从pyspark docu：

pyspark.sql.functions.broadcast(df)[source]

    Marks a DataFrame as small enough for use in broadcast joins.

Docu用于SC广播

class pyspark.Broadcast(sc=None, value=None, pickle_registry=None, path=None, sock_file=None)[source]

    A broadcast variable created with SparkContext.broadcast(). Access its value through value.

因此，第一个更像是对Catalyst的提示，即应该使用广播连接来连接此df，即使通常算法更喜欢sort-merge连接
如果你想了解更多关于连接提示的信息，这里有一个来自Spark代码的示例注解，它描述了这个行为和其他连接提示

// If it is an equi-join, we first look at the join hints w.r.t. the following order:
      //   1. broadcast hint: pick broadcast hash join if the join type is supported. If both sides
      //      have the broadcast hints, choose the smaller side (based on stats) to broadcast.
      //   2. sort merge hint: pick sort merge join if join keys are sortable.
      //   3. shuffle hash hint: We pick shuffle hash join if the join type is supported. If both
      //      sides have the shuffle hash hints, choose the smaller side (based on stats) as the
      //      build side.
      //   4. shuffle replicate NL hint: pick cartesian product if join type is inner like.
      //
      // If there is no hint or the hints are not applicable, we follow these rules one by one:
      //   1. Pick broadcast hash join if one side is small enough to broadcast, and the join type
      //      is supported. If both sides are small, choose the smaller side (based on stats)
      //      to broadcast.
      //   2. Pick shuffle hash join if one side is small enough to build local hash map, and is
      //      much smaller than the other side, and `spark.sql.join.preferSortMergeJoin` is false.
      //   3. Pick sort merge join if the join keys are sortable.
      //   4. Pick cartesian product if join type is inner like.
      //   5. Pick broadcast nested loop join as the final solution. It may OOM but we don't have
      //      other choice.

第二个选项创建广播变量，这就是为什么你必须收集它（因为它需要是值而不是 Dataframe ）有趣的是，当你使用广播加入你的df也将被收集到驱动程序，因为这是广播现在在Spark中的工作方式
在您的情况下，您应该使用pyspark.sql.functions.broadcast（df）。当您希望在每个执行器中提供一些变量的副本以供其他处理时（例如，如果您希望在每个执行器上有一些小的元数据），而不是在连接期间使用pyspark. broadcast，因此最好使用hint