It seems I may have spoken too soon. After executing the job with more data, I can see the following things in the Flink dashboard:- The first subtask is a chained DataSource -> GroupCombine. Even with parallelism set to 24 and a ParameterValuesProvider returning Array(Array("first"), Array("second")), only 1 thread processed all records.- The second subtask is a Sorted Group Reduce, and I see two weird things:+ The first subtask sent 5,923,802 records, yet the second subtask only received 5,575,154 records?+ Again, everything was done in a single thread, even though a groupBy was used.- The third and final subtask is a sink that saves back to the database.Does anyone know why parallelism is not being used?Regards,Alexis.On Thu, Aug 9, 2018 at 11:22 AM Alexis Sarda <alexis.sarda@xxxxxxxxx> wrote:Hi Fabian,Thanks a lot for the help. The scala DataSet, at least in version 1.5.0, declares javaSet as private[flink], so I cannot access it directly. Nevertheless, I managed to get around it by using the java environment:val env = org.apache.flink.api.java.
ExecutionEnvironment.getExecut ionEnvironmentval inputFormat = getInputFormat(query, dbUrl, properties)
val outputFormat = getOutputFormat(dbUrl, properties)
val source = env.createInput(inputFormat)
val sdp = source.getSplitDataProperties
// transform java DataSet to scala DataSet...
.output(outputFormat)It seems to work well, and the semantic annotation does remove a hash partition from the execution plan.Regards,Alexis.On Thu, Aug 9, 2018 at 10:27 AM Fabian Hueske <fhueske@xxxxxxxxx> wrote: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]. getSplitDataPropertiesSo 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 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, Fabian2018-08-08 14:10 GMT+02:00 Alexis Sarda <alexis.sarda@xxxxxxxxx>: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: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.
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 <fhueske@xxxxxxxxx> 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  and validate that the result are identical whether you use SDP or not.Best, Fabian2018-08-07 22:32 GMT+02:00 Alexis Sarda <alexis.sarda@xxxxxxxxx>: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 dataEach new tuple has 3 fields: the host, a new ID, and an Integer=1What 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- mailing-list-archive.2336050.and http://apache-flink-user- n4.nabble.com/Terminology- Split-Group-and-Partition- td11030.html mailing-list-archive.2336050., but I still have some questions: n4.nabble.com/Fwd-Processing- Sorted-Input-Datasets-td20038. html- 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.