ssl - GCP Dataproc - 无法构建 kafka 消费者，无法加载 JKS 类型的 SSL keystore dataproc.jks

Question

我正在尝试在 GCP Dataproc 上运行结构化流程序，该程序从 Kafka 访问数据并打印它。

使用 SSL 访问 Kafka，信任库和 keystore 文件存储在存储桶中。 我正在使用 Google Storage API 访问存储桶，并将文件存储在当前工作目录中。信任库和 keystore 被传递到 Kafka 消费者/生产者。 但是 - 我收到错误

命令:

gcloud dataproc jobs submit pyspark /Users/karanalang/Documents/Technology/gcp/DataProc/StructuredStreaming_Kafka_GCP-Batch-feb2-v2.py  --cluster dataproc-ss-poc  --properties spark.jars.packages=org.apache.spark:spark-sql-kafka-0-10_2.12:3.2.0 --region us-central1

代码如下所示:

spark = SparkSession.builder.appName('StructuredStreaming_VersaSase').getOrCreate()

kafkaBrokers='<broker-ip>:9094'
topic = "versa-sase"
security_protocol="SSL"

# Google Storage API to access the keys in the buckets
client = storage.Client()
bucket = client.get_bucket('ssl-certs-karan')

blob_ssl_truststore = bucket.get_blob('cap12.jks')
ssl_truststore_location = '{}/{}'.format(os.getcwd(), blob_ssl_truststore.name) 
blob_ssl_truststore.download_to_filename(ssl_truststore_location)

ssl_truststore_password="<ssl_truststore_password>"

blob_ssl_keystore = bucket.get_blob('dataproc-versa-sase-p12-1.jks')
ssl_keystore_location = '{}/{}'.format(os.getcwd(), blob_ssl_keystore.name) 
blob_ssl_keystore.download_to_filename(ssl_keystore_location)


ssl_keystore_password="<ssl_keystore_password>"
consumerGroupId = "versa-sase-grp"
checkpoint = "gs://ss-checkpoint/"

print(" SPARK.SPARKCONTEXT -> ", spark.sparkContext)


df = spark.read.format('kafka')\
    .option("kafka.bootstrap.servers",kafkaBrokers)\
    .option("kafka.security.protocol","SSL") \
    .option("kafka.ssl.truststore.location",ssl_truststore_location) \
    .option("kafka.ssl.truststore.password",ssl_truststore_password) \
    .option("kafka.ssl.keystore.location", ssl_keystore_location)\
    .option("kafka.ssl.keystore.password", ssl_keystore_password)\
    .option("subscribe", topic) \
    .option("kafka.group.id", consumerGroupId)\
    .option("startingOffsets", "earliest") \
    .load()

   print(" df -> ", df)
   query = df.selectExpr("CAST(value AS STRING)", "CAST(key AS STRING)", "topic", "timestamp") \
    .write \
    .format("console") \
    .option("numRows",100)\
    .option("checkpointLocation", checkpoint) \
    .option("outputMode", "complete")\
    .option("truncate", "false") \
    .save("output")

错误:

Traceback (most recent call last):
  File "/tmp/3e7304f8e27d4436a2f382280cebe7c5/StructuredStreaming_Kafka_GCP-Batch-feb2-v2.py", line 83, in <module>
    query = df.selectExpr("CAST(value AS STRING)", "CAST(key AS STRING)", "topic", "timestamp") \
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/readwriter.py", line 1109, in save
  File "/usr/lib/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1304, in __call__
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 111, in deco
  File "/usr/lib/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 326, in get_return_value
py4j.protocol.Py4JJavaError22/02/02 23:11:08 DEBUG org.apache.hadoop.ipc.Client: IPC Client (1416219052) connection to dataproc-ss-poc-m/10.128.0.78:8030 from root sending #171 org.apache.hadoop.yarn.api.ApplicationMasterProtocolPB.allocate
22/02/02 23:11:08 DEBUG org.apache.hadoop.ipc.Client: IPC Client (1416219052) connection to dataproc-ss-poc-m/10.128.0.78:8030 from root got value #171
22/02/02 23:11:08 DEBUG org.apache.hadoop.ipc.ProtobufRpcEngine: Call: allocate took 2ms
: An error occurred while calling o84.save.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3) (dataproc-ss-poc-w-0.c.versa-kafka-poc.internal executor 1): org.apache.kafka.common.KafkaException: Failed to construct kafka consumer
    at org.apache.kafka.clients.consumer.KafkaConsumer.<init>(KafkaConsumer.java:823)
    at org.apache.kafka.clients.consumer.KafkaConsumer.<init>(KafkaConsumer.java:665)
    at org.apache.kafka.clients.consumer.KafkaConsumer.<init>(KafkaConsumer.java:613)
    at org.apache.spark.sql.kafka010.consumer.InternalKafkaConsumer.createConsumer(KafkaDataConsumer.scala:124)
    at org.apache.spark.sql.kafka010.consumer.InternalKafkaConsumer.<init>(KafkaDataConsumer.scala:61)
    at org.apache.spark.sql.kafka010.consumer.InternalKafkaConsumerPool$ObjectFactory.create(InternalKafkaConsumerPool.scala:206)
    at org.apache.spark.sql.kafka010.consumer.InternalKafkaConsumerPool$ObjectFactory.create(InternalKafkaConsumerPool.scala:201)
    at org.apache.commons.pool2.BaseKeyedPooledObjectFactory.makeObject(BaseKeyedPooledObjectFactory.java:60)
    at org.apache.commons.pool2.impl.GenericKeyedObjectPool.create(GenericKeyedObjectPool.java:1041)
    at org.apache.commons.pool2.impl.GenericKeyedObjectPool.borrowObject(GenericKeyedObjectPool.java:342)
    at org.apache.commons.pool2.impl.GenericKeyedObjectPool.borrowObject(GenericKeyedObjectPool.java:265)
    at org.apache.spark.sql.kafka010.consumer.InternalKafkaConsumerPool.borrowObject(InternalKafkaConsumerPool.scala:84)
    at org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.retrieveConsumer(KafkaDataConsumer.scala:573)
    at org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.getOrRetrieveConsumer(KafkaDataConsumer.scala:558)
    at org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.$anonfun$getAvailableOffsetRange$1(KafkaDataConsumer.scala:359)
    at org.apache.spark.util.UninterruptibleThread.runUninterruptibly(UninterruptibleThread.scala:77)
    at org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.runUninterruptiblyIfPossible(KafkaDataConsumer.scala:618)
    at org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.getAvailableOffsetRange(KafkaDataConsumer.scala:358)
    at org.apache.spark.sql.kafka010.KafkaSourceRDD.resolveRange(KafkaSourceRDD.scala:123)
    at org.apache.spark.sql.kafka010.KafkaSourceRDD.compute(KafkaSourceRDD.scala:75)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
...

Caused by: org.apache.kafka.common.KafkaException: Failed to load SSL keystore /tmp/3e7304f8e27d4436a2f382280cebe7c5/dataproc-versa-sase-p12-1.jks of type JKS
    at org.apache.kafka.common.security.ssl.DefaultSslEngineFactory$FileBasedStore.load(DefaultSslEngineFactory.java:377)
    at org.apache.kafka.common.security.ssl.DefaultSslEngineFactory$FileBasedStore.<init>(DefaultSslEngineFactory.java:349)
    at org.apache.kafka.common.security.ssl.DefaultSslEngineFactory.createKeystore(DefaultSslEngineFactory.java:299)
    at org.apache.kafka.common.security.ssl.DefaultSslEngineFactory.configure(DefaultSslEngineFactory.java:161)
    at org.apache.kafka.common.security.ssl.SslFactory.instantiateSslEngineFactory(SslFactory.java:138)
    at org.apache.kafka.common.security.ssl.SslFactory.configure(SslFactory.java:95)
    at org.apache.kafka.common.network.SslChannelBuilder.configure(SslChannelBuilder.java:74)
    at org.apache.kafka.common.network.ChannelBuilders.create(ChannelBuilders.java:192)
    at org.apache.kafka.common.network.ChannelBuilders.clientChannelBuilder(ChannelBuilders.java:81)
    at org.apache.kafka.clients.ClientUtils.createChannelBuilder(ClientUtils.java:105)
    at org.apache.kafka.clients.consumer.KafkaConsumer.<init>(KafkaConsumer.java:737)
    ... 53 more
Caused by: java.nio.file.NoSuchFileException: /tmp/3e7304f8e27d4436a2f382280cebe7c5/dataproc-versa-sase-p12-1.jks
    at sun.nio.fs.UnixException.translateToIOException(UnixException.java:86)
    at sun.nio.fs.UnixException.rethrowAsIOException(UnixException.java:102)
    at sun.nio.fs.UnixException.rethrowAsIOException(UnixException.java:107)
    at sun.nio.fs.UnixFileSystemProvider.newByteChannel(UnixFileSystemProvider.java:214)
    at java.nio.file.Files.newByteChannel(Files.java:361)
    at java.nio.file.Files.newByteChannel(Files.java:407)
    at java.nio.file.spi.FileSystemProvider.newInputStream(FileSystemProvider.java:384)
    at java.nio.file.Files.newInputStream(Files.java:152)
    at org.apache.kafka.common.security.ssl.DefaultSslEngineFactory$FileBasedStore.load(DefaultSslEngineFactory.java:370)

在我的 Mac 上，我使用 PKCS 文件 (.p12)，并且能够以 SSL 模式访问 Kafka 集群。但是，在 Dataproc 中 - 预期的文件格式似乎是 JKS。

这是我用来将 .p12 文件转换为 JKS 格式的命令:

keytool -importkeystore -srckeystore dataproc-versa-sase.p12 -srcstoretype pkcs12 -srcalias versa-sase-user -destkeystore dataproc-versa-sase-p12-1.jks -deststoretype jks -deststorepass <password> -destalias versa-sase-user

需要采取什么措施来解决这个问题？ Spark 程序似乎无法访问 JKS 文件？

蒂亚!

Answer 1

如果你想使用jks，我会添加以下选项

.option("kafka.ssl.keystore.type", "JKS")
.option("kafka.ssl.truststore.type", "JKS")

顺便说一句，这也适用于 PKCS12

.option("kafka.ssl.keystore.type", "PKCS12")
.option("kafka.ssl.truststore.type", "PKCS12")

就像之前有人提到的，你可以检查是否是 jdk 兼容性问题，执行如下操作:

keytool -v -list -storetype pkcs12 -keystore kafka-client-jdk8-truststore.p12

如果您收到一条显示 keystore 的消息，那么您就没有问题了，但如果您收到一条消息说找不到标识符，则意味着 jdks 存在差异。