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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. HSQLDB vs Hadoop

HSQLDB vs Hadoop

OverviewComparisonAlternatives

Overview

Hadoop
Hadoop
Stacks2.7K
Followers2.3K
Votes56
GitHub Stars15.3K
Forks9.1K
HSQLDB
HSQLDB
Stacks449
Followers61
Votes0
GitHub Stars86
Forks37

HSQLDB vs Hadoop: What are the differences?

# Introduction
In this comparison, we will outline key differences between HSQLDB and Hadoop, focusing on specific functionalities and use cases of each technology.

1. **Storage Mechanism**: HSQLDB is a relational database management system that stores data in tables with rows and columns, whereas Hadoop is a distributed storage system that stores data across a cluster of machines in a distributed file system like HDFS.
2. **Processing Paradigm**: HSQLDB follows the traditional SQL processing paradigm where queries are executed on a single machine, while Hadoop utilizes the MapReduce programming model for processing data in a distributed manner across nodes in the cluster.
3. **Scalability**: HSQLDB is designed for small to medium-scale applications and struggles with handling huge volumes of data efficiently, whereas Hadoop is built for scalability and can handle massive amounts of data by distributing computation across multiple nodes.
4. **Data Processing Methods**: HSQLDB supports SQL queries for data retrieval and manipulation, whereas Hadoop offers a wide range of data processing methods like MapReduce, Spark, and Hive for complex data analytics and processing tasks.
5. **Fault Tolerance**: HSQLDB lacks built-in fault tolerance mechanisms and may experience data loss in case of hardware failures, while Hadoop provides fault tolerance through data replication and failure recovery mechanisms in the distributed environment.
6. **Use Cases**: HSQLDB is commonly used in desktop applications or small-scale projects where traditional relational database features are required, whereas Hadoop is preferred for big data processing, analytics, and storage in large-scale enterprise applications.

In Summary, HSQLDB is ideal for small to medium-scale applications with traditional database requirements, while Hadoop is designed for massive data processing and storage in distributed environments.

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Detailed Comparison

Hadoop
Hadoop
HSQLDB
HSQLDB

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

It offers a small, fast multi-threaded and transactional database engine with in-memory and disk-based tables and supports embedded and server modes. It includes a powerful command line SQL tool and simple GUI query tools.

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Original code, based on in-depth study of database theory and the SQL Standard; Extensive syntax compatibility modes for porting from other database systems; The fastest overall open-source SQL implementation for small and medium sized databases; Three transaction control models, including lock based and MVCC models; Fully multi-threaded; Compact code footprint
Statistics
GitHub Stars
15.3K
GitHub Stars
86
GitHub Forks
9.1K
GitHub Forks
37
Stacks
2.7K
Stacks
449
Followers
2.3K
Followers
61
Votes
56
Votes
0
Pros & Cons
Pros
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Java syntax
  • 1
    Amazon aws
No community feedback yet
Integrations
No integrations available
Cloud 66
Cloud 66
Leftronic
Leftronic
Spring Boot
Spring Boot
Woopra
Woopra

What are some alternatives to Hadoop, HSQLDB?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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