PySpark 和广播连接示例 [英] PySpark and broadcast join example

问题描述

我使用的是 Spark 1.3

I am using Spark 1.3

# Read from text file, parse it and then do some basic filtering to get   data1
data1.registerTempTable('data1')

# Read from text file, parse it and then do some basic filtering to get data1
data2.registerTempTable('data2')

# Perform join
data_joined = data1.join(data2, data1.id == data2.id);

我的数据非常倾斜，data2(几KB)<<

My data is quite skewed and data2 (few KB) << data1 (10s of GB) and the performance is quite bad. I was reading about broadcast join, but not sure how I can do the same using Python API.

推荐答案

Spark 1.3 不支持使用 DataFrame 的广播连接.在 Spark >= 1.5.0 中，您可以使用 broadcast 函数来应用广播连接:

Spark 1.3 doesn't support broadcast joins using DataFrame. In Spark >= 1.5.0 you can use broadcast function to apply broadcast joins:

from pyspark.sql.functions import broadcast

data1.join(broadcast(data2), data1.id == data2.id)

对于旧版本，唯一的选择是转换为 RDD 并应用与其他语言相同的逻辑.大概是这样的:

For older versions the only option is to convert to RDD and apply the same logic as in other languages. Roughly something like this:

from pyspark.sql import Row
from pyspark.sql.types import StructType

# Create a dictionary where keys are join keys
# and values are lists of rows
data2_bd = sc.broadcast(
    data2.map(lambda r: (r.id, r)).groupByKey().collectAsMap())


# Define a new row with fields from both DFs
output_row = Row(*data1.columns + data2.columns)

# And an output schema
output_schema = StructType(data1.schema.fields + data2.schema.fields)

# Given row x, extract a list of corresponding rows from broadcast
# and output a list of merged rows
def gen_rows(x):
    return [output_row(*x + y) for y in data2_bd.value.get(x.id, [])]

# flatMap and create a new data frame
joined = data1.rdd.flatMap(lambda row: gen_rows(row)).toDF(output_schema)

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