DBMS > Atos Standard Common Repository vs. etcd vs. SQLite vs. Transwarp Hippo vs. Vitess

System Properties Comparison Atos Standard Common Repository vs. etcd vs. SQLite vs. Transwarp Hippo vs. Vitess

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

A distributed reliable key-value store

Widely used embeddable, in-process RDBMS

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store
Key-value store

Secondary database models

Document store
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	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.05
Rank	#386	Overall
	#14	Vector DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

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

www.sqlite.org

www.transwarp.cn/en/subproduct/hippo

vitess.io

Technical documentation

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

www.sqlite.org/docs.html

vitess.io/docs

Developer

Atos Convergence Creators

Dwayne Richard Hipp

The Linux Foundation, PlanetScale

Initial release

2016

2000

2023

2013

Current release

1703

3.4, August 2019

3.46.0 (23 May 2024), May 2024

1.0, May 2023

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Public Domain

commercial

Open Source

Apache Version 2.0, commercial licenses 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.

Implementation language

Java

C++

Server operating systems

Linux

FreeBSD
Linux
Windows

experimental

server-less

Linux
macOS

Docker
Linux
macOS

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

optional

yes

not rigid because of 'dynamic typing' concept.

Vector, Numeric and String

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

yes

with proprietary extensions

APIs and other access methods

LDAP

gRPC
JSON over HTTP

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

RESTful HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

All languages with LDAP bindings

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C++
Java
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic execution of specific operations

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

table locks or row locks depending on storage engine

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

LDAP bind authentication

Role based access control and fine grained access rights

Users with fine-grained authorization concept

no user groups or roles

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