Currently I am running my spark cluster as standalone mode. I am reading data from flat files or Cassandra(depending upon the job) and writing back the processed data to the Cassandra itself.
I was wondering if I switch to Hadoop and start using a Resource manager like YARN or mesos, does it give me an additional performance advantage like execution time and better resource management?
Currently sometime when I am processing huge chunk of data during shuffling with a possibility of stage failure. If I migrate to a YARN, can Resource manager address this issue?
Spark standalone cluster manager can also give you cluster mode capabilities.
Spark standalone cluster will provide almost all the same features as the other cluster managers if you are only running Spark.
When you submit your application in cluster mode all you job related files would be copied on to one of the machines on the cluster which would then submit the job on your behalf, if you submit the application in client mode the machine from which the job is being submitted would be taking care of driver related activities. This means that the machine from which the job has been submitted cannot go offline, whereas in cluster mode the machine from which the job has been submitted can go offline.
Having a Cassandra cluster would also not change any of these behaviors except it can save you network traffic if you can get the nearest contact point for the spark executor(Just like Data locality).
The failed stages gets rescheduled if you use either of the cluster managers.
I was wondering if I switch to Hadoop and start using a Resource manager like YARN or mesos, does it give me an additional performance advantage like execution time and better resource management?
In Standalone cluster model, each application uses all the available nodes in the cluster.
From spark-standalone documentation page:
The standalone cluster mode currently only supports a simple FIFO scheduler across applications. However, to allow multiple concurrent users, you can control the maximum number of resources each application will use. By default, it will acquire all cores in the cluster, which only makes sense if you just run one application at a time.
In other cases (when you are running multiple applications in the cluster) , you can prefer YARN.
Currently sometime when I am processing huge chunk of data during shuffling with a possibility of stage failure. If I migrate to a YARN, can Resource manager address this issue?
Not sure since your application logic is not known. But you can give a try with YARN.
Have a look at related SE question for benefits of YARN over Standalone and Mesos:
Which cluster type should I choose for Spark?
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
I like to study about Hadoop multinode setup and installation, by referring the above tutorial I understand that single node cluster environment can be used as node for the multinode cluster
http://bigdatahandler.com/hadoop-hdfs/hadoop-multi-node-cluster-setup/
Currently I am learning Hadoop using Horton sandbox, can we use a sandbox system as a single node environment?
If not what is the difference between sandbox and traditional Hadoop cluster installation
The sandbox images (from Hortonworks and Cloudera) provide the user with a pre-configured development environment with all the usual tools already available and installed (pig, hive etc.). Since the image is a single "system" it is set-up such that the hadoop cluster is single-node: i.e. everything - HDFS, Hadoop map-reduce etc. - is local to that image. That is a massive benefit, as anyone who has set up a hadoop cluster will tell you! It allows you to get up-and-running with very little operational overhead.
What these sandboxes do not provide, however, is realistic cluster behaviour as you have only one node. But there other possibilities - tools such as Vagrant and Docker - that would allow you to do this (I have not tried it myself).
The big data handler link you shared seems to be about combining several of these standalone, inherently single-node "clusters" so that you have something more realistic. But I would guess setting this up so that YARN, Zookeeper and other services are not duplicated comes with a not insignificant challenge.
I'm trying to get set up on the Amazon Cloud to run some hadoop MapReduce jobs but I'm struggling to successfully create a cluster. I have downloaded the ec2 files, have my certificates and keypair file, but I believe it's the AMIs that are causing me trouble. If I'm trying to run a cluster with a master node and n slave nodes, I start n+1 instances using standard compatible AMIs and then run the code "hadoop-ec2 launch-cluster name n" in the terminal. The master node is successful, but I get an error when the slave nodes start to launch, saying "missing parameter -h (AMI missing)" and I'm not entirely sure how to progress.
Also, some of my jobs will require an alteration in hadoops parameter settings (specifically the mapred-site.xml config file), is it possible to alter this file, and if so, how do I gain access to it? Is hadoop already installed on amazon machines, with this file accessible and alterable?
Thanks
Have you tried Amazon Elastic MapReduce? This is a simple API that brings up Hadoop clusters of a specified size on demand.
That's easier then to create own cluster manually.
But once the jobflow is finished by default it shuts the cluster down, leaving you with outputs on S3. If what you need is simply to do some crunching, this may be the way to go.
In case you need HDFS contents stored permanently (e.g. if you are running HBase on top of Hadoop) you may actually need own cluster on EC2. In this case you may find Cloudera's distribution of Hadoop for Amazon EC2 useful.
Altering Hadoop configuration on nodes it will start is possible using EC2 Bootstrap Actions:
Q: How do I configure Hadoop settings for my job flow?
The Elastic MapReduce default Hadoop configuration is appropriate for most workloads. However, based on your job flow’s specific memory and processing requirements, it may be appropriate to tune these settings. For example, if your job flow tasks are memory-intensive, you may choose to use fewer tasks per core and reduce your job tracker heap size. For this situation, a pre-defined Bootstrap Action is available to configure your job flow on startup. See the Configure Memory Intensive Bootstrap Action in the Developer’s Guide for configuration details and usage instructions. An additional predefined bootstrap action is available that allows you to customize your cluster settings to any value of your choice. See the Configure Hadoop Bootstrap Action in the Developer’s Guide for usage instructions.
About the way you are starting the cluster, please clarify:
If I'm trying to run a cluster with a master node and n slave nodes, I start n+1 instances using standard compatible AMIs and then run the code "hadoop-ec2 launch-cluster name n" in the terminal. The master node is successful, but I get an error when the slave nodes start to launch, saying "missing parameter -h (AMI missing)" and I'm not entirely sure how to progress.
How exactly you are trying start it? What exactly AMIs are you using?
Loving MRToolkit -- great to get away from Java while writing Hadoop jobs. It has become apparent that the library was written to interface with an EC2 cluster, and not with Amazon's elastic map/reduce system. Does anybody have insights into running jobs defined using the toolkit on elastic map/reduce servers? It isn't readily apparent from the web interface, and I'd love to avoid the headache of setting up a cluster by hand on EC2.
I've looked into updloading files under the 'streaming' option (as that's what MRToolkit uses), but Amazon is expecting separate files for the mapper and reducer -- typical MRToolkit style defines them in the a single file as subclasses of predefined Base(Map|Reduce) classes.
Thanks much for any thoughts.
Isaac
It's doable, but not through the web GUI.
Download and install the Ruby Client
Create your cluster: elastic-mapreduce --create --alive [params to size cluster]
Confirm your Elastic Map Reduce Master security group has port 22 open
SSH into your master node
Use git / scp to copy over your application code
Run your app