Sorry, I accidentally sent a reply to just Jungtaek. The current commit structure works for any table where you can stage data in place and commit in a combined operation. Iceberg does this by writing data files in place and committing them in an atomic update to the table metadata. You can also implement this in HBase using the timestamp or version field for MVCC. All readers ignore data newer than version V, writers create records with version V+1, then the current version is updated at once to V+1 from V.
It actually *doesn't* work for Hive tables because table state is tracked in the file system, unless you use a pattern where you write whole partitions and swap the old partition set for the new partition set atomically to commit. On Mon, Sep 10, 2018 at 1:53 PM Jungtaek Lim <kabh...@gmail.com> wrote: > Ah yes. I have been thinking about NoSQL things since output for Spark > workload may not be suitable for RDBMS (in terms of scale, and > performance). For RDBMS it would work essentially (via INSERT ... SELECT). > > I agree the potential failure is pretty short for HDFS case. I just > thought about it theoretically since this is a kind of contract. It > wouldn't hurt most of cases in production. > > 2018년 9월 11일 (화) 오전 5:43, Reynold Xin <r...@databricks.com>님이 작성: > >> Well almost all relational databases you can move data in a transactional >> way. That’s what transactions are for. >> >> For just straight HDFS, the move is a pretty fast operation so while it >> is not completely transactional, the window of potential failure is pretty >> short for appends. For writers at the partition level it is fine because it >> is just renaming directory, which is atomic. >> >> On Mon, Sep 10, 2018 at 1:40 PM Jungtaek Lim <kabh...@gmail.com> wrote: >> >>> When network partitioning happens it is pretty OK for me to see 2PC not >>> working, cause we deal with global transaction. Recovery should be hard >>> thing to get it correctly though. I completely agree it would require >>> massive changes to Spark. >>> >>> What I couldn't find for underlying storages is moving data from staging >>> table to final table in transactional way. I'm not fully sure but as I'm >>> aware of, many storages would not support moving data, and even HDFS sink >>> it is not strictly done in transactional way since we move multiple files >>> with multiple operations. If coordinator just crashes it leaves partial >>> write, and among writers and coordinator need to deal with ensuring it will >>> not be going to be duplicated. >>> >>> Ryan replied me as Iceberg and HBase MVCC timestamps can enable us to >>> implement "commit" (his reply didn't hit dev. mailing list though) but I'm >>> not an expert of both twos and I couldn't still imagine it can deal with >>> various crash cases. >>> >>> 2018년 9월 11일 (화) 오전 5:17, Reynold Xin <r...@databricks.com>님이 작성: >>> >>>> I don't think two phase commit would work here at all. >>>> >>>> 1. It'd require massive changes to Spark. >>>> >>>> 2. Unless the underlying data source can provide an API to coordinate >>>> commits (which few data sources I know provide something like that), 2PC >>>> wouldn't work in the presence of network partitioning. You can't defy the >>>> law of physics. >>>> >>>> Really the most common and simple way I've seen this working is through >>>> staging tables and a final transaction to move data from staging table to >>>> final table. >>>> >>>> >>>> >>>> >>>> >>>> On Mon, Sep 10, 2018 at 12:56 PM Jungtaek Lim <kabh...@gmail.com> >>>> wrote: >>>> >>>>> I guess we all are aware of limitation of contract on DSv2 writer. >>>>> Actually it can be achieved only with HDFS sink (or other filesystem based >>>>> sinks) and other external storage are normally not feasible to implement >>>>> it >>>>> because there's no way to couple a transaction with multiple clients as >>>>> well as coordinator can't take over transactions from writers to do the >>>>> final commit. >>>>> >>>>> XA is also not a trivial one to get it correctly with current >>>>> execution model: Spark doesn't require writer tasks to run at the same >>>>> time >>>>> but to achieve 2PC they should run until end of transaction (closing >>>>> client >>>>> before transaction ends normally means aborting transaction). Spark should >>>>> also integrate 2PC with its checkpointing mechanism to guarantee >>>>> completeness of batch. And it might require different integration for >>>>> continuous mode. >>>>> >>>>> Jungtaek Lim (HeartSaVioR) >>>>> >>>>> 2018년 9월 11일 (화) 오전 4:37, Arun Mahadevan <ar...@apache.org>님이 작성: >>>>> >>>>>> In some cases the implementations may be ok with eventual consistency >>>>>> (and does not care if the output is written out atomically) >>>>>> >>>>>> XA can be one option for datasources that supports it and requires >>>>>> atomicity but I am not sure how would one implement it with the current >>>>>> API. >>>>>> >>>>>> May be we need to discuss improvements at the Datasource V2 API level >>>>>> (e.g. individual tasks would "prepare" for commit and once the driver >>>>>> receives "prepared" from all the tasks, a "commit" would be invoked at >>>>>> each >>>>>> of the individual tasks). Right now the responsibility of the final >>>>>> "commit" is with the driver and it may not always be possible for the >>>>>> driver to take over the transactions started by the tasks. >>>>>> >>>>>> >>>>>> On Mon, 10 Sep 2018 at 11:48, Dilip Biswal <dbis...@us.ibm.com> >>>>>> wrote: >>>>>> >>>>>>> This is a pretty big challenge in general for data sources -- for >>>>>>> the vast majority of data stores, the boundary of a transaction is per >>>>>>> client. That is, you can't have two clients doing writes and >>>>>>> coordinating a >>>>>>> single transaction. That's certainly the case for almost all relational >>>>>>> databases. Spark, on the other hand, will have multiple clients >>>>>>> (consider >>>>>>> each task a client) writing to the same underlying data store. >>>>>>> >>>>>>> DB>> Perhaps we can explore two-phase commit protocol (aka XA) for >>>>>>> this ? Not sure how easy it is to implement this though :-) >>>>>>> >>>>>>> Regards, >>>>>>> Dilip Biswal >>>>>>> Tel: 408-463-4980 <(408)%20463-4980> >>>>>>> dbis...@us.ibm.com >>>>>>> >>>>>>> >>>>>>> >>>>>>> ----- Original message ----- >>>>>>> From: Reynold Xin <r...@databricks.com> >>>>>>> To: Ryan Blue <rb...@netflix.com> >>>>>>> Cc: ross.law...@gmail.com, dev <dev@spark.apache.org> >>>>>>> Subject: Re: DataSourceWriter V2 Api questions >>>>>>> Date: Mon, Sep 10, 2018 10:26 AM >>>>>>> >>>>>>> I don't think the problem is just whether we have a starting point >>>>>>> for write. As a matter of fact there's always a starting point for >>>>>>> write, >>>>>>> whether it is explicit or implicit. >>>>>>> >>>>>>> This is a pretty big challenge in general for data sources -- for >>>>>>> the vast majority of data stores, the boundary of a transaction is per >>>>>>> client. That is, you can't have two clients doing writes and >>>>>>> coordinating a >>>>>>> single transaction. That's certainly the case for almost all relational >>>>>>> databases. Spark, on the other hand, will have multiple clients >>>>>>> (consider >>>>>>> each task a client) writing to the same underlying data store. >>>>>>> >>>>>>> On Mon, Sep 10, 2018 at 10:19 AM Ryan Blue <rb...@netflix.com> >>>>>>> wrote: >>>>>>> >>>>>>> Ross, I think the intent is to create a single transaction on the >>>>>>> driver, write as part of it in each task, and then commit the >>>>>>> transaction >>>>>>> once the tasks complete. Is that possible in your implementation? >>>>>>> >>>>>>> I think that part of this is made more difficult by not having a >>>>>>> clear starting point for a write, which we are fixing in the redesign of >>>>>>> the v2 API. That will have a method that creates a Write to track the >>>>>>> operation. That can create your transaction when it is created and >>>>>>> commit >>>>>>> the transaction when commit is called on it. >>>>>>> >>>>>>> rb >>>>>>> >>>>>>> On Mon, Sep 10, 2018 at 9:05 AM Reynold Xin <r...@databricks.com> >>>>>>> wrote: >>>>>>> >>>>>>> Typically people do it via transactions, or staging tables. >>>>>>> >>>>>>> >>>>>>> On Mon, Sep 10, 2018 at 2:07 AM Ross Lawley <ross.law...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>> Hi all, >>>>>>> >>>>>>> I've been prototyping an implementation of the DataSource V2 writer >>>>>>> for the MongoDB Spark Connector and I have a couple of questions about >>>>>>> how >>>>>>> its intended to be used with database systems. According to the Javadoc >>>>>>> for >>>>>>> DataWriter.commit(): >>>>>>> >>>>>>> *"this method should still "hide" the written data and ask the >>>>>>> DataSourceWriter at driver side to do the final commit via >>>>>>> WriterCommitMessage"* >>>>>>> >>>>>>> Although, MongoDB now has transactions, it doesn't have a way to >>>>>>> "hide" the data once it has been written. So as soon as the DataWriter >>>>>>> has >>>>>>> committed the data, it has been inserted/updated in the collection and >>>>>>> is >>>>>>> discoverable - thereby breaking the documented contract. >>>>>>> >>>>>>> I was wondering how other databases systems plan to implement this >>>>>>> API and meet the contract as per the Javadoc? >>>>>>> >>>>>>> Many thanks >>>>>>> >>>>>>> Ross >>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Ryan Blue >>>>>>> Software Engineer >>>>>>> Netflix >>>>>>> >>>>>>> >>>>>>> >>>>>>> --------------------------------------------------------------------- >>>>>>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org >>>>>> >>>>>> -- Ryan Blue Software Engineer Netflix
