我正在嘗試使用 Kafka 和 Spark 部署 docker 容器，并希望從 pyspark 應用程式中讀取 Kafka 主題。卡夫卡正在作業，我可以寫一個主題，火花也在作業。但是當我嘗試讀取 Kafka 流時，我收到錯誤訊息：
pyspark.sql.utils.AnalysisException: Failed to find data source: kafka. Please deploy the application as per the deployment section of "Structured Streaming Kafka Integration Guide".
我的 Docker Compose yaml 看起來像這樣：
---
version: '3.7'
services:
zookeeper:
image: bitnami/zookeeper:3
ports:
- 2181:2181
environment:
ALLOW_ANONYMOUS_LOGIN: "yes"
kafka:
image: bitnami/kafka:2
ports:
- 9092:9092
environment:
KAFKA_CFG_ZOOKEEPER_CONNECT: zookeeper:2181
ALLOW_PLAINTEXT_LISTENER: "yes"
KAFKA_LISTENERS: >-
INTERNAL://:29092,EXTERNAL://:9092
KAFKA_ADVERTISED_LISTENERS: >-
INTERNAL://kafka:29092,EXTERNAL://localhost:9092
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: >-
INTERNAL:PLAINTEXT,EXTERNAL:PLAINTEXT
KAFKA_INTER_BROKER_LISTENER_NAME: "INTERNAL"
depends_on:
- zookeeper
spark:
image: docker.io/bitnami/spark:3-debian-10
environment:
- SPARK_MODE=master
- SPARK_RPC_AUTHENTICATION_ENABLED=no
- SPARK_RPC_ENCRYPTION_ENABLED=no
- SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
- SPARK_SSL_ENABLED=no
ports:
- '8080:8080'
volumes:
- ./:/home/workspace/
- ./spark/jars:/opt/bitnami/spark/.ivy2
spark-worker-1:
image: docker.io/bitnami/spark:3-debian-10
environment:
- SPARK_MODE=worker
- SPARK_MASTER_URL=spark://spark:7077
- SPARK_WORKER_MEMORY=1G
- SPARK_WORKER_CORES=1
- SPARK_RPC_AUTHENTICATION_ENABLED=no
- SPARK_RPC_ENCRYPTION_ENABLED=no
- SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
- SPARK_SSL_ENABLED=no
volumes:
- ./:/home/workspace/
- ./spark/jars:/opt/bitnami/spark/.ivy2
kafdrop:
image: obsidiandynamics/kafdrop:latest
ports:
- 9000:9000
environment:
KAFKA_BROKERCONNECT: kafka:29092
depends_on:
- kafka
和 pyspark 應用程式：
from pyspark.sql import SparkSession
import os
#os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0,org.apache.kafka:kafka-clients:2.8.1'
# the source for this data pipeline is a kafka topic, defined below
spark = SparkSession.builder.appName("fuel-level").master("local[*]").getOrCreate()
spark.sparkContext.setLogLevel('WARN')
kafkaRawStreamingDF = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "localhost:9092") \
.option("subscribe","SimLab-KUKA") \
.option("startingOffsets","earliest")\
.load()
#this is necessary for Kafka Data Frame to be readable, into a single column value
kafkaStreamingDF = kafkaRawStreamingDF.selectExpr("cast(key as string) key", "cast(value as string) value")
kafkaStreamingDF.writeStream.outputMode("append").format("console").start().awaitTermination()
我是 Spark 和 docker 的新手，所以也許這是一個明顯的錯誤，希望你能幫助我
編輯當我取消注釋 os.env 時，我收到以下錯誤：
Error: Missing application resource.
Usage: spark-submit [options] <app jar | python file | R file> [app arguments]
Usage: spark-submit --kill [submission ID] --master [spark://...]
Usage: spark-submit --status [submission ID] --master [spark://...]
Usage: spark-submit run-example [options] example-class [example args]
Options:
--master MASTER_URL spark://host:port, mesos://host:port, yarn,
k8s://https://host:port, or local (Default: local[*]).
--deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or
on one of the worker machines inside the cluster ("cluster")
(Default: client).
--class CLASS_NAME Your application's main class (for Java / Scala apps).
--name NAME A name of your application.
--jars JARS Comma-separated list of jars to include on the driver
and executor classpaths.
--packages Comma-separated list of maven coordinates of jars to include
on the driver and executor classpaths. Will search the local
maven repo, then maven central and any additional remote
repositories given by --repositories. The format for the
coordinates should be groupId:artifactId:version.
--exclude-packages Comma-separated list of groupId:artifactId, to exclude while
resolving the dependencies provided in --packages to avoid
dependency conflicts.
--repositories Comma-separated list of additional remote repositories to
search for the maven coordinates given with --packages.
--py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to place
on the PYTHONPATH for Python apps.
--files FILES Comma-separated list of files to be placed in the working
directory of each executor. File paths of these files
in executors can be accessed via SparkFiles.get(fileName).
--archives ARCHIVES Comma-separated list of archives to be extracted into the
working directory of each executor.
--conf, -c PROP=VALUE Arbitrary Spark configuration property.
--properties-file FILE Path to a file from which to load extra properties. If not
specified, this will look for conf/spark-defaults.conf.
--driver-memory MEM Memory for driver (e.g. 1000M, 2G) (Default: 1024M).
--driver-java-options Extra Java options to pass to the driver.
--driver-library-path Extra library path entries to pass to the driver.
--driver-class-path Extra class path entries to pass to the driver. Note that
jars added with --jars are automatically included in the
classpath.
--executor-memory MEM Memory per executor (e.g. 1000M, 2G) (Default: 1G).
--proxy-user NAME User to impersonate when submitting the application.
This argument does not work with --principal / --keytab.
--help, -h Show this help message and exit.
--verbose, -v Print additional debug output.
--version, Print the version of current Spark.
Cluster deploy mode only:
--driver-cores NUM Number of cores used by the driver, only in cluster mode
(Default: 1).
Spark standalone or Mesos with cluster deploy mode only:
--supervise If given, restarts the driver on failure.
Spark standalone, Mesos or K8s with cluster deploy mode only:
--kill SUBMISSION_ID If given, kills the driver specified.
--status SUBMISSION_ID If given, requests the status of the driver specified.
Spark standalone, Mesos and Kubernetes only:
--total-executor-cores NUM Total cores for all executors.
Spark standalone, YARN and Kubernetes only:
--executor-cores NUM Number of cores used by each executor. (Default: 1 in
YARN and K8S modes, or all available cores on the worker
in standalone mode).
Spark on YARN and Kubernetes only:
--num-executors NUM Number of executors to launch (Default: 2).
If dynamic allocation is enabled, the initial number of
executors will be at least NUM.
--principal PRINCIPAL Principal to be used to login to KDC.
--keytab KEYTAB The full path to the file that contains the keytab for the
principal specified above.
Spark on YARN only:
--queue QUEUE_NAME The YARN queue to submit to (Default: "default").
Traceback (most recent call last):
File "/Users/janikbischoff/Documents/Uni/PuL/BA/Code/Tests/spark-test.py", line 6, in <module>
spark = SparkSession.builder.appName("fuel-level").master("local[*]").getOrCreate()
File "/Users/janikbischoff/Library/Python/3.8/lib/python/site-packages/pyspark/sql/session.py", line 228, in getOrCreate
sc = SparkContext.getOrCreate(sparkConf)
File "/Users/janikbischoff/Library/Python/3.8/lib/python/site-packages/pyspark/context.py", line 392, in getOrCreate
SparkContext(conf=conf or SparkConf())
File "/Users/janikbischoff/Library/Python/3.8/lib/python/site-packages/pyspark/context.py", line 144, in __init__
SparkContext._ensure_initialized(self, gateway=gateway, conf=conf)
File "/Users/janikbischoff/Library/Python/3.8/lib/python/site-packages/pyspark/context.py", line 339, in _ensure_initialized
SparkContext._gateway = gateway or launch_gateway(conf)
File "/Users/janikbischoff/Library/Python/3.8/lib/python/site-packages/pyspark/java_gateway.py", line 108, in launch_gateway
raise RuntimeError("Java gateway process exited before sending its port number")
RuntimeError: Java gateway process exited before sending its port number
uj5u.com熱心網友回復：
缺少應用程式資源
這意味著您正在使用python而不是運行代碼spark-submit
我能夠通過復制您的環境以及使用findspark來重現錯誤，它似乎PYSPARK_SUBMIT_ARGS在該容器中不起作用，即使變數確實被加載了......
解決方法是在執行時傳遞引數。
spark-submit \
--packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0 \
script.py
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