6d7ab7b52b
### What changes were proposed in this pull request?
Fixed explain formatted mode so it doesn't have duplicate node IDs when InMemoryRelation is present in query plan.
### Why are the changes needed?
Having duplicated node IDs in the plan makes it confusing.
### Does this PR introduce _any_ user-facing change?
Yes, explain formatted string will change.
Notice how `ColumnarToRow` and `InMemoryRelation` have node id of 2.
Before changes =>
```
== Physical Plan ==
AdaptiveSparkPlan (14)
+- == Final Plan ==
* BroadcastHashJoin Inner BuildLeft (9)
:- BroadcastQueryStage (5)
: +- BroadcastExchange (4)
: +- * Filter (3)
: +- * ColumnarToRow (2)
: +- InMemoryTableScan (1)
: +- InMemoryRelation (2)
: +- * ColumnarToRow (4)
: +- Scan parquet default.t1 (3)
+- * Filter (8)
+- * ColumnarToRow (7)
+- Scan parquet default.t2 (6)
+- == Initial Plan ==
BroadcastHashJoin Inner BuildLeft (13)
:- BroadcastExchange (11)
: +- Filter (10)
: +- InMemoryTableScan (1)
: +- InMemoryRelation (2)
: +- * ColumnarToRow (4)
: +- Scan parquet default.t1 (3)
+- Filter (12)
+- Scan parquet default.t2 (6)
(1) InMemoryTableScan
Output [1]: [k#x]
Arguments: [k#x], [isnotnull(k#x)]
(2) InMemoryRelation
Arguments: [k#x], CachedRDDBuilder(org.apache.spark.sql.execution.columnar.DefaultCachedBatchSerializer401788d5,StorageLevel(disk, memory, deserialized, 1 replicas),*(1) ColumnarToRow
+- FileScan parquet default.t1[k#x] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex(1 paths)[file:/Users/mike.chen/code/apacheSpark/spark/spark-warehouse/org.apach..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<k:int>
,None)
(3) Scan parquet default.t1
Output [1]: [k#x]
Batched: true
Location: InMemoryFileIndex [file:/Users/mike.chen/code/apacheSpark/spark/spark-warehouse/org.apache.spark.sql.ExplainSuiteAE/t1]
ReadSchema: struct<k:int>
(4) ColumnarToRow [codegen id : 1]
Input [1]: [k#x]
(5) BroadcastQueryStage
Output [1]: [k#x]
Arguments: 0
(6) Scan parquet default.t2
Output [1]: [key#x]
Batched: true
Location: InMemoryFileIndex [file:/Users/mike.chen/code/apacheSpark/spark/spark-warehouse/org.apache.spark.sql.ExplainSuiteAE/t2]
PushedFilters: [IsNotNull(key)]
ReadSchema: struct<key:int>
(7) ColumnarToRow
Input [1]: [key#x]
(8) Filter
Input [1]: [key#x]
Condition : isnotnull(key#x)
(9) BroadcastHashJoin [codegen id : 2]
Left keys [1]: [k#x]
Right keys [1]: [key#x]
Join condition: None
(10) Filter
Input [1]: [k#x]
Condition : isnotnull(k#x)
(11) BroadcastExchange
Input [1]: [k#x]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=#x]
(12) Filter
Input [1]: [key#x]
Condition : isnotnull(key#x)
(13) BroadcastHashJoin
Left keys [1]: [k#x]
Right keys [1]: [key#x]
Join condition: None
(14) AdaptiveSparkPlan
Output [2]: [k#x, key#x]
Arguments: isFinalPlan=true
```
After Changes =>
```
== Physical Plan ==
AdaptiveSparkPlan (17)
+- == Final Plan ==
* BroadcastHashJoin Inner BuildLeft (12)
:- BroadcastQueryStage (8)
: +- BroadcastExchange (7)
: +- * Filter (6)
: +- * ColumnarToRow (5)
: +- InMemoryTableScan (1)
: +- InMemoryRelation (2)
: +- * ColumnarToRow (4)
: +- Scan parquet default.t1 (3)
+- * Filter (11)
+- * ColumnarToRow (10)
+- Scan parquet default.t2 (9)
+- == Initial Plan ==
BroadcastHashJoin Inner BuildLeft (16)
:- BroadcastExchange (14)
: +- Filter (13)
: +- InMemoryTableScan (1)
: +- InMemoryRelation (2)
: +- * ColumnarToRow (4)
: +- Scan parquet default.t1 (3)
+- Filter (15)
+- Scan parquet default.t2 (9)
(1) InMemoryTableScan
Output [1]: [k#x]
Arguments: [k#x], [isnotnull(k#x)]
(2) InMemoryRelation
Arguments: [k#x], CachedRDDBuilder(org.apache.spark.sql.execution.columnar.DefaultCachedBatchSerializer3ccb12d,StorageLevel(disk, memory, deserialized, 1 replicas),*(1) ColumnarToRow
+- FileScan parquet default.t1[k#x] Batched: true, DataFilters: [], Format: Parquet, Location: InMemoryFileIndex(1 paths)[file:/Users/mike.chen/code/apacheSpark/spark/spark-warehouse/org.apach..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<k:int>
,None)
(3) Scan parquet default.t1
Output [1]: [k#x]
Batched: true
Location: InMemoryFileIndex [file:/Users/mike.chen/code/apacheSpark/spark/spark-warehouse/org.apache.spark.sql.ExplainSuiteAE/t1]
ReadSchema: struct<k:int>
(4) ColumnarToRow [codegen id : 1]
Input [1]: [k#x]
(5) ColumnarToRow [codegen id : 1]
Input [1]: [k#x]
(6) Filter [codegen id : 1]
Input [1]: [k#x]
Condition : isnotnull(k#x)
(7) BroadcastExchange
Input [1]: [k#x]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=#x]
(8) BroadcastQueryStage
Output [1]: [k#x]
Arguments: 0
(9) Scan parquet default.t2
Output [1]: [key#x]
Batched: true
Location: InMemoryFileIndex [file:/Users/mike.chen/code/apacheSpark/spark/spark-warehouse/org.apache.spark.sql.ExplainSuiteAE/t2]
PushedFilters: [IsNotNull(key)]
ReadSchema: struct<key:int>
(10) ColumnarToRow
Input [1]: [key#x]
(11) Filter
Input [1]: [key#x]
Condition : isnotnull(key#x)
(12) BroadcastHashJoin [codegen id : 2]
Left keys [1]: [k#x]
Right keys [1]: [key#x]
Join condition: None
(13) Filter
Input [1]: [k#x]
Condition : isnotnull(k#x)
(14) BroadcastExchange
Input [1]: [k#x]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=#x]
(15) Filter
Input [1]: [key#x]
Condition : isnotnull(key#x)
(16) BroadcastHashJoin
Left keys [1]: [k#x]
Right keys [1]: [key#x]
Join condition: None
(17) AdaptiveSparkPlan
Output [2]: [k#x, key#x]
Arguments: isFinalPlan=true
```
### How was this patch tested?
add test
Closes #34036 from ChenMichael/SPARK-36795-Duplicate-node-id-with-inMemoryRelation.
Authored-by: Michael Chen <mike.chen@workday.com>
Signed-off-by: Hyukjin Kwon <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, pandas API on Spark for pandas workloads, 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.