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.
Related
How does MapReduce processing work if the inputs/output are from local file system?
Does MapReduce job execution happen asynchronously across the Hadoop cluster?
If yes, how does that happen?
In which usecase, do we actually need to use this approach?
MapReduce Works even same in local system (mapper->reducer)
(only its matter of efficiency as it will be less efficient in local system rather than cluster).
Yes,MapReduce job execution happen asynchronously across the Hadoop cluster(it depends on what kind of scheduler you are using in your mapreduce program)
click for more about scheduler
In most of the case this used for testing purpose (running mapreduce program in local system).
I understand that running Nutch in deploy mode is distributed crawling based on Hadoop but I couldn't fully understand what when we run it in local mode. Is Nutch independent of Hadoop in that case? And is the crawling process in local mode not based on MapReduce?
Nutch is based on MapReduce, regardless of how it runs. The Hadoop libs are dependencies of Nutch, in local mode, Nutch puts the Hadoop related libs on the classpath and runs it all in a single JVM. In distributed mode, the 'hadoop' command is called.
See Nutch script
PS: if you use Nutch on a single machine, it makes sense to run it in pseudo distributed mode so that you get the MapReduce UI to monitor the crawl + parallelism etc...
My Application is connected to an HBase and does a lot of communication (hundreds or thousands of reads/writes per second). This strongly affects performance, probably due to I/O operations HBase does on every request.
Doo.dle are calls to my code - the difference between blue and red is time consumed by HBase.
Currently, I've only tested in standalone mode, where HBase stores data using the local file system. I was wondering, whether using one in distributed mode with an actual HDFS could significantly improve performance, or just yield the same results. I'm trying to get a clue before losing too much time into getting a cluster up and running.
A second question I've asked myself is whether a standalone HBase could be configured to just persist data to memory (RAM) instead of writing it to the file system for performance measures.
In the standalone mode,HBase does not use HDFS and it runs all HBase daemons and a local ZooKeeper all up in the same JVM
In a Pseudo-distributed mode, Hbase can run against the local filesystem or it can run against an instance of the Hadoop Distributed File System. So there is no difference between standalone and pseudo-distributed considering the performance.
The Fully-distributed mode requires the use of HDFS which means that the tasks will run over jobs and that's take time according to my experience.
So using Hbase in fully-distributed mode with an actual HDFS could significantly improve performance.
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'm able to run a local mapper and reducer built using ruby with an input file.
I'm unclear about the behavior of the distributed system though.
For the production system, I have a HDFS set up across two machines. I know that if I store a large file on the HDFS, it will have some blocks on both machines to allow for parallelization. Do I also need to store the actual mappers and reducer files (my ruby files in this case) on the HDFS as well?
Also, how would I then go about actually running the streaming job so that it runs in a parallel manner on both systems?
If you were to use mapper/reducers written in ruby (or anything other than Java), you would have to use hadoop-streaming. Hadoop streaming has an option to package your mapper/reducer files when sending your job to the cluster. The following link should have what you are looking for.
http://hadoop.apache.org/common/docs/r0.15.2/streaming.html