Pyspark 2.4.0，使用读取流从 kafka 读取 avro - Python [英] Pyspark 2.4.0, read avro from kafka with read stream - Python

问题描述

我正在尝试使用 PySpark 2.4.0 从 Kafka 读取 avro 消息.

I am trying to read avro messages from Kafka, using PySpark 2.4.0.

spark-avro 外部模块可以提供这个读取avro的方案文件:

The spark-avro external module can provide this solution for reading avro files:

df = spark.read.format("avro").load("examples/src/main/resources/users.avro") 
df.select("name", "favorite_color").write.format("avro").save("namesAndFavColors.avro")

但是，我需要阅读流式 avro 消息.库文档建议使用 from_avro() 函数，该函数仅适用于 Scala 和 Java.

However, I need to read streamed avro messages. The library documentation suggests using the from_avro() function, which is only available for Scala and Java.

是否有其他模块支持读取从 Kafka 流式传输的 avro 消息?

Are there any other modules that support reading avro messages streamed from Kafka?

推荐答案

您可以包含 spark-avro 包，例如使用 --packages(调整版本以匹配 spark 安装):

You can include spark-avro package, for example using --packages (adjust versions to match spark installation):

bin/pyspark --packages org.apache.spark:spark-avro_2.11:2.4.0

并提供您自己的包装器:

and provide your own wrappers:

from pyspark.sql.column import Column, _to_java_column 

def from_avro(col, jsonFormatSchema): 
    sc = SparkContext._active_spark_context 
    avro = sc._jvm.org.apache.spark.sql.avro
    f = getattr(getattr(avro, "package$"), "MODULE$").from_avro
    return Column(f(_to_java_column(col), jsonFormatSchema)) 


def to_avro(col): 
    sc = SparkContext._active_spark_context 
    avro = sc._jvm.org.apache.spark.sql.avro
    f = getattr(getattr(avro, "package$"), "MODULE$").to_avro
    return Column(f(_to_java_column(col))) 

示例用法(取自 官方测试套件):

from pyspark.sql.functions import col, struct


avro_type_struct = """
{
  "type": "record",
  "name": "struct",
  "fields": [
    {"name": "col1", "type": "long"},
    {"name": "col2", "type": "string"}
  ]
}"""


df = spark.range(10).select(struct(
    col("id"),
    col("id").cast("string").alias("id2")
).alias("struct"))
avro_struct_df = df.select(to_avro(col("struct")).alias("avro"))
avro_struct_df.show(3)

+----------+
|      avro|
+----------+
|[00 02 30]|
|[02 02 31]|
|[04 02 32]|
+----------+
only showing top 3 rows

avro_struct_df.select(from_avro("avro", avro_type_struct)).show(3)

+------------------------------------------------+
|from_avro(avro, struct<col1:bigint,col2:string>)|
+------------------------------------------------+
|                                          [0, 0]|
|                                          [1, 1]|
|                                          [2, 2]|
+------------------------------------------------+
only showing top 3 rows

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