python3+spark2.1+kafka0.8+sparkStreaming

python代码：

import time
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
from operator import add

sc = SparkContext(master="local[1]",appName="PythonSparkStreamingRokidDtSnCount")
ssc = StreamingContext(sc, 2)
zkQuorum = ‘localhost:2181‘
topic = {‘rokid‘:1}
groupid = "test-consumer-group"
lines = KafkaUtils.createStream(ssc, zkQuorum, groupid, topic)
lines1 = lines.flatMap(lambda x: x.split("\n"))
valuestr = lines1.map(lambda x: x.value.decode())
valuedict = valuestr.map(lambda x:eval(x))
message = valuedict.map(lambda x: x["message"])
rdd2 = message.map(lambda x: (time.strftime("%Y-%m-%d",time.localtime(float(x.split("\u0001")[0].split("\u0002")[1])/1000))+"|"+x.split("\u0001")[1].split("\u0002")[1],1)).map(lambda x: (x[0],x[1]))
rdd3 = rdd2.reduceByKey(add)
rdd3.saveAsTextFiles("/tmp/wordcount")
rdd3.pprint()
ssc.start()
ssc.awaitTermination()

执行SparkStreaming：

spark/bin/spark-submit --jars spark-streaming-kafka-0-8-assembly_2.11-2.1.0.jar ReadFromKafkaStreaming.py

其中spark-streaming-kafka-0.98-assembly_2.11-2.1.0.jar从以下网站下载
http://search.maven.org

作为入门参考。

"""
 Counts words in UTF8 encoded, ‘\n‘ delimited text received from the network every second.
 Usage: kafka_wordcount.py <zk> <topic>
 To run this on your local machine, you need to setup Kafka and create a producer first, see
 http://kafka.apache.org/documentation.html#quickstart
 and then run the example
    `$ bin/spark-submit --jars       external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar       examples/src/main/python/streaming/kafka_wordcount.py       localhost:2181 test`
"""
from __future__ import print_function

import sys

from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils

if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: kafka_wordcount.py <zk> <topic>", file=sys.stderr)
        exit(-1)

    sc = SparkContext(appName="PythonStreamingKafkaWordCount")
    ssc = StreamingContext(sc, 1)

    zkQuorum, topic = sys.argv[1:]
    kvs = KafkaUtils.createStream(ssc, zkQuorum, "spark-streaming-consumer", {topic: 1})
    lines = kvs.map(lambda x: x[1])
    counts = lines.flatMap(lambda line: line.split(" "))         .map(lambda word: (word, 1))         .reduceByKey(lambda a, b: a+b)
    counts.pprint()

    ssc.start()
    ssc.awaitTermination()

bin/spark-submit --packages org.apache.spark:spark-streaming-kafka_2.10:1.6.2 examples/src/main/python/streaming/kafka_wordcount.py localhost:2181 test

kafka 使用：

1.3 Quick Start

This tutorial assumes you are starting fresh and have no existing Kafka or ZooKeeper data. Since Kafka console scripts are different for Unix-based and Windows platforms, on Windows platforms use bin\windows\ instead of bin/, and change the script extension to .bat.

Step 1: Download the code

Download the 0.11.0.0 release and un-tar it.

Step 2: Start the server

Kafka uses ZooKeeper so you need to first start a ZooKeeper server if you don‘t already have one. You can use the convenience script packaged with kafka to get a quick-and-dirty single-node ZooKeeper instance.

Now start the Kafka server:

Step 3: Create a topic

Let‘s create a topic named "test" with a single partition and only one replica:

We can now see that topic if we run the list topic command:

Alternatively, instead of manually creating topics you can also configure your brokers to auto-create topics when a non-existent topic is published to.

Step 4: Send some messages

Kafka comes with a command line client that will take input from a file or from standard input and send it out as messages to the Kafka cluster. By default, each line will be sent as a separate message.

Run the producer and then type a few messages into the console to send to the server.

Step 5: Start a consumer

Kafka also has a command line consumer that will dump out messages to standard output.