DBMS > Apache Spark (SQL) vs. Memgraph vs. TypeDB

System Properties Comparison Apache Spark (SQL) vs. Memgraph vs. TypeDB

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Editorial information provided by DB-Engines

Name

Description

Apache Spark SQL is a component on top of 'Spark Core' for structured data processing

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

TypeDB provides developers with an expressive, customizable type system to manage their data using an award-winning query language, TypeQL, while building on a high-performance, distributed architecture.

Primary database model

Relational DBMS

Graph DBMS

The type-theoretic data model of TypeDB subsumes the graph database model.
Object oriented DBMS

The data model of TypeDB comprises object-oriented features such as class inheritance and interfaces.
Relational DBMS

The type-theoretic data model of TypeDB subsumes the relational database model.

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

Score	20.40
Rank	#29	Overall
	#18	Relational DBMS

Score	2.57
Rank	#96	Overall
	#8	Graph DBMS

Score	0.58
Rank	#234	Overall
	#20	Graph DBMS
	#10	Object oriented DBMS
	#109	Relational DBMS

Website

spark.apache.org/sql

memgraph.com

typedb.com

Technical documentation

spark.apache.org/docs/latest/sql-programming-guide.html

memgraph.com/docs

typedb.com/docs

Developer

Apache Software Foundation

Memgraph Ltd

Vaticle

Initial release

2014

2017

2016

Current release

3.5.0 ( 2.13), September 2023

2.28.3, June 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSL 1.1; commercial license for enterprise edition available

Open Source

GPL Version 3, 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

Scala

C and C++

Java

Server operating systems

Linux
OS X
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free and schema-optional

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Bolt protocol
Cypher query language

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (IDE)

Supported programming languages

Java
Python
R
Scala

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

All JVM based languages
C
C++
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Sharding

dynamic graph partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using RAFT

Synchronous replication via raft

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

substituted by the relationship feature

Transaction concepts

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

ACID

with snapshot isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

User concepts

Access control

Users, roles and permissions

yes

at REST API level; other APIs in progress

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