Need some help to understand how HDFS and Storm are integrated. Storm can process incoming stream of data using many nodes. My data is, let's say, log entries from different machines. So how do I store that all? Ideally I'd like to store logs from one machine to a one or many files dedicated to that machine. However does does it work? Will I be able to append to the same file in HDFS from many different Storm nodes?
PS: I still working on getting all this running so I can't test this physically... but it does bother me.
Write a file in hdfs with Java
No, you cannot write to the same file from more than one task at a time. Each task would need to write to it's own file in a directory and then you could process them using directory/* if you are using hadoop
Related
Is it possible to run a Hadoop MapReduce program without a cluster? I mean, I am just trying to fiddle around a little with map/reduce, for educational purposes, so all I want is to run few MapReduce programs on my computer, I don't need any job splitting to multiple nodes etc... Don't need any performance boosts or anything, as I said, just for educational purposes.. Do I still need to run a VM to achieve this? I am using IntelliJ Ultimate, and I'm trying to run simple WordCount.. I believe I've set up all necessary libraries and the entire project, and upon running I get this exception:
Exception in thread "main" java.io.IOException: Cannot initialize Cluster.
Please check your configuration for mapreduce.framework.name and the correspond server addresses.
I've found some posts saying that the entire map/reduce process can be run locally on the jvm, but couldn't yet find the way how to do it.
The whole installation tutorial of "pseudo-distributed" mode specifically walks you through the installation of a single node Hadoop cluster
There's also the "Mini cluster" which you'll find some Hadoop projects use for unit&integration tests
I feel like you're just asking if you need HDFS or YARN, though, and the answer is no, Hadoop can read file:// prefixed file paths from disk, with or without a cluster
Keep in mind that splitting is not just between nodes, but also between multiple cores of a single computer. If you're not doing any parallel processing, there's not much reason to use Hadoop other than to learn the API semantics.
Aside: From an "educational perspective", in my career thus far, I find more people writing Spark than MapReduce, and not many jobs asking specifically for MapReduce code
My process creates a huge number of files time to time, I wanted to transfer files from my local directory to some location in HDFS, other than using NiFi, is it possible to develop that flow in java. If yes, please guide me in by giving some reference code in Java.
Please help me out!
You could do a couple of Things :-
1) Use Apache flume :- https://www.dezyre.com/hadoop-tutorial/flume-tutorial. This page says :- "Apache Flume is a distributed system used for aggregating the files to a single location. " This solution should be better than using kafka since it has been designed specifically for files.
2) Write Java code to ssh to your machine and scan for files that were modified after a specific timestamp. If you find such files open an input stream and save it on the machine your java code is running.
3) Alternatively your java code could be running on the machine your files are being created and you could scan for files created after specific timestamp and move them to any new machine
4) If you want to use only kafka. You could write a java code to read files find latest file/row and publish it to a kafka topic. Flume can do all this out of the box.
I don't know if there is a limit on the size of a message in Kafka, but you can use the ByteArraySerializer in the producer/consumer properties. Convert your file to bytes and then reconstruct it on the consumer.
Doing a quick search I found this
message.max.bytes (default:1000000) – Maximum size of a message the
broker will accept. This has to be smaller than the consumer
fetch.message.max.bytes, or the broker will have messages that can’t
be consumed, causing consumers to hang.
I am confused on how the Datanode's in a hadoop cluster runs the java code for the reduce function of a job. Like, how does hadoop send a java code to another computer to execute?
Does Hadoop inject a java code to the nodes? If so, where is the java code located in hadoop?
Or are the reduce functions ran on the master node not the datanodes?
Help me trace this code where the Master Node sends the java code for the reduce function to a datanode.
As shown in the picture, here is what happens:
You run the job on client by using hadoop jar command in which you pass jar file name, class name and other parameters such as input and output
Client will get new application id and then it will copy the jar file and other job resources to HDFS with high replication factor (by default 10 on large clusters)
Then Client will actually submit the application through resource manager
Resource manager keeps track of cluster utilization and submit application master (which co-ordinates the job execution)
Application master will talk to namenode and determine where the blocks for input are located and then work with nodemanagers to submit the tasks (in the form of containers)
Containers are nothing but JVMs and they run map and reduce tasks (mapper and reducer classes), when the JVM is bootstrapped job resources that are on HDFS will be copied to the JVM. For mappers these JVMs will be created on same nodes on which data exists. Once the processing is started the jar file will be executed to process the data locally on that machine (typical).
To answer your question, reducer will be running on one or more data nodes as part of the containers. Java code will be copied as part of the bootstrap process (when JVM is created). Data will be fetched from mappers over the network.
No. Reduce functions are executed on data nodes.
Hadoop transfers packaged code (jar files) to the data node that are going to process data. At run time data nodes download these code and process task.
I am new to Hadoop & MapReduce .We are developing a network monitoring tool (in java).We collect various information of monitored devices periodically , say in every 5 sec. and write that information to HDFS through java client each information as new file(since we'r not using hdfs append facility).In HDFS our data organization would be like this:
/monitored_info
/f1.txt
/f2.txt
.......
/f1020010.txt
Thus each file typically less than 2KB in size.
I know each map task can take upto 1 file, and it will spawn as much as map task and the job will be inefficient. To get rid of this we used merging facility of FileUtil before submitting job:
FileUtil.copyMerge(fileSystem, new Path("monitored_info"), fileSystem,
new Path("mapInputfile"), false, conf, null);
Is it a good practice ? Or is there any other mechanism used for such requirements? Please help...
Check for Apache Kafka and Apache Flume. You can aggregate logs and move to your data store with them.
I'd use Flume personally. Easier to use imho.
If you want to use mapreduce there are different ways we can do that
Hadoop Archives, or HAR files, are a file archiving facility that packs files into HDFS
blocks more efficiently, thereby reducing namenode memory usage while still allowing
transparent access to files
The situation is alleviated somewhat by CombineFileInputFormat, which was designed
to work well with small files. Where FileInputFormat creates a split per file,
CombineFileInputFormat packs many files into each split so that each mapper has more
to process. Crucially, CombineFileInputFormat takes node and rack locality into account
when deciding which blocks to place in the same split, so it does not compromise the
speed at which it can process the input in a typical MapReduce job.
One technique for avoiding the many small files case is to merge small files
into larger files by using a SequenceFile: the keys can act as filenames (or a constant such as NullWritable, if not needed) and the values as file contents.
My hadoop the program originally was launched in a local mode, and now my purpose became start in completely distributed mode. For this purpose it is necessary to provide access to the files which reading is executed in the reducer and mapper functions, from all computers of a cluster and therefore I asked a question on http://answers.mapr.com/questions/4444/syntax-of-option-files-in-hadoop-script (also as it will be not known on what computer to be executed the mapper function (mapper from logic of the program there will be only one and the program will be launched only with one mapper), it is necessary to provide also access on all cluster to the file arriving on an input of the mapper function). In this regard I had a question: Whether it is possible to use hdfs-files directly: that is to copy beforehand files from file system of Linux in file system of HDFS (thereby as I assume, these files become available on all computers of a cluster if it not so, correct please) and then to use HDFS Java API for reading these files, in the reducer and mapper functions which are executing on computers of a cluster?
If on this question the response the positive, give please a copying example from file system of Linux in file system of HDFS and reading these files in java to the program by means of HDFS Java API and and record of its contents at java-string.
Copy all your input files to the master node (this can be done using scp).
Then login to your master node (ssh) and execute something like following to copy files from local filesystem to hdfs:
hadoop fs -put $localfilelocation $destination
Now in your hadoop jobs, you may use the input to be hdfs:///$destination. No need to use any extra API to read from HDFS.
If you really want to read files from HDFS and use as addiotional information other than the input files, then refer this.