DBMS > Datomic vs. DolphinDB vs. Google Cloud Spanner vs. Memgraph vs. Sequoiadb

System Properties Comparison Datomic vs. DolphinDB vs. Google Cloud Spanner vs. Memgraph vs. Sequoiadb

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

An open source graph database built for real-time streaming and compatible with Neo4j

NewSQL database with distributed OLTP and SQL

Primary database model

Document store
Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	3.02
Rank	#98	Overall
	#8	Graph DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

www.datomic.com

www.dolphindb.com

cloud.google.com/spanner

memgraph.com

www.sequoiadb.com

Technical documentation

docs.datomic.com

docs.dolphindb.cn/en/help200/index.html

cloud.google.com/spanner/docs

memgraph.com/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Social network pages

Developer

Cognitect

DolphinDB, Inc

Google

Memgraph Ltd

Sequoiadb Ltd.

Initial release

2012

2018

2017

2013

Current release

1.0.6735, June 2023

v2.00.4, January 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

free community version available

commercial

Open Source

BSL 1.1; commercial license for enterprise edition available

Open Source

Server: AGPL; Client: Apache V2

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

Java, Clojure

C++

C and C++

C++

Server operating systems

All OS with a Java VM

Linux
Windows

hosted

Linux

Data scheme

yes

schema-free and schema-optional

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

SQL-like query language

yes

Query statements complying to ANSI 2011

SQL-like query language

APIs and other access methods

RESTful HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

Bolt protocol
Cypher query language

proprietary protocol using JSON

Supported programming languages

Clojure
Java

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

Go
Java
JavaScript (Node.js)
Python

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

JavaScript

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning

Sharding

dynamic graph partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Multi-source replication with 3 replicas for regional instances.

Multi-source replication using RAFT

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

yes

ACID

Strict serializable isolation

ACID

with snapshot isolation

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

with periodic snapshot and write-ahead logging (WAL) of changes

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Administrators, Users, Groups

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users, roles and permissions

simple password-based access control

More information provided by the system vendor

Specific characteristics

Memgraph directly connects to your streaming infrastructure so you and your team...
» more

Competitive advantages

Business Source License ensures a future for the Memgraph community MAGE algorithm...
» more

Typical application scenarios

Graph algorithms in bioinformatics Social network analysis Cryptocurrency network...
» more

Licensing and pricing models

You can check out our pricing model and licenses on the company website .
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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