DBMS > etcd vs. Hazelcast vs. HEAVY.AI vs. RocksDB vs. TimesTen

System Properties Comparison etcd vs. Hazelcast vs. HEAVY.AI vs. RocksDB vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

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

hazelcast.com

github.com/heavyai/heavydb
www.heavy.ai

rocksdb.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

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

hazelcast.org/imdg/docs

docs.heavy.ai

github.com/facebook/rocksdb/wiki

docs.oracle.com/database/timesten-18.1

Developer

Hazelcast

HEAVY.AI, Inc.

Facebook, Inc.

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2008

2016

2013

1998

Current release

3.4, August 2019

5.3.6, November 2023

5.10, January 2022

9.2.1, May 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD

commercial

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

Java

C++ and CUDA

C++

Server operating systems

FreeBSD
Linux
Windows

experimental

All OS with a Java VM

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

gRPC
JSON over HTTP

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

C++ API
Java API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

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

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
Perl
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

PL/SQL

Triggers

yes, watching key changes

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Replicated Map

Multi-source replication

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

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