From 20fd254101553cb5a4c932c8d03064899112bee6 Mon Sep 17 00:00:00 2001
From: Dongjoon Hyun <dongjoon@apache.org>
Date: Mon, 21 Mar 2016 07:58:57 +0000
Subject: [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.
---
 .../apache/spark/ml/classification/JavaLogisticRegressionSuite.java    | 1 -
 .../java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java    | 3 ++-
 .../java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java    | 3 ++-
 3 files changed, 4 insertions(+), 3 deletions(-)

(limited to 'mllib/src/test')

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);
 
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