DBMS > Google BigQuery vs. Graph Engine vs. SurrealDB

System Properties Comparison Google BigQuery vs. Graph Engine vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Document store
Graph DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

cloud.google.com/bigquery

www.graphengine.io

surrealdb.com

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

surrealdb.com/docs

Developer

Google

Microsoft

SurrealDB Ltd

Initial release

2010

2022

Current release

v1.1.1, January 2024

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

.NET and C

Rust

Server operating systems

hosted

.NET

Linux
macOS
Windows

Data scheme

yes

schema-free

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

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes, based on authentication and database rules

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