Apache Hadoop
Big Data stuff ..
Hadoop
Hadoop is a highly scalable, open-source, distributed computing platform that allows you to store and process large amounts of data. It is used to store and analyze data from a variety of sources, including databases, web servers, and file systems.
Hadoop is designed to be scalable by distributing the processing of data across a large number of computers. It also allows you to store and analyze data in a way that is faster than traditional methods. Hadoop is used to store and analyze data from a variety of sources, including databases, web servers, and file systems.
Components
The master node keeps track of the status of all the data nodes. If a data node goes down, the master node takes over the processing of that block. The slave nodes process the data on their own. HDFS requires a high-speed Internet connection. It is usually best to have at least a 10 Mbps network connection.
HDFS works on a time-based algorithm, which means that every block is processed in a predetermined time interval. This provides a high degree of scalability as all the nodes process the data in real time. HDFS is a great solution for data warehouse and business intelligence tasks. It is also a good solution for high-volume, high-frequency data analysis.
When a client connection is received from a master server, the NameNode running on the master server looks into both the local namespace and the master server’s namespace to find the matching records. The NameNode running on the master server then executes the lookup and returns a list of records that match the query.
DataNodes then get the records and start storing them. A data block is a record of data. Data nodes use the data blocks to store different types of data, such as text, images, videos, etc. NameNode maintains data node connections with clients based on the replication status of DataNodes. If a DataNode goes down, the client can still communicate with the NameNode. The client then gets the latest list of data blocks from the NameNode and communicates with the DataNode that has the newest data block.
Thus, DataNode is a compute-intensive task. It is therefore recommended to use the smallest possible DataNode. As DataNode stores data, it is recommended to choose a node that is close to the centre of the data. In a distributed system, all the nodes have to run the same version of Java, so it is recommended to use open-source Java. If there are multiple DataNodes, then they are expected to work in tandem to achieve a certain level of performance.
A file stored in Hadoop has the default 128 MB or 256 MB block size.
We have to ensure that the storage consumed by our application doesn’t exceed the level of data storage. If the storage consumed by our application is too much, then we have to choose our block size to avoid excessive metadata growth. If you are using a standard block size of 16KB, then you are good to go. You won’t even have to think about it.
Our HDFS block size will be chosen automatically. However, if we are using a large block size, then we have to think about the metadata explosion. We can either keep the metadata as small as possible or keep it as large as possible. HDFS has the option of keeping the metadata as large as possible.
Replication Management
When a node fails, the data stored on it is copied to another healthy node. This process is known as “replication”. If a DataNode fails, the data stored on it is not lost. It is simply stored on another DataNode. This is a good thing because it helps in the high availability of data. When you are using a DataNode as the primary storage for your data, you must keep in mind that it is a highly-available resource. If the primary storage for your data is a DataNode, you must make sure to have a proper backup and restoration mechanism in place.
A given file can have a replication factor of 3 or 1, but it will require 3 times the storage if we keep using a replication factor of 3. The NameNode keeps track of each data node’s block report and whenever a block is under-or over-replicated, it adds or removes replicas accordingly.
A given file can have a replication factor of 3 or 1, but it will require 3 times the storage if we keep using a replication factor of 3. The NameNode keeps track of each data node’s block report and whenever a block is under-or over-replicated, it adds or removes replicas accordingly.
Rack Awareness
When a block is deleted from a rack, the next available block will be placed on the rack. When a block is updated, the previous block is automatically updated in the same rack. This ensures data consistency across the storage network. In case of a fault in any of the data storage nodes, other storage nodes can be alerted through the rack awareness algorithm to take over the responsibility of the failed node.
This helps in providing failover capability across the storage network. This helps in providing high availability of data storage. As data stored in HDFS is massive, it makes sense to use multiple storage nodes for high-speed data access.
HDFS uses a distributed data store architecture to provide high-speed data access to its users. This distributed data store architecture allows for parallel processing of data in multiple data nodes. This parallel processing of data allows for high-speed storage of large data sets.
MapReduce is a data processing language and software framework that allows you to process large amounts of data in a reliable and efficient manner. It is a great fit for data-intensive, real-time and/or streaming applications. Basically, MapReduce allows you to partition your data and process items only when they are needed.
A map-reduce job consists of several maps and reduces functions. Each map function generates, parses, transforms, and filters data before passing it on to the next function. The reduced function groups, aggregates, and partitioning of this intermediate data from the map functions. The map task runs on the same node as the input source. Map tasks are responsible for generating summary statistics about data in the form of a report. The report can be viewed on a web browser or printed.
The output of a map task is the same as that of a reduced task. The only difference is that the map task returns a result whereas the reduced task returns a data structure that is applicable for further analysis. A map task is usually repetitive and is triggered when the data volume on the source is greater than the volume of data that can be processed in a short period of time.
YARN, also known as Yet Another Resource Negotiator, is a resource management and job scheduling/monitoring daemon in Hadoop. A resource manager in YARN isolates resource management from job scheduling and monitoring. A global Resource Manager oversees operations for the entire YARN network, including per-application Application Master. A job or a DAG of jobs may be defined as an application.
The Resource Manager manages resources for all the competing applications in the YARN framework. The Node Manager monitors resource usage by the container and passes it on to Resource Manger. There are resources such as CPU, memory, disk, and connectivity, among others. To perform and monitor the application, the Applcation Master talks to the ResourceManager and the Node Manager to handle and manage resources.
Hadoop is a highly scalable, open-source, distributed computing platform that allows you to store and process large amounts of data. It is used to store and analyze data from a variety of sources, including databases, web servers, and file systems.
Hadoop is designed to be scalable by distributing the processing of data across a large number of computers. It also allows you to store and analyze data in a way that is faster than traditional methods. Hadoop is used to store and analyze data from a variety of sources, including databases, web servers, and file systems.
Components
The master node keeps track of the status of all the data nodes. If a data node goes down, the master node takes over the processing of that block. The slave nodes process the data on their own. HDFS requires a high-speed Internet connection. It is usually best to have at least a 10 Mbps network connection.
HDFS works on a time-based algorithm, which means that every block is processed in a predetermined time interval. This provides a high degree of scalability as all the nodes process the data in real time. HDFS is a great solution for data warehouse and business intelligence tasks. It is also a good solution for high-volume, high-frequency data analysis.
When a client connection is received from a master server, the NameNode running on the master server looks into both the local namespace and the master server’s namespace to find the matching records. The NameNode running on the master server then executes the lookup and returns a list of records that match the query.
DataNodes then get the records and start storing them. A data block is a record of data. Data nodes use the data blocks to store different types of data, such as text, images, videos, etc. NameNode maintains data node connections with clients based on the replication status of DataNodes. If a DataNode goes down, the client can still communicate with the NameNode. The client then gets the latest list of data blocks from the NameNode and communicates with the DataNode that has the newest data block.
Thus, DataNode is a compute-intensive task. It is therefore recommended to use the smallest possible DataNode. As DataNode stores data, it is recommended to choose a node that is close to the centre of the data. In a distributed system, all the nodes have to run the same version of Java, so it is recommended to use open-source Java. If there are multiple DataNodes, then they are expected to work in tandem to achieve a certain level of performance.
Start Container
Hopefully .. you've completed the Setup: Apache Hadoop
To start the container enter the following:
docker start -ai AHWOnce the Docker shell opens, just type restart to restart all processes.
NameNode
The NameNode is the master node and central component of Hadoop's Distributed File System (HDFS). It acts as the "brain" of the file system.
Log into NameNode:
You can upload files to the root directory:
YARN
YARN acts as the operating system for Hadoop clusters by separating resource management from job scheduling and monitoring, allowing multiple data processing engines like MapReduce, Spark, Hive, and others to run simultaneously on the same cluster.
Log into YARN:
DataNode
A DataNode in Hadoop is a worker node in the Hadoop Distributed File System (HDFS) that stores the actual data blocks and serves read/write requests from clients. DataNodes communicate regularly with the NameNode through heartbeat messages to report their health status and the blocks they're storing.
They handle data replication by creating multiple copies of blocks across different nodes to ensure fault tolerance, and they perform block verification to detect corruption. DataNodes also participate in data pipeline operations during file writes and coordinate with other DataNodes to maintain data integrity and availability across the distributed cluster.
Log into the DataNode:
Useful for troubleshooting the Node.
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