Hive HDFS

Hive and HDFS are two fundamental components of the Hadoop ecosystem, and they work together to enable data storage, management, and querying within a Hadoop cluster. Let’s explore the relationship between Hive and HDFS:

1. HDFS (Hadoop Distributed File System):

   • HDFS is the primary storage system in the Hadoop ecosystem. It is a distributed file system designed to store large volumes of data reliably across a cluster of commodity hardware.
   • Data in HDFS is divided into blocks (typically 128 MB or 256 MB in size), and each block is replicated across multiple nodes in the cluster to ensure fault tolerance. This replication factor is configurable.
   • HDFS provides high throughput and is optimized for batch processing and large-scale data storage.

2. Hive:

   • Hive is a data warehousing and query language tool for Hadoop. It provides a higher-level abstraction for querying and analyzing data stored in HDFS.
   • Hive allows users to write SQL-like queries using HiveQL, which is a query language similar to SQL. These queries are translated into MapReduce jobs or other execution engines to process data stored in HDFS.
   • Hive includes a metastore that stores metadata about tables, columns, and partitions. This metadata helps users discover and understand the structure of data stored in HDFS.

The Relationship between Hive and HDFS:

• Hive does not replace or compete with HDFS; instead, it complements it. HDFS is the storage layer where data is physically stored, while Hive is a query and data analysis layer built on top of HDFS.
• When you use Hive to query data, the data remains in HDFS. Hive generates MapReduce jobs or other execution plans to read and process the data stored in HDFS blocks.
• Hive’s metadata store (metastore) keeps track of the schema, tables, and other metadata related to the data stored in HDFS. This metadata helps users and applications interact with data effectively.