I have just moved from a Spark local setup to a Spark standalone cluster. Obviously, loading and saving files no longer works.
I understand that I need to use Hadoop for saving and loading files.
My Spark installation is spark-2.2.1-bin-hadoop2.7
Question 1:
Am I correct that I still need to separately download, install and configure Hadoop to work with my standalone Spark cluster?
Question 2:
What would be the difference between running with Hadoop and running with Yarn? ...and which is easier to install and configure (assuming fairly light data loads)?
A1. Right. The package you mentioned is just packed with hadoop client with specified version and still you need to install hadoop if you want to use hdfs.
A2. Running with yarn means you're using resource manager of spark as yarn. (http://spark.apache.org/docs/latest/job-scheduling.html#scheduling-across-applications) So, when the case you don't need DFS, like when you're only running spark streaming applications, you still can install Hadoop but only run yarn processes to use its resource management functionality.
Related
I am having a Hadoop 3.1.1 multi-node cluster, i want to make use of PySpark to read files from my HDFS into PySpark for ETL operations and then load it to target MySQL databases.
Given below is the ask.
can I install spark in standalone mode?
do I need to install spark on my yarn first?
if no, how can I install spark separately?
You can use any mode for communicating with HDFS and MySQL, including Kubernetes. Or, you just use --master="local[*]" and you don't need a scheduler at all. This is useful, for example, from a Jupyter Notebook.
YARN would be recommended as you already have HDFS, and therefore the scripts to start YARN processes as well.
You don't really "install Spark on YARN". Applications from clients get submitted to the YARN cluster. spark.yarn.archives HDFS path will get unpacked into the classes necessary to run the job.
Refer https://spark.apache.org/docs/latest/running-on-yarn.html
I'm studying distributed computing recently and found out Hadoop Yarn is one of them.
So thought if I just establish Hadoop Yarn cluster, then every application will run distributed.
But now someone told me that HADOOP Yarn cannot do anything by itself and need other things like mapreduce, spark, and hbase.
If this is correct, then is that mean only limited tasks can be run with Yarn?
Or can I apply Yarn's distributed computing to all applications I want?
Hadoop is the name which refers to the entire system.
HDFS is the actual storage system. Think of it as S3 or a distributed Linux filesystem.
YARN is a framework for scheduling jobs and allocating resources. It handles these things for you, but you don't interact very much with it.
Spark and MapReduce are managed by Yarn. With these two, you can actually write your code/applications and give work to the cluster.
HBase uses the HDFS storage (with is file based) and provides NoSql storage.
Theoretically you can run more than just Spark and MapReduce on Yarn and you can use something else then Yarn (Kubernetes is in works or supported now). You can even write your own processing tool, queue/resource management system, storage... Hadoop has many pieces which you may use or not, depending on your case. But the majority of Hadoop systems use Yarn and Spark.
If you want to deploy Docker containers for example, just a Kubernetes cluster would be a better choice. If you need batch/real time processing with Spark, use Hadoop.
YARN itself is a resource manager. You will need to write code that can be deployed onto those resources, and then that could do anything, given that the nodes running the tasks are themselves capable of running the job. For example, you cannot distribute a Python library without first installing the dependencies for that script. Mesos is a bit more generalized / accessible than YARN, if you want more flexibility for the same affect.
YARN mostly supports running JAR files, shell scripts (at least, from Oozie) or Docker containers can be deployed to it as well (refer Apache docs)
You may also refer to the Apache Slider or Twill projects for more information.
I have a Cloudera cluster which is being managed by an admin team. However there is no Zeppelin installed in the cluster.
I would like to install Zeppelin on a separate node and connect with the Cloudera cluster?
Is it feasible to install zeppelin on a node which is not part of the cluster and submit spark jobs to it?
Any reference is really appreciated?
Thanks
Zeppelin is just another Spark client.
For example, on the machine that you want to use Zeppelin on, you should first make sure that spark shell and spark submit work as expected, then Zeppelin configurations become much easier
An easy way to manage that would be to have the admins use Cloudera Manager to install Spark (and Hive and Hadoop) client libraries into this standalone node, then I assume they give you SSH access, or you tell them how to install it
I need to setup spark cluster (1 Master and 2 slaves nodes) on centos7 along with resource manager as YARN. I am new to all this and still exploring. Can somebody share me detailed steps of setting up Spark with Yarn in cluster mode.
Afterwards i have to integrate Livy too(an open source REST interface for using Spark from anywhere).
Inputs are welcome.Thanks
YARN is part of Hadoop. So, a Hadoop installation is necessary to run Spark on YARN.
Check out the page on the Hadoop Cluster Setup.
Then you can utilize the this documentation to learn about Spark on YARN.
Another method to quickly learn about Hadoop, YARN and Spark is to utilize Cloudera Distribution of Hadoop (CDH). Read the CDH 5 Quick Start Guide.
We are currently using the similar setup in aws. AWS EMR is costly hence
we setup our own cluster using ec2 machines with the help of Hadoop Cookbook. The cookbook supports multiple distributions, however we choose HDP.
The setup included following.
Master Setup
Spark (Along with History server)
Yarn Resource Manager
HDFS Name Node
Livy server
Slave Setup
Yarn Node Manager
HDFS Data Node
More information on manually installing can be found in HDP Documentation
You can see the part of that automation in here.
I am trying to test out Spark so I can summarize some data I have in Cassandra. I've been through all the DataStax tutorials and they are very vague as to how you actually enable spark. The only indication I can find is that it comes enabled automatically when you select "Analytics" node during install. However, I have an existing Cassandra node and I don't want to have to use a different machine for testing as I am just evaluating everything on my laptop.
Is it possible to just enable Spark on the same node and deal with any performance implications? If so how can I enable it so that it can be tested?
I see the folders there for Spark (although I'm not positive all the files are present) but when I check to see if it's set to Spark master, it says that no spark nodes are enabled.
dsetool sparkmaster
I am using Linux Ubuntu Mint.
I'm just looking for a quick and dirty way to get my data averaged and so forth and Spark seems like the way to go since it's a massive amount of data, but I want to avoid having to pay to host multiple machines (at least for now while testing).
Yes, Spark is also able to interact with a cluster even if it is not on all the nodes.
Package install
Edit the /etc/default/dse file, and then edit the appropriate line
to this file, depending on the type of node you want:
...
Spark nodes:
SPARK_ENABLED=1
HADOOP_ENABLED=0
SOLR_ENABLED=0
Then restart the DSE service
http://docs.datastax.com/en/datastax_enterprise/4.5/datastax_enterprise/reference/refDseServ.html
Tar Install
Stop DSE on the node and the restart it using the following command
From the install directory:
...
Spark only node: $ bin/dse cassandra -k - Starts Spark trackers on a cluster of Analytics nodes.
http://docs.datastax.com/en/datastax_enterprise/4.5/datastax_enterprise/reference/refDseStandalone.html
Enable spark by changing SPARK_ENABLED=1
using the command: sudo nano /usr/share/dse/resources/dse/conf/dse.default