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author | Dongjoon Hyun <dongjoon@apache.org> | 2016-03-21 07:58:57 +0000 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-03-21 07:58:57 +0000 |
commit | 20fd254101553cb5a4c932c8d03064899112bee6 (patch) | |
tree | 25b1dd840cd2ec27fd875c3b52987d502e6423f5 /mllib | |
parent | e474088144cdd2632cf2fef6b2cf10b3cd191c23 (diff) | |
download | spark-20fd254101553cb5a4c932c8d03064899112bee6.tar.gz spark-20fd254101553cb5a4c932c8d03064899112bee6.tar.bz2 spark-20fd254101553cb5a4c932c8d03064899112bee6.zip |
[SPARK-14011][CORE][SQL] Enable `LineLength` Java checkstyle rule
## What changes were proposed in this pull request?
[Spark Coding Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) has 100-character limit on lines, but it's disabled for Java since 11/09/15. This PR enables **LineLength** checkstyle again. To help that, this also introduces **RedundantImport** and **RedundantModifier**, too. The following is the diff on `checkstyle.xml`.
```xml
- <!-- TODO: 11/09/15 disabled - the lengths are currently > 100 in many places -->
- <!--
<module name="LineLength">
<property name="max" value="100"/>
<property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/>
</module>
- -->
<module name="NoLineWrap"/>
<module name="EmptyBlock">
<property name="option" value="TEXT"/>
-167,5 +164,7
</module>
<module name="CommentsIndentation"/>
<module name="UnusedImports"/>
+ <module name="RedundantImport"/>
+ <module name="RedundantModifier"/>
```
## How was this patch tested?
Currently, `lint-java` is disabled in Jenkins. It needs a manual test.
After passing the Jenkins tests, `dev/lint-java` should passes locally.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #11831 from dongjoon-hyun/SPARK-14011.
Diffstat (limited to 'mllib')
3 files changed, 4 insertions, 3 deletions
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java index 536f0dc58f..e160a5a47e 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java @@ -18,7 +18,6 @@ package org.apache.spark.ml.classification; import java.io.Serializable; -import java.lang.Math; import java.util.List; import org.junit.After; diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java index d493a7fcec..00f4476841 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java @@ -48,7 +48,8 @@ public class JavaOneVsRestSuite implements Serializable { jsql = new SQLContext(jsc); int nPoints = 3; - // The following coefficients and xMean/xVariance are computed from iris dataset with lambda=0.2. + // The following coefficients and xMean/xVariance are computed from iris dataset with + // lambda=0.2. // As a result, we are drawing samples from probability distribution of an actual model. double[] coefficients = { -0.57997, 0.912083, -0.371077, -0.819866, 2.688191, diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java index 5812037dee..bdcbde5e26 100644 --- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java @@ -63,7 +63,8 @@ public class JavaStopWordsRemoverSuite { RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb")) ); StructType schema = new StructType(new StructField[] { - new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty()) + new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, + Metadata.empty()) }); Dataset<Row> dataset = jsql.createDataFrame(data, schema); |