Is there a way to allow a developer to access a hadoop command line without SSH? I would like to place some hadoop clusters in a specific environment where SSH is not permitted. I have searched for alternatives such as a desktop client but so far have not seen anything. I will also need to federate sign on info for developers.
If you're asking about hadoop fs and similar commands, you don't need SSH for this.
You just need to download Hadoop clients and configure the hdfs-site.xml file to point at a remote cluster. However, this is an administrative security hole, so setting up an edge node that does have trusted and audited SSH access is preferred.
Similarly, Hive or HBase or Spark jobs can be ran with the appropriate clients or configuration files without any SSH access, just local libraries
You don't need SSH to use Hadoop. Also Hadoop is a combination of different stacks, which part of Hadoop are you referring to specifically? If you are talking about HDFS you can use web HDFS. If you are talking about YARN you can use API call. There are also various UI tools such as HUE you can use. Notebook apps such as Zeppelin or Jupiter can also be helpful.
I am new to HBase, recently I installed HBase and tried to start it on my Mac. Everything is fine and I could play with HBase. In some articles, it said I should start Hadoop first when using HBase, I am wondering if this prerequisite changed?
Hadoop is not a hard requirement for HBase unless you are running fully distributed which you are not. Running on a single node like you are you can use the local filesystem. See HBase run modes: Standalone and Distributed for more information.
Your local filesystem (the file:// URI) is Hadoop-compatible. Hbase requires a Hadoop compatible storage layer, but that does not mean that it must literally be HDFS.
HDFS will simply provide scalability and reliability
My question is pretty trivial but didnt find anyone actually asking it.
We have a ambari cluster with spark storm hbase and hdfs(among other things).
I dont understand how a user that want to use that cluster use it.
for example, a user want to copy a file to hdfs, run a spark-shell or create new table in hbase shell.
should he get a local account on the server that run the cooresponded service? shouldn't he use a 3rd party machine(his own laptop for example)?
If so ,how one should use hadoop fs, there is no way to specify the server ip like spark-shell has.
what is the normal/right/expected way to run all these tasks from a user prespective.
Thanks.
The expected way to run the described tasks from the command line is as follows.
First, gain access to the command line of a server that has the required clients installed for the services you want to use, e.g. HDFS, Spark, HBase et cetera.
During the process of provisioning a cluster via Ambari, it is possible to define one or more servers where the clients will be installed.
Here you can see an example of an Ambari provisioning process step. I decided to install the clients on all servers.
Afterwards, one way to figure out which servers have the required clients installed is to check your hosts views in Ambari. Here you can find an example of an Ambari hosts view: check the green rectangle to see the installed clients.
Once you have installed the clients on one or more servers, these servers will be able to utilize the services of your cluster via the command line.
Just to be clear, the utilization of a service by a client is location-independent from the server where the service is actually running.
Second, make sure that you are compliant with the security mechanisms of your cluster. In relation to HDFS, this could influence which users you are allowed to use and which directories you can access by using them. If you do not use security mechanisms like e.g. Kerberos, Ranger and so on, you should be able to directly run your stated tasks from the command line.
Third, execute your tasks via command line.
Here is a short example of how to access HDFS without considering security mechanisms:
ssh user#hostxyz # Connect to the server that has the required HDFS client installed
hdfs dfs -ls /tmp # Command to list the contents of the HDFS tmp directory
Take a look on Ambari views, especially on Files view that allows browsing HDFS
I have been using a Hadoop cluster, created using Google's script, for a few months.
Every time I boot the machines I have to manually start Hadoop using:
sudo su hadoop
cd /home/hadoop/hadoop-install/sbin
./start-all.sh
Besides scripting, how can I resolve this?
Or is this just the way it is by default?
(The first boot after cluster creation always starts Hadoop automatically, why not always?)
You have to configure using init.d.
Document provide more details and sample script for datameer. You need to follow similar steps. Script should be smart enough to check all the nodes in the cluster are up before invoking this script using ssh.
While different third-party scripts and "getting started" solutions like Cloud Launcher have varying degrees of support for automatic restart of Hadoop on boot, the officially supported tools are bdutil as a do-it-yourself deployment tool, and Google Cloud Dataproc as a managed service, both of which are already configured with init.d and/or systemd to automatically start Hadoop on boot.
More detailed instructions on using bdutil here.
Are there any dependencies between Spark and Hadoop?
If not, are there any features I'll miss when I run Spark without Hadoop?
Spark is an in-memory distributed computing engine.
Hadoop is a framework for distributed storage (HDFS) and distributed processing (YARN).
Spark can run with or without Hadoop components (HDFS/YARN)
Distributed Storage:
Since Spark does not have its own distributed storage system, it has to depend on one of these storage systems for distributed computing.
S3 – Non-urgent batch jobs. S3 fits very specific use cases when data locality isn’t critical.
Cassandra – Perfect for streaming data analysis and an overkill for batch jobs.
HDFS – Great fit for batch jobs without compromising on data locality.
Distributed processing:
You can run Spark in three different modes: Standalone, YARN and Mesos
Have a look at the below SE question for a detailed explanation about both distributed storage and distributed processing.
Which cluster type should I choose for Spark?
Spark can run without Hadoop but some of its functionality relies on Hadoop's code (e.g. handling of Parquet files). We're running Spark on Mesos and S3 which was a little tricky to set up but works really well once done (you can read a summary of what needed to properly set it here).
(Edit) Note: since version 2.3.0 Spark also added native support for Kubernetes
By default , Spark does not have storage mechanism.
To store data, it needs fast and scalable file system. You can use S3 or HDFS or any other file system. Hadoop is economical option due to low cost.
Additionally if you use Tachyon, it will boost performance with Hadoop. It's highly recommended Hadoop for apache spark processing.
As per Spark documentation, Spark can run without Hadoop.
You may run it as a Standalone mode without any resource manager.
But if you want to run in multi-node setup, you need a resource manager like YARN or Mesos and a distributed file system like HDFS,S3 etc.
Yes, spark can run without hadoop. All core spark features will continue to work, but you'll miss things like easily distributing all your files (code as well as data) to all the nodes in the cluster via hdfs, etc.
Yes, you can install the Spark without the Hadoop.
That would be little tricky
You can refer arnon link to use parquet to configure on S3 as data storage.
http://arnon.me/2015/08/spark-parquet-s3/
Spark is only do processing and it uses dynamic memory to perform the task, but to store the data you need some data storage system. Here hadoop comes in role with Spark, it provide the storage for Spark.
One more reason for using Hadoop with Spark is they are open source and both can integrate with each other easily as compare to other data storage system. For other storage like S3, you should be tricky to configure it like mention in above link.
But Hadoop also have its processing unit called Mapreduce.
Want to know difference in Both?
Check this article: https://www.dezyre.com/article/hadoop-mapreduce-vs-apache-spark-who-wins-the-battle/83
I think this article will help you understand
what to use,
when to use and
how to use !!!
Yes, of course. Spark is an independent computation framework. Hadoop is a distribution storage system(HDFS) with MapReduce computation framework. Spark can get data from HDFS, as well as any other data source such as traditional database(JDBC), kafka or even local disk.
Yes, Spark can run with or without Hadoop installation for more details you can visit -https://spark.apache.org/docs/latest/
Yes spark can run without Hadoop. You can install spark in your local machine with out Hadoop. But Spark lib comes with pre Haddop libraries i.e. are used while installing on your local machine.
You can run spark without hadoop but spark has dependency on hadoop win-utils. so some features may not work, also if you want to read hive tables from spark then you need hadoop.
Not good at english,Forgive me!
TL;DR
Use local(single node) or standalone(cluster) to run spark without Hadoop,but stills need hadoop dependencies for logging and some file process.
Windows is strongly NOT recommend to run spark!
Local mode
There are so many running mode with spark,one of it is called local will running without hadoop dependencies.
So,here is the first question:how to tell spark we want to run on local mode?
After read this official doc,i just give it a try on my linux os:
Must install java and scala,not the core content so skip it.
Download spark package
There are "without hadoop" and "hadoop integrated" 2 type of package
The most important thing is "without hadoop" do NOT mean run without hadoop but just not bundle with hadoop so you can bundle it with your custom hadoop!
Spark can run without hadoop(HDFS and YARN) but need hadoop dependency jar such as parquet/avro etc SerDe class,so strongly recommend to use "integrated" package(and you will found missing some log dependencies like log4j and slfj and other common utils class if chose "without hadoop" package but all this bundled with hadoop integrated pacakge)!
Run on local mode
Most simple way is just run shell，and you will see the welcome log
# as same as ./bin/spark-shell --master local[*]
./bin/spark-shell
Standalone mode
As same as blew,but different with step 3.
# Starup cluster
# if you want run on frontend
# export SPARK_NO_DAEMONIZE=true
./sbin/start-master.sh
# run this on your every worker
./sbin/start-worker.sh spark://VMS110109:7077
# Submit job or just shell
./bin/spark-shell spark://VMS110109:7077
On windows?
I kown so many people run spark on windown just for study,but here is so different on windows and really strongly NOT recommend to use windows.
The most important things is download winutils.exe from here and configure system variable HADOOP_HOME to point where winutils located.
At this moment 3.2.1 is the most latest release version of spark,but a bug is exist.You will got a exception like Illegal character in path at index 32: spark://xxxxxx:63293/D:\classe when run ./bin/spark-shell.cmd,only startup a standalone cluster then use ./bin/sparkshell.cmd or use lower version can temporary fix this.
For more detail and solution you can refer for here
No. It requires full blown Hadoop installation to start working - https://issues.apache.org/jira/browse/SPARK-10944