If I don't do any map/reduce jobs, still JobTracker/TaskTrackers need to be running for some HBase internal dependency?
No you don't need both for running solely HBase.
Just a tip: there are always scripts that just start the HDFS, bin/start-dfs.sh for example.
As mentioned above we don't need Job/Tasktracker if we are dealing with just Hbase. You can use bin/start-dfs.sh to start Name/Dtanodes..Moreover bin/start-all.sh has been deprecated now..So you should prefer using bin/start-dfs.sh to start Name/Datanodes and bin/start-mapred.sh to start Job/Tasktracker..I would suggest using Hbase in pseudo-distributed mode for learning and testing purpose, as in standalone Hbase doesn't use HDFS..You should be a bit careful while configuring though..
Basic case: You don't need JobTracker and TaskTrackers when using only HDFS+HBase (in smaller, testing environment you don't need event HDFS)
When you would like to run MapReduce jobs using data stored in HBase, you'll obviously need both JobTracker and TaskTrackers.
Related
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
We have very complex pipelines which we need to compose and schedule. I see that Hadoop ecosystem has Oozie for this. What are the choices for Spark based jobs when I am running Spark on Mesos or Standalone and doesn't have a Hadoop cluster?
Unlike with Hadoop, it is pretty easy to chains things with Spark. So writing a Spark Scala script might be enough. My first recommendation is tying that.
If you like to keep it SQL like, you can try SparkSQL.
If you have a really complex flow, it is worth looking at Google data flow https://github.com/GoogleCloudPlatform/DataflowJavaSDK.
Oozie can be used in case of Yarn,
for spark there is no built in scheduler available, So you are free to choose any scheduler which works in the cluster mode.
For Mesos I feel Chronos would be the right choice, more info on Chronos
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.
I am not sure about what hadoop can and cannot do, and how easy things are.
I understand hadoop is good at doing mapreduce jobs and at providing hdfs, their distributed filesystem.
What else is hadoop good at / easy to use ?
My problem : I would like to serve data, result of mapreduce. And as I have lot of traffic I would need 3 front end servers. Can Hadoop help me deploy a server on 3 of my n runnning nodes ?
Basically instead of running mapreduce on n machines, I would like to run a custom executable (my server) on 3 machines. And when 1 machine fails, that hadoop takes care of starting the job on another available machine.
Am I supposed to run that on the hadoop cluster ? or should the hadoop cluster be used only for the mapreduce and I should have a separate cloud to serve the data from the hadoop cluster ?
Thanks for sharing your experience.
P.S I am just considering hadoop right now as a solution, Im not tied to it
Your question isn't actually clear but here is my shot.
You want to display the result of your Hadoop job? Usually a Hadoop job writes its result to HDFS. What you can do is to create your own OutputFormat class. You might define a XMLOutputFormat for example.
But the nice thing is that you can create your own Writable. Take a look at Database Access with Apache Hadoop. In this tutorial you can save the output of a Hadoop job to a data base system.
Your frontend then can query the database and show the result.