grundprinzip commented on code in PR #41146:
URL: https://github.com/apache/spark/pull/41146#discussion_r1228238923
##########
connector/connect/server/src/main/scala/org/apache/spark/sql/connect/planner/SparkConnectPlanner.scala:
##########
@@ -786,6 +788,12 @@ class SparkConnectPlanner(val session: SparkSession) {
.logicalPlan
}
+ private def transformCachedRemoteRelation(rel: proto.CachedRemoteRelation):
LogicalPlan = {
+ SparkConnectService.cachedDataFrameManager
+ .get(rel.getUserId, rel.getSessionId, rel.getRelationId)
+ .logicalPlan
+ }
Review Comment:
Conceptually, the cached data should come from the session holder that could
be passed to the planner instead.
##########
connector/connect/common/src/main/protobuf/spark/connect/relations.proto:
##########
@@ -395,6 +396,18 @@ message CachedLocalRelation {
string hash = 3;
}
+// Represents a remote relation that has been cached on server.
+message CachedRemoteRelation {
+ // (Required) An identifier of the user which cached the relation
+ string userId = 1;
+
+ // (Required) An identifier of the Spark session in which the relation is
cached
+ string sessionId = 2;
Review Comment:
The user, session ID can't be trusted coming from the proto. THe cached
relation must only have the actual unique ID of the relation ID and the rest is
resolved from the context of the query.
##########
python/pyspark/sql/connect/session.py:
##########
@@ -476,6 +476,11 @@ def createDataFrame(
createDataFrame.__doc__ = PySparkSession.createDataFrame.__doc__
+ def _createCachedDataFrame(self, relationId: str) -> "DataFrame":
Review Comment:
this seems to be unused here?
##########
connector/connect/server/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectCachedDataFrameManager.scala:
##########
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.connect.service
+
+import scala.collection.mutable
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.connect.common.InvalidPlanInput
+
+/**
+ * This class caches DataFrame on the server side with given ids. The Spark
Connect client can
+ * create a DataFrame reference with the id. When server transforms the
DataFrame reference, it
+ * finds the DataFrame from the cache and replace the reference.
+ *
+ * Each (userId, sessionId) has a corresponding DataFrame map. A cached
DataFrame can only be
+ * accessed from the same user within the same session. The DataFrame will be
removed from the
+ * cache when the session expires.
+ */
+private[connect] class SparkConnectCachedDataFrameManager extends Logging {
+
+ // Each (userId, sessionId) has a DataFrame cache map.
+ private val dataFrameCache = mutable.Map[(String, String),
mutable.Map[String, DataFrame]]()
+
+ def put(userId: String, sessionId: String, dataFrameId: String, value:
DataFrame): Unit =
+ synchronized {
+ val sessionKey = (userId, sessionId)
+ val sessionDataFrameMap = dataFrameCache
+ .getOrElseUpdate(sessionKey, mutable.Map[String, DataFrame]())
+ sessionDataFrameMap.put(dataFrameId, value)
+ }
Review Comment:
This will make this easier as well because you only have one concurrent map
##########
connector/connect/server/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectCachedDataFrameManager.scala:
##########
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.connect.service
+
+import scala.collection.mutable
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.connect.common.InvalidPlanInput
+
+/**
+ * This class caches DataFrame on the server side with given ids. The Spark
Connect client can
+ * create a DataFrame reference with the id. When server transforms the
DataFrame reference, it
+ * finds the DataFrame from the cache and replace the reference.
+ *
+ * Each (userId, sessionId) has a corresponding DataFrame map. A cached
DataFrame can only be
+ * accessed from the same user within the same session. The DataFrame will be
removed from the
+ * cache when the session expires.
+ */
+private[connect] class SparkConnectCachedDataFrameManager extends Logging {
Review Comment:
Can we add this class to the session holder to make sure that this is
properly associated to the right user ID and session.
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