Hi Alexis, The Scala API does not expose a DataSource object but only a Scala DataSet which wraps the Java object. You can get the SplitDataProperties from the Scala DataSet as follows:
val dbData: DataSet[...] = ??? val sdp = dbData.javaSet.asInstanceOf[DataSource].getSplitDataProperties So you first have to get the wrapped Java DataSet, cast it to DataSource and then get the properties. It's not very nice, but should work. In order to use SDPs, you should be a bit familiar how physical data properties are propagated and discarded in the optimizer. For example, applying a simple MapFunction removes all properties because the function might have changed the fields on which a DataSet is partitioned or sorted. You can expose the behavior of a function to the optimizer by using Semantic Annotations [1] Some comments on the code and plan you shared: - You might want to add hostname to ORDER BY to have the output grouped by (ts, hostname). - Check the Global and Local data properties in the plan to validate that the SDP were correctly interpreted. - If the data is already correctly partitioned and sorted, you might not need the Combiners. In either case, you properly want to annotate them with Forward Field annoations. The number of source tasks is unrelated to the number of splits. If you have more tasks than splits, some tasks won't process any data. Best, Fabian [1] https://ci.apache.org/projects/flink/flink-docs-release-1.5/dev/batch/# semantic-annotations 2018-08-08 14:10 GMT+02:00 Alexis Sarda <alexis.sa...@gmail.com>: > Hi Fabian, > > Thanks for the clarification. I have a few remarks, but let me provide > more concrete information. You can find the query I'm using, the > JDBCInputFormat creation, and the execution plan in this github gist: > > https://gist.github.com/asardaes/8331a117210d4e08139c66c86e8c952d > > I cannot call getSplitDataProperties because env.createInput(inputFormat) > returns a DataSet, not a DataSource. In the code, I do this instead: > > val javaEnv = org.apache.flink.api.java.ExecutionEnvironment.getExecutionE > nvironment > val dataSource = new DataSource(javaEnv, inputFormat, rowTypeInfo, > "example") > > which feels wrong (the constructor doesn't accept a Scala environment). Is > there a better alternative? > > I see absolutely no difference in the execution plan whether I use SDP or > not, so therefore the results are indeed the same. Is this expected? > > My ParameterValuesProvider specifies 2 splits, yet the execution plan > shows Parallelism=24. Even the source code is a bit ambiguous, considering > that the constructor for GenericInputSplit takes two parameters: > partitionNumber and totalNumberOfPartitions. Should I assume that there are > 2 splits divided into 24 partitions? > > Regards, > Alexis. > > > > On Wed, Aug 8, 2018 at 11:57 AM Fabian Hueske <fhue...@gmail.com> wrote: > >> Hi Alexis, >> >> First of all, I think you leverage the partitioning and sorting >> properties of the data returned by the database using SplitDataProperties. >> However, please be aware that SplitDataProperties are a rather >> experimental feature. >> >> If used without query parameters, the JDBCInputFormat generates a single >> split and queries the database just once. If you want to leverage >> parallelism, you have to specify a query with parameters in the WHERE >> clause to read different parts of the table. >> Note, depending on the configuration of the database, multiple queries >> result in multiple full scans. Hence, it might make sense to have an index >> on the partitioning columns. >> >> If properly configured, the JDBCInputFormat generates multiple splits >> which are partitioned. Since the partitioning is encoded in the query, it >> is opaque to Flink and must be explicitly declared. >> This can be done with SDPs. The SDP.splitsPartitionedBy() method tells >> Flink that all records with the same value in the partitioning field are >> read from the same split, i.e, the full data is partitioned on the >> attribute across splits. >> The same can be done for ordering if the queries of the JDBCInputFormat >> is specified with an ORDER BY clause. >> Partitioning and grouping are two different things. You can define a >> query that partitions on hostname and orders by hostname and timestamp and >> declare these properties in the SDP. >> >> You can get a SDP object by calling DataSource.getSplitDataProperties(). >> In your example this would be source.getSplitDataProperties(). >> >> Whatever you do, you should carefully check the execution plan >> (ExecutionEnvironment.getExecutionPlan()) using the plan visualizer [1] >> and validate that the result are identical whether you use SDP or not. >> >> Best, Fabian >> >> [1] https://flink.apache.org/visualizer/ >> >> 2018-08-07 22:32 GMT+02:00 Alexis Sarda <alexis.sa...@gmail.com>: >> >>> Hi everyone, >>> >>> I have the following scenario: I have a database table with 3 columns: a >>> host (string), a timestamp, and an integer ID. Conceptually, what I'd like >>> to do is: >>> >>> group by host and timestamp -> based on all the IDs in each group, >>> create a mapping to n new tuples -> for each unique tuple, count how many >>> times it appeared across the resulting data >>> >>> Each new tuple has 3 fields: the host, a new ID, and an Integer=1 >>> >>> What I'm currently doing is roughly: >>> >>> val input = JDBCInputFormat.buildJDBCInputFormat()...finish() >>> val source = environment.createInput(inut) >>> source.partitionByHash("host", "timestamp").mapPartition(...).groupBy(0, >>> 1).aggregate(SUM, 2) >>> >>> The query given to JDBCInputFormat provides results ordered by host and >>> timestamp, and I was wondering if performance can be improved by specifying >>> this in the code. I've looked at http://apache-flink-user-ma >>> iling-list-archive.2336050.n4.nabble.com/Terminology-Split- >>> Group-and-Partition-td11030.html and http://apache-flink-user-m >>> ailing-list-archive.2336050.n4.nabble.com/Fwd-Processing-Sor >>> ted-Input-Datasets-td20038.html, but I still have some questions: >>> >>> - If a split is a subset of a partition, what is the meaning of >>> SplitDataProperties#splitsPartitionedBy? The wording makes me thing >>> that a split is divided into partitions, meaning that a partition would be >>> a subset of a split. >>> - At which point can I retrieve and adjust a SplitDataProperties >>> instance, if possible at all? >>> - If I wanted a coarser parallelization where each slot gets all the >>> data for the same host, would I have to manually create the sub-groups >>> based on timestamp? >>> >>> Regards, >>> Alexis. >>> >>> >>
