Apache Hadoop is an open-source software platform that allows for the distributed processing of large data sets 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. Rather than relying on hardware to deliver high-availability, the software itself is designed to detect and handle failures at the application layer, so delivering a very high degree of fault tolerance.
Components and Ecosystem
It consists of the Hadoop Common package, which provides file system and operating system level abstractions, along with the Hadoop Distributed File System (HDFS) and YARN. HDFS provides a distributed file system that stores data across multiple machines, while providing high throughput access to application data. It also provides redundancy and fault tolerance through replication of data across multiple nodes. YARN is a cluster resource and job scheduling system that allows users to move compute tasks close to the data.
Beyond HDFS and YARN, the ecosystem includes several important projects. MapReduce is a programming model and software framework for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes. Hive provides a SQL-like query language called HiveQL to process structured data stored in HDFS. Pig is a high-level dataflow language and execution framework for writing data analysis programs. HBase is an open source, non-relational, distributed database modeled after Google's Bigtable. Spark is a fast and general compute engine for large-scale data processing.
Benefits
By distributing data and computation across commodity hardware, it can cost-effectively extract value from very large datasets. Hadoop linear scalability means data processing workloads that previously took weeks can now be done in hours using it clusters. Data is also fault tolerant and decentralized, avoiding single points of failure. It is also incredibly flexible, supporting batch processing with MapReduce, streaming data with Spark Streaming, machine learning with Spark MLlib, graph processing with Spark GraphX, and more. Applications can access data in HDFS just as easily as data stored in HBase or elsewhere. Overall, it delivers unmatched scalability, speed, and cost-effectiveness for processing massive datasets.
Processing Data with It
The fundamental data processing mechanism in it is MapReduce. In a MapReduce job, the input data is divided into fixed-size blocks, which are distributed across nodes in the cluster. The application code written by the programmer specifies a map function that processes the input key/value pairs to generate intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. The framework pieces together the execution of the Map and Reduce tasks across large clusters of machines, coordinating and managing failures.
MapReduce jobs are excellent for batch-style processing of large amounts of data in parallel. However, they are not as suitable for interactive or iterative jobs. This led to the development of YARN and improved execution engines like Spark. Under YARN, resources on the cluster are treated generically rather than being dedicated only to MapReduce. Frameworks like Spark and Hive are able to launch their own parallel operations directly on YARN, bypassing MapReduce. Spark in particular improves on MapReduce by supporting in-memory computing for iterative jobs, interactive querying, and machine learning algorithms.
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