[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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HikariCP/2.5.1//HikariCP-2.5.1.jar
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JLargeArrays/1.5//JLargeArrays-1.5.jar
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JTransforms/3.1//JTransforms-3.1.jar
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2020-07-25 13:58:25 -04:00
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RoaringBitmap/0.9.0//RoaringBitmap-0.9.0.jar
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[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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ST4/4.0.4//ST4-4.0.4.jar
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[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
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accessors-smart/1.2//accessors-smart-1.2.jar
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[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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activation/1.1.1//activation-1.1.1.jar
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aircompressor/0.10//aircompressor-0.10.jar
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algebra_2.12/2.0.0-M2//algebra_2.12-2.0.0-M2.jar
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antlr-runtime/3.5.2//antlr-runtime-3.5.2.jar
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2020-11-18 07:20:28 -05:00
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antlr4-runtime/4.8-1//antlr4-runtime-4.8-1.jar
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[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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aopalliance-repackaged/2.6.1//aopalliance-repackaged-2.6.1.jar
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[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
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aopalliance/1.0//aopalliance-1.0.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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arpack_combined_all/0.1//arpack_combined_all-0.1.jar
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2020-11-09 22:07:16 -05:00
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arrow-format/2.0.0//arrow-format-2.0.0.jar
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arrow-memory-core/2.0.0//arrow-memory-core-2.0.0.jar
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arrow-memory-netty/2.0.0//arrow-memory-netty-2.0.0.jar
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arrow-vector/2.0.0//arrow-vector-2.0.0.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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audience-annotations/0.5.0//audience-annotations-0.5.0.jar
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automaton/1.11-8//automaton-1.11-8.jar
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avro-ipc/1.8.2//avro-ipc-1.8.2.jar
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avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar
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avro/1.8.2//avro-1.8.2.jar
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bonecp/0.8.0.RELEASE//bonecp-0.8.0.RELEASE.jar
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breeze-macros_2.12/1.0//breeze-macros_2.12-1.0.jar
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breeze_2.12/1.0//breeze_2.12-1.0.jar
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cats-kernel_2.12/2.0.0-M4//cats-kernel_2.12-2.0.0-M4.jar
|
2020-01-19 21:39:38 -05:00
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chill-java/0.9.5//chill-java-0.9.5.jar
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chill_2.12/0.9.5//chill_2.12-0.9.5.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
|
commons-beanutils/1.9.4//commons-beanutils-1.9.4.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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commons-cli/1.2//commons-cli-1.2.jar
|
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commons-codec/1.10//commons-codec-1.10.jar
|
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commons-collections/3.2.2//commons-collections-3.2.2.jar
|
2020-08-20 16:26:39 -04:00
|
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|
commons-compiler/3.0.16//commons-compiler-3.0.16.jar
|
2020-11-09 21:08:55 -05:00
|
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commons-compress/1.20//commons-compress-1.20.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
|
commons-configuration2/2.1.1//commons-configuration2-2.1.1.jar
|
2020-11-09 17:33:27 -05:00
|
|
|
commons-crypto/1.1.0//commons-crypto-1.1.0.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
|
commons-daemon/1.0.13//commons-daemon-1.0.13.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
|
|
commons-dbcp/1.4//commons-dbcp-1.4.jar
|
|
|
|
|
commons-httpclient/3.1//commons-httpclient-3.1.jar
|
2020-05-29 10:46:53 -04:00
|
|
|
commons-io/2.5//commons-io-2.5.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
|
|
commons-lang/2.6//commons-lang-2.6.jar
|
2020-06-23 03:59:55 -04:00
|
|
|
commons-lang3/3.10//commons-lang3-3.10.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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commons-logging/1.1.3//commons-logging-1.1.3.jar
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commons-math3/3.4.1//commons-math3-3.4.1.jar
|
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commons-net/3.1//commons-net-3.1.jar
|
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commons-pool/1.5.4//commons-pool-1.5.4.jar
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commons-text/1.6//commons-text-1.6.jar
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compress-lzf/1.0.3//compress-lzf-1.0.3.jar
|
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core/1.1.2//core-1.1.2.jar
|
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curator-client/2.13.0//curator-client-2.13.0.jar
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curator-framework/2.13.0//curator-framework-2.13.0.jar
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curator-recipes/2.13.0//curator-recipes-2.13.0.jar
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datanucleus-api-jdo/4.2.4//datanucleus-api-jdo-4.2.4.jar
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datanucleus-core/4.1.17//datanucleus-core-4.1.17.jar
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datanucleus-rdbms/4.1.19//datanucleus-rdbms-4.1.19.jar
|
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derby/10.12.1.1//derby-10.12.1.1.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
|
dnsjava/2.1.7//dnsjava-2.1.7.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
|
|
dropwizard-metrics-hadoop-metrics2-reporter/0.1.2//dropwizard-metrics-hadoop-metrics2-reporter-0.1.2.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
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ehcache/3.3.1//ehcache-3.3.1.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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|
flatbuffers-java/1.9.0//flatbuffers-java-1.9.0.jar
|
|
|
|
|
generex/1.0.2//generex-1.0.2.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
|
geronimo-jcache_1.0_spec/1.0-alpha-1//geronimo-jcache_1.0_spec-1.0-alpha-1.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
|
|
gson/2.2.4//gson-2.2.4.jar
|
|
|
|
|
guava/14.0.1//guava-14.0.1.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
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guice-servlet/4.0//guice-servlet-4.0.jar
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guice/4.0//guice-4.0.jar
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hadoop-annotations/3.2.0//hadoop-annotations-3.2.0.jar
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hadoop-auth/3.2.0//hadoop-auth-3.2.0.jar
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hadoop-client/3.2.0//hadoop-client-3.2.0.jar
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hadoop-common/3.2.0//hadoop-common-3.2.0.jar
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hadoop-hdfs-client/3.2.0//hadoop-hdfs-client-3.2.0.jar
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hadoop-mapreduce-client-common/3.2.0//hadoop-mapreduce-client-common-3.2.0.jar
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hadoop-mapreduce-client-core/3.2.0//hadoop-mapreduce-client-core-3.2.0.jar
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hadoop-mapreduce-client-jobclient/3.2.0//hadoop-mapreduce-client-jobclient-3.2.0.jar
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hadoop-yarn-api/3.2.0//hadoop-yarn-api-3.2.0.jar
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hadoop-yarn-client/3.2.0//hadoop-yarn-client-3.2.0.jar
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hadoop-yarn-common/3.2.0//hadoop-yarn-common-3.2.0.jar
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hadoop-yarn-registry/3.2.0//hadoop-yarn-registry-3.2.0.jar
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hadoop-yarn-server-common/3.2.0//hadoop-yarn-server-common-3.2.0.jar
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hadoop-yarn-server-web-proxy/3.2.0//hadoop-yarn-server-web-proxy-3.2.0.jar
|
2020-04-20 16:38:24 -04:00
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hive-beeline/2.3.7//hive-beeline-2.3.7.jar
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hive-cli/2.3.7//hive-cli-2.3.7.jar
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hive-common/2.3.7//hive-common-2.3.7.jar
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hive-exec/2.3.7/core/hive-exec-2.3.7-core.jar
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hive-jdbc/2.3.7//hive-jdbc-2.3.7.jar
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hive-llap-common/2.3.7//hive-llap-common-2.3.7.jar
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hive-metastore/2.3.7//hive-metastore-2.3.7.jar
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hive-serde/2.3.7//hive-serde-2.3.7.jar
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2020-11-25 15:37:59 -05:00
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hive-service-rpc/3.1.2//hive-service-rpc-3.1.2.jar
|
2020-04-20 16:38:24 -04:00
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hive-shims-0.23/2.3.7//hive-shims-0.23-2.3.7.jar
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hive-shims-common/2.3.7//hive-shims-common-2.3.7.jar
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hive-shims-scheduler/2.3.7//hive-shims-scheduler-2.3.7.jar
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hive-shims/2.3.7//hive-shims-2.3.7.jar
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2020-10-01 15:41:40 -04:00
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hive-storage-api/2.7.2//hive-storage-api-2.7.2.jar
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2020-04-20 16:38:24 -04:00
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hive-vector-code-gen/2.3.7//hive-vector-code-gen-2.3.7.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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|
hk2-api/2.6.1//hk2-api-2.6.1.jar
|
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|
hk2-locator/2.6.1//hk2-locator-2.6.1.jar
|
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hk2-utils/2.6.1//hk2-utils-2.6.1.jar
|
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htrace-core4/4.1.0-incubating//htrace-core4-4.1.0-incubating.jar
|
2020-12-07 02:02:36 -05:00
|
|
|
httpclient/4.5.13//httpclient-4.5.13.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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httpcore/4.4.12//httpcore-4.4.12.jar
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istack-commons-runtime/3.0.8//istack-commons-runtime-3.0.8.jar
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|
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ivy/2.4.0//ivy-2.4.0.jar
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jackson-annotations/2.10.0//jackson-annotations-2.10.0.jar
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jackson-core-asl/1.9.13//jackson-core-asl-1.9.13.jar
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jackson-core/2.10.0//jackson-core-2.10.0.jar
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jackson-databind/2.10.0//jackson-databind-2.10.0.jar
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jackson-dataformat-yaml/2.10.0//jackson-dataformat-yaml-2.10.0.jar
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2020-11-03 01:23:26 -05:00
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jackson-datatype-jsr310/2.11.2//jackson-datatype-jsr310-2.11.2.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
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jackson-jaxrs-base/2.9.5//jackson-jaxrs-base-2.9.5.jar
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jackson-jaxrs-json-provider/2.9.5//jackson-jaxrs-json-provider-2.9.5.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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jackson-mapper-asl/1.9.13//jackson-mapper-asl-1.9.13.jar
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jackson-module-jaxb-annotations/2.10.0//jackson-module-jaxb-annotations-2.10.0.jar
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jackson-module-paranamer/2.10.0//jackson-module-paranamer-2.10.0.jar
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jackson-module-scala_2.12/2.10.0//jackson-module-scala_2.12-2.10.0.jar
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jakarta.activation-api/1.2.1//jakarta.activation-api-1.2.1.jar
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jakarta.annotation-api/1.3.5//jakarta.annotation-api-1.3.5.jar
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jakarta.inject/2.6.1//jakarta.inject-2.6.1.jar
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jakarta.validation-api/2.0.2//jakarta.validation-api-2.0.2.jar
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jakarta.ws.rs-api/2.1.6//jakarta.ws.rs-api-2.1.6.jar
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jakarta.xml.bind-api/2.3.2//jakarta.xml.bind-api-2.3.2.jar
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2020-08-20 16:26:39 -04:00
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janino/3.0.16//janino-3.0.16.jar
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2020-01-25 18:41:55 -05:00
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javassist/3.25.0-GA//javassist-3.25.0-GA.jar
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[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
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javax.inject/1//javax.inject-1.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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javax.jdo/3.2.0-m3//javax.jdo-3.2.0-m3.jar
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javax.servlet-api/3.1.0//javax.servlet-api-3.1.0.jar
|
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javolution/5.5.1//javolution-5.5.1.jar
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jaxb-api/2.2.11//jaxb-api-2.2.11.jar
|
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jaxb-runtime/2.3.2//jaxb-runtime-2.3.2.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
|
jcip-annotations/1.0-1//jcip-annotations-1.0-1.jar
|
2020-05-04 11:14:12 -04:00
|
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|
jcl-over-slf4j/1.7.30//jcl-over-slf4j-1.7.30.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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jdo-api/3.0.1//jdo-api-3.0.1.jar
|
2020-01-25 18:41:55 -05:00
|
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jersey-client/2.30//jersey-client-2.30.jar
|
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jersey-common/2.30//jersey-common-2.30.jar
|
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jersey-container-servlet-core/2.30//jersey-container-servlet-core-2.30.jar
|
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jersey-container-servlet/2.30//jersey-container-servlet-2.30.jar
|
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|
jersey-hk2/2.30//jersey-hk2-2.30.jar
|
|
|
|
|
jersey-media-jaxb/2.30//jersey-media-jaxb-2.30.jar
|
|
|
|
|
jersey-server/2.30//jersey-server-2.30.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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jline/2.14.6//jline-2.14.6.jar
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joda-time/2.10.5//joda-time-2.10.5.jar
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jodd-core/3.5.2//jodd-core-3.5.2.jar
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jpam/1.1//jpam-1.1.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
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json-smart/2.3//json-smart-2.3.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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json/1.8//json-1.8.jar
|
2020-07-25 23:34:31 -04:00
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json4s-ast_2.12/3.7.0-M5//json4s-ast_2.12-3.7.0-M5.jar
|
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json4s-core_2.12/3.7.0-M5//json4s-core_2.12-3.7.0-M5.jar
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json4s-jackson_2.12/3.7.0-M5//json4s-jackson_2.12-3.7.0-M5.jar
|
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json4s-scalap_2.12/3.7.0-M5//json4s-scalap_2.12-3.7.0-M5.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
|
jsp-api/2.1//jsp-api-2.1.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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jsr305/3.0.0//jsr305-3.0.0.jar
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jta/1.1//jta-1.1.jar
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2020-05-04 11:14:12 -04:00
|
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jul-to-slf4j/1.7.30//jul-to-slf4j-1.7.30.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
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kerb-admin/1.0.1//kerb-admin-1.0.1.jar
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kerb-client/1.0.1//kerb-client-1.0.1.jar
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kerb-common/1.0.1//kerb-common-1.0.1.jar
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kerb-core/1.0.1//kerb-core-1.0.1.jar
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kerb-crypto/1.0.1//kerb-crypto-1.0.1.jar
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kerb-identity/1.0.1//kerb-identity-1.0.1.jar
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kerb-server/1.0.1//kerb-server-1.0.1.jar
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kerb-simplekdc/1.0.1//kerb-simplekdc-1.0.1.jar
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kerb-util/1.0.1//kerb-util-1.0.1.jar
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kerby-asn1/1.0.1//kerby-asn1-1.0.1.jar
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kerby-config/1.0.1//kerby-config-1.0.1.jar
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kerby-pkix/1.0.1//kerby-pkix-1.0.1.jar
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kerby-util/1.0.1//kerby-util-1.0.1.jar
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kerby-xdr/1.0.1//kerby-xdr-1.0.1.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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|
kryo-shaded/4.0.2//kryo-shaded-4.0.2.jar
|
2020-11-17 16:41:58 -05:00
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kubernetes-client/4.12.0//kubernetes-client-4.12.0.jar
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kubernetes-model-admissionregistration/4.12.0//kubernetes-model-admissionregistration-4.12.0.jar
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kubernetes-model-apiextensions/4.12.0//kubernetes-model-apiextensions-4.12.0.jar
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kubernetes-model-apps/4.12.0//kubernetes-model-apps-4.12.0.jar
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kubernetes-model-autoscaling/4.12.0//kubernetes-model-autoscaling-4.12.0.jar
|
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kubernetes-model-batch/4.12.0//kubernetes-model-batch-4.12.0.jar
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kubernetes-model-certificates/4.12.0//kubernetes-model-certificates-4.12.0.jar
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kubernetes-model-common/4.12.0//kubernetes-model-common-4.12.0.jar
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kubernetes-model-coordination/4.12.0//kubernetes-model-coordination-4.12.0.jar
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kubernetes-model-core/4.12.0//kubernetes-model-core-4.12.0.jar
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kubernetes-model-discovery/4.12.0//kubernetes-model-discovery-4.12.0.jar
|
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kubernetes-model-events/4.12.0//kubernetes-model-events-4.12.0.jar
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kubernetes-model-extensions/4.12.0//kubernetes-model-extensions-4.12.0.jar
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kubernetes-model-metrics/4.12.0//kubernetes-model-metrics-4.12.0.jar
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kubernetes-model-networking/4.12.0//kubernetes-model-networking-4.12.0.jar
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kubernetes-model-policy/4.12.0//kubernetes-model-policy-4.12.0.jar
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kubernetes-model-rbac/4.12.0//kubernetes-model-rbac-4.12.0.jar
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kubernetes-model-scheduling/4.12.0//kubernetes-model-scheduling-4.12.0.jar
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kubernetes-model-settings/4.12.0//kubernetes-model-settings-4.12.0.jar
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kubernetes-model-storageclass/4.12.0//kubernetes-model-storageclass-4.12.0.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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leveldbjni-all/1.8//leveldbjni-all-1.8.jar
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|
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libfb303/0.9.3//libfb303-0.9.3.jar
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|
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|
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libthrift/0.12.0//libthrift-0.12.0.jar
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log4j/1.2.17//log4j-1.2.17.jar
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2020-09-30 22:00:18 -04:00
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logging-interceptor/3.12.12//logging-interceptor-3.12.12.jar
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2020-01-19 22:05:30 -05:00
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lz4-java/1.7.1//lz4-java-1.7.1.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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machinist_2.12/0.6.8//machinist_2.12-0.6.8.jar
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macro-compat_2.12/1.1.1//macro-compat_2.12-1.1.1.jar
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mesos/1.4.0/shaded-protobuf/mesos-1.4.0-shaded-protobuf.jar
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metrics-core/4.1.1//metrics-core-4.1.1.jar
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metrics-graphite/4.1.1//metrics-graphite-4.1.1.jar
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metrics-jmx/4.1.1//metrics-jmx-4.1.1.jar
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metrics-json/4.1.1//metrics-json-4.1.1.jar
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metrics-jvm/4.1.1//metrics-jvm-4.1.1.jar
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minlog/1.3.0//minlog-1.3.0.jar
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2020-08-02 19:46:11 -04:00
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netty-all/4.1.51.Final//netty-all-4.1.51.Final.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
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nimbus-jose-jwt/4.41.1//nimbus-jose-jwt-4.41.1.jar
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2020-07-23 19:20:17 -04:00
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objenesis/2.6//objenesis-2.6.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
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|
okhttp/2.7.5//okhttp-2.7.5.jar
|
2020-09-30 22:00:18 -04:00
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okhttp/3.12.12//okhttp-3.12.12.jar
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2020-07-23 19:20:17 -04:00
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okio/1.14.0//okio-1.14.0.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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opencsv/2.3//opencsv-2.3.jar
|
2020-10-02 03:06:03 -04:00
|
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orc-core/1.5.12//orc-core-1.5.12.jar
|
|
|
|
|
orc-mapreduce/1.5.12//orc-mapreduce-1.5.12.jar
|
|
|
|
|
orc-shims/1.5.12//orc-shims-1.5.12.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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oro/2.0.8//oro-2.0.8.jar
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osgi-resource-locator/1.0.3//osgi-resource-locator-1.0.3.jar
|
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paranamer/2.8//paranamer-2.8.jar
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parquet-column/1.10.1//parquet-column-1.10.1.jar
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parquet-common/1.10.1//parquet-common-1.10.1.jar
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parquet-encoding/1.10.1//parquet-encoding-1.10.1.jar
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parquet-format/2.4.0//parquet-format-2.4.0.jar
|
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parquet-hadoop/1.10.1//parquet-hadoop-1.10.1.jar
|
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parquet-jackson/1.10.1//parquet-jackson-1.10.1.jar
|
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protobuf-java/2.5.0//protobuf-java-2.5.0.jar
|
2020-02-20 12:08:33 -05:00
|
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|
py4j/0.10.9//py4j-0.10.9.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
|
|
pyrolite/4.30//pyrolite-4.30.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
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re2j/1.1//re2j-1.1.jar
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[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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scala-collection-compat_2.12/2.1.1//scala-collection-compat_2.12-2.1.1.jar
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scala-compiler/2.12.10//scala-compiler-2.12.10.jar
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scala-library/2.12.10//scala-library-2.12.10.jar
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scala-parser-combinators_2.12/1.1.2//scala-parser-combinators_2.12-1.1.2.jar
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scala-reflect/2.12.10//scala-reflect-2.12.10.jar
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scala-xml_2.12/1.2.0//scala-xml_2.12-1.2.0.jar
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shapeless_2.12/2.3.3//shapeless_2.12-2.3.3.jar
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2020-07-25 13:58:25 -04:00
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shims/0.9.0//shims-0.9.0.jar
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2020-05-04 11:14:12 -04:00
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slf4j-api/1.7.30//slf4j-api-1.7.30.jar
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slf4j-log4j12/1.7.30//slf4j-log4j12-1.7.30.jar
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[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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snakeyaml/1.24//snakeyaml-1.24.jar
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2020-10-21 16:04:39 -04:00
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snappy-java/1.1.8//snappy-java-1.1.8.jar
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[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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spire-macros_2.12/0.17.0-M1//spire-macros_2.12-0.17.0-M1.jar
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spire-platform_2.12/0.17.0-M1//spire-platform_2.12-0.17.0-M1.jar
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spire-util_2.12/0.17.0-M1//spire-util_2.12-0.17.0-M1.jar
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spire_2.12/0.17.0-M1//spire_2.12-0.17.0-M1.jar
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stax-api/1.0.1//stax-api-1.0.1.jar
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[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
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stax2-api/3.1.4//stax2-api-3.1.4.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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stream/2.9.6//stream-2.9.6.jar
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super-csv/2.2.0//super-csv-2.2.0.jar
|
2020-01-31 20:41:27 -05:00
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threeten-extra/1.5.0//threeten-extra-1.5.0.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
|
|
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token-provider/1.0.1//token-provider-1.0.1.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
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|
transaction-api/1.1//transaction-api-1.1.jar
|
2020-08-25 11:25:58 -04:00
|
|
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univocity-parsers/2.9.0//univocity-parsers-2.9.0.jar
|
[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
|
|
|
velocity/1.5//velocity-1.5.jar
|
[SPARK-33618][CORE] Use hadoop-client instead of hadoop-client-api to make hadoop-aws work
### What changes were proposed in this pull request?
This reverts commit SPARK-33212 (cb3fa6c9368e64184a5f7b19688181d11de9511c) mostly with three exceptions:
1. `SparkSubmitUtils` was updated recently by SPARK-33580
2. `resource-managers/yarn/pom.xml` was updated recently by SPARK-33104 to add `hadoop-yarn-server-resourcemanager` test dependency.
3. Adjust `com.fasterxml.jackson.module:jackson-module-jaxb-annotations` dependency in K8s module which is updated recently by SPARK-33471.
### Why are the changes needed?
According to [HADOOP-16080](https://issues.apache.org/jira/browse/HADOOP-16080) since Apache Hadoop 3.1.1, `hadoop-aws` doesn't work with `hadoop-client-api`. It fails at write operation like the following.
**1. Spark distribution with `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY
20/11/30 23:01:24 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context available as 'sc' (master = local[*], app id = local-1606806088715).
Spark session available as 'spark'.
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 3.1.0-SNAPSHOT
/_/
Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 1.8.0_272)
Type in expressions to have them evaluated.
Type :help for more information.
scala> spark.read.parquet("s3a://dongjoon/users.parquet").show
20/11/30 23:01:34 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties
+------+--------------+----------------+
| name|favorite_color|favorite_numbers|
+------+--------------+----------------+
|Alyssa| null| [3, 9, 15, 20]|
| Ben| red| []|
+------+--------------+----------------+
scala> Seq(1).toDF.write.parquet("s3a://dongjoon/out.parquet")
20/11/30 23:02:14 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)/ 1]
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
```
**2. Spark distribution without `-Phadoop-cloud`**
```scala
$ bin/spark-shell --conf spark.hadoop.fs.s3a.access.key=$AWS_ACCESS_KEY_ID --conf spark.hadoop.fs.s3a.secret.key=$AWS_SECRET_ACCESS_KEY -c spark.eventLog.enabled=true -c spark.eventLog.dir=s3a://dongjoon/spark-events/ --packages org.apache.hadoop:hadoop-aws:3.2.0,org.apache.hadoop:hadoop-common:3.2.0
...
java.lang.NoSuchMethodError: org.apache.hadoop.util.SemaphoredDelegatingExecutor.<init>(Lcom/google/common/util/concurrent/ListeningExecutorService;IZ)V
at org.apache.hadoop.fs.s3a.S3AFileSystem.create(S3AFileSystem.java:772)
```
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CI.
Closes #30508 from dongjoon-hyun/SPARK-33212-REVERT.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-12-02 04:23:48 -05:00
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woodstox-core/5.0.3//woodstox-core-5.0.3.jar
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[SPARK-30491][INFRA] Enable dependency audit files to tell dependency classifier
### What changes were proposed in this pull request?
Enable dependency audit files to tell the value of artifact id, version, and classifier of a dependency.
For example, `avro-mapred-1.8.2-hadoop2.jar` should be expanded to `avro-mapred/1.8.2/hadoop2/avro-mapred-1.8.2-hadoop2.jar` where `avro-mapred` is the artifact id, `1.8.2` is the version, and `haddop2` is the classifier.
### Why are the changes needed?
Dependency audit files are expected to be consumed by automated tests or downstream tools.
However, current dependency audit files under `dev/deps` only show jar names. And there isn't a simple rule on how to parse the jar name to get the values of different fields. For example, `hadoop2` is the classifier of `avro-mapred-1.8.2-hadoop2.jar`, in contrast, `incubating` is the version of `htrace-core-3.1.0-incubating.jar`.
Reference: There is a good example of the downstream tool that would be enabled as yhuai suggested,
> Say we have a Spark application that depends on a third-party dependency `foo`, which pulls in `jackson` as a transient dependency. Unfortunately, `foo` depends on a different version of `jackson` than Spark. So, in the pom of this Spark application, we use the dependency management section to pin the version of `jackson`. By doing this, we are lifting `jackson` to the top-level dependency of my application and I want to have a way to keep tracking what Spark uses. What we can do is to cross-check my Spark application's classpath with what Spark uses. Then, with a test written in my code base, whenever my application bumps Spark version, this test will check what we define in the application and what Spark has, and then remind us to change our application's pom if needed. In my case, I am fine to directly access git to get these audit files.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Code changes are verified by generated dependency audit files naturally. Thus, there are no tests added.
Closes #27177 from mengCareers/depsOptimize.
Lead-authored-by: Xinrong Meng <meng.careers@gmail.com>
Co-authored-by: mengCareers <meng.careers@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-01-15 23:19:44 -05:00
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xbean-asm7-shaded/4.15//xbean-asm7-shaded-4.15.jar
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xz/1.5//xz-1.5.jar
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zjsonpatch/0.3.0//zjsonpatch-0.3.0.jar
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zookeeper/3.4.14//zookeeper-3.4.14.jar
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2020-10-12 03:27:53 -04:00
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zstd-jni/1.4.5-6//zstd-jni-1.4.5-6.jar
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