What Is Hadoop?
Hadoop is an open-source framework for storing and processing big data in a distributed environment using simple programming models. It is based on the MapReduce algorithm that allows for the processing of large data sets with a parallel, distributed algorithm across a cluster.
Hadoop is highly scalable, allowing for the addition of more nodes without needing to change data formats, how data is loaded, or the applications written. It consists of a storage part known as Hadoop Distributed File System (HDFS) and a processing part which is the MapReduce programming model.
HDFS provides high throughput access to application data and is designed to span large clusters of commodity servers. MapReduce divides the task into small parts, each of which can be processed in parallel on different nodes. This two-part structure can handle large volumes of structured and unstructured data, making Hadoop useful for big data analytics.
What Is Apache Hive?
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive allows for the querying and managing of large datasets residing in distributed storage. It is designed to simplify complex SQL queries, making it easier to handle big data tasks. With Hive, users can execute SQL-like commands (HiveQL) that are internally converted into MapReduce, Tez, or Spark jobs.
Hive’s architecture enables it to support various data formats and sources, including HDFS, Apache HBase, and Amazon S3. It provides a mechanism to project structure onto this data and query the data using a SQL-like language. This makes it possible for analysts familiar with SQL to interact with big data without needing to learn new languages or frameworks.
This integration between Hive and Hadoop allows users to leverage the scalability and efficiency of Hadoop’s distributed computing model while interacting with data through familiar SQL syntax. It democratizes access to big data analytics. By abstracting the complexity of direct MapReduce programming, Hive opens up big data processing to a broader audience, including analysts not versed in Java or complex data processing frameworks.
In this article:
- Is Apache Hive Obsolete?
- Apache Hive Architecture and Components
- Hadoop vs Hive: Key Differences
- Optimizing Hadoop with Intel® Tiber™ App-Level Optimization
Is Apache Hive Obsolete?
The landscape of big data technologies has seen significant shifts since the mid-2010s, leading to questions about the relevance of Apache Hive. One major trend impacting Hive is the migration of data infrastructures to the cloud. Traditional on-premise systems like HDFS have been increasingly replaced by cloud storage solutions such as Amazon S3, Google Storage, and Azure Blob Storage. These cloud services offer superior scalability, flexibility, and cost-efficiency, rendering some of the older Hadoop-based infrastructures less attractive.
Another critical development has been the rise of containerization technologies, particularly Docker and Kubernetes. These tools have revolutionized the way distributed applications are deployed and managed, offering greater stability and scalability. Hadoop’s adaptation to containerization has been slow, and support for Docker containers only became available with Hadoop 3.0 in 2018. This delay has led many organizations to seek alternative solutions that integrate more seamlessly with containerized environments, further reducing reliance on traditional Hadoop components like Hive.
The advent of deep learning has also played a significant role in diminishing Hive’s prominence. Deep learning applications often require high-performance hardware like GPUs and the ability to quickly update and deploy new software versions. Hadoop and its ecosystem, including Hive, were not initially designed to meet these needs. Consequently, data scientists have gravitated towards platforms that offer better support for deep learning workloads.
However, despite its declining popularity, Apache Hive is still actively maintained and is used in production by thousands of organizations.
Apache Hive Architecture and Components
The Apache Hive architecture includes the following components.
Hive Server 2
Hive Server 2 (HS2) supports multi-client concurrency and authentication. It acts as an interface between users and the Hive system, enabling communication through JDBC, ODBC, and Thrift APIs. By handling multiple requests simultaneously, HS2 optimizes resource utilization and improves system efficiency.
HS2’s architecture is built to accommodate a range of client applications, ranging from traditional database access tools to modern analytical software. Its support for open API clients allows users to interact with Hive in a secure manner.
Hive Query Language
Hive Query Language (HQL) is a SQL-like scripting language for data querying and analysis in Apache Hive. HQL simplifies complex data interactions, allowing users to execute SQL-like commands that are internally converted into MapReduce, Tez, or Spark jobs. This enables efficient handling of large datasets across the Hadoop ecosystem without the need for in-depth knowledge of Java or MapReduce coding.
By offering familiar SQL syntax, HQL makes it accessible for analysts to perform data manipulation and analysis on big data stored in HDFS and other supported storage systems. Its capability to manage petabytes of data through simple queries enhances productivity.
The Hive Metastore
The Hive Metastore (HMS) is the central repository for storing metadata about databases, tables, columns, data types, and HDFS storage paths. This metadata supports the management and optimization of data queries by enabling efficient access to schema information during query execution.
By centralizing metadata storage in a relational database management system (RDBMS), HMS ensures consistency and integrity across Hive queries. Integration with an RDBMS for metadata storage allows HMS to provide scalability and reliability in managing metadata for large-scale data environments. It supports creating databases and tables, altering schemas, and querying table statistics.
Hive Beeline Shell
Hive Beeline Shell is a command-line utility that provides an interface for executing HiveQL commands directly against the Hive Server 2 (HS2). It acts as a JDBC client for interaction with HS2, enabling users to submit queries, monitor their execution, and view results.
Beeline supports connecting to HS2 via different transport protocols, allowing secure data operations across distributed environments. Through Beeline, users can execute SQL-like queries within a shell environment, making it easier to automate tasks and integrate Hive operations into scripts or other applications.
Related content: Read our guide to Hadoop architecture
Hadoop vs Hive: Key Differences
Here are some of the main differences between Hadoop and Hive.
Data Processing
Hadoop, as a distributed data storage and processing framework, handles vast amounts of structured and unstructured data across clustered environments. Its MapReduce programming model enables parallel processing of large datasets by dividing tasks into smaller chunks managed across multiple nodes. This is particularly effective for batch processing and complex analytical computations that require extensive data manipulation.
Hive operates as a data warehousing layer on top of Hadoop’s infrastructure, offering an SQL-like interface for querying and managing large datasets. By translating SQL-like queries into MapReduce jobs behind the scenes, Hive simplifies interaction with big data, making it accessible to users familiar with SQL.
Data Manipulation
Hadoop itself does not have a built-in system for high-level data manipulation; instead, it relies on external applications like Hive to provide these capabilities.
Hive manipulates data using Hive Query Language (HQL), which offers SQL-like syntax for interacting with data stored in Hadoop. Through HQL, users can execute a range of data manipulation operations such as inserting, updating, and deleting records within Hive tables. These operations are translated into MapReduce, Tez, or Spark jobs, allowing for efficient processing of large datasets across the distributed storage provided by Hadoop.
Programming
Hadoop, being a low-level framework, requires writing complex MapReduce jobs in Java for data processing tasks. This requires a good grasp of Java programming and understanding of the MapReduce model, making it challenging for those without programming backgrounds.
Hive abstracts away the complexity of writing direct MapReduce jobs through HiveQL, a SQL-like query language. This allows users to perform data analysis and manipulation using familiar SQL syntax without deep knowledge of Java or the underlying MapReduce execution. HiveQL queries are internally converted to MapReduce jobs by Hive, offering a higher-level programming interface.
Data Storage
In Hadoop, data storage is managed by the Hadoop Distributed File System (HDFS), which is designed to store large volumes of data across multiple nodes in a cluster. HDFS breaks down large files into smaller blocks and distributes them across the cluster to enable high throughput and fault tolerance.
Hive uses Hadoop’s distributed storage mechanism but introduces a structured layer with its table-based format that resembles traditional relational databases. Data in Hive is organized into databases, tables, and partitions, which are stored in HDFS or other compatible file systems like Amazon S3. Hive supports file formats like ORC, Parquet, TextFile, and SequenceFile.
Schema
Hadoop does not enforce any schema on the data it stores. This schema-on-read approach means that data can be ingested in its raw form, without predefined structure, allowing for greater flexibility in handling various types of data. When processing this data using MapReduce or other Hadoop-based technologies, the interpretation of the data structure is determined at runtime.
Hive introduces a schema-on-write mechanism. When creating tables in Hive, users define a schema for their data, specifying columns and data types. This schema is then enforced when data is loaded into Hive tables. The defined schema allows Hive to optimize query execution by understanding the structure of the data beforehand.
Related content: Read our guide to Hadoop vs Spark
Optimizing Hadoop with Intel® Tiber™ App-Level Optimization
Optimizing Apache Hadoop environments is essential for enhancing the performance of big data tasks such as data engineering, data science, and machine learning. Intel Tiber App-Level Optimization provides a comprehensive optimization solution that supports all major Hadoop use cases, including batch and streaming data processing, SQL analytics, and large-scale data science. This tool integrates seamlessly with leading data storage and infrastructure platforms like Kafka, MongoDB, Elasticsearch, Delta Lake, Kubernetes, and Cassandra, ensuring efficient and streamlined Hadoop operations.
By implementing App-Level Optimization, teams can complete Hadoop jobs more quickly, benefiting from enhanced YARN resource allocation, optimized Spark dynamic allocation, and various JVM runtime optimizations. The solution also addresses memory arena optimization and accelerates cryptographic and compression operations, leading to significant reductions in memory and CPU usage. This optimization not only boosts performance but also delivers cost savings, as evidenced by Claroty’s experience of a 50% memory reduction, 20% CPU reduction, and an 18% overall cost reduction after deploying App-Level Optimization.