DBMS > Dragonfly vs. Google BigQuery vs. GridGain vs. XTDB

System Properties Comparison Dragonfly vs. Google BigQuery vs. GridGain vs. XTDB

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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

GridGain is an in-memory computing platform, built on Apache Ignite

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

Relational DBMS

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Document store

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

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

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

cloud.google.com/bigquery

www.gridgain.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

www.dragonflydb.io/docs

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

www.xtdb.com/docs

Developer

DragonflyDB team and community contributors

Google

GridGain Systems, Inc.

Juxt Ltd.

Initial release

2023

2010

2007

2019

Current release

1.0, March 2023

GridGain 8.5.1

1.19, September 2021

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

commercial, open source

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java, C++, .Net, Python, REST, SQL

Clojure

Server operating systems

Linux

hosted

Linux
OS X
Solaris
Windows
z/OS

All OS with a Java 8 (and higher) VM
Linux

Data scheme

scheme-free

yes

schema-free

Typing

predefined data types such as float or date

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

yes

yes, extensible-data-notation format

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

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

limited SQL, making use of Apache Calcite

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP REST
JDBC

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

C#
C++
Java
PHP
Python
Ruby
Scala

Clojure
Java

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

Triggers

publish/subscribe channels provide some trigger functionality

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes (replicated cache)

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Password-based authentication

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

Google Cloud Identity & Access Management (IAM)

Role-based access control
Security Hooks for custom implementations

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