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From "Thakrar, Jayesh" <jthak...@conversantmedia.com>
Subject Re: DataSourceWriter V2 Api questions
Date Thu, 13 Sep 2018 15:08:04 GMT
Agree on the “constraints” when working with Cassandra.
But remember, this is a weak attempt to make two non-transactional systems appear to the outside
world as a transactional system.
Scaffolding/plumbing/abstractions will have to be created in the form of say, a custom data
access layer.

Anyway, Ross is trying to get some practices used by other adopters of the V2 API while trying
to implement a driver/connector for MongoDB.

Probably views can be used similar to partitions in mongoDB?
Essentially each batch load goes into a separate mongoDB table and will result in view redefinition
after a successful load.
And finally to avoid too many tables in a view, you may have to come up with a separate process
to merge the underlying tables on a periodic basis.
It gets messy and probably moves you towards a write-once only tables, etc.

Finally using views in a generic mongoDB connector may not be good and flexible enough.


From: Russell Spitzer <russell.spitzer@gmail.com>
Date: Tuesday, September 11, 2018 at 9:58 AM
To: "Thakrar, Jayesh" <jthakrar@conversantmedia.com>
Cc: Arun Mahadevan <arunm@apache.org>, Jungtaek Lim <kabhwan@gmail.com>, Wenchen
Fan <cloud0fan@gmail.com>, Reynold Xin <rxin@databricks.com>, Ross Lawley <ross.lawley@gmail.com>,
Ryan Blue <rblue@netflix.com>, dev <dev@spark.apache.org>, "dbiswal@us.ibm.com"
<dbiswal@us.ibm.com>
Subject: Re: DataSourceWriter V2 Api questions

That only works assuming that Spark is the only client of the table. It will be impossible
to force an outside user to respect the special metadata table when reading so they will still
see all of the data in transit. Additionally this would force the incoming data to only be
written into new partitions which is not simple to do from a C* perspective as balancing the
distribution of new rows would be non trivial. If we had to do something like this we would
basically be forced to write to some disk format first and then when we move the data into
C* we still have the same problem that we started with.

On Tue, Sep 11, 2018 at 9:41 AM Thakrar, Jayesh <jthakrar@conversantmedia.com<mailto:jthakrar@conversantmedia.com>>
wrote:
So if Spark and the destination datastore are both non-transactional, you will have to resort
to an external mechanism for “transactionality”.

Here are some options for both RDBMS and non-transaction datastore destination.
For now assuming that Spark is used in batch mode (and not streaming mode).

RDBMS Options
Use staging table as discussed in the thread.

As an extension of the above, use partitioned destination tables and load data into a staging
table and then use partition management to include the staging table into the partitioned
table.
This this implies a partition per Spark batch run.

Non-transactional Datastore Options
Use another metadata table.
Load the data into a staging table equivalent or even Cassandra partition(s).
Start the transaction by making a “start of transaction” entry into the metadata table
along with partition keys to be populated.
As part of Spark batch commit, update the metadata entry with appropriate details – e.g.
partition load time, etc.
In the event of a failed / incomplete batch, the metadata table entry will be incomplete and
the corresponding partition keys can be dropped.
So essentially you use the metadata table to load/drop/skip the data to be moved/retained
into the final destination.

Misc
Another option is to use Spark to stage data into a filesystem (distributed, HDFS) and then
use RDBMS utilities to transactionally load data into the destination table.


From: Russell Spitzer <russell.spitzer@gmail.com<mailto:russell.spitzer@gmail.com>>
Date: Tuesday, September 11, 2018 at 9:08 AM
To: Arun Mahadevan <arunm@apache.org<mailto:arunm@apache.org>>
Cc: Jungtaek Lim <kabhwan@gmail.com<mailto:kabhwan@gmail.com>>, Wenchen Fan <cloud0fan@gmail.com<mailto:cloud0fan@gmail.com>>,
Reynold Xin <rxin@databricks.com<mailto:rxin@databricks.com>>, Ross Lawley <ross.lawley@gmail.com<mailto:ross.lawley@gmail.com>>,
Ryan Blue <rblue@netflix.com<mailto:rblue@netflix.com>>, dev <dev@spark.apache.org<mailto:dev@spark.apache.org>>,
<dbiswal@us.ibm.com<mailto:dbiswal@us.ibm.com>>

Subject: Re: DataSourceWriter V2 Api questions

I'm still not sure how the staging table helps for databases which do not have such atomicity
guarantees. For example in Cassandra if you wrote all of the data temporarily to a staging
table, we would still have the same problem in moving the data from the staging table into
the real table. We would likely have as similar a chance of failing and we still have no way
of making the entire staging set simultaneously visible.

On Tue, Sep 11, 2018 at 8:39 AM Arun Mahadevan <arunm@apache.org<mailto:arunm@apache.org>>
wrote:
>Some being said it is exactly-once when the output is eventually exactly-once, whereas
others being said there should be no side effect, like consumer shouldn't see partial write.
I guess 2PC is former, since some partitions can commit earlier while other partitions fail
to commit for some time.
Yes its more about guaranteeing atomicity like all partitions eventually commit or none commits.
The visibility of the data for the readers is orthogonal (e.g setting the isolation levels
like serializable for XA) and in general its difficult to guarantee that data across partitions
are visible at once. The approach like staging table and global commit works in a centralized
set up but can be difficult to do in a distributed manner across partitions (e.g each partition
output goes to a different database)

On Mon, 10 Sep 2018 at 21:23, Jungtaek Lim <kabhwan@gmail.com<mailto:kabhwan@gmail.com>>
wrote:
IMHO that's up to how we would like to be strict about "exactly-once".

Some being said it is exactly-once when the output is eventually exactly-once, whereas others
being said there should be no side effect, like consumer shouldn't see partial write. I guess
2PC is former, since some partitions can commit earlier while other partitions fail to commit
for some time.

Being said, there may be couple of alternatives other than the contract Spark provides/requires,
and I'd like to see how Spark community wants to deal with others. Would we want to disallow
alternatives, like "replay + deduplicate write (per a batch/partition)" which ensures "eventually"
exactly-once but cannot ensure the contract?

Btw, unless achieving exactly-once is light enough for given sink, I think the sink should
provide both at-least-once (also optimized for the semantic) vs exactly-once, and let end
users pick one.

2018년 9월 11일 (화) 오후 12:57, Russell Spitzer <russell.spitzer@gmail.com<mailto:russell.spitzer@gmail.com>>님이
작성:
Why is atomic operations a requirement? I feel like doubling the amount of writes (with staging
tables) is probably a tradeoff that the end user should make.
On Mon, Sep 10, 2018, 10:43 PM Wenchen Fan <cloud0fan@gmail.com<mailto:cloud0fan@gmail.com>>
wrote:
Regardless the API, to use Spark to write data atomically, it requires
1. Write data distributedly, with a central coordinator at Spark driver.
2. The distributed writers are not guaranteed to run together at the same time. (This can
be relaxed if we can extend the barrier scheduling feature)
3. The new data is visible if and only if all distributed writers success.

According to these requirements, I think using a staging table is the most common way and
maybe the only way. I'm not sure how 2PC can help, we don't want users to read partial data,
so we need a final step to commit all the data together.

For RDBMS data sources, I think a simple solution is to ask users to coalesce the input RDD/DataFrame
into one partition, then we don't need to care about multi-client transaction. Or using a
staging table like Ryan described before.



On Tue, Sep 11, 2018 at 5:10 AM Jungtaek Lim <kabhwan@gmail.com<mailto:kabhwan@gmail.com>>
wrote:
> And regarding the issue that Jungtaek brought up, 2PC doesn't require tasks to be running
at the same time, we need a mechanism to take down tasks after they have prepared and bring
up the tasks during the commit phase.

I guess we already got into too much details here, but if it is based on client transaction
Spark must assign "commit" tasks to the executor which task was finished "prepare", and if
it loses executor it is not feasible to force committing. Staging should come into play for
that.

We should also have mechanism for "recovery": Spark needs to ensure it finalizes "commit"
even in case of failures before starting a new batch.

So not an easy thing to integrate correctly.
2018년 9월 11일 (화) 오전 6:00, Arun Mahadevan <arunm@apache.org<mailto:arunm@apache.org>>님이
작성:
>Well almost all relational databases you can move data in a transactional way. That’s
what transactions are for.

It would work, but I suspect in most cases it would involve moving data from temporary tables
to the final tables

Right now theres no mechanisms to let the individual tasks commit in a two-phase manner (Not
sure if the CommitCordinator might help). If such an API is provided, the sources could use
it as they wish (e.g. use XA support provided by mysql to implement it in a more efficient
way than the driver moving from temp tables to destination tables).

Definitely there are complexities involved, but I am not sure if the network partitioning
comes into play here since the driver can act as the co-ordinator and can run in HA mode.
And regarding the issue that Jungtaek brought up, 2PC doesn't require tasks to be running
at the same time, we need a mechanism to take down tasks after they have prepared and bring
up the tasks during the commit phase.

Most of the sources would not need any of the above and just need a way to support Idempotent
writes and like Ryan suggested we can enable this (if there are gaps in the current APIs).


On Mon, 10 Sep 2018 at 13:43, Reynold Xin <rxin@databricks.com<mailto:rxin@databricks.com>>
wrote:
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 <kabhwan@gmail.com<mailto:kabhwan@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 <rxin@databricks.com<mailto:rxin@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 <kabhwan@gmail.com<mailto:kabhwan@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 <arunm@apache.org<mailto:arunm@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 <dbiswal@us.ibm.com<mailto: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<tel:(408)%20463-4980>
dbiswal@us.ibm.com<mailto:dbiswal@us.ibm.com>


----- Original message -----
From: Reynold Xin <rxin@databricks.com<mailto:rxin@databricks.com>>
To: Ryan Blue <rblue@netflix.com<mailto:rblue@netflix.com>>
Cc: ross.lawley@gmail.com<mailto:ross.lawley@gmail.com>, dev <dev@spark.apache.org<mailto: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<mailto: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<mailto: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<mailto: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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