Objective The objective of this article is to present a workflow to capture and republish data in Kafka topics. Note that NiFi is NOT a Kafka Replication/Backup tool. This is just a workflow based approach to capture kafka data and publish it back to a fresh topic if necessary. Although storage of Kafka data is not mandatory is several cases, it may be essential in cases such as Archival – Dump Kafka Data in HDFS Corrections/Inspections – In cases where an invalid event may disrupt a downstream system, to correct and restore the messages. Start by creating two processor groups – One for the backup workflow, second for the restore workflow Backup Workflow Backup is a long running workflow which archives data to HDFS. A file in HDFS may contain a batch of events or a single event, based on how frequently messages are getting published to the source kafka topic. Hence, user does not have to stop/start this backup workflow frequently. Restore Workflow Restore a manually triggered job/workflow in Nifi and it has to be modeled based on the topic being restored. Some considerations: Restore data to a temporary topic. A restore to original topic may duplicate the data in the original topic. If you want to restore data to original topic, do that only after cleaning the original topic (re-create the original topic) Stop backup workflow which re-publishing to original topic: To avoid duplication of backed up data in HDFS by Nifi. An alternate approach would be to move or rename the HDFS backup directory for the topic. Steps:- Stop Backup KafkaConsumer processor. Edit Restore Workflow - ListHDFS processor - to update the HDFS directory to the topic which needs to be restored. Edit Restore Workflow - PublishKafka processor - to update the topic which needs to be restored. Delete and Recreate the restore topic (if it already exists) Start the Restore Workflow. After the restore is complete and verified - Stop the restore workflow Move the backup directory (which has the original backup files) Start the backup process (this should take all the data to a new directory to avoid duplication) Backup Workflow Some Technical Consideration for Backup Workflow:- 1.Backup location is HDFS 2.Kafka Key is to be saved along with the Kafka Payload 3.Message Metadata like, partition info, message offset should be stored along with the message 4.Messages format is either text or json Variables Used in the Backup Workflow The following variables have been used the following processors to avoid hardcoding values. kafka.brokers kafka.kerberos-service-name kerberos.principal kerberos.keytab hadoop.configuration.resources Destination Directory: /edl_data/backup/kafka/${kafka.topic} Data Format Each file thus created may have a batch of Kafka messages. Each message would have the following format. Note that in case there is a missing key or payload, that part would just be left empty. [topicname-partition-offset]<@>[message key] <@>[message payload] Example:- topic1-0-231<@> {"ID":"535"}<@> {"vwcustomer":{"id":"13631918","name":"Mikel","age":51,"date":"2018-10-04T15:16:06Z"}} ConsumeKafka This processor has a list of topic(s), whose messages should be consumed and eventually backed up in HDFS. Topic Names: Can have a list of topics that would be consumed by this processor. GroupID: the nifi-kafka-consumer group id. if you want to consume all the messages, please reset this to a new consumer group for a new backup Max Poll Records: Why is this set to 1? To get the key for each Kafka message. If we poll a batch of kafka messages, the message key is lost and not stored as an attribute for the flow file. In order to save the kafka key, max poll records should be set to 1. This way each message is sent to a flow file having a kafka.key attribute. UpdateAttribute This processor captures the kafka-metadata that would be used later to store this information along with the message key and value. ReplaceText This processor is used to append the kafka.topic.metadata and the kafka.key to the beginning of the message. Note that the delimiter used is separate metadata, key and value is "<@>". If this delimiter is updated, make sure to update the same in the ExtractText processor of the Restore Workflow. MergeContent If we do NOT use this processor, then each kafka message (metadata<@>key<@>value)- would be written to a separate HDFS file. Instead, we want to merge multiple kafka messages to a given file in HDFS, if possible. We use MergeContent processor to this. Note that we are merging content based on the attribute kafka.topic; hence messages with same topic should end up together. For information on how batching would occur and other properties, Check: https://nifi.apache.org/docs/nifi-docs/components/org.apache.nifi/nifi-standard-nar/1.6.0/org.apache.nifi.processors.standard.MergeContent/index.html PutHDFS Eventually, we backup the files to HDFS. Note that if the kafka.topic destination directory does not exist, PutHDFS will create it. Restore Kafka Messages Some Technical Consideration for Restore Workflow:- 1.Restore Messages from HDFS backup location to Kafka Topic 2.Restore Key and payload 3.Order of restored messages: An attempt has been made to preserve order while restoring kafka messages. Kafka maintains order only on a partition. Hence, this Restore workflow is for topics with 1 partition. 4.If order of restored messages is not important, than this workflow can be made significantly simpler. 5.As per the backup workflow, this is for messages in text or json format. ENSURE THAT BACKUP IS STOPPED BEFORE RESTORE IS STARTED. This prevents a backup/restore loop thus duplicating the messages. ListHDFS and FetchHDFS These processors fetch the hdfs files from the configured directory location in ListHDFS. The files will not be deleted in the source directory. SplitText and ExtractText These processors are used to extract the Kafka offset from the metadata section stored in the backed up hdfs files. The offset attribute extracted is used to sequence the messages using “priority” attribute and “PriorityAttributePrioritizer” Update Attribute, EnforceOrder and MergeContent Update Attribute creates a new 'priority' attribute in every flowfile (kafka message), and assigns the offset value to it. This 'priority' attribute is used in the following relationships to prioritize processing of the messages. Enforce Order is used to order flow files based on the attribute "kafka.offset". The wait timeout is set to 10 seconds. This means that if there is a missing offset (order attribute) in the sequence, the processor will wait for a 10 seconds window in which time, if it gets the event, it continues to process and take the message to success relationship. If event does not arrive within the window, the message will be taken to overtook relationship. Refer:- https://issues.apache.org/jira/browse/NIFI-3414 https://gist.github.com/ijokarumawak/88fc30a2300845b3c27a79113fc72d41 MergeContent batches the messages back into 1 flow file. ExtractText, UpdateAttribute, PublishKafka Finally, we extract the metadata, key and value. Publish kafka key and value using PublishKafka processor. Note, that kafka topic names should be updated in PublishKafka processor. We use UpdateAttribute processor yet again to order messages before publishing them. ... View more

Objective -To store multiple row versions in HBase to evaluate the impact on performance when doing reading all versions vs. getting the latest version. To put this differently, would storing multiple versions affect the performance when querying the latest version. -Using NiFi to be able to quickly ingest millions or rows into HBase Warning -Do not store more than a few versions in HBase. This can have negative impacts. HBase is NOT designed to store more than a few versions of a cell. Step 1: Create Sample Workflow using NiFi to ingest data into HBase table -Create HBase Table Dataset: https://www.citibikenyc.com/system-data create 'venkataw:nycstations','nycstationfam' 0 row(s) in 1.3070 seconds hbase(main):014:0> desc 'venkataw:nycstations' Table venkataw.nycstations is ENABLED venkataw.nycstations COLUMN FAMILIES DESCRIPTION {NAME => 'nycstationfam', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', COMPRESSION => 'NONE', MIN_VERSIONS => '0', BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_SCOPE => '0'} 1 row(s) in 0.1870 seconds put 'venkataw:nycstations', 1224, 'nycstationfam:name', 'citiWunnava' put 'venkataw:nycstations',1224,'nycstationfam:short_name','citiW' put 'venkataw:nycstations',1224,'nycstationfam:lat','-90.12' put 'venkataw:nycstations',1224,'nycstationfam:lon','.92' put 'venkataw:nycstations',1224,'nycstationfam:region_id','9192' put 'venkataw:nycstations',1224,'nycstationfam:capacity','100202' put 'venkataw:nycstations',1224,'nycstationfam:rental_url','http://www.google.com/' hbase(main):016:0> scan 'venkataw:nycstations' ROW COLUMN+CELL 1224 column=nycstationfam:capacity, timestamp=1538594876306, value=100202 1224 column=nycstationfam:lat, timestamp=1538594875626, value=-90.12 1224 column=nycstationfam:lon, timestamp=1538594875643, value=.92 1224 column=nycstationfam:name, timestamp=1538594875555, value=citiWunnava 1224 column=nycstationfam:region_id, timestamp=1538594875660, value=9192 1224 column=nycstationfam:rental_url, timestamp=1538594902755, value=http://www.google.com/ 1224 column=nycstationfam:short_name, timestamp=1538594875606, value=citiW alter 'venkataw:nycstations', NAME=>'nycstationfam',VERSIONS => 10000 Step 2: NiFi Workflow to publish data to HBase table The above NiFi workflow consumes messages from a web server and published it to HBase. Configuration for the processors is as follows: GetHTTP processor reads the REST endpoint every 5 seconds. We extract the stations object using SplitJson processor Finally we use the PutHBaseJson processor we ingest the data to the destination HBase Table created above. Notice that I am trying to randomly assign row identifier so that eventually I get multiple rows versions for the same identifier The PutHBaseJson processor uses the HBase Client Controller Servicer to connect to HBase using Kerberos credentials. Step 3: Run queries to read the latest version and all available versions I tried querying all versions vs. latest versions in HBase with the following queries hbase(main):002:0> get 'venkataw:nycstations', 99886 , {COLUMN=> ['nycstationfam:station_id','nycstationfam:name','nycstationfam:short_name','nycstationfam:lat','nycstationfam:lon','nycstationfam:region_id','nycstationfam:capacity','nycstationfam:rental_url']} COLUMN CELL nycstationfam:capacity timestamp=1507322481470, value=31 nycstationfam:lat timestamp=1507322481470, value=40.71286844 nycstationfam:lon timestamp=1507322481470, value=-73.95698119 nycstationfam:name timestamp=1507322481470, value=Grand St & Havemeyer St nycstationfam:region_id timestamp=1507322481470, value=71 nycstationfam:rental_url timestamp=1507322481470, value=http://app.citibikenyc.com/S6Lr/IBV092JufD?station_id=471 nycstationfam:short_name timestamp=1507322481470, value=5267.08 nycstationfam:station_id timestamp=1507322481470, value=471 8 row(s) in 0.0600 seconds get 'venkataw:nycstations', 99828 , {COLUMN=> ['nycstationfam:station_id','nycstationfam:name','nycstationfam:short_name','nycstationfam:lat','nycstationfam:lon','nycstationfam:region_id','nycstationfam:capacity','nycstationfam:rental_url'],VERSIONS => 100} {done for diff. rowids} 24 row(s) in 0.0200 seconds 16 row(s) in 0.0300 seconds 8 row(s) in 0.0310 seconds 232 row(s) in 0.1850 seconds 8 row(s) in 0.0570 seconds 152 row(s) in 0.0380 seconds 184 row(s) in 0.0420 seconds 208 row(s) in 0.1550 seconds 1 row:- 8 row(s) in 0.0050 seconds 8 row(s) in 0.0040 seconds 8 row(s) in 0.0060 seconds all versions:- 14765 row(s) in 2.4350 seconds 14351 row(s) in 1.1620 seconds 14572 row(s) in 2.4210 seconds In the above results * The green rows are for the latest version reads * The yellow rows are all version reads Notice how latest version reads are fairly consistent and have a smaller response times. Also notice as the number of versions (rows) increase, the response times for all-version reads keep increasing. So based on this observation, as expected, it would seem like a query to get the latest version would consistently perform well when compared to a query which returns ‘n’ versions. ... View more

The following link has some sample code to connect to a secure Hive Server2 via the default connection string and via knox. https://github.com/vspw/hiveJDBC It uses the following Jar files:- commons-configuration-xx.jar (UGI set configurations and metrics) commons-logging-xx.jar (*Not mandatory) hadoop-auth-xx.jar hadoop-common-xx.jar (UGI stuff, hadoop configurations) hive-jdbc-1.2xx-standalone.jar log4j-xx-api-xx.jar (for log4j) log4j-api-xx.jar (for log4j) log4j-core.xx.jar (for log4j) xercesImpl-xx.jar It also reads a bunch of properties like "keytabLocationWindows", "jdbcConnStringDirect", "hiveQuery" etc. from a properties file and logging is enabled with log4j2.xml. The class: "HiveJDBCKnox" , has some instructions to make knox connectivity work on windows platform as well. ... View more

Objective:- Atlas, by default comes with certain types for Hive, Storm, Falcon etc. However, there might be cases where you would like to capture some custom metadata in Atlas. This can be metadata related to ETL processes, enterprise-operations etc. The article explains how to create custom Atlas types and provides some insight on establishing lineage between said types. Use Case:- Consider a simple Use Case where Raw Textual data is analyzed via a ML process and the results are stored in HDFS. For instance, the raw source data is a dump of access logs on professors and research assistants referring research papers. The ML process would try to come up with recommendations on research papers for further reading for these end users. To capture metadata and lineage for this workflow, we would want to have three custom types in Atlas. a.) ResearchPaperAccessDataset: To capture the metadata for the input dataset. b.) ResearchPaperRecommendationResults: To capture the metadata for the resultant output after the ML process has completed its analysis. c.) ResearchPaperMachineLearning: To capture the metadata for the ML process itself, which analyzes the Input dataset. The eventual lineage we want to capture would look something like this:- Bonus: The last part of this article has some information to create new Traits using REST API and then to associate it with an existing atlas entity. Files:- The files being used in this article are present in github. a.) atlas_type_ResearchPaperDataSet.json b.) atlas_entity_ResearchPaperDataSet.json c.) atlas_type_RecommendationResults.json d.) atlas_entity_RecommendationResults.json e.) atlas_type_process_ML.json f.) atlas_entity_process_ML.json Steps:- 1. Create Custom Atlas ResearchPaperAccessDataset Type:- https://github.com/vspw/atlas-custom-types/blob/master/atlas_type_ResearchPaperDataSet.json [root@zulu atlas]# curl -i -X POST -H 'Content-Type: application/json' -H 'Accept: application/json' -u admin 'http://yellow.hdp.com:21000/api/atlas/types' -d @atlas_type_ResearchPaperDataSet.json Enter host password for user 'admin':***** {"requestId":"qtp84739718-14 - bed149b3-b360-4bf5-b46b-8f25ac7692c3","types":[{"name":"ResearchPaperAccessDataset"}]} Notice the superType for "ResearchPaperAccessDataset" Type: ["DataSet"] "DataSet" in turn has superTypes of ["Referenceable","Asset"] "Asset" Type has attributes such as -> name, description, owner "Referenceable" Type has attributes such as -> qualifiedName Depending on whether these attributes are mandatory or not (based on the multiplicity required), the entity Type we create next, for "ResearchPaperAccessDataset" should have definitions for these attributes. 2. Create Entity for ResearchPaperAccessDataset Type:- https://github.com/vspw/atlas-custom-types/blob/master/atlas_entity_ResearchPaperDataSet.json [root@zulu atlas]# curl -i -X POST -H 'Content-Type: application/json' -H 'Accept: application/json' -u admin 'http://yellow.hdp.com:21000/api/atlas/entities' -d @atlas_entity_ResearchPaperDataSet.json {"requestId":"qtp84739718-15 - 827d5151-a6fb-4ccb-909f-f4ac5f8d8f26","entities":{"created":["40dc03dc-16d6-4281-826d-c4884cd1dad5"]},"definition":{"jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Reference","id":{"jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Id","id":"40dc03dc-16d6-4281-826d-c4884cd1dad5","version":0,"typeName":"ResearchPaperAccessDataset","state":"ACTIVE"},"typeName":"ResearchPaperAccessDataset","values":{"name":"GeoThermal-1224","createTime":"2017-03-25T20:07:12.000Z","description":"GeoThermal Research Input Dataset 1224","resourceSetID":1224,"researchPaperGroupName":"WV-SP-INT-HWX","qualifiedName":"ResearchPaperAccessDataset.1224-WV-SP-INT-HWX","owner":"EDM_RANDD"},"traitNames":[],"traits":{}}} 3. Create Custom ResearchPaperRecommendationResults Type:- https://github.com/vspw/atlas-custom-types/blob/master/atlas_type_RecommendationResults.json [root@zulu atlas]# curl -i -X POST -H 'Content-Type: application/json' -H 'Accept: application/json' -u admin 'http://yellow.hdp.com:21000/api/atlas/types' -d @atlas_type_RecommendationResults.json Enter host password for user 'admin': {"requestId":"qtp84739718-15 - 9da58639-479f-41fb-819d-b11b4464011e","types":[{"name":"ResearchPaperRecommendationResults"}]} 4. Create Entity for ResearchPaperRecommendationResults Type:- https://github.com/vspw/atlas-custom-types/blob/master/atlas_entity_RecommendationResults.json [root@zulu atlas]# curl -i -X POST -H 'Content-Type: application/json' -H 'Accept: application/json' -u admin 'http://yellow.hdp.com:21000/api/atlas/entities' -d @atlas_entity_RecommendationResults.json Enter host password for user 'admin': {"requestId":"qtp84739718-16 - b7ebe7d8-e671-4e94-a6c7-506947c7d5e5","entities":{"created":["43b6da13-31ee-4bbe-980e-84ed4b759f11"]},"definition":{"jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Reference","id":{"jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Id","id":"43b6da13-31ee-4bbe-980e-84ed4b759f11","version":0,"typeName":"ResearchPaperRecommendationResults","state":"ACTIVE"},"typeName":"ResearchPaperRecommendationResults","values":{"name":"RecommendationsGeoThermal-4995149","createTime":"2017-03-25T21:00:12.000Z","description":"GeoThermal Recommendations Mar 2017","qualifiedName":"ResearchPaperRecommendationResults.4995149-GeoThermal","researchArea":"GeoThermal","hdfsDestination":"hdfs:\/\/xena.hdp.com:8020\/edm\/data\/prod\/recommendations","owner":"EDM_RANDD","recommendationsResultsetID":4995149},"traitNames":[],"traits":{}}} 5. Create a Special Process Type (ResearchPaperMachineLearning) which would complete the lineage information:- https://github.com/vspw/atlas-custom-types/blob/master/atlas_type_process_ML.json Notice the superTypes for "ResearchPaperMachineLearning" - ["Process"], The "Process" type in turn constitutes superTypes "Referenceable" and "Asset". And besides the attributes inherited from the above superTypes, "Process" has the following attributes:- - inputs - outputs Our custom type (ResearchPaperMachineLearning) has attributes such as : operationType, userName, startTime and endTime. Hence we need to collectively define all these types in the entity we create after we are done with creating this type. [root@zulu atlas]# curl -i -X POST -H 'Content-Type: application/json' -H 'Accept: application/json' -u admin 'http://yellow.hdp.com:21000/api/atlas/types' -d @atlas_type_process_ML.json Enter host password for user 'admin': {"requestId":"qtp84739718-135 - 4f4cf931-0922-4d5c-b876-061f1bc1e7af","types":[{"name":"ResearchPaperMachineLearning"}]} 6. Create an entity for the Process Type:- https://github.com/vspw/atlas-custom-types/blob/master/atlas_entity_process_ML.json [root@zulu atlas]# curl -i -X POST -H 'Content-Type: application/json' -H 'Accept: application/json' -u admin 'http://yellow.hdp.com:21000/api/atlas/entities' -d @atlas_entity_process_ML.json Enter host password for user 'admin':**** {"requestId":"qtp84739718-18 - abbc3513-fa09-4a63-a8e5-af4b7b5f2d9a","entities":{"created":["4bd5263e-761b-4c0c-b629-c3d9fc87626f"]},"definition":{"jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Reference","id":{"jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Id","id":"4bd5263e-761b-4c0c-b629-c3d9fc87626f","version":0,"typeName":"ResearchPaperMachineLearning","state":"ACTIVE"},"typeName":"ResearchPaperMachineLearning","values":{"name":"ML_Iteration567019","startTime":"2017-03-26T20:20:13.675Z","description":"ML_Iteration567019 For GeoThermal DataSets","operationType":"DecisionTreeAndRegression","outputs":[{"jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Id","id":"43b6da13-31ee-4bbe-980e-84ed4b759f11","version":0,"typeName":"DataSet","state":"ACTIVE"}],"endTime":"2017-03-26T20:27:23.675Z","inputs":[{"jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Id","id":"40dc03dc-16d6-4281-826d-c4884cd1dad5","version":0,"typeName":"DataSet","state":"ACTIVE"}],"qualifiedName":"ResearchPaperMachineLearning.ML_Iteration567019","owner":"EDM_RANDD","clusterName":"turing","queryGraph":null,"userName":"hdpdev-edm-appuser-recom"},"traitNames":[],"traits":{}}} So after creating all the necessary Types and Entities we should be able to see the respective types created in Atlas UI and query entities and create new entities as usual. In this case we had a java application that used to create and deliver the entity json files for the above workflow after each iteration of the ML process completed successfully (Since the attributes values in the entities json file should be altered dynamically based on the iteration and results) You should also be able to see the types created thus far in the search objects. Creating a Trait and Associating tagging an Atlas Entity:- Note that we can create new Trait/Tag types in Atlas similar to how we have created our custom types. https://github.com/vspw/atlas-custom-types/blob/master/atlas_trait_type.json [root@zulu atlas]# curl -i -X POST -H 'Content-Type: application/json' -H 'Accept: application/json' -u admin 'http://yellow.hdp.com:21000/api/atlas/types' -d @atlas_trait_type.json Associating a trait to an existing Entity:- curl -i -X POST -H 'Content-Type: application/json' -H 'Accept: application/json' -u admin 'http://yellow.hdp.com:21000/api/atlas/entities/b58571af-1ef1-40e4-a89b-0a2ade4eeab3/traits' -d @associate_trait.json associate_trait.json { "jsonClass":"org.apache.atlas.typesystem.json.InstanceSerialization$_Struct", "typeName":"PublicData", "values":{ "name":"addTrait" } } ... View more

The document lists the steps to be performed to enable users on windows workstation to access the HDP cluster hosted on a different realm. Windows Workstation (realm: WIN.EXAMPLE.COM) HDP (realm: HDP.EXAMPLE.COM) Accessing WebUI components for Namenode, YARN, MapReduce, Oozie etc. Install and Setup MIT Kerberos Install Firefox Enable MIT Kerberos on Firefox Get Kerberos Ticket using MIT Kerberos Utility Open NN, RM UIs using Firefox Install and Setup MIT Kerberos For 64-bit machines (includes both 32 and 64 bit libraries) http://web.mit.edu/kerberos/dist/kfw/4.0/kfw-4.0.1-amd64.msi For 32-bit machines (includes only 32 bit libraries) http://web.mit.edu/kerberos/dist/kfw/4.0/kfw-4.0.1-i386.msi Default location of configuration file in Windows machine is "C:\Program Files\MIT\Kerberos" directory. Note: This is a hidden folder. Copy the krb5.conf file (from the HDP KDC) to above mentioned location and rename krb5.conf to krb5.ini. Configure the following environment properties for MIT Kerberos. - KRB5_CONFIG: Path for the kerberos ini file. - KRB5CCNAME: Path for the kerberos credential cache file. Create a writable directory. Eg: c:\temp (or any path where user has access to and make a note of the path) for the krb5cache file specified in the path created by the utility. Save and reboot the machine. Install Firefox Install Mozilla Firefox from below link and follow instructions on webpage for installation. https://www.mozilla.org/en-US/firefox/new/ Enable MIT Kerberos on Firefox Open Firefox, type about:config in URL and hit enter Search for and change below parameters network.negotiate-auth.trusted-uris = .domain.com network.negotiate-auth.using-native-gsslib = false network.negotiate-auth.gsslib = C:\Program Files\MIT\Kerberos\bin\gssapi32.dll network.auth.use-sspi = false network.negotiate-auth.allow-non-fqdn = true Get Kerberos Ticket using MIT Kerberos Utility 1. Click start button 2. Click All Programs 3. Click Kerberos for windows program group 4. Use "MIT Kerberos Ticket Manager" to obtain a ticket for the principal that will be used to connect to HDP cluster. a. Click Get Ticket b. Enter Principal and Password as below. Open NN, RM UIs using Firefox Open Firefox, ambari UI -> HDFS -> Quick Links -> NN UI or directly open the NN UI: http://xx-xxx-xxxx.aws.hdp.com:50070 If you don't have a kerberos ticket, then this opens a dialog box for MIT kerberos client requesting Principal and password for the HDP KDC user. ([email protected] and ******). If your ticket is invalid or has expired, then you would see an "Unauthorized User" exception on the browser. Upon successful authentication this should open the NN UI and others. Remember to refresh the ticket as it expires overtime (using getTicket in the MIT kerberos client utility). This authentication is also required to establish connection to Hive Server2 for operating on hive tables. Appendix:- https://bugzilla.mozilla.org/show_bug.cgi?id=628210 The above link has some debugging techniques which can be used to debug firefox authentication using gsslib. ... View more