>>>>>> > >>>>>>> On Mon, Mar 16, 2020 at 4:15 PM Piotr Nowojski < > >> pi...@ververica.com> > >>>>>>> wrote: > >>>>>>> > >>>>>>>> Hi Jingsong, > >>>>>>>> > >>>>>>>>> First way is reusing Batch sink in FLINK-14254, It has > >> handled > >>>> the > >>>>>>>> partition and metastore logic well. > >>>>>>>>> - unify batch and streaming > >>>>>>>>> - Using FileOutputFormat is consistent with FileInputFormat. > >>>>>>>>> - Add exactly-once related logic. Just 200+ lines code. > >>>>>>>>> - It's natural to support more table features, like partition > >>>>> commit, > >>>>>>>> auto compact and etc.. > >>>>>>>>> > >>>>>>>>> Second way is reusing Datastream StreamingFileSink: > >>>>>>>>> - unify streaming sink between table and Datastream. > >>>>>>>>> - It maybe hard to introduce table related features to > >>>>>>> StreamingFileSink. > >>>>>>>>> > >>>>>>>>> I prefer the first way a little. What do you think? > >>>>>>>> > >>>>>>>> I would be surprised if adding “exactly-once related logic” is > >> just > >>>>> 200 > >>>>>>>> lines of code. There are things like multi part file upload to > >> s3 > >>>> and > >>>>>>> there > >>>>>>>> are also some pending features like [1]. I would suggest to > >>>>> ask/involve > >>>>>>>> Klou in this discussion. > >>>>>>>> > >>>>>>>> If it’s as easy to support exactly-once streaming with current > >>>> batch > >>>>>>> sink, > >>>>>>>> that begs the question, why do we need to maintain > >>>> StreamingFileSink? > >>>>>>>> > >>>>>>>> The worst possible outcome from my perspective will be, if we > >> have > >>>>>>> another > >>>>>>>> example of an operator/logic implemented independently both in > >>>>>> DataStream > >>>>>>>> API and Table API. Because I’m pretty sure they will not be > >> fully > >>>>>>>> compatible, each with it’s own set of limitations, quirks and > >>>>> features. > >>>>>>>> Especially that we have on our long term roadmap and wish list > >> to > >>>>> unify > >>>>>>>> such kind of operators. > >>>>>>>> > >>>>>>>> Piotrek > >>>>>>>> > >>>>>>>> [1] https://issues.apache.org/jira/browse/FLINK-11499 < > >>>>>>>> https://issues.apache.org/jira/browse/FLINK-11499> > >>>>>>>> > >>>>>>>>> On 16 Mar 2020, at 06:55, Jingsong Li < > >> jingsongl...@gmail.com> > >>>>>> wrote: > >>>>>>>>> > >>>>>>>>> Thanks Jinhai for involving. > >>>>>>>>> > >>>>>>>>>> we need add 'connector.sink.username' for > >> UserGroupInformation > >>>>> when > >>>>>>> data > >>>>>>>>> is written to HDFS > >>>>>>>>> > >>>>>>>>> Yes, I am not an expert of HDFS, but it seems we need do this > >>>>> "doAs" > >>>>>> in > >>>>>>>> the > >>>>>>>>> code for access external HDFS. I will update document. > >>>>>>>>> > >>>>>>>>> Best, > >>>>>>>>> Jingsong Lee > >>>>>>>>> > >>>>>>>>> On Mon, Mar 16, 2020 at 12:01 PM Jingsong Li < > >>>>> jingsongl...@gmail.com > >>>>>>> > >>>>>>>> wrote: > >>>>>>>>> > >>>>>>>>>> Thanks Piotr and Yun for involving. > >>>>>>>>>> > >>>>>>>>>> Hi Piotr and Yun, for implementation, > >>>>>>>>>> > >>>>>>>>>> FLINK-14254 [1] introduce batch sink table world, it deals > >> with > >>>>>>>> partitions > >>>>>>>>>> thing, metastore thing and etc.. And it just reuse > >>>>>> Dataset/Datastream > >>>>>>>>>> FileInputFormat and FileOutputFormat. Filesystem can not do > >>>>> without > >>>>>>>>>> FileInputFormat, because it need deal with file things, > >> split > >>>>>> things. > >>>>>>>> Like > >>>>>>>>>> orc and parquet, they need read whole file and have > >> different > >>>>> split > >>>>>>>> logic. > >>>>>>>>>> > >>>>>>>>>> So back to file system connector: > >>>>>>>>>> - It needs introducing FilesystemTableFactory, > >>>>> FilesystemTableSource > >>>>>>> and > >>>>>>>>>> FilesystemTableSink. > >>>>>>>>>> - For sources, reusing Dataset/Datastream FileInputFormats, > >>>> there > >>>>>> are > >>>>>>> no > >>>>>>>>>> other interface to finish file reading. > >>>>>>>>>> > >>>>>>>>>> For file sinks: > >>>>>>>>>> - Batch sink use FLINK-14254 > >>>>>>>>>> - Streaming sink has two ways. > >>>>>>>>>> > >>>>>>>>>> First way is reusing Batch sink in FLINK-14254, It has > >> handled > >>>> the > >>>>>>>>>> partition and metastore logic well. > >>>>>>>>>> - unify batch and streaming > >>>>>>>>>> - Using FileOutputFormat is consistent with FileInputFormat. > >>>>>>>>>> - Add exactly-once related logic. Just 200+ lines code. > >>>>>>>>>> - It's natural to support more table features, like > >> partition > >>>>>> commit, > >>>>>>>> auto > >>>>>>>>>> compact and etc.. > >>>>>>>>>> > >>>>>>>>>> Second way is reusing Datastream StreamingFileSink: > >>>>>>>>>> - unify streaming sink between table and Datastream. > >>>>>>>>>> - It maybe hard to introduce table related features to > >>>>>>>> StreamingFileSink. > >>>>>>>>>> > >>>>>>>>>> I prefer the first way a little. What do you think? > >>>>>>>>>> > >>>>>>>>>> Hi Yun, > >>>>>>>>>> > >>>>>>>>>>> Watermark mechanism might not be enough. > >>>>>>>>>> > >>>>>>>>>> Watermarks of subtasks are the same in the "snapshotState". > >>>>>>>>>> > >>>>>>>>>>> we might need to also do some coordination between > >> subtasks. > >>>>>>>>>> > >>>>>>>>>> Yes, JobMaster is the role to control subtasks. Metastore > >> is a > >>>>> very > >>>>>>>>>> fragile single point, which can not be accessed by > >> distributed, > >>>> so > >>>>>> it > >>>>>>> is > >>>>>>>>>> uniformly accessed by JobMaster. > >>>>>>>>>> > >>>>>>>>>> [1]https://issues.apache.org/jira/browse/FLINK-14254 > >>>>>>>>>> > >>>>>>>>>> Best, > >>>>>>>>>> Jingsong Lee > >>>>>>>>>> > >>>>>>>>>> On Fri, Mar 13, 2020 at 6:43 PM Yun Gao < > >> yungao...@aliyun.com> > >>>>>> wrote: > >>>>>>>>>> > >>>>>>>>>>> Hi, > >>>>>>>>>>> > >>>>>>>>>>> Very thanks for Jinsong to bring up this discussion! > >> It > >>>>>> should > >>>>>>>>>>> largely improve the usability after enhancing the > >> FileSystem > >>>>>>> connector > >>>>>>>> in > >>>>>>>>>>> Table. > >>>>>>>>>>> > >>>>>>>>>>> I have the same question with Piotr. From my side, I > >>>> think > >>>>> it > >>>>>>>>>>> should be better to be able to reuse existing > >>>> StreamingFileSink. > >>>>> I > >>>>>>>> think We > >>>>>>>>>>> have began > >>>>>>>>>>> enhancing the supported FileFormat (e.g., ORC, > >> Avro...), > >>>>> and > >>>>>>>>>>> reusing StreamFileSink should be able to avoid repeat work > >> in > >>>> the > >>>>>>> Table > >>>>>>>>>>> library. Besides, > >>>>>>>>>>> the bucket concept seems also matches the semantics > >> of > >>>>>>> partition. > >>>>>>>>>>> > >>>>>>>>>>> For the notification of adding partitions, I'm a > >> little > >>>>>>> wondering > >>>>>>>>>>> that the Watermark mechanism might not be enough since > >>>>>>> Bucket/Partition > >>>>>>>>>>> might spans > >>>>>>>>>>> multiple subtasks. It depends on the level of > >>>> notification: > >>>>>> if > >>>>>>> we > >>>>>>>>>>> want to notify for the bucket on each subtask, using > >> watermark > >>>> to > >>>>>>>> notifying > >>>>>>>>>>> each subtask > >>>>>>>>>>> should be ok, but if we want to notifying for the > >> whole > >>>>>>>>>>> Bucket/Partition, we might need to also do some > >> coordination > >>>>>> between > >>>>>>>>>>> subtasks. > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> Best, > >>>>>>>>>>> Yun > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> > >>>>> ------------------------------------------------------------------ > >>>>>>>>>>> From:Piotr Nowojski <pi...@ververica.com> > >>>>>>>>>>> Send Time:2020 Mar. 13 (Fri.) 18:03 > >>>>>>>>>>> To:dev <dev@flink.apache.org> > >>>>>>>>>>> Cc:user <u...@flink.apache.org>; user-zh < > >>>>> user...@flink.apache.org > >>>>>>> > >>>>>>>>>>> Subject:Re: [DISCUSS] FLIP-115: Filesystem connector in > >> Table > >>>>>>>>>>> > >>>>>>>>>>> Hi, > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> Which actual sinks/sources are you planning to use in this > >>>>> feature? > >>>>>>> Is > >>>>>>>> it about exposing StreamingFileSink in the Table API? Or do you > >>>> want > >>>>> to > >>>>>>>> implement new Sinks/Sources? > >>>>>>>>>>> > >>>>>>>>>>> Piotrek > >>>>>>>>>>> > >>>>>>>>>>>> On 13 Mar 2020, at 10:04, jinhai wang < > >> jinhai...@gmail.com> > >>>>>> wrote: > >>>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>>> Thanks for FLIP-115. It is really useful feature for > >> platform > >>>>>>>> developers who manage hundreds of Flink to Hive jobs in > >> production. > >>>>>>>>>>> > >>>>>>>>>>>> I think we need add 'connector.sink.username' for > >>>>>>>> UserGroupInformation when data is written to HDFS > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> 在 2020/3/13 下午3:33，“Jingsong Li”<jingsongl...@gmail.com> > >> 写入: > >>>>>>>>>>>> > >>>>>>>>>>>> Hi everyone, > >>>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>>> I'd like to start a discussion about FLIP-115 Filesystem > >>>>>> connector > >>>>>>>> in Table > >>>>>>>>>>>> [1]. > >>>>>>>>>>>> This FLIP will bring: > >>>>>>>>>>>> - Introduce Filesystem table factory in table, support > >>>>>>>>>>>> csv/parquet/orc/json/avro formats. > >>>>>>>>>>>> - Introduce streaming filesystem/hive sink in table > >>>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>>> CC to user mail list, if you have any unmet needs, > >> please > >>>> feel > >>>>>>> free > >>>>>>>> to > >>>>>>>>>>>> reply~ > >>>>>>>>>>>> > >>>>>>>>>>>> Look forward to hearing from you. > >>>>>>>>>>>> > >>>>>>>>>>>> [1] > >>>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>> > >>>>>>> > >>>>>> > >>>>> > >>>> > >> > https://cwiki.apache.org/confluence/display/FLINK/FLIP-115%3A+Filesystem+connector+in+Table > >>>>>>>>>>>> > >>>>>>>>>>>> Best, > >>>>>>>>>>>> Jingsong Lee > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>>> > >>>>>>>>>> > >>>>>>>>>> -- > >>>>>>>>>> Best, Jingsong Lee > >>>>>>>>>> > >>>>>>>>> > >>>>>>>>> > >>>>>>>>> -- > >>>>>>>>> Best, Jingsong Lee > >>>>>>>> > >>>>>>>> > >>>>>>> > >>>>>>> -- > >>>>>>> Best, Jingsong Lee > >>>>>>> > >>>>>> > >>>>> > >>>> > >>>> > >>>> -- > >>>> Best, Jingsong Lee > >>>> > >> > > -- Best, Jingsong Lee
