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

Hadoop vs PostgreSQL vs TiDB

OverviewComparisonAlternatives

Overview

PostgreSQL
PostgreSQL
Stacks103.2K
Followers83.9K
Votes3.6K
GitHub Stars19.0K
Forks5.2K
Hadoop
Hadoop
Stacks2.7K
Followers2.3K
Votes56
GitHub Stars15.3K
Forks9.1K
TiDB
TiDB
Stacks76
Followers177
Votes28
GitHub Stars39.3K
Forks6.0K

Hadoop vs PostgreSQL vs TiDB: What are the differences?

# Introduction

This Markdown code highlights the key differences between Hadoop, PostgreSQL, and TiDB.

1. **Scalability**: Hadoop is designed to scale horizontally by adding more nodes to the cluster, resulting in increased storage and processing power. In contrast, PostgreSQL and TiDB traditionally scale vertically by enhancing the performance of individual nodes, albeit some sharding capabilities.
   
2. **Data Model**: PostgreSQL follows a traditional relational database model with support for ACID transactions, while TiDB offers a hybrid model combining the distributed architecture of Hadoop with the relational capabilities of PostgreSQL, providing the best of both worlds.   
   
3. **Consistency vs. Availability**: PostgreSQL prioritizes consistency over availability, ensuring data integrity but potentially impacting performance during network partitions. TiDB leans towards availability, allowing concurrent transactions even under network failures at the cost of possible data conflicts.
   
4. **Deployment Complexity**: Hadoop typically requires more expertise in deployment and maintenance due to its distributed nature, while PostgreSQL and TiDB are easier to set up and manage with better compatibility with existing tools and applications.
   
5. **Use Case Suitability**: Hadoop excels in processing large volumes of unstructured data for analytics, whereas PostgreSQL and TiDB are ideal for transactional applications that demand strong consistency and real-time capabilities for OLTP workloads.
   
6. **Ecosystem and Community**: Hadoop has an extensive ecosystem with various tools and frameworks for big data processing, whereas PostgreSQL and TiDB benefit from mature communities and extensive documentation, ensuring reliable support and continuous development.

In Summary, the key differences between Hadoop, PostgreSQL, and TiDB lie in scalability, data model, consistency vs. availability trade-offs, deployment complexity, use case suitability, and ecosystem/community support.

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Detailed Comparison

PostgreSQL
PostgreSQL
Hadoop
Hadoop
TiDB
TiDB

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.

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.

Inspired by the design of Google F1, TiDB supports the best features of both traditional RDBMS and NoSQL.

--
Horizontal scalability;Asynchronous schema changes;Consistent distributed transactions;Compatible with MySQL protocol;Written in Go;NewSQL over TiKV;Multiple storage engine support
Statistics
GitHub Stars
19.0K
GitHub Stars
15.3K
GitHub Stars
39.3K
GitHub Forks
5.2K
GitHub Forks
9.1K
GitHub Forks
6.0K
Stacks
103.2K
Stacks
2.7K
Stacks
76
Followers
83.9K
Followers
2.3K
Followers
177
Votes
3.6K
Votes
56
Votes
28
Pros & Cons
Pros
  • 765
    Relational database
  • 511
    High availability
  • 439
    Enterprise class database
  • 383
    Sql
  • 304
    Sql + nosql
Cons
  • 10
    Table/index bloatings
Pros
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Java syntax
  • 1
    Amazon aws
Pros
  • 9
    Open source
  • 7
    Horizontal scalability
  • 5
    Strong ACID
  • 3
    HTAP
  • 2
    Enterprise Support

What are some alternatives to PostgreSQL, Hadoop, TiDB?

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.

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.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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