Community Articles
Find and share helpful community-sourced technical articles
Announcements
Alert: Welcome to the Unified Cloudera Community. Former HCC members be sure to read and learn how to activate your account here.
Contributor

The purpose of this repo is to provide quick examples and utilities to work on a Spark and Hive integration on HDP 3.1.4

Prerequisites:

  • HiveServer2Interactive (LLAP) must be installed, up and running
  • Bash and Python interpreter must be available
  • Ideally, for connections using HTTP transport protocol, in the Ambari -> Hive -> Configs, hive.server2.thrift.http.cookie.auth.enabled should be set to true
  • Set hadoop.proxyuser.hive.hosts=* in the Ambari -> HDFS -> Configs -> Custom core-site section (core-site.xml, or mention at least the LLAP Hosts, HS2 Hosts and Spark client hosts separated by commas).

Once the LLAP service is up and running, the next step for this setup requires the following properties to be configured in the Spark client:

spark.datasource.hive.warehouse.load.staging.dir=
spark.datasource.hive.warehouse.metastoreUri=
spark.hadoop.hive.llap.daemon.service.hosts=
spark.jars=
spark.security.credentials.hiveserver2.enabled=
spark.sql.hive.hiveserver2.jdbc.url=
spark.sql.hive.hiveserver2.jdbc.url.principal=
spark.submit.pyFiles=
spark.hadoop.hive.zookeeper.quorum=

While the above information can be manually collected as explained in our Cloudera official documentation, the following steps will help in collecting the standard information and avoid making mistakes during the copy and paste process of parameter values between HS2I and Spark. Steps:

--Notice the connection is to the LLAP Host--

ssh root@my-llap-host 
cd /tmp
wget https://raw.githubusercontent.com/dbompart/hive_warehouse_connector/master/hwc_info_collect.sh
chmod +x  hwc_info_collect.sh
./hwc_info_collect.sh

The above script will immediately provide enough information with regard to the following:

  • The information to enable the Spark and Hive integration (HWConnector)
  • A working spark-shell command to test initial connectivity
  • A short how-to list all Databases in Hive, in scala.

Done !!!

LDAP/AD Authentication

In an LDAP enabled authentication setup, the username and password will be passed in plaintext. The recommendation is to Kerberize the cluster. Otherwise, expect to see the username and password exposed in clear text amongst the logs.

 

To provide username and password, we will have to specify them as part of the JDBC URL string in the following format:

jdbc:hive2://zk1:2181,zk2:2181,zk3:2181/;user=myusername;password=mypassword;serviceDiscoveryMode=zooKeeper;zooKeeperNamespace=hiveserver2-interactive

Note: We will need to URL encode the password if it has a special character. For example, user=hr1;password=BadPass#1 will translate to user=hr1;password=BadPass%231

 

This method is not fully supported for Spark HWC integration nor it is recommended.

Kerberos Authentication

For Kerberos authentication, the following pre-conditions have to be met:

  1. Initial "kinit" has to always be executed and validated.

    $ kinit dbompart@EXAMPLE.COM
    Password for dbompart@EXAMPLE.COM:
    
    $ klist -v
    Credentials cache: API:501:9
        Principal: dbompart@EXAMPLE.COM
    Cache version: 0
    
    Server: krbtgt/EXAMPLE.COM@EXAMPLE.COM
    Client: dbompart@EXAMPLE.COM
    Ticket etype: aes128-cts-hmac-sha1-96
    Ticket length: 256
    Auth time:  Feb 11 16:11:36 2013
    End time:   Feb 12 02:11:22 2013
    Renew till: Feb 18 16:11:36 2013
    Ticket flags: pre-authent, initial, renewable, forwardable
    Addresses: addressless
  2. For YARN, HDFS, Hive and HBase long-running jobs, DelegationTokens have to be fetched. Hence, provide "--keytab" and "--principal" extra arguments, i.e.:

    spark-submit $arg1 $arg2 $arg3 $arg-etc --keytab my_file.keytab 
    --principal dbompart@EXAMPLE.COM --class a.b.c.d app.jar
  3. For Kafka, a JAAS file has to be provided:

    With a keytab, recommended for long running jobs:

    KafkaClient { 
    com.sun.security.auth.module.Krb5LoginModule required 
    useKeyTab=true 
    keyTab="./my_file.keytab" 
    storeKey=true 
    useTicketCache=false 
    serviceName="kafka" 
    principal="user@EXAMPLE.COM"; 
    };

    Without a keytab, usually used for batch jobs:

    KafkaClient { 
    com.sun.security.auth.module.Krb5LoginModule required 
    useTicketCache=true 
    renewTicket=true 
    serviceName="kafka"; 
    };

    And, it also has to be mentioned at the JVM level:

    spark-submit $arg1 $arg2 $arg3 $arg-etc --files jaas.conf 
    --conf spark.driver.extraJava.Options="-Djava.security.auth.login.config=./jaas.conf"
    --conf spark.executor.extraJavaOptions=-Djava.security.auth.login.config=./jaas.conf"

Livy2 - Example

curl -X POST --data '{"kind": "pyspark", "queue": "default", "conf": { "spark.jars": "/usr/hdp/current/hive_warehouse_connector/hive-warehouse-connector-assembly-1.0.0.3.1.4.32-1.jar", "spark.submit.pyFiles":"/usr/hdp/current/hive_warehouse_connector/pyspark_hwc-1.0.0.3.1.4.32-1.zip", "spark.hadoop.hive.llap.daemon.service.hosts": "@llap0","spark.sql.hive.hiveserver2.jdbc.url": "jdbc:hive2://[node2.cloudera.com:2181,node3.cloudera.com:2181,node4.cloudera.com:2181/;serviceDiscoveryMode=zooKeeper;zooKeeperNamespace=hiveserver2-interactive](http://node2.cloudera.com:2181,node3.cloudera.com:2181,node4.cloudera.com:2181/;serviceDiscoveryMode=zooKeeper;zooKeeperNamespace=hiveserver2-interactive)", "spark.yarn.security.credentials.hiveserver2.enabled": "false","spark.sql.hive.hiveserver2.jdbc.url.principal": "hive/[_HOST@EXAMPLE.COM](mailto:_HOST@EXAMPLE.COM)", "spark.datasource.hive.warehouse.load.staging.dir": "/tmp", "spark.datasource.hive.warehouse.metastoreUri": "thrift://node3.cloudera.com:9083", "spark.hadoop.hive.zookeeper.quorum": "[node2.cloudera.com:2181,node3.cloudera.com:2181,node4.cloudera.com:2181](http://node2.cloudera.com:2181,node3.cloudera.com:2181,node4.cloudera.com:2181)"}}' -H "X-Requested-By: admin" -H "Content-Type: application/json" --negotiate -u : [http://node3.cloudera.com:8999/sessions/](http://node3.cloudera.com:8999/sessions/) | python -mjson.tool

Submitting a brief example to show databases (hive.showDatabases()):

curl --negotiate -u : http://node2.cloudera.com:8999/sessions/2/statements -X POST -H 'Content-Type: application/json' -H "X-Requested-By: admin" -d '{"code":"from pyspark_llap import HiveWarehouseSession"}'
curl --negotiate -u : http://node2.cloudera.com:8999/sessions/2/statements -X POST -H 'Content-Type: application/json' -H "X-Requested-By: admin" -d '{"code":"hive = HiveWarehouseSession.session(spark).build()"}'
curl --negotiate -u : http://node2.cloudera.com:8999/sessions/2/statements -X POST -H 'Content-Type: application/json' -H "X-Requested-By: admin" -d '{"code":"hive.showDatabases().show()"}'

Quick reference for basic API commands to check on the application status:

# Check sessions. Based on the ID field, update the following curl commands to replace "2" with $ID.
curl --negotiate -u : http://node2.cloudera.com:8999/sessions/ | python -mjson.tool

# Check session status
curl --negotiate -u : http://node2.cloudera.com:8999/sessions/2/status | python -mjson.tool

# Check session logs
curl --negotiate -u : http://node2.cloudera.com:8999/sessions/2/log | python -mjson.tool

# Check session statements.
curl --negotiate -u : http://node2.cloudera.com:8999/sessions/2/statements | python -mjson.tool

Zeppelin - Example

Livy2 Interpreter

Assumptions:

  • The cluster is kerberized.
  • LLAP has already been enabled.
  • We got the initial setup information using the script hwc_info_collect.sh
  • In Ambari->Spark2->Configs->Advanced livy2-conf, the property livy.spark.deployMode should be set to either "yarn-cluster" or just plain "cluster". Note: Client mode is not supported.

Extra steps:

  • Add the following property=value in Ambari->Spark2->Configs->Custom livy2-conf section: - livy.file.local-dir-whitelist=/usr/hdp/current/hive_warehouse_connector/

We can test our configurations before setting them statically in the Interpreter:

Notebook > First paragraph:

%livy2.conf
livy.spark.datasource.hive.warehouse.load.staging.dir=$value
livy.spark.datasource.hive.warehouse.metastoreUri=$value
livy.spark.hadoop.hive.llap.daemon.service.hosts=$value
livy.spark.jars=file:///$value
livy.spark.security.credentials.hiveserver2.enabled=true
livy.spark.sql.hive.hiveserver2.jdbc.url=$value
livy.spark.sql.hive.hiveserver2.jdbc.url.principal=$value
livy.spark.submit.pyFiles=file:///$value
livy.spark.hadoop.hive.zookeeper.quorum=$value

Please note that compared to a regular spark-shell or spark-submit, this time we'll have to specify the filesystem scheme file:///, otherwise it'll try to reference a path on HDFS by default.

 

Notebook > Second paragraph:

 

%livy2
import com.hortonworks.hwc.HiveWarehouseSession
import com.hortonworks.hwc.HiveWarehouseSession._
val hive = HiveWarehouseSession.session(spark).build()
hive.showDatabases().show()

 

Creating a Table with Dummy data in Hive

For this specific task, we can expedite the table creation and dummy data ingest by referring to the Cloudera's VideoKB. In the above link, the Python script (HiveRandom.zip) should help you create and load a table based on an input table schema.

 

Another short bash script is available show_create_cleaner.sh, and it can be used as the following:

 

wget https://raw.githubusercontent.com/dbompart/hive_warehouse_connector/master/show_create_cleaner.sh
chmod +x  show_create_cleaner.sh
./show_create_cleaner.sh show_create_table_output_file.txt

This bash script is a quick cleaner, it will make the Show create table stmt output re-usable in Hive or Spark by using --clean which will also remove the Table's Location and Table's Properties sections, i.e.:

./show_create_cleaner.sh show_create_table_output_file.txt --clean

Common errors

No service instances found in registry

Check again the configuration settings, especially the llap.daemon.service.hosts value and also the corresponding zNode which should be available and readable in from Zookeeper.

error: object hortonworks is not a member of package com

This usually means that either the HWC jar or zip files were not successfully uploaded to the Spark classpath. We can confirm this by looking at the logs and searching for:

Uploading resource file:/usr/hdp/current/hive_warehouse_connector/hive-warehouse-connector-assembly-1.0.0.3.1.4.32-1.jar

 

Cannot run get splits outside HS2

Add hive.fetch.task.conversion="more" To Custom hiveserver2-interactive section. And check the LLAP logs if needed.

 

Query returns no more than 1000

Follow the HWS API guide. This usually means that execute() method was incorrectly used instead of executeQuery().

 

"Blacklisted configuration values in session config: spark.master"

In the /etc/livy2/conf/spark-blacklist.conf file on the Livy2 server host, reconfigure this file to allow/disallow for configurations to be modified.

 

Unable to read HiveServer2 configs from ZooKeeper. Tried all existing HiveServer2 URIS from ZooKeeper

LLAP may not be up and running or there is a problem on reading its znode.

Suggested documentation

97 Views
0 Kudos
Don't have an account?
Coming from Hortonworks? Activate your account here
Version history
Revision #:
5 of 5
Last update:
‎03-17-2020 10:37 AM
Updated by:
 
Contributors
Top Kudoed Authors