DBMS > BoltDB vs. Hazelcast vs. HBase vs. RocksDB vs. ScyllaDB

System Properties Comparison BoltDB vs. Hazelcast vs. HBase vs. RocksDB vs. ScyllaDB

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

A widely adopted in-memory data grid

Wide-column store based on Apache Hadoop and on concepts of BigTable

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Cassandra and DynamoDB compatible wide column store

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

Key-value store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Website

github.com/boltdb/bolt

Technical documentation

hazelcast.org/imdg/docs

hbase.apache.org/book.html

github.com/facebook/rocksdb/wiki

docs.scylladb.com

Developer

Hazelcast

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Facebook, Inc.

ScyllaDB

Initial release

2013

2008

2013

2015

Current release

5.3.6, November 2023

2.3.4, January 2021

9.2.1, May 2024

ScyllaDB Open Source 5.4.1, January 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache version 2

Open Source

BSD

Open Source

Open Source (AGPL), commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Linux
Unix
Windows

using Cygwin

Linux

Data scheme

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

the object must implement a serialization strategy

Secondary indexes

yes

cluster global secondary indices

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements (CQL)

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

C++ API
Java API

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Coprocessors in Java

yes, Lua

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Replicated Map

Multi-source replication
Source-replica replication

yes

selectable replication factor

Representation of geographical distribution of servers is possible

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency or Eventual Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

yes

one or two-phase-commit; repeatable reads; read commited

Single row ACID (across millions of columns)

yes

Atomicity and isolation are supported for single operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

in-memory tables

User concepts

Access control

Role-based access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
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Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
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Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
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Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
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