ODP currently does not support Ozone as the default file system. Yet ODP Ozone is configured to work independently of HDFS.
Prerequisites
To enable ofs support with applications, configure applications to use necessary jars and ozone-site.xml.
- Install Ozone client from Ambari UI on the node where you want to enable ofs support with Hadoop service.
- Add the
ozone-filesystem-hadoop3.jarto the application classpath:
# hadoop-envexport HADOOP_CLASSPATH=$HADOOP_CLASSPATH:/usr/odp/current/ozone-client/share/ozone/lib/ozone-filesystem-hadoop3-1.4.0.3.2.3.3-3.jar- Add the following configs to
core-site.xml:
<property> <name>fs.ofs.impl</name <value>org.apache.hadoop.fs.ozone.RootedOzoneFileSystem</value></property>- Add the following configs from ozone-site.xml to hdfs-site.xml:
| Property | Value |
|---|---|
| ozone.om.service.ids | omservice |
| ozone.om.address.omservice.om0 | <om-node1-host>:9862 |
| ozone.om.address.omservice.om1 | <om-node2-host>:9862 |
| ozone.om.address.omservice.om2 | <om-node3-host>:9862 |
| ozone.om.nodes.omservice | om0,om1,om2 |
- Include the
ozone-filesystem-hadoop3-1.4.0*.jarfile in themapreduce.application.classpathproperty in themapred-site.xmlfile. - Restart Hadoop services.
HDFS with OFS
To access hdfs dfs operations with ozone storage:
$ hdfs dfs [options] OFS_URIExamples:
- List files:
$ hdfs dfs -ls ofs://omservice/SLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/usr/odp/3.2.3.3-3/hadoop/lib/slf4j-reload4j-1.7.35.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/usr/odp/3.2.3.3-3/hadoop-hdfs/lib/slf4j-reload4j-1.7.35.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Reload4jLoggerFactory]24/03/20 16:15:08 INFO client.ClientTrustManager: Loading certificates for client.Found 2 itemsdrwxrwxrwx - hdfs hadoop 0 2024-03-20 17:58 ofs://omservice/s3vdrwxrwxrwx - hdfs hadoop 0 2024-03-20 18:37 ofs://omservice/testvol- Create directory:
$ hdfs dfs -mkdir ofs://omservice/testvol/testbucketSLF4J: Class path contains multiple SLF4J bindings.SLF4J: Found binding in [jar:file:/usr/odp/3.2.3.3-3/hadoop/lib/slf4j-reload4j-1.7.35.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/usr/odp/3.2.3.3-3/hadoop-hdfs/lib/slf4j-reload4j-1.7.35.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: Found binding in [jar:file:/usr/odp/3.2.3.3-3/tez/lib/slf4j-reload4j-1.7.35.jar!/org/slf4j/impl/StaticLoggerBinder.class]SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.SLF4J: Actual binding is of type [org.slf4j.impl.Reload4jLoggerFactory]24/05/02 13:18:57 INFO client.ClientTrustManager: Loading certificates for client.24/05/02 13:18:58 INFO rpc.RpcClient: Creating Bucket: testvol/testbucket, with bucket layout FILE_SYSTEM_OPTIMIZED, ambari-qa as owner, Versioning false, Storage Type set to DISK and Encryption set to false, Replication Type set to server-side default replication type, Namespace Quota set to -1, Space Quota set to -1- Upload file:
$ vi /tmp/README.mdhi,this is README.md$ hdfs dfs -put /tmp/README.md ofs://omservice/testvol/testbucket/$ hdfs dfs -ls ofs://omservice/testvol/testbucket/Found 1 items-rw-rw-rw- 3 ambari-qa ambari-qa 22 2024-04-30 13:31 ofs://omservice/testvol/testbucket/README.md- Reading file:
$ hdfs dfs -cat ofs://omservice/testvol/testbucket/README.md24/05/02 13:37:40 INFO client.ClientTrustManager: Loading certificates for client.24/05/02 13:37:40 INFO impl.MetricsConfig: Loaded properties from hadoop-metrics2.properties24/05/02 13:37:40 INFO impl.MetricsSystemImpl: Scheduled Metric snapshot period at 10 second(s).24/05/02 13:37:40 INFO impl.MetricsSystemImpl: XceiverClientMetrics metrics system startedhi,this is README.mdretry.RetryInvocationHandler: com.google.protobuf.ServiceException: INFO logs may be ignored as client hits all OM hosts one by one to identify leader OM.
24/05/02 13:37:40 INFO retry.RetryInvocationHandler: com.google.protobuf.ServiceException: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.ozone.om.exceptions.OMNotLeaderException): OM:om1 is not the leader. Suggested leader is OM:om2[odp03.acceldata.dvl]. at org.apache.hadoop.ozone.protocolPB.OzoneManagerProtocolServerSideTranslatorPB.createNotLeaderException(OzoneManagerProtocolServerSideTranslatorPB.java:292) at org.apache.hadoop.ozone.protocolPB.OzoneManagerProtocolServerSideTranslatorPB.createLeaderErrorException(OzoneManagerProtocolServerSideTranslatorPB.java:274) at org.apache.hadoop.ozone.protocolPB.OzoneManagerProtocolServerSideTranslatorPB.submitReadRequestToOM(OzoneManagerProtocolServerSideTranslatorPB.java:267) at org.apache.hadoop.ozone.protocolPB.OzoneManagerProtocolServerSideTranslatorPB.internalProcessRequest(OzoneManagerProtocolServerSideTranslatorPB.java:211) at org.apache.hadoop.ozone.protocolPB.OzoneManagerProtocolServerSideTranslatorPB.processRequest(OzoneManagerProtocolServerSideTranslatorPB.java:171) at org.apache.hadoop.hdds.server.OzoneProtocolMessageDispatcher.processRequest(OzoneProtocolMessageDispatcher.java:89) at org.apache.hadoop.ozone.protocolPB.OzoneManagerProtocolServerSideTranslatorPB.submitRequest(OzoneManagerProtocolServerSideTranslatorPB.java:162) at org.apache.hadoop.ozone.protocol.proto.OzoneManagerProtocolProtos$OzoneManagerService$2.callBlockingMethod(OzoneManagerProtocolProtos.java) at org.apache.hadoop.ipc.ProtobufRpcEngine$Server.processCall(ProtobufRpcEngine.java:484) at org.apache.hadoop.ipc.ProtobufRpcEngine2$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine2.java:595) at org.apache.hadoop.ipc.ProtobufRpcEngine2$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine2.java:573) at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:1227) at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:1094) at org.apache.hadoop.ipc.Server$RpcCall.run(Server.java:1017) at java.security.AccessController.doPrivileged(Native Method) at javax.security.auth.Subject.doAs(Subject.java:422) at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1899) at org.apache.hadoop.ipc.Server$Handler.run(Server.java:3048), while invoking $Proxy11.submitRequest over nodeId=om1,nodeAddress=odp02.acceldata.dvl:9862 after 1 failover attempts. Trying to failover immediately.Yarn with Ozone
YARN enables the execution of jobs that interact with data stored in or written to the Ozone file system.
- If Ranger authorization is enabled, grant the necessary permissions to OFS (Ozone File System) buckets, HDFS paths, and YARN queues to allow the required operations as per the job requirements.
- Authenticate users with Kerberos credentials when operating in a secure cluster.
- Submit the job.
Below is an example job that performs word count on data from a file in OFS and stores the output file containing the word count result back into OFS.
# yarn jar /path/to/jar /path/to/inputfile /ath/to/output$ yarn --config /etc/hadoop/conf jar /usr/odp/3.2.3.3-3/hadoop-mapreduce/hadoop-mapreduce-examples.jar wordcount ofs://omservice/testvol/testbucket/hdfsOzone.txt ofs://omservice/testvol/testbucket/wordcount_output$ hdfs dfs -cat ofs://omservice/testvol/testbucket/wordcount_output/part-r-0000024/05/03 11:06:36 INFO client.ClientTrustManager: Loading certificates for client.24/05/03 11:06:37 INFO impl.MetricsConfig: Loaded properties from hadoop-metrics2.properties24/05/03 11:06:37 INFO impl.MetricsSystemImpl: Scheduled Metric snapshot period at 10 second(s).24/05/03 11:06:37 INFO impl.MetricsSystemImpl: XceiverClientMetrics metrics system startedHDFS. 1Hello 2Ozone. 1from 1Job failing with : INFO mapreduce.Job: Task Id : task-id , Status : FAILEDError: java.io.IOException: Cannot resolve OM host omservice in the URI``
Configure MapReduce job to use ozone-site.xml. Alternatively, you can pass the configurations during runtime:
$ yarn jar /usr/odp/3.2.3.3-3/hadoop-mapreduce/hadoop-mapreduce-examples.jar wordcount -Dozone.om.service.ids=omservice -Dozone.om.nodes.omservice=om0,om1,om2 -Dozone.om.address.omservice.om0=odp01.acceldata.dvl:9862 -Dozone.om.address.omservice.om1=odp02.acceldata.dvl:9862 -Dozone.om.address.omservice.om2=odp03.acceldata.dvl:9862 ofs://omservice/testvol/testbucket/hdfsOzone.txt ofs://omservice/testvol/testbucket/wordcount_output10Hive with Ozone
Although Hive installation and operations use HDFS as the default file system, Ozone can be configured to be a parallel file system for Hive operations.
Configure Hive to work with Ozone:
- Navigate to the Ambari UI > Hive > Configs > Advanced Hive-env and add:
export HIVE_AUX_JARS_PATH=${HIVE_AUX_JARS_PATH}:/usr/odp/current/ozone-client/share/ozone/lib/ozone-filesystem-hadoop3-1.4.0.3.2.3.3-3.jar- Restart Hive.
- If Ranger authorization is enabled, grant the necessary permissions to OFS (Ozone File System) buckets, HDFS paths, and Hive URL to allow the required operations as per the job requirements.
- Authenticate users with Kerberos credentials when operating in a secure cluster.
Store tables in OFS:
To create tables in OFS, add LOCATION '<OFS_URI> to CREATE TABLE command. This will make Hive tables reside at the specified location in Ozone. All data changes here after will be in effect at the table in the given OFS_URI.
Here are sample Hive operations with Hive accessing OFS :
- Connect to Beeline
- Create new table in OFS
0: jdbc:hive2://odp01.ha.ubuntu.ce:2181,odp02> CREATE EXTERNAL TABLE IF NOT EXISTS `employee`(. . . . . . . . . . . . . . . . . . . . . . .> `id` bigint,. . . . . . . . . . . . . . . . . . . . . . .> `name` string,. . . . . . . . . . . . . . . . . . . . . . .> `age` smallint). . . . . . . . . . . . . . . . . . . . . . .> STORED AS parquet. . . . . . . . . . . . . . . . . . . . . . .> LOCATION 'ofs://omservice/testvol/user/employee';INFO : Compiling command(queryId=hive_20240313142803_8810af7b-f2f3-401f-ba40-5559e446d18e): CREATE EXTERNAL TABLE IF NOT EXISTS `employee`(`id` bigint,`name` string,`age` smallint)STORED AS parquetLOCATION 'ofs://omservice/testvol/user/employee'INFO : Semantic Analysis Completed (retrial = false)INFO : Created Hive schema: Schema(fieldSchemas:null, properties:null)INFO : Completed compiling command(queryId=hive_20240313142803_8810af7b-f2f3-401f-ba40-5559e446d18e); Time taken: 5.791 secondsINFO : Operation CREATETABLE obtained 1 locksINFO : Executing command(queryId=hive_20240313142803_8810af7b-f2f3-401f-ba40-5559e446d18e): CREATE EXTERNAL TABLE IF NOT EXISTS `employee`(`id` bigint,`name` string,`age` smallint)STORED AS parquetLOCATION 'ofs://omservice/testvol/user/employee'INFO : Starting task [Stage-0:DDL] in serial modeINFO : Completed executing command(queryId=hive_20240313142803_8810af7b-f2f3-401f-ba40-5559e446d18e); Time taken: 2.497 secondsNo rows affected (8.958 seconds)- Validate the new table:
0: jdbc:hive2://odp01.ha.ubuntu.ce:2181,odp02> show tables;INFO : Compiling command(queryId=hive_20240313143033_f33adeb6-bf59-45ac-bfb4-4e654c063ec1): show tablesINFO : Semantic Analysis Completed (retrial = false)INFO : Created Hive schema: Schema(fieldSchemas:[FieldSchema(name:tab_name, type:string, comment:from deserializer)], properties:null)INFO : Completed compiling command(queryId=hive_20240313143033_f33adeb6-bf59-45ac-bfb4-4e654c063ec1); Time taken: 0.269 secondsINFO : Executing command(queryId=hive_20240313143033_f33adeb6-bf59-45ac-bfb4-4e654c063ec1): show tablesINFO : Starting task [Stage-0:DDL] in serial modeINFO : Completed executing command(queryId=hive_20240313143033_f33adeb6-bf59-45ac-bfb4-4e654c063ec1); Time taken: 0.281 seconds+-----------+| tab_name |+-----------+| employee |+-----------+- Add values to the table:
0: jdbc:hive2://odp01.ha.ubuntu.ce:2181,odp02> INSERT INTO employee(id, name, age) VALUES (1, "dora", 34) ;- Validate the newly added tables:
0: jdbc:hive2://odp01.ha.ubuntu.ce:2181,odp02> select * from employee;+--------------+----------------+---------------+| employee.id | employee.name | employee.age |+--------------+----------------+---------------+| 1 | dora | 34 |+--------------+----------------+---------------+1 row selected (6.1 seconds)Spark with Ozone
While Ozone is capable of operating independently, the current version of Ambari does not facilitate Spark installation without HDFS integration.
Apache Spark can access data from Apache Ozone and perform tasks.
To access Apache Ozone, configure Spark:
- Configure Spark shell to
use /usr/odp/current/ozone-client/share/ozone/lib/ozone-filesystem-hadoop3-client-1.4.0.3.2.3.3-3.jar. - If Ranger authorization is enabled, grant the necessary permissions to OFS (Ozone File System) buckets to allow the required operations as per the job requirements.
- Authenticate users with Kerberos credentials when operating in a secure cluster.
Accessing Apache Ozone Data in Apache Spark:
- Create sample data to be read by Spark:
$ vi /tmp/employee.csvid,name,age1,Ranga,332,Nishanth,43,Raja,60- Upload the employee.csv file to Ozone:
$ ozone -config /etc/hadoop-ozone/conf/ozone.om sh key put /testvol/testbuck/employee.csv /tmp/employee.csv- Provide necessary permissions under Ozone policies, for the Spark user to access the respective bucket and file, if Ranger authorization is enabled.
- Allow the Spark user to submit Yarn applications.
- Launch spark-shell:
spark-shell --keytab /etc/security/keytabs/spark.headless.keytab --principal <spark-principal> --conf spark.yarn.access.hadoopFileSystems=<OFS_URI> --jars=/usr/odp/current/ozone-client/share/ozone/lib/ozone-filesystem-hadoop3-1.4.0.3.2.3.3-3.jar- Access the .csv file content in Ozone as Spark df:
x
scala> val df=spark.read.option("header", "true").option("inferSchema","true").csv("ofs://omservice/testvol/testbuck/employee.csv")scala> df.show()24/04/30 15:53:02 INFO DAGScheduler: ResultStage 2 (show at <console>:26) finished in 0.987 s24/04/30 15:53:02 INFO DAGScheduler: Job 2 finished: show at <console>:26, took 1.016383 s+---+--------+---+| id| name|age|+---+--------+---+| 1| Ranga| 33|| 2|Nishanth| 4|| 3| Raja| 60|+---+--------+---+Custom PySpark Job
To run a Spark job using OFS, run the following command:
spark-submit --conf spark.yarn.access.hadoopFileSystems=<OFS_URI> --jars=/usr/odp/current/ozone-client/share/ozone/lib/ozone-filesystem-hadoop3-1.4.0.3.2.3.3-3.jar <SPARK_JOB> [parameteres-for-job-if-any]For a secure cluster, add --keytab <keytab> --principal <principal> values to above command.
Here is a sample custom job that functions to access Ozone data with Apache Spark and to write the output to Ozone.
- Custom Pyspark application using ofs to access data and write output:
$ vi /tmp/PySparkSample.pyfrom pyspark.sql import SparkSessionfrom pyspark.sql.functions import colspark = SparkSession.builder \ .appName("PySpark Example") \ .getOrCreate()# Load data from input.txtinput_file_path = "ofs://omservice/testvol/testbuck/input.txt"df = spark.read.text(input_file_path)df_transformed = df.select((col("value").cast("int") * 2).cast("string").alias("transformed_data"))#write the result data to output.txtoutput_file_path = "ofs://omservice/testvol/testbuck/output.txt"df_transformed.write.mode("overwrite").text(output_file_path)spark.stop()- Upload the sample input file to ofs:
$ vi /tmp/input.txt102030$ ozone --config /etc/hadoop-ozone/conf/ozone.om sh key put /testvol/testbuck/input.txt /tmp/input.txt- Provide necessary permissions to the Spark user to access respective bucket and key, in case of Ranger authorization being enabled.
- To run PySpark app in a secure cluster:
spark-submit --keytab /etc/security/keytabs/spark.headless.keytab --principal <spark-principal> --conf spark.yarn.access.hadoopFileSystems=ofs://omservice/ --jars=/usr/odp/current/ozone-client/share/ozone/lib/ozone-filesystem-hadoop3-1.4.0.3.2.3.3-3.jar /tmp/PySparkSample.py 10- To validate the output in ofs:
$ hdfs dfs -ls ofs://omservice/testvol/testbuck/output.txt/Found 2 items-rw-rw-rw- 3 hdfs hdfs 0 2024-04-30 17:08 ofs://omservice/testvol/testbuck/output.txt/_SUCCESS-rw-rw-rw- 3 hdfs hdfs 9 2024-04-30 17:08 ofs://omservice/testvol/testbuck/output.txt/part-00000-e9cc165a-b8cd-4a2b-9722-f9802053ea77-c000.txt$ hdfs dfs -ls ofs://omservice/testvol/testbuck/output.txt/part-00000-e9cc165a-b8cd-4a2b-9722-f9802053ea77-c000.txt204060Was this page helpful?