We have a kerberoized hadoop cluster, where HBase is running. Apache drill is running in distributed mode, in another cluster. Now, we need to query the Kerberos enabled HBase from the apache drill, using web UI. The Hadoop cluster is actually running in AWS, the HBase uses s3 as storage. Please help me with steps to achieve successful queries to HBase.
Apache_Drill_version:1.16.0 Hadoop version: 2
Usually, to query HBase, we run kinit with the keytab manually, then get into HBase shell in Hadoop cluster. We wanted to make use of drill, to query in a SQL fashion easily, better readability.
Related
I am new to Hadoop / Pig and I have just started reading the docs.
There are lots of blogs on installing Hadoop in cluster mode.
I know that Pig runs on top of Hadoop.
My question is: Hadoop is installed on all the cluster nodes.
Should I also install Pig on all the cluster nodes or only on the master node?
You would want to install Hive Metastore and Hive Server on 2 different nodes. By default, hive uses derby database, but most of the people choose to go with MySQL so there will be a MYSQL server daemon also.
So not to confuse you anymore :
Install HiveServer and WebHcat Server on one node
Install Hive Metastore and MySQL server on another node.
This is the best practice. If you have any other doubt you can ask!
I cannot tell if the question is about Hive or Pig, but there's a difference between clients and servers.
For Hive, the master services are the Metastore and HiveServer2. You can install these daemons on the same server to improve network traffic between the metastore and the Hive query compiler. You only need one client to communicate with those masters.
For Pig, it communicates directly to YARN and HDFS (optionally Hive, if you use Hcatalog). Again, it's only a client, so only one hosts needs it.
It is generally preferred to have a dedicated set of machines for Hive and the backing RDBMS for the metastore (Mysql or Postgres being the more popular options)
You also don't need to "install Pig in the cluster". For example, I could grab the Hadoop XML configs and run some Pig code against the YARN cluster from any outside computer after downloading Pig locally (same applies to Spark)
I have a Kerberized CDH cluster, where there are some daily oozie workflows running. All of them use shell, impala-shell, hive and sqoop to ingest data to Hive tables (lets call these tables SensitiveTables)
Now, I want to create 2 new BI users to use the cluster and experiment with some other ingested data.
The requirement is that these new BI users:
should not have access to the SensitiveTables
should be able to spark-submit jobs to the cluster
(optionally) use Hue
Apart from setting-up Apache Sentry (which is the recommended way to go), is there any chance to meet those requirements using file-permissions or ACL and Service Level Authorization ?
So far, I managed (via hadoop fs -chmod o-rwx /user/hive/warehouse/sensitive) to restrict access to SensitiveTables via Hive (which uses user impersonation), but failed to do so via Impala (which submits all jobs to the cluster as user impala). Is there anything else I should try?
Thank you,
Gee
After a lot of research and based on the assumptions I described, the answer is NO. Furthermore, the metastore can not be protected this way.
I want to efficiently develop a Hadoop job using Cassandra as input and output.
As I know MapReduce jobs in Hadoop uses HDFS to store intermediate results.
Is it possible to make Hadoop store intermediate results in Cassandra File System? If yes then how to achieve that?
I wonder if it possible to completely disable HDFS if I am using Hadoop only Cassandra as underlying data storage system.
I am using Cassandra 2.0.11 and Hadoop 1.0.4 (If the above is possible only in Hadoop 2.x I would also apreciate that information)
I'm having a problem about hive server that I don't understand. I've just set up a hadoop cluster and want to access to it from a hive service. First try I did was running the hive server in one of the cluster machines.
Everything worked nicely but I wanted to move the hive service to another machine outside the hadoop cluster.
So I just started a new machine outside this hadoop cluster. I've just install hive (+ hadoop libraries) and copied the hadoop config from the cluster. When I run the hiveserver almost everything goes ok. I can connect with the hive cli from a different machine to my hiveserver, create new tables in the hive warehouse within the hdfs filesystem in the hadoop cluster, query then and so on.
The thing I don't understand is that hiveserver seems to not recognize old tables which were created in my first try.
Some notes about my config are that all tables are handled by Hive and stored in HDFS. Hive configuration is the default one. I suppose that it has to do with my hive metastore but it couldn't say what.
Thank you!!
I am a newbie to Hadoop / Hive and I have just started reading the docs. There are lots of blogs on installing Hadoop in cluster mode. Also, I know that Hive runs on top of Hadoop.
My question is: Hadoop is installed on all the cluster nodes. Should I also install Hive on all the cluster nodes or only on the master node?
No, it is not something you install on worker nodes. Hive is a Hadoop client. Just run Hive according to the instructions you see at the Hive site.
From Cloudera's Hive installation Guide:
Install Hive on your client machine(s) from which you submit jobs; you do not need to install it on the nodes in your Hadoop cluster.
Hive is basically used for processing structured and semi-structured data in Hadoop. We can also perform Analysis of large datasets which is present in HDFS and also in Amazon S3 filesystem using Hive. In order to query data hive also provides query language known as HiveQL which is similar to SQL. Using Hive one can easily run Ad-hoc queries for the data analysis. Using Hive we don’t need to write complex Map-Reduce jobs, we just need to submit SQL queries. Hive converts these SQL queries into MapReduce jobs.
Finally Hive SQL will get converted to MapReduce jobs and we don't have to submit MapReduce job from all node in a Hadoop cluster, in the same way we don't need Hive to be installed in all node of Hadoop cluster