使 Spark 代码更高效、更简洁

Question

我有以下代码可以清理 returns 具有两列的数据框的文档语料库 (pipelineClean(corpus))：

“id”：长
“令牌”：数组[字符串]。

之后，代码生成一个包含以下列的数据框：

“术语”：字符串
"postingList": List[Array[Long, Long]]（第一个 long 是文档中记录的另一个术语频率）

pipelineClean(corpus)
        .select($"id" as "documentId", explode($"tokens") as "term") // explode creates a new row for each element in the given array column
        .groupBy("term", "documentId").count //group by and then count number of rows per group, returning a df with groupings and the counting
        .where($"term" =!= "") // seems like there are some tokens that are empty, even though Tokenizer should remove them
        .withColumn("posting", struct($"documentId", $"count")) // merge columns as a single {docId, termFreq}
        .select("term", "posting")
        .groupBy("term").agg(collect_list($"posting") as "postingList") // we do another grouping in order to collect the postings into a list
        .orderBy("term")
        .persist(StorageLevel.MEMORY_ONLY_SER)

我的问题是：是否可以让这段代码更短 and/or 更有效率？例如，是否可以在单个 groupBy?

中进行分组

Answer 1

除了跳过 withColumn 调用并直接使用 select:

之外，您似乎无能为力。

.select(col("term"), struct(col("documentId"), col("count")) as "posting")

而不是

.withColumn("posting", struct($"documentId", $"count")) // merge columns as a single {docId, termFreq}
        .select("term", "posting")

使 Spark 代码更高效、更简洁

Make a Spark code more efficient and cleaner

scala

apache-spark

apache-spark-sql