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 <dbiswal@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
dbiswal@us.ibm.com
 
 
----- Original message -----
From: Reynold Xin <rxin@databricks.com>
To: Ryan Blue <rblue@netflix.com>
Cc: ross.lawley@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 <rblue@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 <rxin@databricks.com> wrote:
Typically people do it via transactions, or staging tables.
 
 
On Mon, Sep 10, 2018 at 2:07 AM Ross Lawley <ross.lawley@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
 

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