HBase vs TiDB

Overview

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

TiDB

Stacks76

Followers177

Votes28

GitHub Stars39.3K

Forks6.0K

HBase vs TiDB: What are the differences?

Introduction: In the world of databases, HBase and TiDB both serve as powerful tools, each with its unique set of strengths and advantages.

Data Model: HBase follows a column-oriented data model, which is ideal for storing sparse data with many columns, while TiDB follows a row-oriented data model, making it better suited for transactional workloads with complex relationships between rows.
Consistency: HBase provides strong consistency, ensuring that all clients see the same data at the same time, while TiDB offers snapshot isolation, allowing for improved concurrency without sacrificing consistency.
Scalability: HBase is horizontally scalable, meaning it can easily handle large amounts of data by adding more servers to a cluster, whereas TiDB offers horizontal scalability through automated sharding, enabling it to handle massive dataset growth effortlessly.
Storage Engine: HBase uses Apache Hadoop's HDFS as its underlying storage engine, whereas TiDB utilizes TiKV, a distributed key-value store, providing higher performance and better fault tolerance.
Query Language Support: HBase supports only Java API and REST, making it challenging for users of other languages, whereas TiDB supports SQL, making it easier for a broader range of users to interact with the database.
Consistency Level Control: HBase allows users to configure the consistency level at the operation level, offering more flexibility but also complexity, while TiDB simplifies this by providing a global consistency level setting for the entire cluster.

In Summary, HBase and TiDB differ in their data models, consistency models, scalability approaches, storage engines, query language support, and consistency level control.

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Detailed Comparison

HBase	TiDB
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.	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 5.5K	GitHub Stars 39.3K
GitHub Forks 3.4K	GitHub Forks 6.0K
Stacks 511	Stacks 76
Followers 498	Followers 177
Votes 15	Votes 28
Pros & Cons
Pros 9 Performance 5 OLTP 1 Fast Point Queries	Pros 9 Open source 7 Horizontal scalability 5 Strong ACID 3 HTAP 2 Enterprise Support

What are some alternatives to HBase, TiDB?

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

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

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

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 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

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 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

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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HBase vs TiDB: What are the differences?

Introduction: In the world of databases, HBase and TiDB both serve as powerful tools, each with its unique set of strengths and advantages.

Data Model: HBase follows a column-oriented data model, which is ideal for storing sparse data with many columns, while TiDB follows a row-oriented data model, making it better suited for transactional workloads with complex relationships between rows.
Consistency: HBase provides strong consistency, ensuring that all clients see the same data at the same time, while TiDB offers snapshot isolation, allowing for improved concurrency without sacrificing consistency.
Scalability: HBase is horizontally scalable, meaning it can easily handle large amounts of data by adding more servers to a cluster, whereas TiDB offers horizontal scalability through automated sharding, enabling it to handle massive dataset growth effortlessly.
Storage Engine: HBase uses Apache Hadoop's HDFS as its underlying storage engine, whereas TiDB utilizes TiKV, a distributed key-value store, providing higher performance and better fault tolerance.
Query Language Support: HBase supports only Java API and REST, making it challenging for users of other languages, whereas TiDB supports SQL, making it easier for a broader range of users to interact with the database.
Consistency Level Control: HBase allows users to configure the consistency level at the operation level, offering more flexibility but also complexity, while TiDB simplifies this by providing a global consistency level setting for the entire cluster.

In Summary, HBase and TiDB differ in their data models, consistency models, scalability approaches, storage engines, query language support, and consistency level control.