Hadoop is an open-source distributed computing framework for storing and processing large-scale datasets. Here are the basics of Hadoop:
Hadoop architecture: Hadoop consists of two core components, the Hadoop Distributed File System (HDFS) and Hadoop MapReduce. HDFS is a scalable, distributed file system for storing large-scale datasets. MapReduce is a distributed computing framework for processing large-scale data in parallel in clusters.
Hadoop Ecosystem: The Hadoop ecosystem includes many other tools and projects to enhance the functionality and performance of Hadoop. For example, Apache Hive can provide a SQL-like query language for data analysis on Hadoop. Apache Pig provides a script-like language for writing data stream processing tasks. Apache Spark is a fast, general-purpose big data processing framework that can be integrated with Hadoop.
Hadoop cluster: A Hadoop cluster consists of multiple computers, each of which is called a node. There are two types of nodes in a cluster: master nodes and worker nodes. The primary node consists of a primary server (namenode) and a secondary server (secondary namenode) that is used to manage the metadata of the file system. A worker consists of one or more datanodes that store and process data.
Hadoop data processing process: In Hadoop, data is split into multiple chunks and stored and processed on different nodes in the cluster. MapReduce is the core computing model of Hadoop, which consists of two phases: the Map phase and the Reduce phase. In the map phase, the data is split into small chunks and processed in parallel on different nodes. In the reduce phase, the results are merged and summarized. This parallel processing method can improve the processing efficiency of large data sets.
lua-import the luasocket-http library.
local http=require"luasocket.http"
Get the ** link.
video_url=""ï¼›Crawler IP acquisition.
Create an HTTP connection.
local res,code=http.request(video_url,})
Print the response results.
print(res)
Advantages of Hadoop: Hadoop has the following advantages:
Scalability: Hadoop can add or remove nodes from the cluster to accommodate data processing needs at different scales.
Fault tolerance: Hadoop can automatically handle node failures to ensure data reliability and consistency.
Cost-effective: Hadoop uses inexpensive hardware to form clusters, which is more cost-effective than traditional data processing solutions.
Handle diverse data: Hadoop can handle structured, semi-structured, and unstructured data, including text, images, audio, and more.
These are the basics of Hadoop, and understanding them can help you understand how Hadoop works and how to use it.