DBMS > etcd vs. HarperDB vs. Microsoft Access vs. Snowflake vs. Splice Machine

System Properties Comparison etcd vs. HarperDB vs. Microsoft Access vs. Snowflake vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Cloud-based data warehousing service for structured and semi-structured data

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Key-value store

Document 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	0.55
Rank	#248	Overall
	#38	Document stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

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

www.harperdb.io

www.microsoft.com/en-us/microsoft-365/access

www.snowflake.com

splicemachine.com

Technical documentation

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

docs.harperdb.io/docs

developer.microsoft.com/en-us/access

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

Developer

HarperDB

Microsoft

Snowflake Computing Inc.

Splice Machine

Initial release

2017

1992

2014

Current release

3.4, August 2019

3.1, August 2021

1902 (16.0.11328.20222), March 2019

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community edition available

commercial

Bundled with Microsoft Office

commercial

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Node.js

C++

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

Linux
OS X

Windows

Not a real database server, but making use of DLLs

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

dynamic schema

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

SQL-like data manipulation statements

yes

but not compliant to any SQL standard

yes

APIs and other access methods

gRPC
JSON over HTTP

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

ADO.NET
DAO
ODBC
OLE DB

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

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#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes

since Access 2010 using the ACE-engine

user defined functions

yes

Java

Triggers

yes, watching key changes

yes

since Access 2010 using the ACE-engine

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

none

yes

Shared Nothhing Auto-Sharding, Columnar 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.

yes

the nodes on which a table resides can be defined

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

yes, using LMDB

yes

but no files for transaction logging

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 and roles

a simple user-level security was built in till version Access 2003

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.