84815d0550
### What changes were proposed in this pull request? Please refer https://issues.apache.org/jira/browse/SPARK-24634 to see rationalization of the issue. This patch adds a new metric to count the number of inputs arrived later than watermark plus allowed delay. To make changes simpler, this patch doesn't count the exact number of input rows which are later than watermark plus allowed delay. Instead, this patch counts the inputs which are dropped in the logic of operator. The difference of twos are shown in streaming aggregation: to optimize the calculation, streaming aggregation "pre-aggregates" the input rows, and later checks the lateness against "pre-aggregated" inputs, hence the number might be reduced. The new metric will be provided via two places: 1. On Spark UI: check the metrics in stateful operator nodes in query execution details page in SQL tab 2. On Streaming Query Listener: check "numLateInputs" in "stateOperators" in QueryProcessEvent. ### Why are the changes needed? Dropping late inputs means that end users might not get expected outputs. Even end users may indicate the fact and tolerate the result (as that's what allowed lateness is for), but they should be able to observe whether the current value of allowed lateness drops inputs or not so that they can adjust the value. Also, whatever the chance they have multiple of stateful operators in a single query, if Spark drops late inputs "between" these operators, it becomes "correctness" issue. Spark should disallow such possibility, but given we already provided the flexibility, at least we should provide the way to observe the correctness issue and decide whether they should make correction of their query or not. ### Does this PR introduce _any_ user-facing change? Yes. End users will be able to retrieve the information of late inputs via two ways: 1. SQL tab in Spark UI 2. Streaming Query Listener ### How was this patch tested? New UTs added & existing UTs are modified to reflect the change. And ran manual test reproducing SPARK-28094. I've picked the specific case on "B outer C outer D" which is enough to represent the "intermediate late row" issue due to global watermark. https://gist.github.com/jammann/b58bfbe0f4374b89ecea63c1e32c8f17 Spark logs warning message on the query which means SPARK-28074 is working correctly, ``` 20/05/30 17:52:47 WARN UnsupportedOperationChecker: Detected pattern of possible 'correctness' issue due to global watermark. The query contains stateful operation which can emit rows older than the current watermark plus allowed late record delay, which are "late rows" in downstream stateful operations and these rows can be discarded. Please refer the programming guide doc for more details.; Join LeftOuter, ((D_FK#28 = D_ID#87) AND (B_LAST_MOD#26-T30000ms = D_LAST_MOD#88-T30000ms)) :- Join LeftOuter, ((C_FK#27 = C_ID#58) AND (B_LAST_MOD#26-T30000ms = C_LAST_MOD#59-T30000ms)) : :- EventTimeWatermark B_LAST_MOD#26: timestamp, 30 seconds : : +- Project [v#23.B_ID AS B_ID#25, v#23.B_LAST_MOD AS B_LAST_MOD#26, v#23.C_FK AS C_FK#27, v#23.D_FK AS D_FK#28] : : +- Project [from_json(StructField(B_ID,StringType,false), StructField(B_LAST_MOD,TimestampType,false), StructField(C_FK,StringType,true), StructField(D_FK,StringType,true), value#21, Some(UTC)) AS v#23] : : +- Project [cast(value#8 as string) AS value#21] : : +- StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider3a7fd18c, kafka, org.apache.spark.sql.kafka010.KafkaSourceProvider$KafkaTable396d2958, org.apache.spark.sql.util.CaseInsensitiveStringMapa51ee61a, [key#7, value#8, topic#9, partition#10, offset#11L, timestamp#12, timestampType#13], StreamingRelation DataSource(org.apache.spark.sql.SparkSessiond221af8,kafka,List(),None,List(),None,Map(inferSchema -> true, startingOffsets -> earliest, subscribe -> B, kafka.bootstrap.servers -> localhost:9092),None), kafka, [key#0, value#1, topic#2, partition#3, offset#4L, timestamp#5, timestampType#6] : +- EventTimeWatermark C_LAST_MOD#59: timestamp, 30 seconds : +- Project [v#56.C_ID AS C_ID#58, v#56.C_LAST_MOD AS C_LAST_MOD#59] : +- Project [from_json(StructField(C_ID,StringType,false), StructField(C_LAST_MOD,TimestampType,false), value#54, Some(UTC)) AS v#56] : +- Project [cast(value#41 as string) AS value#54] : +- StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider3f507373, kafka, org.apache.spark.sql.kafka010.KafkaSourceProvider$KafkaTable7b6736a4, org.apache.spark.sql.util.CaseInsensitiveStringMapa51ee61b, [key#40, value#41, topic#42, partition#43, offset#44L, timestamp#45, timestampType#46], StreamingRelation DataSource(org.apache.spark.sql.SparkSessiond221af8,kafka,List(),None,List(),None,Map(inferSchema -> true, startingOffsets -> earliest, subscribe -> C, kafka.bootstrap.servers -> localhost:9092),None), kafka, [key#33, value#34, topic#35, partition#36, offset#37L, timestamp#38, timestampType#39] +- EventTimeWatermark D_LAST_MOD#88: timestamp, 30 seconds +- Project [v#85.D_ID AS D_ID#87, v#85.D_LAST_MOD AS D_LAST_MOD#88] +- Project [from_json(StructField(D_ID,StringType,false), StructField(D_LAST_MOD,TimestampType,false), value#83, Some(UTC)) AS v#85] +- Project [cast(value#70 as string) AS value#83] +- StreamingRelationV2 org.apache.spark.sql.kafka010.KafkaSourceProvider2b90e779, kafka, org.apache.spark.sql.kafka010.KafkaSourceProvider$KafkaTable36f8cd29, org.apache.spark.sql.util.CaseInsensitiveStringMapa51ee620, [key#69, value#70, topic#71, partition#72, offset#73L, timestamp#74, timestampType#75], StreamingRelation DataSource(org.apache.spark.sql.SparkSessiond221af8,kafka,List(),None,List(),None,Map(inferSchema -> true, startingOffsets -> earliest, subscribe -> D, kafka.bootstrap.servers -> localhost:9092),None), kafka, [key#62, value#63, topic#64, partition#65, offset#66L, timestamp#67, timestampType#68] ``` and we can find the late inputs from the batch 4 as follows:  which represents intermediate inputs are being lost, ended up with correctness issue. Closes #28607 from HeartSaVioR/SPARK-24634-v3. Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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Apache Spark
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Building Spark
Spark is built using Apache Maven. To build Spark and its example programs, run:
./build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
./bin/spark-shell
Try the following command, which should return 1,000,000,000:
scala> spark.range(1000 * 1000 * 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
./bin/pyspark
And run the following command, which should also return 1,000,000,000:
>>> spark.range(1000 * 1000 * 1000).count()
Example Programs
Spark also comes with several sample programs in the examples directory.
To run one of them, use ./bin/run-example <class> [params]. For example:
./bin/run-example SparkPi
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the examples
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Running Tests
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
Please see the guidance on how to run tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.