I have been trying to install Hadoop on a single node following the instructions written here. There are two sets of instructions, one for running a MapReduce job locally, and another for YARN.
What is difference between running a MapReduce job locally and running on YARN?
If you use local the map and reduce tasks are run in the same jvm. Usually this mode is used when we want to debug the code. Whereas if we use yarn resource manager which is in MRV2 comes into play and mappers and reducers will run in different nodes and different jvms with in the same node(if it is pseudo distributed mode).
In regards to being able to run machine learning jobs with Spark. Which is a better choice the Yarn scheduler or the Spark Standalone scheduler?
There is no difference when it comes to run the actual spark job.
Yarn/Mesos helps you to schedule resources if you have different spark applictions running and/or other components running in your cluster (which support Yarn/Mesos of course).
The Spark standalone cluster cannot manage resources. That is if you start a Spark application and it uses all the ressources, the second application will not find any resources left. That means you have to do this by yourself (e.g. adapting Spark config accordingly)
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 wanted to know that what is the performance gain or loss if I use pig in local mode (which internally calls Map reduce) vs using PIG-withouthadoop.jar file.?
Does PIG-withouthadoop.jar really does not use hadoop ???
And If I only want to use Pig without clusters, like design a data flow, then what should I use,? Pig in local mode OR pig-withouthadoop.jar file??
Currently I have written my script using pig local mode and while trying to deploy in server and set up PIG in local mode, I think I also need HADOOP_HOME to be set in the environment variables before setting the PIG_HOME variable
Kindly advice ..
Thanks in advance. :)
Let me answer your question in a sequence:
1) When we talk about performance, then if we assume the file size and the Pig script to be constant, while running in local mode and Hadoop mode. Then, definitely the processing will be faster in local mode as all the task is getting performed in a single JVM and but in case of Hadoop mode, the input file will be carried to the data nodes, then the Pig script or UDFs will also get carried to the cluster. This will demand more time, although, in both the cases the pig scripts and UDFs will internally get converted to map and reduce task and also the number of map and reduce class constructed will always be same in both the cases. We can check this by using EXPLAIN command.
2) No. Pig internally contains a bundle of Hadoop jars. So, if you haven't started the Hadoop by using start-all.sh command, pig will work as it uses the internal Hadoop bundled jars. Now, the interesting part is, if you have installed hadoop and then use pig without starting the Hadoop, then sometimes it will not work because the of Hadoop version mismatch. So to be in safe side start Hadoop explicitly. So, Pig always uses Hadoop. :)
3) Always use Hadoop local mode if the file size is less. As already explained, Pig by default comes with Hadoop jars.
4) Yes you need to set this, if you are using Hadoop explicitly.
Local mode will literally run Pig, HDFS and MR1 (or YARN+MR2) in one JVM.
It's not really relevant to compare performance difference in local vs cluster modes. Local mode is generally used for testing or running small MR jobs that can work on 1 node.
With regards to pig-withouthadoop.jar, I can see how the jar's name can be construed to mean that Pig won't using Hadoop. But that is not the case.
Pig packages two jars relevant to execution:
pig.jar, which is an "uber jar" that also includes all hadoop and mapreduce jars. You can literally take that jar on a box which does not already have hadoop installed, and run pig (after setting the right configs and environment.)
But most clusters already have hadoop installed and configured. In that case, you use pig-withouthadoop.jar. This jar is half the size of the uber jar, for obvious reasons.
Either ways you'll need to ensure hadoop configs hdfs-site.xml, mapred-site.xml etc are in standard location (/etc/hadoop/conf/ typically) for Pig to work.
What is the actual difference between running PIG scripts locally and on mapreduce?
I understand mapreduce mode is when you run it on a cluster that has hdfs installed. Does this mean local mode does not need HDFS and so even mapreduce jobs don't get triggered? What is the difference and when do you the other?
Local mode will build a simulated mapreduce job running off of a local file on disk. In theory equivalent to MapReduce, but it's not a "real" mr job. You shouldn't be able to tell the difference from a user perspective.
Local mode is great for development.
Local mode: All scripts are run on a single machine without requiring Hadoop MapReduce and HDFS. This can be useful for developing and testing Pig logic. If you’re using a small set of data to developer or test your code, then local mode could be faster than going through the MapReduce infrastructure.
Local mode doesn’t require Hadoop. When you run in Local mode, the Pig program runs in the context of a local Java Virtual Machine, and data access is via the local file system of a single machine. Local mode is actually a local simulation of MapReduce in Hadoop’s LocalJobRunner class.
MapReduce mode (also known as Hadoop mode): Pig is executed on the Hadoop cluster. In this case, the Pig Script gets converted into a series of MapReduce jobs that are then run on the Hadoop cluster.
If you have a terabyte of data that you want to perform operations on and you want to interactively develop a program, you may soon find things slowing down considerably, and you may start growing your storage. Local mode allows you to work with a subset of your data in a more interactive manner so that you can figure out the logic (and work out the bugs) of your Pig program.
After you have things set up as you want them and your operations are running smoothly, you can then run the script against the full data set using MapReduce mode.