Question

我正在使用以下形式的JSON对象：

{
    "docId" : "1",
    "links" : {
        "a link": ["endpoint 1", "endpoint 2"],
        "another link": ["endpoint 3"] 
    },
    "authors" : ["Thomas", "Peter"],
    "text": "This is the document text!"    
}

为了使用SparkSession.read.json加载它，我将所有这些JSON对象写入一个文件concatenated.txt，其中每行代表一个完整的文档：

{"docId": "1", ...}
{"docId": "2", ...}
{"docId": "3", ...}

问题是如果我运行

df_data = spark.read.json('concatenated.txt')

它不起作用，因为我遇到了java.lang.OutOfMemoryError。我不明白确切的问题，但我想它无法推断出正确的类型。出于这个原因，我将以下模式定义为解决方法。到目前为止这是有效的：

schema = StructType([
        StructField("docId", StringType(), True),
        StructField("links", StringType(), True),
        StructField("authors", StringType(), True),
        StructField("text", StringType(), True)                              
])

df_data = spark.read.json('concatenated.txt', schema=schema)

但当然，这需要我做一些像：

import json 
# ..
df_data.flatMap(lambda x: json.loads(x.authors))

以获得实际的基础对象。

我的问题是如何加载JSON数据，以便所有复杂对象实际上都是在加载时构建的。因此，authors应该是始终包含list（或None），而links是dict，其值也属于list类型。

示例数据：

{ "docId" : "1", "links" : { "a link": ["endpoint 1", "endpoint 2"], "another link": ["endpoint 3"] }, "authors" : ["Thomas", "Peter"], "text": "This is the document text!" }
{ "docId" : "2", "links" : { "a link": ["endpoint 1", "endpoint 2"], "another link": ["endpoint 3"] }, "authors" : ["Thomas", "Peter"], "text": "This is the document text!" }
{ "docId" : "3", "links" : { "a link": ["endpoint 1", "endpoint 2"], "another link": ["endpoint 3"] }, "authors" : ["Thomas", "Peter"], "text": "This is the document text!" }
{ "docId" : "4", "links" : { "a link": ["endpoint 1", "endpoint 2"], "another link": ["endpoint 3"] }, "authors" : ["Thomas", "Peter"], "text": "This is the document text!" }
{ "docId" : "5", "links" : { "a link": ["endpoint 1", "endpoint 2"], "another link": ["endpoint 3"] }, "authors" : ["Thomas", "Peter"], "text": "This is the document text!" }
{ "docId" : "6", "links" : { "a link": ["endpoint 1", "endpoint 2"], "another link": ["endpoint 3"] }, "authors" : ["Thomas", "Peter"], "text": "This is the document text!" }

Answer 1

文件加载正常（Spark 1.6，cloudera VM 5.12）。这里是。检查您的spark.driver.memory。

>>> sqlContext.sql("select * from json.`file:///home/cloudera/data4.json`").show()
+---------------+-----+--------------------+--------------------+
|        authors|docId|               links|                text|
+---------------+-----+--------------------+--------------------+
|[Thomas, Peter]|    1|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    2|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    3|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    4|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    5|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    6|[WrappedArray(end...|This is the docum...|
+---------------+-----+--------------------+--------------------+

>>> sqlContext.read.json("file:///home/cloudera/data4.json").show()
+---------------+-----+--------------------+--------------------+
|        authors|docId|               links|                text|
+---------------+-----+--------------------+--------------------+
|[Thomas, Peter]|    1|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    2|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    3|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    4|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    5|[WrappedArray(end...|This is the docum...|
|[Thomas, Peter]|    6|[WrappedArray(end...|This is the docum...|
+---------------+-----+--------------------+--------------------+

如何使用嵌套列表和字典加载JSON对象？

1 个答案: