Thanks for the info Ryan – very helpful! From: Ryan Blue <rb...@netflix.com> Reply-To: "rb...@netflix.com" <rb...@netflix.com> Date: Wednesday, September 19, 2018 at 3:17 PM To: "Thakrar, Jayesh" <jthak...@conversantmedia.com> Cc: Wenchen Fan <cloud0...@gmail.com>, Hyukjin Kwon <gurwls...@gmail.com>, Spark Dev List <dev@spark.apache.org> Subject: Re: data source api v2 refactoring
Hi Jayesh, The existing sources haven't been ported to v2 yet. That is going to be tricky because the existing sources implement behaviors that we need to keep for now. I wrote up an SPIP to standardize logical plans while moving to the v2 sources. The reason why we need this is that too much is delegated to sources today. For example, sources are handed a SaveMode to overwrite data, but what exactly gets overwritten isn't defined and it varies by the source that gets used. That's not a good thing and we want to clean up what happens so that users know that a query behaves the same way across all v2 sources. CTAS shouldn't succeed for one source but fail for another if the table already exists. Standardizing plans makes it difficult to port the existing sources to v2 because we need to implement the behavior of the v2 plans, which may not be the existing v1 behavior. I think what we should do is keep the existing v1 sources working as they do today, and add a way to opt in for v2 behavior. One good way to do this is to use a new write API that is more clear; I proposed one in the SPIP I mentioned earlier. SQL is a bit easier because the behavior for SQL is fairly well-defined. The problem is mostly with the existing DF write API, DataFrameWriter. It would be great to open a discussion about the compatibility between v1 and v2 and come up with a plan on this list. rb On Fri, Sep 7, 2018 at 2:12 PM Thakrar, Jayesh <jthak...@conversantmedia.com<mailto:jthak...@conversantmedia.com>> wrote: Ryan et al, Wondering if the existing Spark based data sources (e.g. for HDFS, Kafka) have been ported to V2. I remember reading threads where there were discussions about the inefficiency/overhead of converting from Row to InternalRow that was preventing certain porting effort etc. I ask because those are the most widely used data sources and have a lot of effort and thinking behind them, and if they have ported over to V2, then they can serve as excellent production examples of V2 API. Thanks, Jayesh From: Ryan Blue <rb...@netflix.com.INVALID> Reply-To: <rb...@netflix.com<mailto:rb...@netflix.com>> Date: Friday, September 7, 2018 at 2:19 PM To: Wenchen Fan <cloud0...@gmail.com<mailto:cloud0...@gmail.com>> Cc: Hyukjin Kwon <gurwls...@gmail.com<mailto:gurwls...@gmail.com>>, Spark Dev List <dev@spark.apache.org<mailto:dev@spark.apache.org>> Subject: Re: data source api v2 refactoring There are a few v2-related changes that we can work in parallel, at least for reviews: * SPARK-25006, #21978<https://github.com/apache/spark/pull/21978>: Add catalog to TableIdentifier - this proposes how to incrementally add multi-catalog support without breaking existing code paths * SPARK-24253, #21308<https://github.com/apache/spark/pull/21308>: Add DeleteSupport API - this is a small API addition, which doesn't affect the refactor * SPARK-24252, #21306<https://github.com/apache/spark/pull/21306>: Add v2 Catalog API - this is a different way to create v2 tables, also doesn't affect the refactor I agree that the PR for adding SQL support should probably wait on the refactor. I have also been meaning to share our implementation, which isn't based on the refactor. It handles CTAS, RTAS, InsertInto, DeleteFrom, and AlterTable from both SQL and the other methods in the DF API, saveAsTable and insertInto. It follows the structure that I proposed on the SQL support PR to convert SQL plans to v2 plans and uses the new TableCatalog to implement CTAS and RTAS. rb On Fri, Sep 7, 2018 at 12:27 AM Wenchen Fan <cloud0...@gmail.com<mailto:cloud0...@gmail.com>> wrote: Hi Ryan, You are right that the `LogicalWrite` mirrors the read side API. I just don't have a good naming yet, and write side changes will be a different PR. Hi Hyukjin, That's my expectation, otherwise we keep rebasing the refactor PR and never get it done. On Fri, Sep 7, 2018 at 3:02 PM Hyukjin Kwon <gurwls...@gmail.com<mailto:gurwls...@gmail.com>> wrote: BTW, do we hold Datasource V2 related PRs for now until we finish this refactoring just for clarification? 2018년 9월 7일 (금) 오전 12:52, Ryan Blue <rb...@netflix.com.invalid>님이 작성: Wenchen, I'm not really sure what you're proposing here. What is a `LogicalWrite`? Is it something that mirrors the read side in your PR? I think that I agree that if we have a Write independent of the Table that carries the commit and abort methods, then we can create it directly without a WriteConfig. So I tentatively agree with what you propose, assuming that I understand it correctly. rb On Tue, Sep 4, 2018 at 8:42 PM Wenchen Fan <cloud0...@gmail.com<mailto:cloud0...@gmail.com>> wrote: I'm switching to my another Gmail account, let's see if it still gets dropped this time. Hi Ryan, I'm thinking about the write path and feel the abstraction should be the same. We still have logical and physical writing. And the table can create different logical writing based on how to write. e.g., append, delete, replaceWhere, etc. One thing I'm not sure about is the WriteConfig. With the WriteConfig, the API would look like trait Table { WriteConfig newAppendWriteConfig(); WriteConfig newDeleteWriteConfig(deleteExprs); LogicalWrite newLogicalWrite(writeConfig); } Without WriteConfig, the API looks like trait Table { LogicalWrite newAppendWrite(); LogicalWrite newDeleteWrite(deleteExprs); } It looks to me that the API is simpler without WriteConfig, what do you think? Thanks, Wenchen On Wed, Sep 5, 2018 at 4:24 AM Ryan Blue <rb...@netflix.com.invalid> wrote: Latest from Wenchen in case it was dropped. ---------- Forwarded message --------- From: Wenchen Fan <wenc...@databricks.com<mailto:wenc...@databricks.com>> Date: Mon, Sep 3, 2018 at 6:16 AM Subject: Re: data source api v2 refactoring To: <mri...@gmail.com<mailto:mri...@gmail.com>> Cc: Ryan Blue <rb...@netflix.com<mailto:rb...@netflix.com>>, Reynold Xin <r...@databricks.com<mailto:r...@databricks.com>>, <dev@spark.apache.org<mailto:dev@spark.apache.org>> Hi Mridul, I'm not sure what's going on, my email was CC'ed to the dev list. Hi Ryan, The logical and physical scan idea sounds good. To add more color to Jungtaek's question, both micro-batch and continuous mode have the logical and physical scan, but there is a difference: for micro-batch mode, a physical scan outputs data for one epoch, but it's not true for continuous mode. I'm not sure if it's necessary to include streaming epoch in the API abstraction, for features like metrics reporting. On Sun, Sep 2, 2018 at 12:31 PM Mridul Muralidharan <mri...@gmail.com<mailto:mri...@gmail.com>> wrote: Is it only me or are all others getting Wenchen’s mails ? (Obviously Ryan did :-) ) I did not see it in the mail thread I received or in archives ... [1] Wondering which othersenderswere getting dropped (if yes). Regards Mridul [1] http://apache-spark-developers-list.1001551.n3.nabble.com/data-source-api-v2-refactoring-td24848.html On Sat, Sep 1, 2018 at 8:58 PM Ryan Blue <rb...@netflix.com.invalid> wrote: Thanks for clarifying, Wenchen. I think that's what I expected. As for the abstraction, here's the way that I think about it: there are two important parts of a scan: the definition of what will be read, and task sets that actually perform the read. In batch, there's one definition of the scan and one task set so it makes sense that there's one scan object that encapsulates both of these concepts. For streaming, we need to separate the two into the definition of what will be read (the stream or streaming read) and the task sets that are run (scans). That way, the streaming read behaves like a factory for scans, producing scans that handle the data either in micro-batches or using continuous tasks. To address Jungtaek's question, I think that this does work with continuous. In continuous mode, the query operators keep running and send data to one another directly. The API still needs a streaming read layer because it may still produce more than one continuous scan. That would happen when the underlying source changes and Spark needs to reconfigure. I think the example here is when partitioning in a Kafka topic changes and Spark needs to re-map Kafka partitions to continuous tasks. rb On Fri, Aug 31, 2018 at 5:12 PM Wenchen Fan <wenc...@databricks.com<mailto:wenc...@databricks.com>> wrote: Hi Ryan, Sorry I may use a wrong wording. The pushdown is done with ScanConfig, which is not table/stream/scan, but something between them. The table creates ScanConfigBuilder, and table creates stream/scan with ScanConfig. For streaming source, stream is the one to take care of the pushdown result. For batch source, it's the scan. It's a little tricky because stream is an abstraction for streaming source only. Better ideas are welcome! On Sat, Sep 1, 2018 at 7:26 AM Ryan Blue <rb...@netflix.com<mailto:rb...@netflix.com>> wrote: Thanks, Reynold! I think your API sketch looks great. I appreciate having the Table level in the abstraction to plug into as well. I think this makes it clear what everything does, particularly having the Stream level that represents a configured (by ScanConfig) streaming read and can act as a factory for individual batch scans or for continuous scans. Wenchen, I'm not sure what you mean by doing pushdown at the table level. It seems to mean that pushdown is specific to a batch scan or streaming read, which seems to be what you're saying as well. Wouldn't the pushdown happen to create a ScanConfig, which is then used as Reynold suggests? Looking forward to seeing this PR when you get it posted. Thanks for all of your work on this! rb On Fri, Aug 31, 2018 at 3:52 PM Wenchen Fan <wenc...@databricks.com<mailto:wenc...@databricks.com>> wrote: Thank Reynold for writing this and starting the discussion! Data source v2 was started with batch only, so we didn't pay much attention to the abstraction and just follow the v1 API. Now we are designing the streaming API and catalog integration, the abstraction becomes super important. I like this proposed abstraction and have successfully prototyped it to make sure it works. During prototyping, I have to work around the issue that the current streaming engine does query optimization/planning for each micro batch. With this abstraction, the operator pushdown is only applied once per-query. In my prototype, I do the physical planning up front to get the pushdown result, and add a logical linking node that wraps the resulting physical plan node for the data source, and then swap that logical linking node into the logical plan for each batch. In the future we should just let the streaming engine do query optimization/planning only once. About pushdown, I think we should do it at the table level. The table should create a new pushdow handler to apply operator pushdowm for each scan/stream, and create the scan/stream with the pushdown result. The rationale is, a table should have the same pushdown behavior regardless the scan node. Thanks, Wenchen On Fri, Aug 31, 2018 at 2:00 PM Reynold Xin <r...@databricks.com<mailto:r...@databricks.com>> wrote: I spent some time last week looking at the current data source v2 apis, and I thought we should be a bit more buttoned up in terms of the abstractions and the guarantees Spark provides. In particular, I feel we need the following levels of "abstractions", to fit the use cases in Spark, from batch, to streaming. Please don't focus on the naming at this stage. When possible, I draw parallels to what similar levels are named in the currently committed api: 0. Format: This represents a specific format, e.g. Parquet, ORC. There is currently no explicit class at this level. 1. Table: This should represent a logical dataset (with schema). This could be just a directory on the file system, or a table in the catalog. Operations on tables can include batch reads (Scan), streams, writes, and potentially other operations such as deletes. The closest to the table level abstraction in the current code base is the "Provider" class, although Provider isn't quite a Table. This is similar to Ryan's proposed design. 2. Stream: Specific to streaming. A stream is created out of a Table. This logically represents a an instance of a StreamingQuery. Pushdowns and options are handled at this layer. I.e. Spark guarnatees to data source implementation pushdowns and options don't change within a Stream. Each Stream consists of a sequence of scans. There is no equivalent concept in the current committed code. 3. Scan: A physical scan -- either as part of a streaming query, or a batch query. This should contain sufficient information and methods so we can run a Spark job over a defined subset of the table. It's functionally equivalent to an RDD, except there's no dependency on RDD so it is a smaller surface. In the current code, the equivalent class would be the ScanConfig, which represents the information needed, but in order to execute a job, ReadSupport is needed (various methods in ReadSupport takes a ScanConfig). To illustrate with pseudocode what the different levels mean, a batch query would look like the following: val provider = reflection[Format]("parquet") val table = provider.createTable(options) val scan = table.createScan(scanConfig) // scanConfig includes pushdown and options // run tasks on executors A streaming micro-batch scan would look like the following: val provider = reflection[Format]("parquet") val table = provider.createTable(options) val stream = table.createStream(scanConfig) while(true) { val scan = streamingScan.createScan(startOffset) // run tasks on executors } Vs the current API, the above: 1. Creates an explicit Table abstraction, and an explicit Scan abstraction. 2. Have an explicit Stream level and makes it clear pushdowns and options are handled there, rather than at the individual scan (ReadSupport) level. Data source implementations don't need to worry about pushdowns or options changing mid-stream. For batch, those happen when the scan object is created. This email is just a high level sketch. I've asked Wenchen to prototype this, to see if it is actually feasible and the degree of hacks it removes, or creates. -- Ryan Blue Software Engineer Netflix -- Ryan Blue Software Engineer Netflix -- Ryan Blue Software Engineer Netflix -- Ryan Blue Software Engineer Netflix -- Ryan Blue Software Engineer Netflix -- Ryan Blue Software Engineer Netflix
