HBase vs Neo4j

Overview

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

HBase vs Neo4j: What are the differences?

Introduction

HBase and Neo4j are both popular database management systems used for different purposes. They have key differences that set them apart.

Data Model: HBase is a column-oriented database that stores data in rows and columns, whereas Neo4j is a graph database that uses nodes and relationships to represent data. HBase is suitable for structured data with multiple columns, while Neo4j excels in representing complex and interconnected data relationships.
Query Language: HBase uses a structured query language called HQL, which is similar to SQL but with some differences. On the other hand, Neo4j uses Cypher, a powerful graph query language designed specifically for graph databases. Cypher allows for expressive querying of complex relationships, making it more suitable for graph-related operations.
Scalability: HBase is a horizontally scalable database, meaning it can handle large amounts of data by adding more servers to the HBase cluster. Neo4j, on the other hand, is primarily vertically scalable, meaning it can handle larger loads by increasing the resources (such as CPU and memory) of a single server. This makes HBase more suitable for big data scenarios where the volume of data grows rapidly.
ACID Compliance: HBase offers limited ACID (Atomicity, Consistency, Isolation, Durability) compliance, providing strong consistency guarantees for single-row operations but eventual consistency for multi-row operations. Neo4j, on the other hand, supports full ACID compliance, ensuring transaction atomicity, consistency, isolation, and durability for all operations, making it more suitable for applications that require strict data integrity.
Data Access Patterns: HBase is optimized for high-speed random read and write operations, making it a good choice for applications with frequent data access patterns. Neo4j, on the other hand, excels in traversing complex relationships and performing graph-based operations efficiently, making it suitable for applications that heavily rely on graph algorithms and analytics.
Community and Ecosystem: HBase has a larger and more mature community, with extensive documentation, tutorials, and libraries available. It also has a broader ecosystem with integration support for various Hadoop ecosystem tools. Neo4j, although growing rapidly, has a smaller community but has strong support for graph-related operations, including native graph algorithms and visualization tools.

In Summary, HBase is a column-oriented database optimized for structured data with high-speed random read and write operations, while Neo4j is a graph database that excels in representing complex relationships and performing graph-based operations efficiently.

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Advice on HBase, Neo4j

Jaime

none at none

Aug 31, 2020

Needs advice

Hi, I want to create a social network for students, and I was wondering which of these three Oriented Graph DB's would you recommend. I plan to implement machine learning algorithms such as k-means and others to give recommendations and some basic data analyses; also, everything is going to be hosted in the cloud, so I expect the DB to be hosted there. I want the queries to be as fast as possible, and I like good tools to monitor my data. I would appreciate any recommendations or thoughts.

Context:

I released the MVP 6 months ago and got almost 600 users just from my university in Colombia, But now I want to expand it all over my country. I am expecting more or less 20000 users.

56.4k views56.4k

Comments

Detailed Comparison

HBase	Neo4j
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.	Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
-	intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API
Statistics
GitHub Stars 5.5K	GitHub Stars 15.3K
GitHub Forks 3.4K	GitHub Forks 2.5K
Stacks 511	Stacks 1.2K
Followers 498	Followers 1.4K
Votes 15	Votes 351
Pros & Cons
Pros 9 Performance 5 OLTP 1 Fast Point Queries	Pros 69 Cypher – graph query language 61 Great graphdb 33 Open source 31 Rest api 27 High-Performance Native API Cons 9 Comparably slow 4 Can't store a vertex as JSON 1 Doesn't have a managed cloud service at low cost

What are some alternatives to HBase, Neo4j?

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.

Related Comparisons

HBase vs Neo4j: What are the differences?

Introduction

HBase and Neo4j are both popular database management systems used for different purposes. They have key differences that set them apart.

Data Model: HBase is a column-oriented database that stores data in rows and columns, whereas Neo4j is a graph database that uses nodes and relationships to represent data. HBase is suitable for structured data with multiple columns, while Neo4j excels in representing complex and interconnected data relationships.
Query Language: HBase uses a structured query language called HQL, which is similar to SQL but with some differences. On the other hand, Neo4j uses Cypher, a powerful graph query language designed specifically for graph databases. Cypher allows for expressive querying of complex relationships, making it more suitable for graph-related operations.
Scalability: HBase is a horizontally scalable database, meaning it can handle large amounts of data by adding more servers to the HBase cluster. Neo4j, on the other hand, is primarily vertically scalable, meaning it can handle larger loads by increasing the resources (such as CPU and memory) of a single server. This makes HBase more suitable for big data scenarios where the volume of data grows rapidly.
ACID Compliance: HBase offers limited ACID (Atomicity, Consistency, Isolation, Durability) compliance, providing strong consistency guarantees for single-row operations but eventual consistency for multi-row operations. Neo4j, on the other hand, supports full ACID compliance, ensuring transaction atomicity, consistency, isolation, and durability for all operations, making it more suitable for applications that require strict data integrity.
Data Access Patterns: HBase is optimized for high-speed random read and write operations, making it a good choice for applications with frequent data access patterns. Neo4j, on the other hand, excels in traversing complex relationships and performing graph-based operations efficiently, making it suitable for applications that heavily rely on graph algorithms and analytics.
Community and Ecosystem: HBase has a larger and more mature community, with extensive documentation, tutorials, and libraries available. It also has a broader ecosystem with integration support for various Hadoop ecosystem tools. Neo4j, although growing rapidly, has a smaller community but has strong support for graph-related operations, including native graph algorithms and visualization tools.

HBase vs Neo4j

Overview

HBase vs Neo4j: What are the differences?