Question

在this question之后，我现在运行以下代码：

List<StructField> fields = new ArrayList<>();
fields.add(DataTypes.createStructField("A",DataTypes.LongType,true));
fields.add(DataTypes.createStructField("B",DataTypes.DoubleType,true));
StructType schema1 = DataTypes.createStructType(fields);
Dataset<Row> df1 = spark.sql("select 1 as A, 2.2 as B");
Dataset<Row> finalDf1 = spark.createDataFrame(df1.javaRDD(), schema1);

fields = new ArrayList<>();
fields.add(DataTypes.createStructField("B",DataTypes.DoubleType,true));
fields.add(DataTypes.createStructField("A",DataTypes.LongType,true));
StructType schema2 = DataTypes.createStructType(fields);
Dataset<Row> df2 = spark.sql("select 2.2 as B, 1 as A");
Dataset<Row> finalDf2 = spark.createDataFrame(df2.javaRDD(), schema2);

finalDf1.printSchema();
finalDf2.printSchema();
System.out.println(finalDf1.schema());
System.out.println(finalDf2.schema());
System.out.println(finalDf1.schema().equals(finalDf2.schema()));

以下是输出：

root
 |-- A: long (nullable = true)
 |-- B: double (nullable = true)

root
 |-- B: double (nullable = true)
 |-- A: long (nullable = true)

StructType(StructField(A,LongType,true), StructField(B,DoubleType,true))
StructType(StructField(B,DoubleType,true), StructField(A,LongType,true))
false

虽然各列的排列顺序不同，但是这两个数据集的列和列类型都完全相同。为了获得true，这里需要进行哪些比较？

Answer 1

假设cols顺序不匹配，并且相同的名称是相同的语义，并且需要相同的列数。

使用SCALA的示例，您应该能够适应JAVA：

import spark.implicits._
val df = sc.parallelize(Seq(
        ("A", "X", 2, 100), ("A", "X", 7, 100), ("B", "X", 10, 100),
        ("C", "X", 1, 100), ("D", "X", 50, 100), ("E", "X", 30, 100)
        )).toDF("c1", "c2", "Val1", "Val2")
val names = df.columns

val df2 = sc.parallelize(Seq(
       ("A", "X", 2, 1))).toDF("c1", "c2", "Val1", "Val2")
val names2 = df2.columns

names.sortWith(_ < _) sameElements names2.sortWith(_ < _)

返回true或false，请尝试输入。

Answer 2

如果它们的顺序不同，则它们不相同。即使它们都具有相同的列数和相同的名称。如果要查看两个模式是否具有相同的列名，请从两个数据帧的列表中获取该模式，然后编写代码进行比较。参见下面的Java示例

public static void main(String[] args)
{

    List<String> firstSchema =Arrays.asList(DataTypes.createStructType(ConfigConstants.firstSchemaFields).fieldNames());
    List<String> secondSchema = Arrays.asList(DataTypes.createStructType(ConfigConstants.secondSchemaFields).fieldNames());


    if(schemasHaveTheSameColumnNames(firstSchema,secondSchema))
    {
        System.out.println("Yes, schemas have the same column names");
    }else
    {
        System.out.println("No, schemas do not have the same column names");
    }
}

private static boolean schemasHaveTheSameColumnNames(List<String> firstSchema, List<String> secondSchema)
{
    if(firstSchema.size() != secondSchema.size())
    {
        return false;
    }else 
    {
        for (String column : secondSchema)
        {
            if(!firstSchema.contains(column))
                return false;
        }
    }
    return true;
}

Answer 3

遵循先前的答案，似乎是比较hasMoreEntries = events['has_more']; while (hasMoreEntries): url = "https://api.dropboxapi.com/2/team_log/get_events/continue" headers = { "Authorization": 'Bearer %s' % aTokenAudit, "Content-Type": "application/json" } data = { "cursor": events['cursor'] } r = requests.post(url, headers=headers, data=json.dumps(data)) events = r.json() hasMoreEntries = events['has_more']; for event in events['events']: counter+=1; print 'member id %s has done %s activites' % (memberId, counter)（列和类型）（而不仅仅是名称）的最快方法如下：

StructFields

Spark Java：当列的顺序不同时，如何比较架构？

3 个答案: