 |
| Big Data Hadoop | IndianTechnoEra |
What is Hadoop?
Hadoop is an open source software platform for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
The Hadoop platform includes the Hadoop distributed file system (HDFS) for storage, the MapReduce programming model for parallel processing of large data sets, and the Apache Hadoop software library for distributed computing.
Hadoop also includes a library of distributed programming models and utilities for deploying and managing distributed applications on large clusters of computers.
What are key advantages of Apache Hadoop?
1. Scalability: Hadoop clusters can be easily scaled up or down by adding or removing nodes, allowing it to quickly adapt to changing computing needs.
2. Fault Tolerance: Hadoop uses data replication and automated failover to ensure that data is not lost when a node goes down.
3. Cost-Effective: Hadoop can store and process large amounts of data on commodity hardware, making it a cost-effective solution.
4. Easy to Use: Hadoop is designed to be an easy-to-use platform, with a high-level API that makes it easy to develop applications.
5. Flexibility: Hadoop can process a variety of data including structured, unstructured, and real-time data.
6. High Availability: Hadoop ensures that data is always available and accessible, even if a node goes down.
Hadoop vs. RDBMS
Hadoop and RDBMS both offer powerful tools for managing and analyzing data, but they have very different approaches. Hadoop is an open-source framework for storing and processing large datasets on a distributed computing system. It is designed to work with commodity hardware and can scale horizontally to support large datasets. It is well suited for problems that require large amounts of data or data that is too large for a single machine.
RDBMS, on the other hand, is a traditional database system that stores data in a structured format. It is designed to work with single machines, and is best suited for problems that require a lot of data manipulation and querying. It is not as well suited for large datasets, as it can become slow and inefficient when working with large amounts of data.
Differences between Hadoop and RDBMS
 |
Differences Between Hadoop and RDBMS | IndianTechnoEra |
In summary, Hadoop is best suited for large datasets, while RDBMS is better for smaller datasets that require more complex data manipulation and querying.
Hadoop Architecture
What are Hadoop components?
1. HDFS (Hadoop Distributed File System): HDFS is a distributed file system that runs on commodity hardware. It is highly fault-tolerant and designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications that have large data sets.
2. YARN (Yet Another Resource Negotiator): YARN is a cluster resource management system for Hadoop. It provides a platform for applications to run on a Hadoop cluster, managing resources such as CPU, memory, and storage. It is responsible for scheduling tasks and managing resources across the cluster.
3. MapReduce: MapReduce is a programming model for processing large data sets on clusters of computers. It divides large jobs into smaller tasks that can be run in parallel on multiple machines. MapReduce is used for data intensive applications such as web indexing and data mining.
4. Common: It contains the libraries and utilities needed by other Hadoop modules.
5. HBase: HBase is a distributed, column-oriented database. It is built on top of HDFS and provides random, real-time read/write access to large datasets. It is used for applications such as web indexing and real-time analytics.
6. Hive: Hive is a data warehousing system for Hadoop that provides an SQL-like query language for querying data stored in HDFS. Hive provides data summarization, query, and analysis.
7. Pig: Pig is a high-level data flow language and execution framework for parallel computation. It is used to process large datasets stored in HDFS. Pig programs are written in a data flow language called Pig Latin.
8. Oozie: Oozie is a workflow scheduler system for Hadoop. It is used to manage and schedule Hadoop jobs. Oozie can be used to schedule and manage complex multi-stage data processing applications.
9. Flume: It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming data.
10. Spark: Apache Spark is an open-source, distributed processing framework and in-memory computing engine for big data analytics. It is built atop the Hadoop Distributed File System (HDFS). It is designed to provide an easy-to-use and unified framework for large-scale data processing. It is designed to process data in parallel and provides enhanced speeds for in-memory data processing. It is also used to perform analytics on large datasets stored in HDFS. Spark is written in Scala and provides APIs in Python and Java.
Spark can also be used in conjunction with other Hadoop components such as Hive, HBase, and ZooKeeper.
11. Zookeeper: Hadoop component Zookeeper is a distributed coordination service that helps manage large distributed systems. It is used to store configuration information and provides services such as synchronization, group services, and naming. It provides a consistent view of the system provides fault-tolerance. It is a critical component of any Hadoop cluster and is used to coordinate, manage, and synchronize the various nodes in the cluster.
HDFS Design and goals
Hadoop Distributed File System (HDFS) is the primary storage system used by Apache Hadoop. It is a distributed file system designed to store and manage large amounts of data across a cluster of commodity hardware. HDFS is designed to run on low-cost commodity hardware and provide high throughput access to application data.
The goals of HDFS are to:
1. Provide high throughput access to application data.
2. Store and manage large amounts of data reliably.
3. Offer a simple and cost-effective solution for distributed storage and processing.
4. Support the execution of large data-intensive applications.
5. Provide an efficient and fault-tolerant method for data replication and distribution.
6. Minimize hardware cost and maintenance overhead.
anatomy of file read and write in HDFS,
HDFS File Read and Write Anatomy:
1. A client application sends a request to the NameNode for a file read or write operation.
2. The NameNode checks if the requested file exists and returns the list of DataNodes that store the file blocks.
3. The client application establishes a pipeline between the DataNodes that store the file blocks.
4. The DataNodes start serving the file blocks to the client application.
5. The client application reads or writes the data to the file blocks.
6. The DataNodes send an acknowledgment to the client application on completion of the operation.
7. The client application sends a success or failure message to the NameNode on completion of the operation.
8. The NameNode updates the metadata related to the file accordingly.
Replica placement strategy,
Replica placement strategy in big data is a process of optimizing the deployment of replicated data across multiple nodes in a distributed system. It involves determining the best locations for replicas for high availability and data consistency. The goal is to minimize the network latency, data transfer cost, and the number of replicas that need to be maintained.
Replica placement strategies are important for distributed data systems because they enable better performance and scalability. The strategies can also be used to improve data reliability and availability, as well as to reduce data storage costs. Different strategies can be used depending on the specific requirements of the system, including the amount of data stored, the number of nodes, the type of workload, and the availability requirements.
For example, when replicating data across multiple nodes, a common strategy is to create replicas across geographically dispersed data centers. This allows the system to be resilient to network issues and natural disasters, as well as reducing the amount of data that needs to be transferred for data synchronization. Other strategies involve creating replicas across nodes with different hardware architectures, such as different types of disks, to improve the performance of the system.
Another strategy is to use a combination of multiple replication strategies, such as multiple full replicas and partial replicas
Working with HDFS Commands
1. hdfs dfs -ls
This command is used to list the contents of the current directory and any subdirectories.
2. hdfs dfs -mkdir
This command is used to create a new directory in HDFS.
3. hdfs dfs -put
This command is used to copy the specified file or directory from the local filesystem to the specified destination in HDFS.
4. hdfs dfs -get
This command is used to copy the specified file or directory from HDFS to the local filesystem.
5. hdfs dfs -rm
This command is used to remove the specified directory or file from HDFS.
6. hdfs dfs -chmod
This command is used to change the permissions of a file or directory in HDFS.
7. hdfs dfs -chown
This command is used to change the owner of a file or directory in HDFS.
8. hdfs dfs -mv
This command is used to move the specified file or directory from one location to another in HDFS.
Hadoop file system interfaces
Hadoop file system interfaces include the Hadoop Distributed File System (HDFS), the Hadoop Compatible File System (HCFS), the Hadoop Archive File System (HARFS), the Amazon S3 File System (S3FS), the Google Cloud Storage File System (GCSFS), the Azure Blob Storage File System (ABFS), the WebHDFS interface, and the Hadoop File System Shell (FSShell).
Hadoop 1.0 vs Hadoop 2.0
Hadoop Ecosystem
Hadoop Ecosystem is a set of open-source software components that work together to enable distributed processing of large datasets across clusters of computers using simple programming models.
It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The core of the Apache Hadoop Echo System consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model.
Hadoop Echo System also includes a number of related tools such as Pig, Hive, HBase, Zookeeper, Oozie, and Sqoop. These tools provide capabilities such as data ingestion, data warehousing, data analysis, machine learning, and many other data-driven applications.
These tools are commonly used in combination with Apache Spark, which is an open-source distributed computing framework that provides in-memory processing capabilities.
Data Streaming
Data streaming in Hadoop is the process of transferring data from one source to another in real-time. This process is often used to transfer large amounts of data from one cluster to another. It is also used for the analysis of data in the Hadoop Distributed File System (HDFS). Data streaming enables the analysis of data in chunks, which can help reduce the size of the data being transferred. Data streaming also helps to reduce latency and improve the performance of the system.
Data flow in hadoop
Data flow in Hadoop is governed by the Hadoop Distributed File System (HDFS). HDFS stores and replicates data across multiple nodes in the Hadoop cluster. It is designed to provide value-added services such as data replication, scalability, and fault tolerance.
Data enters the Hadoop cluster when the user submits an application to run on the cluster. The application is then broken up into tasks, which are distributed to the various nodes in the cluster.
On each node, the data is then processed by the MapReduce framework (or some other parallel processing framework). MapReduce divides data into key-value pairs and then performs parallel processing over the data. The output of each task is then sent back to the user.
Finally, the user can access the output of the tasks through the HDFS. HDFS provides a distributed file system for storing and accessing the data. Data stored in HDFS is replicated across multiple nodes in the cluster, ensuring fault tolerance and scalability.
Data flow models in hadoop
There are four main data flow models in Hadoop:
1. Batch Processing: Batch processing is a process of data ingestion, transformation and loading (ETL) into Hadoop. It is the most common form of data processing in Hadoop and involves the use of MapReduce to process large volumes of data.
2. Stream Processing: Stream processing is the real time processing of data streams. It is used to process and analyze data in real time as it enters the system. Apache Storm, Apache Spark Streaming and Apache Flink are some of the popular stream processing engines used in Hadoop.
3. Complex Event Processing (CEP): CEP is a type of stream processing used to detect patterns and correlations in data streams. It is mainly used in applications such as fraud detection, financial trading and customer behavior analysis. Apache Kafka and Apache Samza are some of the popular CEP tools used in Hadoop.
4. Lambda Architecture: Lambda architecture is a data processing architecture that combines batch processing, stream processing and CEP. It is mainly used for applications that require real time data processing with high throughput and low latency. Apache Spark and Apache Apex are some of the popular Lambda
Flume
Flume is a service for collecting, aggregating, and moving large amounts of data from multiple sources into the Hadoop Distributed File System (HDFS).
It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming data into HDFS.
Flume has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms for failover and recovery.
Flume provides a powerful and reliable way of streaming data into HDFS. Its flexible architecture allows for a range of sources and destinations to be used, including HDFS, HBase, S3, and other services.
It also allows for data to be transformed and enriched as it moves from source to destination. Flume is highly extensible, allowing for custom sources, sinks, and channel selectors to be implemented.
This extensibility makes it possible to integrate Flume with a wide range of data sources and destinations.
Flumes Architecture
Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming data into a centralized data store. It has a simple and flexible architecture based on streaming data flows.
 |
| Flumes Architecture | IndianTechnoEra |
At its core, Flume consists of three main components:
1. Sources: Sources generate and/or collect events, which are the basic unit of data in Flume. Examples of sources include web servers, log files, and network sockets.
2. Channels: Channels are the entities that temporarily store events between the Source and Sink. They provide persistent storage, allowing data to be replayed in case of failure.
3. Sinks: Sinks are the destinations where events are stored. Examples of sinks include HDFS, HBase, and files.
In addition, Flume has several optional components, such as Interceptors, Selectors, and Sink Processors. These components provide additional features, such as filtering and routing of events.
Advantage of flumes in hadoop
1. Cost Savings: Flumes are cost-effective compared to other tools for collecting, aggregating, and moving large amounts of data.
3. Flexibility: Flumes can be used to collect data from a variety of sources, including log files, network traffic, and streaming data.
4. Reliability: Flumes are designed to be fault tolerant and provide reliable data delivery.
5. Fault Tolerance: If a node in the cluster goes down, Flume will automatically re-route the data to another node in the cluster.