将 RDD 中的 JSON 行转换为 Apache Spark 中的数据帧 [英] Convert lines of JSON in RDD to dataframe in Apache Spark

问题描述

我在 S3 中有大约 17,000 个文件，如下所示:

{"hour": "00", "month": "07", "second": "00", "year": "1970", "timezone": "-00:00",天":12"，分钟":00"}{"hour": "00", "month": "07", "second": "01", "year": "1970", "timezone": "-00:00", "day": "12", "分钟": "00"}{"hour": "00", "month": "07", "second": "02", "year": "1970", "timezone": "-00:00", "day": "12", "分钟": "00"}{"hour": "00", "month": "07", "second": "03", "year": "1970", "timezone": "-00:00", "day": "12", "分钟": "00"}{"hour": "00", "month": "07", "second": "04", "year": "1970", "timezone": "-00:00", "day": "12", "分钟": "00"}

我每天有一个文件.每个文件包含每秒的记录.∴ 一个文件中有 86,000 条记录.每个文件都有一个文件名，如YYYY-MM-DD".

我使用 boto3 生成存储桶中的文件列表.这里我只选择了 10 个使用前缀的文件.

导入boto3s3_list = []s3 = boto3.resource('s3')my_bucket = s3.Bucket('time-waits-for-no-man')对于 my_bucket.objects.filter(Prefix='1972-05-1) 中的对象:s3_list.append(object.key)

此函数返回文件列表(S3 键).然后我定义一个函数来获取文件并返回行:

def FileRead(s3Key):s3obj = boto3.resource('s3').Object(bucket_name='bucket', key=s3Key)内容 = s3obj.get()['Body'].read().decode('utf-8')产量行(**内容)

然后我使用 flatMap 分发这个函数:

job = sc.parallelize(s3_list)foo = job.flatMap(FileRead)

问题

但是，我无法弄清楚如何将这些行正确地泵入数据帧中.

<预><代码>>>>foo.toDF().show()+--------------------+|_1|+--------------------+|{"小时": "00", "米...||{"小时": "00", "米...||{"小时": "00", "米...||{"小时": "00", "米...||{"小时": "00", "米...||{"小时": "00", "米...||{"小时": "00", "米...||{"小时": "00", "米...||{"小时": "00", "米...||{"小时": "00", "米...|+--------------------+>>>foo.toDF().count()10

请有人告诉我怎么做吗?

解决方案

你或许应该直接使用 json reader (spark.read.json/sqlContext.read.json) 但如果您知道架构，您可以尝试手动解析 JSON 字符串:

from pyspark.sql.types import StructField, StructType, StringType从 pyspark.sql 导入行导入json字段 = ['天', '小时', '分钟', '月', '秒', '时区', '年']架构 = 结构类型([StructField(field, StringType(), True) 用于字段中的字段])定义解析(S，字段):尝试:d = json.loads(s[0])返回 [tuple(d.get(field) for field in fields)]除了:返回 []spark.createDataFrame(foo.flatMap(lambda s: parse(s, fields)), schema)

你也可以使用get_json_object:

from pyspark.sql.functions import get_json_objectdf.select([get_json_object("value", "$.{0}".format(field)).alias(field)字段中的字段])

I have some 17,000 files in S3 that look like this:

{"hour": "00", "month": "07", "second": "00", "year": "1970", "timezone": "-00:00", "day": "12", "minute": "00"}
{"hour": "00", "month": "07", "second": "01", "year": "1970", "timezone": "-00:00", "day": "12", "minute": "00"}
{"hour": "00", "month": "07", "second": "02", "year": "1970", "timezone": "-00:00", "day": "12", "minute": "00"}
{"hour": "00", "month": "07", "second": "03", "year": "1970", "timezone": "-00:00", "day": "12", "minute": "00"}
{"hour": "00", "month": "07", "second": "04", "year": "1970", "timezone": "-00:00", "day": "12", "minute": "00"}

I have one file per day. Each file contains a record for each second. ∴ 86,000 records in a file. Each file has a file name like "YYYY-MM-DD".

Using boto3 I generate a list of the files in the bucket. Here I am selecting only 10 files using the prefix.

import boto3
s3_list = []
s3 = boto3.resource('s3')
my_bucket = s3.Bucket('time-waits-for-no-man')
for object in my_bucket.objects.filter(Prefix='1972-05-1):
    s3_list.append(object.key)

This function returns a list of files(S3 keys). I then define a function to fetch a file and return the rows:

def FileRead(s3Key):
    s3obj = boto3.resource('s3').Object(bucket_name='bucket', key=s3Key)
    contents = s3obj.get()['Body'].read().decode('utf-8')
    yield Row(**contents)

I then distribute this function using flatMap:

job = sc.parallelize(s3_list)
foo = job.flatMap(FileRead)

Problem

I'm not able to work out how to properly pump these rows into a Dataframe however.

>>> foo.toDF().show()
+--------------------+                                                          
|                  _1|
+--------------------+
|{"hour": "00", "m...|
|{"hour": "00", "m...|
|{"hour": "00", "m...|
|{"hour": "00", "m...|
|{"hour": "00", "m...|
|{"hour": "00", "m...|
|{"hour": "00", "m...|
|{"hour": "00", "m...|
|{"hour": "00", "m...|
|{"hour": "00", "m...|
+--------------------+

>>> foo.toDF().count()
10  

Please could someone show me how to do this?

解决方案

You should probably use json reader directly (spark.read.json / sqlContext.read.json) but if you know the schema you can try parsing JSON string manually:

from pyspark.sql.types import StructField, StructType, StringType
from pyspark.sql import Row
import json

fields = ['day', 'hour', 'minute', 'month', 'second', 'timezone', 'year']
schema =  StructType([
  StructField(field, StringType(), True) for field in fields
])

def parse(s, fields):
    try:
        d = json.loads(s[0])
        return [tuple(d.get(field) for field in fields)]
    except:
        return []

spark.createDataFrame(foo.flatMap(lambda s: parse(s, fields)), schema)

You can also use get_json_object:

from pyspark.sql.functions import get_json_object

df.select([
    get_json_object("value", "$.{0}".format(field)).alias(field)
    for field in fields
])

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