Joining Two Datasets with Predicate Pushdown - scala

Question

I have a Dataset that i created from a RDD and try to join it with another Dataset which is created from my Phoenix Table:
val dfToJoin = sparkSession.createDataset(rddToJoin)
val tableDf = sparkSession
.read
.option("table", "table")
.option("zkURL", "localhost")
.format("org.apache.phoenix.spark")
.load()
val joinedDf = dfToJoin.join(tableDf, "columnToJoinOn")
When i execute it, it seems that the whole database table is loaded to do the join.
Is there a way to do such a join so that the filtering is done on the database instead of in spark?
Also: dfToJoin is smaller than the table, i do not know if this is important.
Edit: Basically i want to join my Phoenix table with an Dataset created through spark, without fetching the whole table into the executor.
Edit2: Here is the physical plan:
*Project [FEATURE#21, SEQUENCE_IDENTIFIER#22, TAX_NUMBER#23,
WINDOW_NUMBER#24, uniqueIdentifier#5, readLength#6]
+- *SortMergeJoin [FEATURE#21], [feature#4], Inner
:- *Sort [FEATURE#21 ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(FEATURE#21, 200)
: +- *Filter isnotnull(FEATURE#21)
: +- *Scan PhoenixRelation(FEATURES,localhost,false)
[FEATURE#21,SEQUENCE_IDENTIFIER#22,TAX_NUMBER#23,WINDOW_NUMBER#24]
PushedFilters: [IsNotNull(FEATURE)], ReadSchema:
struct<FEATURE:int,SEQUENCE_IDENTIFIER:string,TAX_NUMBER:int,
WINDOW_NUMBER:int>
+- *Sort [feature#4 ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(feature#4, 200)
+- *Filter isnotnull(feature#4)
+- *SerializeFromObject [assertnotnull(input[0, utils.CaseClasses$QueryFeature, true], top level Product input object).feature AS feature#4, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, assertnotnull(input[0, utils.CaseClasses$QueryFeature, true], top level Product input object).uniqueIdentifier, true) AS uniqueIdentifier#5, assertnotnull(input[0, utils.CaseClasses$QueryFeature, true], top level Product input object).readLength AS readLength#6]
+- Scan ExternalRDDScan[obj#3]
As you can see the equals-filter is not contained in the pushed-filters list, so it is obvious that no predicate pushdown is happening.

Answer 1

Spark will fetch the Phoenix table records to appropriate executors(not the entire table to one executor)
As the is no direct filter on Phoenix table df, we see only *Filter isnotnull(FEATURE#21) in physical plan.
As you are mentioning Phoenix table data is less when you apply filter on it. You push the filter to phoenix table on feature column by finding feature_ids in other dataset.
//This spread across workers - fully distributed
val dfToJoin = sparkSession.createDataset(rddToJoin)
//This sits in driver - not distributed
val list_of_feature_ids = dfToJoin.dropDuplicates("feature")
.select("feature")
.map(r => r.getString(0))
.collect
.toList
//This spread across workers - fully distributed
val tableDf = sparkSession
.read
.option("table", "table")
.option("zkURL", "localhost")
.format("org.apache.phoenix.spark")
.load()
.filter($"FEATURE".isin(list_of_feature_ids:_*)) //added filter
//This spread across workers - fully distributed
val joinedDf = dfToJoin.join(tableDf, "columnToJoinOn")
joinedDf.explain()