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| Title | Views | Posted |
|---|---|---|
| 6214 | 02-18-2021 11:53 AM |
02-18-2021
11:53 AM
Hi @jagadeesan here is the short update. After numerous failed attempts to build SPARK_HADOOP_VERSION=2.3.0.cloudera4 we added airflow node to CDH env. That was done by installing cloudera agent and registering the node to ClouderaManager. Next step was to edit docker-compose airflow.yaml file: -volumes mount ...
# java home
- /usr/java/jdk1.8.0_162:/usr/java/jdk1.8.0_162
# krb5.conf
- /etc/krb5.conf:/etc/krb5.conf:ro,z
# CDH bin
- /opt/cloudera/parcels:/opt/cloudera/parcels
# /etc
- /etc/hadoop:/etc/hadoop:rw,z
- /etc/spark2:/etc/spark2:rw,z
- /etc/sqoop:/etc/sqoop:rw,z
# sqoop
- /var/lib/sqoop:/var/lib/sqoop
... -env var ###java
JAVA_HOME=/usr/java/jdk1.8.0_162 Inside the container, symlinks were missing, so: ###java
ln -s /usr/java/jdk1.8.0_162/jre/bin/java /etc/alternatives/java
ln -s /etc/alternatives/java /usr/bin/java
#
ln -s /usr/java/jdk1.8.0_162/bin/java-rmi.cgi /etc/alternatives/java-rmi.cgi
ln -s /etc/alternatives/java-rmi.cgi /usr/bin/java-rmi.cgi
#
ln -s /usr/java/jdk1.8.0_162/bin/javac /etc/alternatives/javac
ln -s /etc/alternatives/javac /usr/bin/javac
#
ln -s /usr/java/jdk1.8.0_162/bin/javaws /etc/alternatives/javaws
ln -s /etc/alternatives/javaws /usr/bin/javaws
#
ln -s /usr/java/jdk1.8.0_162/bin/javapackager /etc/alternatives/javapackager
ln -s /etc/alternatives/javapackager /usr/bin/javapackager
#
ln -s /usr/java/jdk1.8.0_162/bin/javap /etc/alternatives/javap
ln -s /etc/alternatives/javap /usr/bin/javap
#
ln -s /usr/java/jdk1.8.0_162/bin/javah /etc/alternatives/javah
ln -s /etc/alternatives/javah /usr/bin/javah
#
ln -s /usr/java/jdk1.8.0_162/bin/javafxpackager /etc/alternatives/javafxpackager
ln -s /etc/alternatives/javafxpackager /usr/bin/javafxpackager
#
ln -s /usr/java/jdk1.8.0_162/bin/javadoc /etc/alternatives/javadoc
ln -s /etc/alternatives/javadoc /usr/bin/javadoc
###spark2-submit
ln -s /opt/cloudera/parcels/SPARK2-2.3.0.cloudera4-1.cdh5.13.3.p0.611179/bin/spark2-submit /etc/alternatives/spark2-submit
ln -s /etc/alternatives/spark2-submit /usr/bin/spark2-submit
ln -s /etc/spark2/conf.cloudera.spark2_on_yarn /etc/alternatives/spark2-conf
###hdfs
ln -s /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/bin/hdfs /etc/alternatives/hdfs
ln -s /etc/alternatives/hdfs /usr/bin/hdfs
ln -s /etc/hadoop/conf.cloudera.yarn /etc/alternatives/hadoop-conf
###sqoop
ln -s /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/bin/sqoop /etc/alternatives/sqoop
ln -s /etc/alternatives/sqoop /usr/bin/sqoop
ln -s /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/etc/sqoop/conf.dist /etc/alternatives/sqoop-conf After this, spark2-submit works as expected.
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01-24-2021
09:13 AM
Hi @jagadeesan thank you for your reply. This airflow docker node is not in Cloudera environment, it's spark, hive, hadoop and java dependencies are not managed by CDH CM node. CDH 5.15 comes with spark 2.3.0, hadoop 2.6.0, hive 1.1.0 and java8 version. To submit a job to yarn, all versions must be the same (I know I've read this somewhere, I'm not going crazy yet :D). That's why we downloaded spark-2.3.0-bin-without-hadoop, hive-1.1.0 and so on. We even tried to build CDH spark. SPARK_HADOOP_VERSION=2.3.0.cloudera4 SPARK_YARN=true sbt assembly But it throws error: [warn] module not found: org.apache.hadoop#hadoop-client;2.6.0-cdh5.13.3
[warn] ==== public: tried
[warn] https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-client/2.6.0-cdh5.13.3/hadoop-client-2.6.0-cdh5.13.3.pom Our next shot is to add this airflow node in CDH env and assign spark, hive and hdfs gateways. Apparently, this type of node does not need additional license...
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01-13-2021
12:52 AM
Hi all, we are executing pyspark and spark-submit to kerberized CDH 5.15v from remote airflow docker container not managed by CDH CM node, e.g. airflow container is not in CDH env. Versions of hive, spark and java are the same as on CDH. There is a valid kerberos ticket before executing spark-submit or pyspark. Python script: from pyspark.sql import SparkSession, functions as F
spark = SparkSession.builder.enableHiveSupport().appName('appName').getOrCreate()
sa_df=spark.sql("SELECT * FROM lnz_ch.lnz_cfg_codebook") Error is: To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.3.0
/_/
Using Python version 3.6.12 (default, Oct 13 2020 21:45:01)
SparkSession available as 'spark'.
>>> from pyspark.sql import SparkSession, functions as F
>>> spark = SparkSession.builder.enableHiveSupport().appName('appName').getOrCreate()
>>> sa_df=spark.sql("SELECT * FROM lnz_ch.lnz_cfg_codebook")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/python/pyspark/sql/session.py", line 708, in sql
return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
File "/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/python/lib/py4j-0.10.6-src.zip/py4j/java_gateway.py", line 1160, in __call__
File "/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/python/lib/py4j-0.10.6-src.zip/py4j/protocol.py", line 320, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o24.sql.
: java.lang.NoSuchFieldError: METASTORE_CLIENT_SOCKET_LIFETIME
at org.apache.spark.sql.hive.HiveUtils$.formatTimeVarsForHiveClient(HiveUtils.scala:195)
at org.apache.spark.sql.hive.HiveUtils$.newClientForMetadata(HiveUtils.scala:286)
at org.apache.spark.sql.hive.HiveExternalCatalog.client$lzycompute(HiveExternalCatalog.scala:66)
at org.apache.spark.sql.hive.HiveExternalCatalog.client(HiveExternalCatalog.scala:65)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists$1.apply$mcZ$sp(HiveExternalCatalog.scala:195)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists$1.apply(HiveExternalCatalog.scala:195)
at org.apache.spark.sql.hive.HiveExternalCatalog$$anonfun$databaseExists$1.apply(HiveExternalCatalog.scala:195)
at org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:97)
at org.apache.spark.sql.hive.HiveExternalCatalog.databaseExists(HiveExternalCatalog.scala:194)
at org.apache.spark.sql.internal.SharedState.externalCatalog$lzycompute(SharedState.scala:114)
at org.apache.spark.sql.internal.SharedState.externalCatalog(SharedState.scala:102)
at org.apache.spark.sql.hive.HiveSessionStateBuilder.externalCatalog(HiveSessionStateBuilder.scala:39)
at org.apache.spark.sql.hive.HiveSessionStateBuilder.catalog$lzycompute(HiveSessionStateBuilder.scala:54)
at org.apache.spark.sql.hive.HiveSessionStateBuilder.catalog(HiveSessionStateBuilder.scala:52)
at org.apache.spark.sql.hive.HiveSessionStateBuilder$$anon$1.<init>(HiveSessionStateBuilder.scala:69)
at org.apache.spark.sql.hive.HiveSessionStateBuilder.analyzer(HiveSessionStateBuilder.scala:69)
at org.apache.spark.sql.internal.BaseSessionStateBuilder$$anonfun$build$2.apply(BaseSessionStateBuilder.scala:293)
at org.apache.spark.sql.internal.BaseSessionStateBuilder$$anonfun$build$2.apply(BaseSessionStateBuilder.scala:293)
at org.apache.spark.sql.internal.SessionState.analyzer$lzycompute(SessionState.scala:79)
at org.apache.spark.sql.internal.SessionState.analyzer(SessionState.scala:79)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:638)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748) Same error is retured from yarn when executing spark-submit. Details: googling this error, we assume that the versions of spark and hive in airflow container are "somehow mismatched". Error still occurs if we specify spark-submit or pyspark like this: spark-shell \
--jars \
/var/lib/airflow/spark/apache-hive-1.1.0-bin/lib/hive-metastore-1.1.0.jar,\
/var/lib/airflow/spark/apache-hive-1.1.0-bin/lib/hive-exec-1.1.0.jar,\
/var/lib/airflow/spark/apache-hive-1.1.0-bin/lib/hive-common-1.1.0.jar,\
/var/lib/airflow/spark/apache-hive-1.1.0-bin/lib/hive-serde-1.1.0.jar,\
/var/lib/airflow/spark/apache-hive-1.1.0-bin/lib/guava-14.0.1.jar,\
/var/lib/airflow/spark/HiveJDBC4.jar \
--conf spark.sql.hive.metastore.version=1.1.0 \
--conf spark.sql.hive.metastore.jars=/var/lib/airflow/spark/spark-2.3.0-bin-without-hadoop/jars/* As you can see, we are heavily experimenting with --jars argument :D. Any ideas? Thank you. Links https://issues.apache.org/jira/browse/SPARK-14492 https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-properties.html https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/hive/
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Apache Spark