DBMS > etcd vs. GridDB vs. HBase vs. Heroic vs. RocksDB

System Properties Comparison etcd vs. GridDB vs. HBase vs. Heroic vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

Scalable in-memory time series database optimized for IoT and Big Data

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

Primary database model

Secondary database models

Key-value store
Relational DBMS

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

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

Website

etcd.io
github.com/etcd-io/etcd

griddb.net

hbase.apache.org

github.com/spotify/heroic

rocksdb.org

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.griddb.net

hbase.apache.org/book.html

spotify.github.io/heroic

github.com/facebook/rocksdb/wiki

Developer

Toshiba Corporation

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Spotify

Facebook, Inc.

Initial release

2013

2008

2014

2013

Current release

3.4, August 2019

5.1, August 2022

2.3.4, January 2021

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

BSD

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.

Implementation language

C++

Java

C++

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux

Linux
Unix
Windows

using Cygwin

Linux

Data scheme

schema-free

yes

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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.

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

gRPC
JSON over HTTP

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

HQL (Heroic Query Language, a JSON-based language)
HTTP API

C++ API
Java API

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Triggers

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Source-replica replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency or Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID at container level

Single row ACID (across millions of columns)

yes

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

User concepts

Access control

Access rights for users can be defined per database

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

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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