Hidoop is a platform developed in Java, allowing the execution of applications based on MapReduce programming model on a computer cluster.
This project is our very first work on the theme of competing applications for intensive computing and mass data processing.
It consists in a lite version of Hadoop (developed by Apache) and it is composed of two modules :
- 📁 A distributed file-system managing data storing on a cluster - inspired by Hadoop Distributed File System (HDFS)
- 💠 An implementation of the MapReduce programming model for large-scale data processing - inspired by Hadoop MapReduce
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This application allows its user to process large data sets across multiple servers using the MapReduce programming model.
This programming model is used to parallelize the processing of large-scale data among a cluster.
Each server from the cluster processes a small part of data.
In this project, processing data takes place in 3 steps :
- Data (provided as a file) is cut into small chunks and spread over the servers (process managed by HDFS)
- Each server processes a small part amount data (Map)
- Server results are collected and aggregated (Reduce)
Map and Reduce processes depend on the purpose of the MapReduce application, and can be entirely designed by user.
The strength of this model lies in the parallelization of processes.
For example, MapReduce programming model can be use to parallelize the counting of the number of occurrences of a specific word in a large dataset.
- Current version must be run on a Linux system.
- Current version also needs MATE Terminal to be installed on the system.
Indeed, starting the current version causes the opening of different terminals allowing the monitoring of the activity of the different services. - All servers (or machine) must be in a same network, accessible by the machine running the project.
- Servers need to be accessible via SSH.
- All servers must have a recent version of Java (1.8 or more recent).
- Java compiler used must produce code that is executable by all servers.
Project must be cloned on a Linux system, which will be responsible for the deployment of the platform within the cluster.
Servers that are part of the cluster must be filled in file config/servs.config. Addresses must be written line by line as in the example below :
salameche.enseeiht.fr
carapuce.enseeiht.fr
bulbizarre.enseeiht.fr
nidoran.enseeiht.fr
magicarpe.enseeiht.fr
Server written first (at the top of the list) will automatically be set as Master Server, i.e. the server running central processes (NameNode and JobManager).
Following settings can be configured in file congif/settings.config (KV format file).
These setting have to be written key-value pairs (watch out for white spaces), in any order, according to the following indications :
- Chunk size (chunksize key, strictly positive integer) : size of chunks (Mo) when files are written on HDFS
- Data path on servers (datapath key, string) : path to folder where data will be stored (Hidoop executables and data processed) on each server
- NameNode's port (portnamenode key, strictly positive integer) : port used by NameNode
- DataNodes' port (portdatanode key, strictly positive integer) : port used by DataNodes
- JobMaster's port (portjobmaster key, strictly positive integer) : port used by JobMaster (also called JobManager)
- Daemons' port (portdaemon key, strictly positive integer) : port used by Daemons
See the example below :
chunksize<->128
datapath<->/work/hidoop/
portnamenode<->4023
portdatanode<->4027
portjobmaster<->4985
portdaemon<->4321
Once the project is configured (see section above), running the platform is a 2-step procedure.
First executable files have to be deployed on the cluster, then the platform has to be launched.
Open a terminal in project's root folder and execute hidoop-init.sh bash script by typing the following command :
./hidoop-init.sh <username>
<username> has to be replaced with the username to be used for SSH connection on servers.
Note : SSH command used within bash scripts is ssh <username>@<serveraddress>, e.g. ssh lvincent@salameche.enseeiht.fr.
A logo signalling the begining of the deployment should appear.
Deployment might take a few time : executable files are copied on each machine of the cluster.
In a terminal in project's root folder, execute hidoop-run.sh bash script by typing the following command :
./hidoop-run.sh <username>
Same remark as for the deployment, <username> has to be replaced with the username to be used for SSH connection on servers.
After a few seconds, two terminals with tabs should show-up as in the picture below, displaying the status of the different entities of Hidoop running on servers.
On the first terminal, you can track the status of the NameNode and the four DataNodes.
On the second terminal, you can track the status of the JobManager and the four Daemons.
The role and functioning of these entities is explained in more detail in this section for the more curious.
Hidoop is now ready for use !
To clean up everything related to Hidoop from the cluster (binaries, data and Java processes), execute hidoop-clear.sh_ bash script from project's root folder :
./hidoop-clear.sh <username>
Same remark as for the deployment, <username> has to be replaced with the username to be used for SSH connection on servers. The Java processes of NameNode, DataNodes, JobManager and Daemons should be killed and the data stored on each server should be erased.
To run MapReduce applications processing data, the data must first be distributed on the cluster using HDFS. Data is cut into small chunks and spread over the servers.
In this project, HDFS provides 3 main functionnalities :
- Write data : cut a provided file into chunk and spread them over the servers
- Read data : retrieve data that has been written on servers by write process in order to rebuild the original file
- Delete data : delete data stored by servers concerning a specified file
The following instructions can only work if Hidoop platform is running on the cluster (see section above).
Data to process must be stored as a file. Current version of the project supports two types of format : Line and KV.
In Line format, text is written line by line. A line of text is considered a unit of data, so lines' length should not be too disparate.
Here is an example of Line format file content :
This is the content of the file.
It should be written line by line,
lines' length should not be too disparate,
file can be as large as desired.
In KV format, text file is composed of key-value pairs. Each pair is written on a line, separator is <-> symbol.
Here is an example of KV format file content :
movie<->inception
series<->game-of-thrones
dish<->pizza
colour<->blue
To write a large file in HDFS (corresponds to spreading data among servers), open a terminal in project's root folder and execute following command :
java -cp bin hdfs.HdfsClient write <line|kv> <sourcefilename> [replicationfactor]
- <line|kv> corresponds to input file format, line or KV format.
- <sourcefilename> is the name of the file to proceed.
- [replicationfactor] is an optional argument. It corresponds to the replication factor of the file, i.e. the number of time each chunk is duplicated on the cluster, in order to anticipate server failures. Default value is 1.
Note : -cp is a shortcut for -classpath.
To read a file from HDFS (corresponds to retrieving data from servers), open a terminal in project's root folder and execute following command :
java -cp bin hdfs.HdfsClient read <filename> <destfilename>
- <sourcefilename> is the name of the file (written on HDFS previously) to read from the servers.
- <destfilename> is the name of the file to store data retrieved by process (rebuilt file).
To delete a file from HDFS (corresponds to deleting data from servers), open a terminal in project's root folder and execute following command :
java -cp bin hdfs.HdfsClient delete <sourcefilename>
- <sourcefilename> is the name of the file (written on HDFS previously) to delete from HDFS.
MapReduce application models are given in src/application package.
Note : It is also possible to run MapReduce applications that do not take a file as a parameter (for example QuasiMonteCarlo application that generates a number of points in a unitary square and which calculates the number of points inside the circle inscribed in the square in order to approach the value of pi).
Wordcount is a MapReduce application runnable on Hiddop platform.
This application counts the number of occurrences of each word in a large text file in Line format.
The application is located in package src/application/.
Note : We have also implemented an iterative version to compare the differences of performance on very large files (> 10 GB).
Make sure you have written the file in HDFS before launching the application. Then, execute following code from a terminal opened in project's root folder :
java -cp bin application.WordCount_MapReduce <filename>
- <filename> is the name of the file (written on HDFS previously) to process.
Example of use :
Let's say you would want to count the number of occurrences of all every word in a large 50GB text file, stored on your system at data/filesample.txt.
The first step is to write the file in HDFS :
java -cp bin hdfs.HdfsClient write line data/filesample.txt 1
The next step is to run the WordCount application by specifying the file name (without the path because HDFS is a flat hierarchy) :
java -cp bin application.WordCount_MapReduce filesample.txt
The result of the process is written in the file results/resf-filesample.txt, in KV format.
Note : It is possible to compare the performances of MapReduce applications with their iterative versions, also present in the package src/application.
Keep in mind that time savings will only be noticeable on very large files (> 10 Go), not taking into account time spent writing files on HDFS.
Indeed, the MapReduce process is quite expensive and is only useful on large data sets.
We have developed a MapReduce version of the QuasiMonteCarlo algorithm.
It generates a number of X points in a unit square during the Map operation and then calculates the number of points inside the circle inscribed in this square during the Reduce operation.
It then derives an approximation of 𝝅.
This application is complementary to the WordCount application because it does not take a file as a parameter on which to execute operations.
Moreover, the application does not manage the distribution of Map and Reduce operations on the cluster in the same way.
Indeed, since no Map operation is performed on chunks because the application does not take a file in parameter, a Map and Reduce operation is launched on each available daemon.
To launch the QuasiMonteCarlo application, simply and run the following command from project's root folder :
java -cp bin application.QuasiMonteCarlo_MapReduce
Note : By default, the application generates 10⁶ points per Daemon.
The PageRank application is based on Google's page ranking algorithm developed by Larry Page. This algorithm allows to rank web pages in Google's search results.
The principle is to assign to each page a value (or page rank) proportional to the number of times a user would pass by this page while surfing on the Web (by clicking on links on other pages). For more details on this algorithm, don't hesitate to watch this Youtube video series.
Note : The performance gain is visible only if you run the algorithm on large graphs (the web is huge, hence the usefulness of the MapReduce version).
In the data/ folder, we have provided you with two files small-network and big-network which represent small and medium size networks (4 pages for the first one and 26 for the second one). The application runs on files in KV format, for example :
A<->B,C
B<->A
C<->A,B
This file represents a graph consisting of 3 nodes (pages). A page A which contains links to pages B and C. A page B which redirects to page A and finally a page C which leads to pages A and B. We did not use jsoup to analyze existing web pages but it could be an idea for improvement.
To launch the PageRank application, simply go to the prject's root folder and run the following command :
java -cp bin application.PageRank_MapReduce <filename> <numberiterations>
- <filename> is the network on which you want to calculate page rank values.
- <numberiterations> is the number of iterations you want to run. The higher the number, the more the algorithm converges.
Example of use:
To run the PageRank algorithm on the small network, execute the command :
java -cp bin application.PageRank_MapReduce data/small-network 3
Note: Very few iterations are needed to converge in such a simple case (here, 3 iterations is enough to observe convergence).
The proposed implementation of the HDFS service is composed of 2 main entities, NameNode and DataNode.
It also provides a class with static methods to perform all possible operations on the file system, HdfsClient.
The NameNode is the Java process running on the master server of the cluster.
It supervises the entire network, stores information about the files stored on the file system (metadata and file chunks locations), and provides an interface to make queries on the data.
The DataNode is the Java process running on each of the cluster's slave servers.
It supervises the data stored on the server (chunks), performing the operations controlled by the NameNode (receiving/sending/deleting chunks).
The processes are inter-connected by RMI, and exchange data via Socket in TCP mode.
The 3 possible operations on the HDFS file system are those performed by the static methods of the HdfsClient class, namely :
-
HdfsWrite : writes a file on the file system. The file is split into chunks, and each chunk is sent to one or more DataNode (according to the replication factor). The choice of the DataNode receiving the chunk is determined by the NameNode.
-
HdfsRead : reads a file on HDFS. The NameNode provides locations of the different chunks composing the file. For each chunk, the client tries to retrieve data from at least one of the servers containing a copy. A connection is established with the DataNode of the server, which provides the content of the chunk to the client.
-
HdfsDelete : deletes a file from HDFS. The NameNode provides locations of the different chunks composing the file. For each chunk, the client sends a request to the corresponding DataNode, asking for data deletion. The DataNode deletes the chunk and notifies the NameNode.
The implementation of the MapReduce concept is composed of 2 major entities: JobManager and Daemon.
The JobManager is the Java process, that has a similar role to the NameNode but on the application side. It supervises all the Daemons Map and Reduce's ongoing operations within the cluster. It also keeps track of the Jobs launched and would allow, in a future version, to manage failures during a Map operation.
The Daemon is the Java process that execute an action defined by the Map operation. The results of each map will be aggregated and returned to the client thanks to the Reduce operation.
A Job executes the Map and Reduce methods of a MapReduce application (i.e. WordCount_MapReduce) within the cluster. It will retrieve the list of Daemons available thanks to the JobManager. It will also retrieve the list of fragments if the application requires an input file, written in HDFS. Then the Job will execute the Map operations and ask the JobManager for the progress of operations until all the maps are done. At the very end, it will read the result file of the map thanks to HDFS and start the Reduce operation.
- Parallelization of client-side operations (HdfsClient).
- Cluster monitoring improvement (NameNode).
- Implementation of an HDFS Shell, facilitating the use of the file system.
- Global architecture improvement.
- Implementation of new file formats.
- Parallelization of the Reduce operation.
- Manage breakdowns during the course of a MapReduce application.
- Optimize the distribution of resources according to the CPU used for example (currently distributed according to the number of maps per server).
- Make sure that the Job is executed on a cluster node and not on the client machine (to get closer to the real Hadoop architecture).
- Improve the Job interface to make it more generic and accessible to other types of MapReduce applications.
- In the PageRank application, add a whole part allowing to analyze a web page with Jsoup in order to find all the links (existing web pages and web pages which do not exist any more).
- Develop more applications based on the MapReduce concept (i.e. algorithms on the exact coverage like Donald Knuth's DLX algorithm allowing to solve sudokus or other puzzles of that kind).