I installed kafka and zookeeper in windows system. i have started kafka and zookeeper servers, created topic "javainuse-topic" , started producer and consumer with the below commands
.\bin\windows\zookeeper-server-start.bat .\config\zookeeper.properties
.\bin\windows\kafka-server-start.bat .\config\server.properties
.\bin\windows\kafka-topics.bat --create --zookeeper localhost:2181
--replication-factor 1 --partitions 1 --topic javainuse-topic
.\bin\windows\kafka-console-producer.bat --broker-list localhost:9092
--topic javainuse-topic
.\bin\windows\kafka-console-consumer.bat --bootstrap-server
localhost:9092 --topic javainuse-topic --from-beginning
i am able to transfer data successfully from producer to consumer. So, i have wrote below code in eclipse and tried to execute it in local. but i am not able to view the consumer data in my eclipse console.
import os
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.3.0 pyspark-shell'
import sys
import time
from pyspark import SparkContext, SparkConf
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
n_secs = 1
topic = "javainuse-topic"
conf = SparkConf().setAppName("KafkaStreamProcessor").setMaster("local[*]")
sc = SparkContext(conf=conf)
sc.setLogLevel("WARN")
ssc = StreamingContext(sc, n_secs)
kafkaStream = KafkaUtils.createDirectStream(ssc, [topic], {
'bootstrap.servers':'localhost:9092',
'group.id':'javainuse-topic',
'fetch.message.max.bytes':'15728640',
'auto.offset.reset':'largest'})
# Group ID is completely arbitrary
lines = kafkaStream.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()
time.sleep(6) # Run stream for 10 minutes just in case no detection of producer
# ssc.awaitTermination()
ssc.stop(stopSparkContext=True,stopGraceFully=True)
You might try again but this time setting auto.offset.reset to 'earliest' (or 'smallest' if you are using the old consumer).
kafkaStream = KafkaUtils.createDirectStream(ssc, [topic], {
'bootstrap.servers':'localhost:9092',
'group.id':'javainuse-topic',
'fetch.message.max.bytes':'15728640',
'auto.offset.reset':'earliest'})
# Group ID is completely arbitrary
Related
When we are trying to stream the data from SSL enabled Kafka topic we are facing below error . Can you please help us on this issue .
19/11/07 13:26:54 INFO ConsumerFetcherManager: [ConsumerFetcherManager-1573151189884] Added fetcher for partitions ArrayBuffer()
19/11/07 13:26:54 WARN ConsumerFetcherManager$LeaderFinderThread: [spark-streaming-consumer_dvtcbddc101.corp.cox.com-1573151189725-d40a510f-leader-finder-thread], Failed to find leader for Set([inst_monitor_status_test,2], [inst_monitor_status_test,0], [inst_monitor_status_test,1])
java.lang.NullPointerException
at org.apache.kafka.common.utils.Utils.formatAddress(Utils.java:408)
at kafka.cluster.Broker.connectionString(Broker.scala:62)
at kafka.client.ClientUtils$$anonfun$fetchTopicMetadata$5.apply(ClientUtils.scala:89)
at kafka.client.ClientUtils$$anonfun$fetchTopicMetadata$5.apply(ClientUtils.scala:89)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at kafka.client.ClientUtils$.fetchTopicMetadata(ClientUtils.scala:89)
at kafka.consumer.ConsumerFetcherManager$LeaderFinderThread.doWork(ConsumerFetcherManager.scala:66)
at kafka.utils.ShutdownableThread.run(ShutdownableThread.scala:60)
Pyspark code :
from __future__ import print_function
import sys
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
from pyspark import SparkConf, SparkContext
from operator import add
import sys
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
import json
from kafka import SimpleProducer, KafkaClient
from kafka import KafkaProducer
def handler(message):
records = message.collect()
for record in records:
print(record)
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, 10)
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()
kvs.foreachRDD(handler)
ssc.start()
ssc.awaitTermination()
Spark submit command :
Spark submit:
/usr/hdp/2.6.1.0-129/spark2/bin/spark-submit --packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.1.0,org.apache.spark:spark-sql-kafka-0-10_2.11:2.1.0,org.apache.spark:spark-sql-kafka-0-10_2.11:2.3.0 dsstream2.py host:2181 inst_monitor_status_test
Thanks for your inputs . I have passed the SSL parameters in following method and working fine as expected.
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
from pyspark.streaming import StreamingContext
import time
# Spark Streaming context :
spark = SparkSession.builder.appName('PythonStreamingDirectKafkaWordCount').getOrCreate()
sc = spark.sparkContext
ssc = StreamingContext(sc, 20)
# Kafka Topic Details :
KAFKA_TOPIC_NAME_CONS = "topic_name"
KAFKA_OUTPUT_TOPIC_NAME_CONS = "topic_to_hdfs"
KAFKA_BOOTSTRAP_SERVERS_CONS = 'kafka_server:9093'
# Creating readstream DataFrame :
df = spark.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", KAFKA_BOOTSTRAP_SERVERS_CONS) \
.option("subscribe", KAFKA_TOPIC_NAME_CONS) \
.option("startingOffsets", "earliest") \
.option("kafka.security.protocol","SASL_SSL")\
.option("kafka.client.id" ,"Clinet_id")\
.option("kafka.sasl.kerberos.service.name","kafka")\
.option("kafka.ssl.truststore.location", "/home/path/kafka_trust.jks") \
.option("kafka.ssl.truststore.password", "password_rd") \
.option("kafka.sasl.kerberos.keytab","/home/path.keytab") \
.option("kafka.sasl.kerberos.principal","path") \
.load()
df1 = df.selectExpr( "CAST(value AS STRING)")
# Creating Writestream DataFrame :
df1.writeStream \
.option("path","target_directory") \
.format("csv") \
.option("checkpointLocation","chkpint_directory") \
.outputMode("append") \
.start()
ssc.awaitTermination()
I am trying to write a spark script that monitors a directory & processes data as it streams in.
In the below, i dont get any errors, but it also doesn't print the files,
Does anyone have any ideas?
import findspark
findspark.init()
import pyspark
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
conf = (SparkConf()
.setMaster("local")
.setAppName("My app")
.set("spark.executor.memory", "1g"))
sc = SparkContext.getOrCreate(conf=conf)
ssc = StreamingContext(sc, 1) #microbatched every 1 second
lines = ssc.textFileStream('file:///C:/Users/kiera/OneDrive/Documents/logs')#directory of log files, Does not work for subdirectories
lines.pprint()
ssc.start()
ssc.awaitTermination()
I am naive in Big data, I am trying to connect kafka to spark.
Here is my producer code
import os
import sys
import pykafka
def get_text():
## This block generates my required text.
text_as_bytes=text.encode(text)
producer.produce(text_as_bytes)
if __name__ == "__main__":
client = pykafka.KafkaClient("localhost:9092")
print ("topics",client.topics)
producer = client.topics[b'imagetext'].get_producer()
get_text()
This is printing my generated text on console consumer when I do
bin/kafka-console-consumer.sh --zookeeper localhost:2181 --topic imagetext --from-beginning
Now I want this text to be consumed using Spark and this is my Jupyter code
import findspark
findspark.init()
import os
from pyspark import SparkContext, SparkConf
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
import json
os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars /spark-2.1.1-bin-hadoop2.6/spark-streaming-kafka-0-8-assembly_2.11-2.1.0.jar pyspark-shell'
conf = SparkConf().setMaster("local[2]").setAppName("Streamer")
sc = SparkContext(conf=conf)
ssc = StreamingContext(sc,5)
print('ssc =================== {} {}')
kstream = KafkaUtils.createDirectStream(ssc, topics = ['imagetext'],
kafkaParams = {"metadata.broker.list": 'localhost:9092'})
print('contexts =================== {} {}')
lines = kstream.map(lambda x: x[1])
lines.pprint()
ssc.start()
ssc.awaitTermination()
ssc.stop(stopGraceFully = True)
But this is producing output on my Jupyter as
Time: 2018-02-21 15:03:25
-------------------------------------------
-------------------------------------------
Time: 2018-02-21 15:03:30
-------------------------------------------
Not the text that is on my console consumer..
Please help, unable to figure out the mistake.
I found another solution to it. While the solution of putting get_text() in a loop works, it is not the right solution. You data was not in continuous fashion when it was sent in Kafka. As a result, Spark streaming should not get it in such a way.
Kafka-python library provides a get(timeout) functionality so that Kafka waits for a request.
producer.send(topic,data).get(timeout=10)
Since you are using pykafka, I am not sure whether it will work. Nevertheless, you can still try once and dont put get_text() in loop.
Just change your port in the consumer from 9092 to 2181 as it is the Zookeeper. From the producer side, it has to be connected to the Kafka with port number 9092. And from the streamer side, it has to be connected to the Zookeeper with port number 2181.
Using a Kafka Stream in PySpark, is it possible to seek to the beginning of a Kafka topic without creating a new consumer group?
For example, I have the following code snippet:
...
import os
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.2.0 pyspark-shell'
from pyspark import SparkContext
from pyspark.sql import SparkSession
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
sc = SparkContext('local[2]', appName="MyStreamingApp_01")
sc.setLogLevel("INFO")
ssc.StreamingContext(sc, 30)
spark = SparkSession(sc)
kafkaStream = KafkaUtils.createStream(ssc, zookeeper_ip, 'group-id', {'messages': 1})
counted = kafkaStream.count()
...
My goal is to do something along the lines of
kafkaStream.seekToBeginningOfTopic()
Currently, I'm creating a new consumer group to re-read from the beginning of the topic, e.g.:
kafkaStream = KafkaUtils.createStream(ssc, zookeeper, 'group-id-2', {'messages': 1}, {"auto.offset.reset": "smallest"})
Is this the proper way to consume a topic from the beginning using PySpark?
I want to setup a streaming application using Apache Kafka and Spark streaming. Kafka is running on a seperate unix machine version 0.9.0.1 and spark v1.6.1 is a part of a hadoop cluster.
I have started the zookeeper and kafka server and want to stream in messages from a log file using console producer and consumed by spark streaming application using direct method (no receivers). I have written code in python and executing using the below command:
spark-submit --jars spark-streaming-kafka-assembly_2.10-1.6.1.jar streamingDirectKafka.py
getting below error:
/opt/mapr/spark/spark-1.6.1/python/lib/pyspark.zip/pyspark/streaming/kafka.py", line 152, in createDirectStream
py4j.protocol.Py4JJavaError: An error occurred while calling o38.createDirectStreamWithoutMessageHandler.
: java.lang.ClassCastException: kafka.cluster.BrokerEndPoint cannot be cast to kafka.cluster.Broker
Could you please help?
Thanks!!
from pyspark import SparkContext, SparkConf
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
if __name__ == "__main__":
conf = SparkConf().setAppName("StreamingDirectKafka")
sc = SparkContext(conf = conf)
ssc = StreamingContext(sc, 1)
topic = ['test']
kafkaParams = {"metadata.broker.list": "apsrd7102:9092"}
lines = (KafkaUtils.createDirectStream(ssc, topic, kafkaParams)
.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()
Looks like you are using incompatible version of Kafka. From the documentation as of Spark 2.0 - Kafka 0.8.x is supported.
http://spark.apache.org/docs/latest/streaming-programming-guide.html#advanced-sources