Currently we are working on Hive, which by default uses map reduce as processing framework in our MapR cluster. Now we want to change from map reduce to spark for better performance. As per my understanding we need to set hive.execution.engine=spark.
Now my question is Hive on spark is currently supported by MapR ? if yes, what are configuration changes that we need to do ?
Your help is very much appreciated. Thanks
No, MapR (5.2) doesn't support that. From their docs,
MapR does not support Hive on Spark. Therefore, you cannot use Spark as an execution engine for Hive. However, you can run Hive and Spark on the same cluster. You can also use Spark SQL and Drill to query Hive tables.
Cheers.
I know and understand that your question is about using Spark as data processing engine for Hive; and as you can see in the various answer it is today not officially supported by MapR.
However, if you goal is to make Hive faster, and do not use MapReduce you can switch to Tez, for this install the MEP 3.0.
See: http://maprdocs.mapr.com/home/Hive/HiveandTez.html
I have a Hadoop (2.7.2) setup over a Cassandra (3.7) Cluster. I have no problem with using Hadoop MapReduce. Similarly, I have no problem to create tables and keyspace in CQLSH. However, I have been trying to install PIG over hadoop, so as to access the tables in Cassandra. (Installation of PIG is as such fine) It is where I'm having trouble.
I have come across numerous websites, most are either for outdated versions of Cassandra or just plain vague.
The one thing I gleaned from this website is that we can load access the cassandra tables in pig using CqlStorage / CqlNativeStorage. However, in the latest version, it seems this support has been removed (since 2015).
Now my question is, are there any workarounds?
I would be running mapreduce jobs over cassandra tables, and use PiG for querying, mostly.
Thanks in Advance.
All pig support was Deprecated in 2.2 and removed in 3.0. https://issues.apache.org/jira/browse/CASSANDRA-10542
So I think you are a bit out of luck here. You may be able to use old classes with modern C* but Pig is very niche right now. SparkSql is definitely the current favorite child (I may be biased since I work on the Spark + Cassandra Connector) and allows for very flexible querying of C* data.
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
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.