Stuart, _key and _val fields is quite a dirty hack that was added years ago and is virtually never used now. We never recommend to use these fields and I would definitely avoid building new features based on them.
Having said that, I'm not arguing the use case, but we need better implementation approach here. I suggest we think it over and come back to this next week :) I'm sure Nikolay will also chime in and share his thoughts. -Val On Fri, Jul 27, 2018 at 12:39 PM Stuart Macdonald <stu...@stuwee.org> wrote: > If your predicates and joins are expressed in Spark SQL, you cannot > currently optimise those and also gain access to the key/val objects. If > you went without the Ignite Spark SQL optimisations and expressed your > query in Ignite SQL, you still need to use the _key/_val columns. The > Ignite documentation has this specific example using the _val column (right > at the end): > https://apacheignite-fs.readme.io/docs/ignitecontext-igniterdd > > Stuart. > > On 27 Jul 2018, at 20:05, Valentin Kulichenko < > valentin.kuliche...@gmail.com> > wrote: > > Well, the second approach would use the optimizations, no? > > -Val > > > On Fri, Jul 27, 2018 at 11:49 AM Stuart Macdonald <stu...@stuwee.org> > wrote: > > Val, > > > Yes you can already get access to the cache objects as an RDD or > > Dataset but you can’t use the Ignite-optimised DataFrames with these > > mechanisms. Optimised DataFrames have to be passed through Spark SQL’s > > Catalyst engine to allow for predicate pushdown to Ignite. So the > > usecase we’re talking about here is when we want to be able to push > > Spark filters/joins to Ignite to optimise, but still have access to > > the underlying cache objects, which is not possible currently. > > > Can you elaborate on the reason _key and _val columns in Ignite SQL > > will be removed? > > > Stuart. > > > On 27 Jul 2018, at 19:39, Valentin Kulichenko < > > valentin.kuliche...@gmail.com> wrote: > > > Stuart, Nikolay, > > > I really don't like the idea of exposing '_key' and '_val' fields. This > > is > > legacy stuff that hopefully will be removed altogether one day. Let's not > > use it in new features. > > > Actually, I don't even think it's even needed. Spark docs [1] suggest two > > ways of creating a typed dataset: > > 1. Based on RDD. This should be supported using IgniteRDD I believe. > > 2. Based on DataFrame providing a class. This would just work out of the > > box I guess. > > > Of course, this needs to be tested and verified, and there might be > > certain > > pieces missing to fully support the use case. But generally I like these > > approaches much more. > > > > > https://spark.apache.org/docs/2.3.1/sql-programming-guide.html#creating-datasets > > > -Val > > > On Fri, Jul 27, 2018 at 6:31 AM Stuart Macdonald <stu...@stuwee.org> > > wrote: > > > Here’s the ticket: > > > https://issues.apache.org/jira/browse/IGNITE-9108 > > > Stuart. > > > > On Friday, 27 July 2018 at 14:19, Nikolay Izhikov wrote: > > > Sure. > > > Please, send ticket number in this thread. > > > пт, 27 июля 2018 г., 16:16 Stuart Macdonald <stu...@stuwee.org > > (mailto: > > stu...@stuwee.org)>: > > > Thanks Nikolay. For both options if the cache object isn’t a simple > > type, > > we’d probably do something like this in our Ignite SQL statement: > > > select cast(_key as binary), cast(_val as binary), ... > > > Which would give us the BinaryObject’s byte[], then for option 1 we > > keep > > the Ignite format and introduce a new Spark Encoder for Ignite binary > > types > > ( > > > > > > https://spark.apache.org/docs/2.1.0/api/java/org/apache/spark/sql/Encoder.html > > ), > > so that the end user interface would be something like: > > > IgniteSparkSession session = ... > > Dataset<Row> dataFrame = ... > > Dataset<MyValClass> valDataSet = > > > > dataFrame.select(“_val_).as(session.binaryObjectEncoder(MyValClass.class)) > > > Or for option 2 we have a behind-the-scenes Ignite-to-Kryo UDF so that > > the > > user interface would be standard Spark: > > > Dataset<Row> dataFrame = ... > > DataSet<MyValClass> dataSet = > > dataFrame.select(“_val_).as(Encoders.kryo(MyValClass.class)) > > > I’ll create a ticket and maybe put together a test case for further > > discussion? > > > Stuart. > > > On 27 Jul 2018, at 09:50, Nikolay Izhikov <nizhi...@apache.org > > (mailto:nizhi...@apache.org <nizhi...@apache.org>)> wrote: > > > Hello, Stuart. > > > I like your idea. > > > 1. Ignite BinaryObjects, in which case we’d need to supply a Spark > > Encoder > > implementation for BinaryObjects > > > 2. Kryo-serialised versions of the objects. > > > > Seems like first option is simple adapter. Am I right? > > If yes, I think it's a more efficient way comparing with > > transformation of > > each object to some other(Kryo) format. > > > Can you provide some additional links for both options? > > Where I can find API or(and) examples? > > > As a second step, we can apply same approach to the regular key, value > > caches. > > > Feel free to create a ticket. > > > В Пт, 27/07/2018 в 09:37 +0100, Stuart Macdonald пишет: > > > Ignite Dev Community, > > > > Within Ignite-supplied Spark DataFrames, I’d like to propose adding > > support > > > for _key and _val columns which represent the cache key and value > > objects > > > similar to the current _key/_val column semantics in Ignite SQL. > > > > If the cache key or value objects are standard SQL types (eg. String, > > Int, > > > etc) they will be represented as such in the DataFrame schema, > > otherwise > > > they are represented as Binary types encoded as either: 1. Ignite > > > BinaryObjects, in which case we’d need to supply a Spark Encoder > > > implementation for BinaryObjects, or 2. Kryo-serialised versions of > > the > > > objects. Option 1 would probably be more efficient but option 2 would > > be > > > more idiomatic Spark. > > > > This feature would be controlled with an optional parameter in the > > Ignite > > > data source, defaulting to the current implementation which doesn’t > > supply > > > _key or _val columns. The rationale behind this is the same as the > > Ignite > > > SQL _key and _val columns: to allow access to the full cache objects > > from a > > > SQL context. > > > > Can I ask for feedback on this proposal please? > > > > I’d be happy to contribute this feature if we agree on the concept. > > > > Stuart. >
