使用 pyspark 将 spark 数据帧转换为嵌套 JSON

Question

我正在尝试将 spark 数据帧转换为 JSON。这个数据框中大约有 100 万行，下面是示例代码，但性能确实很差。所需的输出将是一个 member_id 在 JSON 文件中显示一次，与 tag_name 在一个 member_id 下显示相同。如果有任何可能的方法可以更快地完成此操作，请告诉我。

示例代码：

iresult = sdf.groupBy('member_id','tag_name').agg(ch.collect_list(ch.struct('detail_name','detail_value')).alias('detail')).\

groupBy('member_id').agg(ch.collect_list(ch.struct('tag_name','detail')).alias('tag'))\

.agg(ch.to_json(ch.collect_list(ch.struct('member_id','tag'))).alias('result'))

result.show()

detail.csv:

member_id, tag_name, detail_name, detail_value
-------------------------------------------------------
abc123, m1, Service_A, 20
abc123, m1, Service_B, 20
abc123, m2, Service_C, 10
xyz456, m3, Service A, 5
xyz456, m3, Service A, 10

期望输出JSON：

{   "member_id": "abc123",
    "tag"：[ {"tag_name": "m1",
            "detail":[{ "detail_name": "Service_A",
                        "detail_value": "20"},
                    {   "detail_name": "Service_B",
                        "detail_value": "20"}]},
            {"tag_name": "m2",
            "detail":[{ "detail_name": "Service_C",
                        "detail_value": "10"}]}]},
{   "member_id": "xyz456",
    "tag"：[{"tag_name": "m3",
            "detail":[{ "detail_name": "Service_A",
                        "detail_value": "5"},
                      { "detail_name": "Service_A",
                        "detail_value": "10"}]}]}

duplicate.csv:

member_id, tag_name, detail_name, detail_value
-------------------------------------------------------
abc123, m1, problem_no, 'abc123xyz'
abc123, m1, problem_no, 'abc456zzz'
xyz456, m1, problem_no, 'abc123xyz'
xyz456, m1, problem_no, 'abc456zzz'

重复输出 JSON:

{   "member_id": "abc123",
    "tag"：[ {"tag_name": "m1",
            "detail":[{ "detail_name": "problem_no",
                        "detail_value": "abc123xyz"},
                    {   "detail_name": "problem_no",
                        "detail_value": "abc456zzz"},
                      { "detail_name": "problem_no",
                        "detail_value": "abc123xyz"},
                    {   "detail_name": "problem_no",
                        "detail_value": "abc456zzz"}]}]},
{   "member_id": "xyz456",
    "tag"：[ {"tag_name": "m1",
            "detail":[{ "detail_name": "problem_no",
                        "detail_value": "abc123xyz"},
                    {   "detail_name": "problem_no",
                        "detail_value": "abc456zzz"},
                      { "detail_name": "problem_no",
                        "detail_value": "abc123xyz"},
                    {   "detail_name": "problem_no",
                        "detail_value": "abc456zzz"}]}]}

Answer 1

您介意通过 sql 语句实现它吗？

逐层构造struct，最后使用to_json函数生成json字符串。

df.createOrReplaceTempView('tmp')
sql = """
    select to_json(collect_list(struct(member_id,tag))) as member
    from
        (select member_id,collect_list(struct(tag_name,detail)) as tag
        from
            (select member_id,tag_name,collect_list(struct(detail_name,detail_value)) as detail
            from tmp
            group by member_id,tag_name)
        group by member_id)
"""
df = spark.sql(sql)
df.show(truncate=False)

使用 pyspark 将 spark 数据帧转换为嵌套 JSON

Convert spark dataframe to nested JSON using pyspark

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apache-spark-sql