DBMS > Dragonfly vs. Google BigQuery vs. InfluxDB vs. Lovefield vs. Sadas Engine

System Properties Comparison Dragonfly vs. Google BigQuery vs. InfluxDB vs. Lovefield vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Large scale data warehouse service with append-only tables

DBMS for storing time series, events and metrics

Embeddable relational database for web apps written in pure JavaScript

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Secondary database models

Spatial DBMS

with GEO package

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

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

cloud.google.com/bigquery

www.influxdata.com/products/influxdb-overview

google.github.io/lovefield

www.sadasengine.com

Technical documentation

www.dragonflydb.io/docs

cloud.google.com/bigquery/docs

docs.influxdata.com/influxdb

github.com/google/lovefield/blob/master/docs/spec_index.md

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

DragonflyDB team and community contributors

Google

SADAS s.r.l.

Initial release

2023

2010

2013

2014

2006

Current release

1.0, March 2023

2.7.6, April 2024

2.1.12, February 2017

8.0

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache 2.0

commercial

free trial version 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

C++

JavaScript

C++

Server operating systems

Linux

hosted

Linux
OS X

through Homebrew

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

AIX
Linux
Windows

Data scheme

scheme-free

yes

schema-free

yes

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

yes

Numeric data and Strings

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

yes

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP/JSON API

HTTP API
JSON over UDP

JDBC
ODBC
Proprietary protocol

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

JavaScript

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

Triggers

publish/subscribe channels provide some trigger functionality

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

in enterprise version only

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

Depending on used storage engine

yes

using MemoryDB

yes

managed by 'Learn by Usage'

User concepts

Access control

Password-based authentication

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

Google Cloud Identity & Access Management (IAM)

simple rights management via user accounts

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

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