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
Related
I have scenario in which i have to pull data from Hadoop cluster into AWS.
I understand running dist-cp on the hadoop cluster is a way to copy the data into s3, but i have a restriction here, i wont be able to run any commands in the cluster. I should be able to pull the files from hadoop cluster into AWS. The data is available in hive.
I thought of the below options:
1) Sqoop data from Hive ? Is it possible ?
2) S3-distcp (running it on aws), if so what would be the configuration needed ?
Any Suggestions ?
If the hadoop cluster is visible from EC2-land, you could run a distcp command there, or, if it's a specific bit of data, some hive query which uses hdfs:// as input and writes out to s3. You'll need to deal with kerberos auth though: you cannot use distcp in an un-kerberized cluster to read data from a kerberized one, though you can go the other way.
You can also run distcp locally in 1+ machine, though you are limited by the bandwidth of those individual systems. distcp is best when it schedules the uploads on the hosts which actually have the data.
Finally, if it is incremental backup you are interested in, you can use the HDFS audit log as a source of changed files...this is what incremental backup tools tend to use
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 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.
I am new to Hadoop and much interested in Hadoop Administration,so i tried to install Hadoop 2.2.0 in Ubuntu 12.04 as pseudo distributed mode and installed successfully and run some example jar files also ,now i am trying learn further ,trying to learn data back up and recovery part now,can anyone tell ways to take data back back up and recovery it in hadoop 2.2.0 ,and also please suggest any good books for Hadoop Adminstration and steps to learn Hadoop Adminstration.
Thanks in Advance.
There is no classic backup and recovery functionality in Hadoop. There are several reasons for this:
HDFS uses block level replication for data protection via redundancy.
HDFS scales out massively in size, and it is becoming more economic to backup to disk, rather than tape.
The size of "Big Data" doesn't lend itself to being easily backed up.
Instead of backups, Hadoop uses data replication. Internally, it creates multiple copies of each block of data (by default, 3 copies). It also has a function called 'distcp', which allows you to replicate copies of data between clusters. This is what's typically done for "backups" by most Hadoop operators.
Some companies, like Cloudera, are incorporating the distcp tool into creating a 'backup' or 'replication' service for their distribution of Hadoop. It operates against a specific directory in HDFS, and replicates it to another cluster.
If you really wanted to create a backup service for Hadoop, you can create one manually yourself. You would need some mechanism of accessing the data (NFS gateway, webFS, etc), and could then use tape libraries, VTLs, etc. to create backups.
I'm newbie on hadoop.
I heard that mapR is better way to mount hadoop HDFS rather than fuse.
But most of the related article just describe about mapR hadoop not pure apache hadoop.
Anyone has experience of mounting pure apache hadoop with mapR?
Thanks in advance.
MapR is much more than just a way to mount HDFS.
MapR includes Hadoop and many Apache eco-system components and many other non-Apache components such as Cascading. It also includes LucidWorks which includes Solr.
MapR also includes a reimplementation of HDFS called MaprFS. MaprFS has higher performance, has read-write semantics, allows read during write, supports transactionally correct mirrors and snapshots, has no name node, scales without the futzing of federation, is inherently HA without all the mess of the HA NameNode and which is accessible via a distributed NFS system.
Oh, MaprFS also supports the HBase API in addition to POSIX-ish access via NFS and in addition to the HDFS API.
The map-reduce layer in MapR has been partially re-written to make use of the extremely high performance capabilities of the file system. This is how MapR was able to break the minute sort record last fall.
So naming aside, MapR includes all the open source software that you would get with any other distribution and much more besides. "Pure Hadoop" is next to useless. You need Pig and/or Hive. You probably should look into Cascading/Scalding. You may need Mahout. You definitely will need to connect your system to legacy data sources and reporting systems which is what NFS makes easy.
Keep in mind that mounting HDFS via NFS or Fuze doesn't get you where you want to be. HDFS just doesn't have suitable semantics for access via NFS or normal file system API's. It just has too many compromises.
With MapR, on the other hand, you can even run databases like MySQL or Postgress on top of the clusters file system via NFS.
MapR comes in three editions.
M3 is free and gives you all the performance and scalability, but limits you to a single NFS server and no mirrors, snapshots, volume locality or HBase compatible API (you can run HBase itself, of course). HA is also degraded in M3 so that it takes an hour to fail over certain functions.
M5 costs money after the free trial period and gives you snapshots, mirrors, the ability to force some data to different topologies and unlimited NFS servers.
M7 also costs money and adds the HBase API to all that M5 can do.
See mapr.com for more info.
To sum up what Ted said as well,
You're not really "mounting pure apache hadoop with mapR?". Hadoop shouldn't be confused with HDFS. While they tend to be interchangeable during conversation, HDFS explicitly refers to the actual distributed filesystem (hence the DFS in HDFS). HDFS has to be interacted with using specific hadoop commands, i.e. "hadoop dfs ls /" will list the root contents of hdfs.
MapR went above and beyond what hadoop provides you be default. One, you can interact with the filesystem using the more efficient maprfs (a rewrite of hdfs). The other thing you can do is actually NFS mount the HDFS/MapRFS so that you can manipulate the filesystem natively without having to do anything special. It gets treated like any other NFS filesystem, except in this case, it's distributed across your cluster.