DBMS > Firebolt vs. Graph Engine vs. Postgres-XL vs. Splice Machine

System Properties Comparison Firebolt vs. Graph Engine vs. Postgres-XL vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

Technical documentation

docs.firebolt.io

www.graphengine.io/docs/manual

www.postgres-xl.org/documentation

splicemachine.com/how-it-works

Developer

Firebolt Analytics Inc.

Microsoft

Splice Machine

Initial release

2020

2010

2014

since 2012, originally named StormDB

2014

Current release

10 R1, October 2018

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Mozilla public license

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

.NET and C

Java

Server operating systems

hosted

.NET

Linux
macOS

Linux
OS X
Solaris
Windows

Data scheme

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes

APIs and other access methods

.Net
ODBC
RESTful HTTP API

RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Go
JavaScript (Node.js)
Python

C#
C++
F#
Visual Basic

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Server-side scripts

Stored procedures

yes

user defined functions

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

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

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

fine grained access rights according to SQL-standard

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

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