如何使用Apache Spark数据帧(Python)执行Switch语句 [英] How to perform a Switch statement with Apache Spark Dataframes (Python)
问题描述
我正在尝试对数据执行某项操作，在该操作中，如果某个值符合条件之一，则该值将映射到预定值列表，否则将映射到穿透值.
I'm trying to perform an operation on my data where a certain value will be mapped to a list of pre-determined values if it matches one of the criteria, or to a fall-through value otherwise.
这将是等效的SQL:
CASE
WHEN user_agent LIKE \'%CanvasAPI%\' THEN \'api\'
WHEN user_agent LIKE \'%candroid%\' THEN \'mobile_app_android\'
WHEN user_agent LIKE \'%iCanvas%\' THEN \'mobile_app_ios\'
WHEN user_agent LIKE \'%CanvasKit%\' THEN \'mobile_app_ios\'
WHEN user_agent LIKE \'%Windows NT%\' THEN \'desktop\'
WHEN user_agent LIKE \'%MacBook%\' THEN \'desktop\'
WHEN user_agent LIKE \'%iPhone%\' THEN \'mobile\'
WHEN user_agent LIKE \'%iPod Touch%\' THEN \'mobile\'
WHEN user_agent LIKE \'%iPad%\' THEN \'mobile\'
WHEN user_agent LIKE \'%iOS%\' THEN \'mobile\'
WHEN user_agent LIKE \'%CrOS%\' THEN \'desktop\'
WHEN user_agent LIKE \'%Android%\' THEN \'mobile\'
WHEN user_agent LIKE \'%Linux%\' THEN \'desktop\'
WHEN user_agent LIKE \'%Mac OS%\' THEN \'desktop\'
WHEN user_agent LIKE \'%Macintosh%\' THEN \'desktop\'
ELSE \'other_unknown\'
END AS user_agent_type
我对Spark还是很陌生，因此我对该程序的第一次尝试是使用查找字典，并像这样在RDD中逐行调整值:
I am fairly new to Spark, and so my first attempt at this program uses a lookup dictionary and adjusts the values line by line in an RDD like so:
USER_AGENT_VALS = {
'CanvasAPI': 'api',
'candroid': 'mobile_app_android',
'iCanvas': 'mobile_app_ios',
'CanvasKit': 'mobile_app_ios',
'Windows NT': 'desktop',
'MacBook': 'desktop',
'iPhone': 'mobile',
'iPod Touch': 'mobile',
'iPad': 'mobile',
'iOS': 'mobile',
'CrOS': 'desktop',
'Android': 'mobile',
'Linux': 'desktop',
'Mac OS': 'desktop',
'Macintosh': 'desktop'
}
def parse_requests(line: list,
id_data: dict,
user_vals: dict = USER_AGENT_VALS):
"""
Expects an input list which maps to the following indexes:
0: user_id
1: context(course)_id
2: request_month
3: user_agent_type
:param line: A list of values.
:return: A list
"""
found = False
for key, value in user_vals.items():
if key in line[3]:
found = True
line[3] = value
if not found:
line[3] = 'other_unknown'
# Retrieves the session_id count from the id_data dictionary using
# the user_id as the key.
session_count = id_data[str(line[0])]
line.append(session_count)
line.extend(config3.ETL_LIST)
return [str(item) for item in line]
我当前的代码中的数据在dataframe中，我不确定如何最有效地执行上述操作.我知道它们是不可变的，因此需要将其作为新的数据帧返回，但是我的问题是如何最好地做到这一点.这是我的代码:
My current code has the data in a dataframe, and I'm not exactly sure how to perform the above operation most efficiently. I know they are immutable so it needs to be returned as a new dataframe, but my question is how best to do this. Here is my code:
from boto3 import client
import psycopg2 as ppg2
from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.functions import current_date, date_format, lit, StringType
EMR_CLIENT = client('emr')
conf = SparkConf().setAppName('Canvas Requests Logs')
sc = SparkContext(conf=conf)
sql_context = SQLContext(sc)
# for dependencies
# sc.addPyFile()
USER_AGENT_VALS = {
'CanvasAPI': 'api',
'candroid': 'mobile_app_android',
'iCanvas': 'mobile_app_ios',
'CanvasKit': 'mobile_app_ios',
'Windows NT': 'desktop',
'MacBook': 'desktop',
'iPhone': 'mobile',
'iPod Touch': 'mobile',
'iPad': 'mobile',
'iOS': 'mobile',
'CrOS': 'desktop',
'Android': 'mobile',
'Linux': 'desktop',
'Mac OS': 'desktop',
'Macintosh': 'desktop'
}
if __name__ == '__main__':
df = sql_context.read.parquet(
r'/Users/mharris/PycharmProjects/etl3/pyspark/Datasets/'
r'usage_data.gz.parquet')
course_data = df.filter(df['context_type'] == 'Course')
request_data = df.select(
df['user_id'],
df['context_id'].alias('course_id'),
date_format(df['request_timestamp'], 'MM').alias('request_month'),
df['user_agent']
)
sesh_id_data = df.groupBy('user_id').count()
joined_data = request_data.join(
sesh_id_data,
on=request_data['user_id'] == sesh_id_data['user_id']
).drop(sesh_id_data['user_id'])
all_fields = joined_data.withColumn(
'etl_requests_usage', lit('DEV')
).withColumn(
'etl_datetime_local', current_date()
).withColumn(
'etl_transformation_name', lit('agg_canvas_logs_user_agent_types')
).withColumn(
'etl_pdi_version', lit(r'Apache Spark')
).withColumn(
'etl_pdi_build_version', lit(r'1.6.1')
).withColumn(
'etl_pdi_hostname', lit(r'N/A')
).withColumn(
'etl_pdi_ipaddress', lit(r'N/A')
).withColumn(
'etl_checksum_md5', lit(r'N/A')
)
作为PS，有没有比我做的更好的添加列的方法?
As a PS, is there a better way to add columns than the way I've done it?
推荐答案
如果您愿意，甚至可以直接使用SQL表达式:
If you want you can even use your SQL expression directly:
expr = """
CASE
WHEN user_agent LIKE \'%Android%\' THEN \'mobile\'
WHEN user_agent LIKE \'%Linux%\' THEN \'desktop\'
ELSE \'other_unknown\'
END AS user_agent_type"""
df = sc.parallelize([
(1, "Android"), (2, "Linux"), (3, "Foo")
]).toDF(["id", "user_agent"])
df.selectExpr("*", expr).show()
## +---+----------+---------------+
## | id|user_agent|user_agent_type|
## +---+----------+---------------+
## | 1| Android| mobile|
## | 2| Linux| desktop|
## | 3| Foo| other_unknown|
## +---+----------+---------------+
否则，您可以将其替换为when和like以及otherwise的组合:
otherwise you can replace it with a combination of when and like and otherwise:
from pyspark.sql.functions import col, when
from functools import reduce
c = col("user_agent")
vs = [("Android", "mobile"), ("Linux", "desktop")]
expr = reduce(
lambda acc, kv: when(c.like(kv[0]), kv[1]).otherwise(acc),
vs,
"other_unknown"
).alias("user_agent_type")
df.select("*", expr).show()
## +---+----------+---------------+
## | id|user_agent|user_agent_type|
## +---+----------+---------------+
## | 1| Android| mobile|
## | 2| Linux| desktop|
## | 3| Foo| other_unknown|
## +---+----------+---------------+
您还可以在单个select中添加多个列:
You can also add multiple columns in a single select:
exprs = [c.alias(a) for (a, c) in [
('etl_requests_usage', lit('DEV')),
('etl_datetime_local', current_date())]]
df.select("*", *exprs)
这篇关于如何使用Apache Spark数据帧(Python)执行Switch语句的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
