Introduction to Hive PDF

Introduction to Hive Apache Hive is a data warehouse software that facilitates querying and managing large datasets residing in distributed storage systems like Hadoop. It provides an SQL-like interface to query data, and its primary purpose is to enable easy data summarization, analysis, and query...

Introduction to Hive Apache Hive is a data warehouse software that facilitates querying and managing large datasets residing in distributed storage systems like Hadoop. It provides an SQL-like interface to query data, and its primary purpose is to enable easy data summarization, analysis, and query execution on massive datasets stored in Hadoop’s HDFS. History Hive was initially developed at Facebook to enable SQL-like querying for Hadoop data. It was released as an open-source project by Apache in 2010 and has since evolved with various versions improving its performance, scalability, and flexibility. Recent Releases Hive continues to see updates, with recent releases focusing on improvements in speed (through features like LLAP - Long Lived and Processed), integration with modern tools, and support for transactional operations. As of now, Hive is fully compatible with Apache Hadoop and continues to evolve alongside it. Key Features  SQL-like queries: Hive provides an easy-to-learn SQL-like language called HiveQL.  Supports large datasets: It efficiently handles petabytes of data.  Integration with Hadoop: Hive queries are converted into MapReduce jobs or directed acyclic graphs (DAGs) for distributed processing.  User-Defined Functions (UDFs): Hive allows custom scripts and UDFs for extending its capabilities.  Partitioning and Bucketing: Supports the partitioning of large tables for faster query results and bucketing for optimized storage and processing. Integration and Workflow Hive operates by translating queries written in HiveQL into MapReduce, Tez, or Spark jobs that are executed in a distributed fashion on a Hadoop cluster. Hive integrates with data storage systems like HDFS and HBase, making it an essential tool in a Hadoop ecosystem. Data Units Hive manages and organizes data using the following hierarchy:  Databases: A namespace for tables.  Tables: Structured storage for data similar to relational databases.  Partitions: Subsets of data within tables, allowing efficient querying.  Buckets: Further divisions within partitions to optimize performance. Hive Architecture Hive consists of three main components:  1. Hive Client: Where users submit HiveQL queries. 2. Driver: Compiles the query into execution plans (DAGs). 3. Metastore: Stores metadata about the structure of tables and partitions. 4. Execution Engine: Converts HiveQL into MapReduce, Tez, or Spark jobs and executes them. Hive Data Types Hive supports two types of data:  Primitive Data Types: These include integers (INT, BIGINT), floating points (FLOAT, DOUBLE), strings (STRING, VARCHAR), booleans (BOOLEAN), etc.  Collection Data Types: o ARRAY: Collection of elements. o MAP: Key-value pairs. o STRUCT: Grouping of fields with various types. Hive File Formats  Text File: Plain text data.  Sequence File: A flat file format for storing data in a serialized key-value pair.  RCFile (Record Columnar File): A columnar storage format to improve read/write performance. Hive Query Language HiveQL is a SQL-like language that includes:  DDL (Data Definition Language): o CREATE, ALTER, DROP tables, partitions, views, databases.  DML (Data Manipulation Language): o SELECT, INSERT, UPDATE, DELETE. Starting Hive Shell The Hive shell provides an interface to interact with Hive. You can start it by running the hive command on the terminal after setting up the environment. Database, Tables, Partitions, Buckets  Database: Organizes tables under namespaces.  Tables: Store structured data.  Partitions: Divide tables based on values of specific columns.  Buckets: Further divide partitions to distribute data into manageable segments. Views, Subqueries, Joins, Aggregation  Views: Virtual tables created from query results.  Subqueries: Queries within another query.  Joins: Combine rows from two or more tables based on a related column.   Aggregation: Functions like SUM, COUNT, AVG, etc.  GROUP BY and HAVING: Group data and apply conditions. RCFile Implementation RCFile is a columnar storage file format optimized for fast query execution. It stores data in a highly compressed format and organizes it by columns rather than rows. SerDe (Serializer/Deserializer) SerDe is a framework in Hive that allows reading and writing from various file formats. It is responsible for interpreting the input and output of records. UDF (User-Defined Functions) Hive provides a way to define custom functions, called UDFs, which can be written in Java and used to extend Hive's functionality. This introduction offers a foundational understanding of Hive, covering its architecture, data types, file formats, and HiveQL language essentials.