PySpark 找不到 Kafka 源

Question

我正在尝试使用 Kafka 和 Spark 部署 docker 容器，并希望从 pyspark 应用程序读取 Kafka 主题。卡夫卡正在工作，我可以写一个主题，火花也在工作。但是当我尝试读取 Kafka 流时，我收到错误消息：

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 如下所示：

---
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 应用程序：

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，所以也许这是一个明显的错误，希望你能帮助我

编辑 当我取消注释 os.env 时，我收到以下错误：

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

Answer 1

缺少应用程序资源

这意味着您正在使用 python 而不是 spark-submit 运行代码

我能够通过复制您的环境以及using findspark 来重现该错误，似乎PYSPARK_SUBMIT_ARGS 在该容器中不起作用，即使变量确实已加载...

解决方法是在执行时传递参数。

spark-submit \
  --packages org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0 \
  script.py