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Posted to reviews@spark.apache.org by "sryza (via GitHub)" <gi...@apache.org> on 2025/07/24 03:48:45 UTC
[PR] Sinks [spark]
sryza opened a new pull request, #51644:
URL: https://github.com/apache/spark/pull/51644
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Re: [PR] [SPARK-52937][SDP] Sinks [spark]
Posted by "JiaqiWang18 (via GitHub)" <gi...@apache.org>.
JiaqiWang18 commented on code in PR #51644:
URL: https://github.com/apache/spark/pull/51644#discussion_r2389852041
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/DatasetManager.scala:
##########
@@ -179,7 +200,7 @@ object DatasetManager extends Logging {
}
// Wipe the data if we need to
- if ((isFullRefresh || !table.isStreamingTable) && existingTableOpt.isDefined) {
+ if (!table.isStreamingTable && existingTableOpt.isDefined) {
context.spark.sql(s"TRUNCATE TABLE ${table.identifier.quotedString}")
Review Comment:
we don't need this truncate right, because we already do it in `State.reset`?
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Re: [PR] [SPARK-52937][SDP] Sinks [spark]
Posted by "JiaqiWang18 (via GitHub)" <gi...@apache.org>.
JiaqiWang18 commented on code in PR #51644:
URL: https://github.com/apache/spark/pull/51644#discussion_r2389852041
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/DatasetManager.scala:
##########
@@ -179,7 +200,7 @@ object DatasetManager extends Logging {
}
// Wipe the data if we need to
- if ((isFullRefresh || !table.isStreamingTable) && existingTableOpt.isDefined) {
+ if (!table.isStreamingTable && existingTableOpt.isDefined) {
context.spark.sql(s"TRUNCATE TABLE ${table.identifier.quotedString}")
Review Comment:
we don't need this truncate right, because we already do it in `State.reset`?
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Re: [PR] [SPARK-52937][SDP] Sinks [spark]
Posted by "sryza (via GitHub)" <gi...@apache.org>.
sryza closed pull request #51644: [SPARK-52937][SDP] Sinks
URL: https://github.com/apache/spark/pull/51644
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Re: [PR] [SPARK-52937][SDP] Sinks [spark]
Posted by "JiaqiWang18 (via GitHub)" <gi...@apache.org>.
JiaqiWang18 commented on code in PR #51644:
URL: https://github.com/apache/spark/pull/51644#discussion_r2380329356
##########
sql/pipelines/src/test/scala/org/apache/spark/sql/pipelines/graph/SystemMetadataSuite.scala:
##########
@@ -0,0 +1,540 @@
+/*
+ * 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.pipelines.graph
+
+import java.nio.file.Files
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.sql.execution.streaming.{MemoryStream, StreamingQueryWrapper}
+import org.apache.spark.sql.pipelines.utils.{ExecutionTest, TestGraphRegistrationContext}
+import org.apache.spark.sql.test.SharedSparkSession
+
+class SystemMetadataSuite
+ extends ExecutionTest
+ with SystemMetadataTestHelpers
+ with SharedSparkSession {
+ test("flow checkpoint for ST wrote to the expected location") {
+ val session = spark
+ import session.implicits._
+
+ // create a pipeline with only a single ST
+ val graph = new TestGraphRegistrationContext(spark) {
+ val mem: MemoryStream[Int] = MemoryStream[Int]
+ mem.addData(1, 2, 3)
+ registerView("a", query = dfFlowFunc(mem.toDF()))
+ registerTable("st")
+ registerFlow("st", "st", query = readStreamFlowFunc("a"))
+ }.toDataflowGraph
+
+ val testStorageRoot = Files.createTempDirectory("TestUpdateContext").normalize.toString
+ val updateContext1 = TestPipelineUpdateContext(
+ unresolvedGraph = graph,
+ spark = spark,
+ storageRootOpt = Option(testStorageRoot)
+ )
+
+ // start an update in continuous mode, checkpoints are only created to streaming query
+ updateContext1.pipelineExecution.startPipeline()
+ updateContext1.pipelineExecution.awaitCompletion()
+
+ val graphExecution1 = updateContext1.pipelineExecution.graphExecution.get
+ val executionGraph1 = graphExecution1.graphForExecution
+
+ val stIdentifier = fullyQualifiedIdentifier("st")
+
+ val expectedStorageSuffix = "st"
+
+ // assert that the checkpoint dir for the ST is created as expected
+ assertFlowCheckpointDirExists(
+ tableOrSinkElement = executionGraph1.table(stIdentifier),
+ flowElement = executionGraph1.flow(stIdentifier),
+ expectedStorageName = expectedStorageSuffix,
+ // the default checkpoint version is 0
+ expectedCheckpointVersion = 0,
+ graphExecution = graphExecution1,
+ updateContext = updateContext1
+ )
+
+ // start another update in full refresh, expected a new checkpoint dir to be created
+ // with version number incremented to 1
+ val updateContext2 = TestPipelineUpdateContext(
+ unresolvedGraph = graph,
+ spark = spark,
+ storageRootOpt = Option(testStorageRoot),
+ fullRefreshTables = AllTables
+ )
+
+ updateContext2.pipelineExecution.startPipeline()
+ updateContext2.pipelineExecution.awaitCompletion()
+ val graphExecution2 = updateContext2.pipelineExecution.graphExecution.get
+ val executionGraph2 = graphExecution2.graphForExecution
+
+ // due to full refresh, assert that new checkpoint dir is created with version number
+ // incremented to 1
+ assertFlowCheckpointDirExists(
+ tableOrSinkElement = executionGraph2.table(stIdentifier),
+ flowElement = executionGraph1.flow(stIdentifier),
+ expectedStorageName = expectedStorageSuffix,
+ // new checkpoint directory is created
+ expectedCheckpointVersion = 1,
+ graphExecution = graphExecution2,
+ updateContext2
+ )
+ }
+
+ test(
+ "flow checkpoint for ST (with flow name different from table name) wrote to the expected " +
+ "location"
+ ) {
+ val session = spark
+ import session.implicits._
+
+ val graph = new TestGraphRegistrationContext(spark) {
+ val mem: MemoryStream[Int] = MemoryStream[Int]
+ mem.addData(1, 2, 3)
+ registerView("a", query = dfFlowFunc(mem.toDF()))
+ registerTable("st")
+ registerFlow("st", "st_flow", query = readStreamFlowFunc("a"))
+ }.toDataflowGraph
+
+ val testStorageRoot = Files.createTempDirectory("TestUpdateContext").normalize.toString
+ val updateContext1 = TestPipelineUpdateContext(
+ unresolvedGraph = graph,
+ spark = spark,
+ storageRootOpt = Option(testStorageRoot)
+ )
+
+ // start an update in continuous mode, checkpoints are only created to streaming query
+ updateContext1.pipelineExecution.startPipeline()
+ updateContext1.pipelineExecution.awaitCompletion()
+
+ val graphExecution1 = updateContext1.pipelineExecution.graphExecution.get
+ val executionGraph1 = graphExecution1.graphForExecution
+
+ val stIdentifier = fullyQualifiedIdentifier("st")
+
+ val expectedStorageSuffix = "st_flow"
+
+ // assert that the checkpoint dir for the ST is created as expected
+ assertFlowCheckpointDirExists(
+ tableOrSinkElement = executionGraph1.table(stIdentifier),
+ flowElement = executionGraph1.flow(stIdentifier),
+ expectedStorageName = expectedStorageSuffix,
+ // the default checkpoint version is 0
+ expectedCheckpointVersion = 0,
+ graphExecution = graphExecution1,
+ updateContext = updateContext1
+ )
+
+ // start another update in full refresh, expected a new checkpoint dir to be created
+ // with version number incremented to 1
Review Comment:
also for my understanding, why do we expect the version number to be `1` now? How does the new checkpoint versioning logic interact with refresh options?
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/SystemMetadata.scala:
##########
@@ -0,0 +1,125 @@
+/*
+ * 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.pipelines.graph
+
+import scala.util.Try
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.sql.classic.SparkSession
+
+sealed trait SystemMetadata {}
+
+/**
+ * Represents the system metadata associated with a [[Flow]].
+ */
+case class FlowSystemMetadata(
+ context: PipelineUpdateContext,
+ flow: Flow,
+ graph: DataflowGraph
+) extends SystemMetadata {
+
+ /**
+ * Returns the checkpoint root directory for a given flow.
+ * @return the checkpoint root directory for `flow`
+ */
+ private def flowCheckpointsDirOpt(): Option[Path] = {
Review Comment:
for my understanding, are we changing the checkpoint directory location, and why do we need to explicitly manage checkpoint location ourselves? Is it for implementing our custom versioned checkpoint?
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/SystemMetadata.scala:
##########
@@ -0,0 +1,125 @@
+/*
+ * 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.pipelines.graph
+
+import scala.util.Try
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.sql.classic.SparkSession
+
+sealed trait SystemMetadata {}
+
+/**
+ * Represents the system metadata associated with a [[Flow]].
+ */
+case class FlowSystemMetadata(
+ context: PipelineUpdateContext,
+ flow: Flow,
+ graph: DataflowGraph
+) extends SystemMetadata {
+
+ /**
+ * Returns the checkpoint root directory for a given flow.
+ * @return the checkpoint root directory for `flow`
+ */
+ private def flowCheckpointsDirOpt(): Option[Path] = {
+ Option(if (graph.sink.contains(flow.destinationIdentifier)) {
+ val storageRoot = context.storageRootOpt.getOrElse(
+ // TODO: better error
+ throw new IllegalArgumentException("Storage root must be defined for flow checkpoints")
+ )
+ val storageFlowName = flow.identifier.table
+ new Path(new Path(storageRoot, "checkpoints"), storageFlowName)
+ } else if (graph.table.contains(flow.destinationIdentifier)) {
+ val storageFlowName = flow.identifier.table
+ new Path(
+ new Path(graph.table(flow.destinationIdentifier).path, "checkpoints"),
+ storageFlowName
+ )
+ } else {
+ // TODO: raise an error
+ throw new IllegalArgumentException(
+ s"Flow ${flow.identifier} does not have a valid destination for checkpoints."
+ )
+ })
+ }
+
+ /** Returns the location for the most recent checkpoint of a given flow. */
+ def latestCheckpointLocation: String = {
+ val checkpointsDir = flowCheckpointsDirOpt().get
+ SystemMetadata.getLatestCheckpointDir(checkpointsDir)
+ }
+
+ /**
+ * Same as [[latestCheckpointLocation()]] but returns [[None]] if the flow checkpoints directory
+ * does not exist.
+ */
+ def latestCheckpointLocationOpt(): Option[String] = {
+ flowCheckpointsDirOpt().map { flowCheckpointsDir =>
+ SystemMetadata.getLatestCheckpointDir(flowCheckpointsDir)
+ }
+ }
+}
+
+object SystemMetadata {
+ private def spark = SparkSession.getActiveSession.get
+
+ /**
+ * Finds the largest checkpoint version subdirectory path within a checkpoint directory, or
+ * creates and returns a version 0 subdirectory path if no versions exist.
+ * @param rootDir The root/parent directory where all the numbered checkpoint subdirectories are
+ * stored
+ * @param createNewCheckpointDir If true, a new latest numbered checkpoint directory should be
+ * created and returned
+ * @return The string URI path to the latest checkpoint directory
+ */
+ def getLatestCheckpointDir(
+ rootDir: Path,
+ createNewCheckpointDir: Boolean = false
+ ): String = {
Review Comment:
for my own understanding, why do we need versioned checkpoint directory?
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/GraphRegistrationContext.scala:
##########
@@ -45,17 +49,20 @@ class GraphRegistrationContext(
views += viewDef
}
+ def registerSink(sinkDef: Sink): Unit = {
+ sinks += sinkDef
+ }
+
def registerFlow(flowDef: UnresolvedFlow): Unit = {
flows += flowDef.copy(sqlConf = defaultSqlConf ++ flowDef.sqlConf)
}
def toDataflowGraph: DataflowGraph = {
- if (tables.isEmpty && views.collect { case v: PersistedView =>
- v
- }.isEmpty) {
- throw new AnalysisException(
- errorClass = "RUN_EMPTY_PIPELINE",
- messageParameters = Map.empty)
+ if (tables.isEmpty && views.collect {
+ case v: PersistedView =>
+ v
+ }.isEmpty && sinks.isEmpty) {
Review Comment:
so if a pipeline only has sinks, does it count as a valid pipeline?
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/PipelineExecution.scala:
##########
@@ -46,6 +46,10 @@ class PipelineExecution(context: PipelineUpdateContext) {
def startPipeline(): Unit = synchronized {
// Initialize the graph.
val resolvedGraph = resolveGraph()
+ if (context.fullRefreshTables.nonEmpty) {
+ State.reset(resolvedGraph, context)
Review Comment:
does this need to happen after `DatasetManager.materializeDatasets`, seems like we only know `normalizedPath` after `materializeTable` [here](https://github.com/sryza/spark/blob/ac211435c7a4ca1a34a52e61c0647355ebd29476/sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/DatasetManager.scala#L210)
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/FlowExecution.scala:
##########
@@ -261,3 +271,30 @@ class BatchTableWrite(
}
}
}
+
+/** A `StreamingFlowExecution` that writes a streaming `DataFrame` to a `Table`. */
+class SinkWrite(
+ val identifier: TableIdentifier,
+ val flow: ResolvedFlow,
+ val graph: DataflowGraph,
+ val updateContext: PipelineUpdateContext,
+ val checkpointPath: String,
+ val trigger: Trigger,
+ val destination: Sink,
+ val sqlConf: Map[String, String]
+) extends StreamingFlowExecution {
+
+ override def getOrigin: QueryOrigin = flow.origin
+
+ def startStream(): StreamingQuery = {
+ val data = graph.reanalyzeFlow(flow).df
Review Comment:
why do we need to reanalyze the flow before execution? Seems like we are doing this for table writes as well
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Re: [PR] [SPARK-52937][SDP] Sinks [spark]
Posted by "JiaqiWang18 (via GitHub)" <gi...@apache.org>.
JiaqiWang18 commented on PR #51644:
URL: https://github.com/apache/spark/pull/51644#issuecomment-3337149265
>We were not resetting checkpoint dirs on full refresh.
Seems like currently, if the table `st` lives in `spark-warehouse/st` and I do a `spark-pipelines run --conf spark.sql.catalogImplementation=hive --full-refresh-all`, all the content of the directory gets deleted along with the checkpoint sub directories in it, probably because we call `TRUNCATE TABLE` [here](https://github.com/sryza/spark/blob/dde895c722af503d57ab235907a00373a7935178/sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/DatasetManager.scala#L292). I guess if we move the checkpoints outside of the table's directory, we need to reset manually.
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Re: [PR] [SPARK-52937][SDP] Sinks [spark]
Posted by "sryza (via GitHub)" <gi...@apache.org>.
sryza commented on code in PR #51644:
URL: https://github.com/apache/spark/pull/51644#discussion_r2382985709
##########
sql/pipelines/src/main/scala/org/apache/spark/sql/pipelines/graph/GraphRegistrationContext.scala:
##########
@@ -45,17 +49,20 @@ class GraphRegistrationContext(
views += viewDef
}
+ def registerSink(sinkDef: Sink): Unit = {
+ sinks += sinkDef
+ }
+
def registerFlow(flowDef: UnresolvedFlow): Unit = {
flows += flowDef.copy(sqlConf = defaultSqlConf ++ flowDef.sqlConf)
}
def toDataflowGraph: DataflowGraph = {
- if (tables.isEmpty && views.collect { case v: PersistedView =>
- v
- }.isEmpty) {
- throw new AnalysisException(
- errorClass = "RUN_EMPTY_PIPELINE",
- messageParameters = Map.empty)
+ if (tables.isEmpty && views.collect {
+ case v: PersistedView =>
+ v
+ }.isEmpty && sinks.isEmpty) {
Review Comment:
Do you know whether a pipeline with a table but not flows targeting it is a valid pipeline? I would expect the same behavior for sinks. I.e.
- 1 sink + 1 flow -> valid
- 1 sink + 0 flows -> not sure
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