I have used the next docker-compose to build the containers:
version: '3'
services:
spark-master:
image: docker.io/bitnami/spark:2
environment:
- SPARK_MODE=master
- SPARK_RPC_AUTHENTICATION_ENABLED=no
- SPARK_RPC_ENCRYPTION_ENABLED=no
- SPARK_LOCAL_STORAGE_ENCRYPTION_ENABLED=no
- SPARK_SSL_ENABLED=no
volumes:
- type: bind
source: ./conf/log4j.properties
target: /opt/bitnami/spark/conf/log4j.properties
ports:
- '8080:8080'
- '7077:7077'
networks:
- spark
spark-worker-1:
image: docker.io/bitnami/spark:2
environment:
- SPARK_MODE=worker
- SPARK_MASTER_URL=spark://spark-master: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:
- type: bind
source: ./conf/log4j.properties
target: /opt/bitnami/spark/conf/log4j.properties
ports:
- '8081:8081'
networks:
- spark
depends_on:
- spark-master
networks:
spark:
driver: bridge
And the inspection of the network:
Finally, I use the next configuration in the Spark Session object:
def get_spark_context(app_name: str) -> SparkSession:
conf = SparkConf()
conf.setAll(
[
(
"spark.master",
os.environ.get("SPARK_MASTER_URL", "spark://spark-master:7077"),
),
("spark.driver.host", os.environ.get("SPARK_DRIVER_HOST", "local[*]")),
("spark.submit.deployMode", "client"),
('spark.ui.showConsoleProgress', 'true'),
("spark.driver.bindAddress", "0.0.0.0"),
("spark.app.name", app_name)
]
)
return SparkSession.builder.config(conf=conf).getOrCreate()
I'm using Spark Structured streaming, when I try to create the SparkSession object I get the next error:
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 21/09/09 15:58:04 WARN SparkContext: Please ensure that the number of slots available on your executors is limited by the number of cores to task cpus and not another custom resource. If cores is not the limiting resource then dynamic allocation will not work properly! 21/09/09 15:58:07 WARN TransportClientFactory: DNS resolution failed for spark-master:7077 took 2266 ms 21/09/09 15:58:07 WARN StandaloneAppClient$ClientEndpoint: Failed to connect to master spark-master:7077 org.apache.spark.SparkException: Exception thrown in awaitResult:
at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:302)
at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
at org.apache.spark.rpc.RpcEnv.setupEndpointRefByURI(RpcEnv.scala:101)
at org.apache.spark.rpc.RpcEnv.setupEndpointRef(RpcEnv.scala:109)
at org.apache.spark.deploy.client.StandaloneAppClient$ClientEndpoint$$anon$1.run(StandaloneAppClient.scala:106)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748) Caused by: java.io.IOException: Failed to connect to spark-master:7077
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:253)
at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:195)
at org.apache.spark.rpc.netty.NettyRpcEnv.createClient(NettyRpcEnv.scala:204)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:202)
at org.apache.spark.rpc.netty.Outbox$$anon$1.call(Outbox.scala:198)
... 4 more Caused by: java.net.UnknownHostException: spark-master
at java.net.InetAddress.getAllByName0(InetAddress.java:1281)
at java.net.InetAddress.getAllByName(InetAddress.java:1193)
at java.net.InetAddress.getAllByName(InetAddress.java:1127)
at java.net.InetAddress.getByName(InetAddress.java:1077)
at io.netty.util.internal.SocketUtils$8.run(SocketUtils.java:156)
at io.netty.util.internal.SocketUtils$8.run(SocketUtils.java:153)
at java.security.AccessController.doPrivileged(Native Method)
at io.netty.util.internal.SocketUtils.addressByName(SocketUtils.java:153)
at io.netty.resolver.DefaultNameResolver.doResolve(DefaultNameResolver.java:41)
at io.netty.resolver.SimpleNameResolver.resolve(SimpleNameResolver.java:61)
at io.netty.resolver.SimpleNameResolver.resolve(SimpleNameResolver.java:53)
at io.netty.resolver.InetSocketAddressResolver.doResolve(InetSocketAddressResolver.java:55)
at io.netty.resolver.InetSocketAddressResolver.doResolve(InetSocketAddressResolver.java:31)
at io.netty.resolver.AbstractAddressResolver.resolve(AbstractAddressResolver.java:106)
at io.netty.bootstrap.Bootstrap.doResolveAndConnect0(Bootstrap.java:200)
at io.netty.bootstrap.Bootstrap.access$000(Bootstrap.java:46)
at io.netty.bootstrap.Bootstrap$1.operationComplete(Bootstrap.java:180)
at io.netty.bootstrap.Bootstrap$1.operationComplete(Bootstrap.java:166)
at io.netty.util.concurrent.DefaultPromise.notifyListener0(DefaultPromise.java:577)
at io.netty.util.concurrent.DefaultPromise.notifyListenersNow(DefaultPromise.java:551)
at io.netty.util.concurrent.DefaultPromise.notifyListeners(DefaultPromise.java:490)
at io.netty.util.concurrent.DefaultPromise.setValue0(DefaultPromise.java:615)
at io.netty.util.concurrent.DefaultPromise.setSuccess0(DefaultPromise.java:604)
at io.netty.util.concurrent.DefaultPromise.trySuccess(DefaultPromise.java:104)
at io.netty.channel.DefaultChannelPromise.trySuccess(DefaultChannelPromise.java:84)
at io.netty.channel.AbstractChannel$AbstractUnsafe.safeSetSuccess(AbstractChannel.java:984)
at io.netty.channel.AbstractChannel$AbstractUnsafe.register0(AbstractChannel.java:504)
at io.netty.channel.AbstractChannel$AbstractUnsafe.access$200(AbstractChannel.java:417)
at io.netty.channel.AbstractChannel$AbstractUnsafe$1.run(AbstractChannel.java:474)
at io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:164)
at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:472)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:500)
at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:989)
at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
... 1 more INFO:py4j.java_gateway:Error while receiving.
The docker ps command returns the next result:
I don't exactly know where the problem is, all I want is running a python app using pyspark in a Docker container.