What are the similarities and differences between Hadoop and Distributed Data Processing Spark VS Ha

Spark and Hadoop are two different open-source big data processing frameworks, Spark can run on Hadoop and can replace some components in Hadoop, such as MapReduce. However, Spark and Hadoop are not in direct competition, but can work together to improve the efficiency and performance of big data processing.

Hadoop is a distributed storage and computing framework that can be used to store and process large-scale data. HDFS (Hadoop Distributed File System) is used to store data, while MapReduce is used for data processing. Hadoop has been around for more than a decade and is one of the important infrastructures in the field of big data and has been widely used.

Spark is a general-purpose big data processing framework that can be used for data processing, machine learning, image processing, and other tasks. Spark is superior to Hadoop's MapReduce in terms of computing speed and memory usage efficiency, so it has higher efficiency and performance when processing large-scale data.

While Spark is superior to Hadoop in some ways, Spark also has some limitations, such as not necessarily being better than Hadoop for processing large-scale data. In addition, the Hadoop ecosystem is much better than Spark, with more components and tools to choose from.

Therefore, Spark does not directly replace Hadoop, but is used with Hadoop to improve the efficiency and performance of big data processing. Spark and Hadoop can be selected and combined according to factors such as the size, type, and processing method of data to achieve better processing results.

Must be on a Hadoop cluster, and its data ** is HDFS, which is essentially a compute framework on yarn, like MR.

Hadoop is the foundation, where HDFS provides file storage and YARN manages resources. You can run computing frameworks such as MapReduce, Spark, and TEZ.

The real advantage of Spark over Hadoop is speed, where most of Spark's operations are in memory, while Hadoop's MapReduce system writes all data back to the physical storage medium after each operation to ensure full recovery in the event of a problem, but Spark's elastic distributed data store also enables this.

To understand that HDFS is only used to store data in a distributed manner, Spark has a total of four modes, local, standlone, yarn, and mesos. Only the yarn mode will use the yarn cluster of Hadoop.

The differences between Spark and Hadoop are as follows:

1. Sequence of birth: Hadoop belongs to the first generation of open source big data processing platforms, while Spark belongs to the second generation. Spark, which belongs to the next generation, is definitely better than the first generation of Hadoop in terms of overall evaluation.

2. Different computing: Spark and Hadoop are different in the specific implementation of distributed computing; The MapReduce algorithm framework in Hadoop, a computing job, performs a map-reduce process; In a Spark job, multiple map-reduce processes can be cascaded.

3. Different platforms: Spark is a computing platform, while Hadoop is a composite platform (including a computing engine, a distributed file storage system, and a resource scheduling system for distributed computing), so if Spark is compared with Hadoop, Hadoop is mainly because its computing part is declining, while Spark is currently in full swing, and related technologies are in high demand, and the offer is easy to get.

4. Data storage: When Hadoop's MapReduce is used for computation, the intermediate results generated each time are stored in the local disk; The intermediate results generated by Spark during computation are stored in memory.

5. Data processing: Hadoop needs to load data from the disk every time it performs data processing, resulting in a large overhead of the disk. When Spark performs data processing, it only needs to load the data into memory, and then directly load the intermediate result dataset in memory, reducing the overhead of disk by 1O.

The Hadoop framework focuses on offline high-volume computing, while the Spark framework focuses on in-memory and real-time computing.

Hadoop and Apache Spark are both big data frameworks, but they exist for different purposes. Hadoop is essentially more of a distributed data infrastructure: it distributes huge data sets to multiple nodes in a cluster of common computers for storage, meaning you don't need to buy and maintain expensive server hardware.

At the same time, Hadoop indexes and tracks this data, making big data processing and analysis more efficient than ever before. Spark is a tool designed to process distributed data and does not store distributed data.

In addition to the well-known HDFS distributed data storage function, Hadoop also provides a data processing function called MapReduce. So here we can completely abandon Spark and use Hadoop's own MapReduce to complete data processing.

Conversely, Spark doesn't have to be attached to Hadoop to survive. But as mentioned above, it doesn't provide a file management system, so it has to be integrated with other distributed file systems to work. Here we can choose Hadoop's HDFS, or we can choose other cloud-based data system platforms.

But Spark is still used in Hadoop by default, after all, everyone thinks that they are the best combination.

Spark: A fast and general-purpose computing engine designed for large-scale data processing, it is an open-source cluster computing environment similar to Hadoop, with the advantages of Hadoop MapReduce, Spark is an alternative to MapReduce, and is compatible with HDFS and Hive, and can be integrated into the Hadoop ecosystem to make up for the shortcomings of MapReduce.

Spark is mainly used for big data computing, and Hadoop will be used for big data storage in the future(such as HDFS, Hive, HBase, etc.), and resource scheduling (yarn). Spark + Hadoop is currently the most popular combination in the field of big data

The functional scenarios of the two are different.

Hadoop and Spark are not directly comparable.

Hadoop is a comprehensive big data software system, including MapReduce, Yarn, and HDFS

Spark, on the other hand, is a distributed computing engine programming framework.

2.Let's just compare distributed computing.

Both MapReduce and Spark can realize distributed parallel processing of data, but the specific implementation mechanism is slightly different, MapReduce program, a program can only contain one MAP stage and one Reduce stage.

Spark, on the other hand, can organize multiple map-reduce processes into a DAG logical process in a single program, which is relatively more efficient.

1) Different application scenarios.

Hadoop and Spark are both big data frameworks, but their application scenarios are different. Hadoop is a distributed data storage architecture that distributes huge data sets to multiple nodes in a cluster of common computers for storage, reducing hardware costs. Spark is a tool designed to process distributed data with the help of HDFS data storage.

2) The processing speed is different.

Hadoop's MapReduce processes data step by step, reading data from disk, processing it once, writing the result to disk, then reading the updated data from disk, processing it again, and finally storing the result to disk, which affects the processing speed. Spark reads data from disk, puts intermediate data into memory, completes all necessary analysis and processing, and writes the results back to the cluster, so Spark is faster.

3) Fault tolerance is different.

Hadoop writes the processed data to disk every time, and there is little to no power outage or data loss in the event of an error. Spark's data objects are stored in the elastic distributed dataset RDD, which is a collection of read-only objects distributed across a set of nodes, and if a portion of the dataset is lost, they can be reconstructed according to the data derivation process. Moreover, RDD can be fault-tolerant through checkpoints when calculating.

Hadoop is a big data processing technology that has been around for about a decade and is considered to be the preferred solution for big data collection processing. MapReduce is an excellent solution for one-way computation, but it is not very efficient for use cases that require multi-way computation and algorithms. Each step in the data processing process requires a map phase and a reduce phase, and to take advantage of this solution, all use cases need to be converted to a mapreduce pattern.

Before the next step begins, the output data of the previous job must be stored in a distributed file system. As a result, replication and disk storage can cause this to be slower. In addition, Hadoop solutions often contain clusters that are difficult to install and manage.

And to handle different big data use cases, there are many different tools that need to be integrated (such as Mahout for machine learning and Storm for streaming data processing).

If you want to do more complex work, you have to concatenate a series of mapreduce jobs and execute them sequentially. Each job is high-latency, and the next job can only start after the previous job is completed.

Spark, on the other hand, allows developers to develop complex multi-step data pipelines using directed acyclic graphs (DAGs). It also supports in-memory data sharing across directed acyclic graphs so that different jobs can work together on the same data.

Spark runs on top of the existing Hadoop Distributed File System (HDFS) and provides additional enhancements. It supports deploying Spark applications to existing Hadoop V1 clusters (with simr Spark-inside-MapReduce) or Hadoop V2 Yarn clusters, or even Apache Mesos.

We should think of Spark as an alternative to Hadoop MapReduce, not Hadoop. The intent is not to replace Hadoop, but to provide a comprehensive and unified solution for managing different big data use cases and requirements.