DBMS > Apache Impala vs. DolphinDB vs. Fauna vs. GridGain vs. Redis

System Properties Comparison Apache Impala vs. DolphinDB vs. Fauna vs. GridGain vs. Redis

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

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

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Key-value store

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Secondary database models

Document store

Relational DBMS

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	157.80
Rank	#6	Overall
	#1	Key-value stores

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.dolphindb.cn/en/help200/index.html

docs.fauna.com

www.gridgain.com/docs/index.html

docs.redis.com/latest/index.html
redis.io/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DolphinDB, Inc

Fauna, Inc.

GridGain Systems, Inc.

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Initial release

2013

2018

2014

2007

2009

Current release

4.1.0, June 2022

v2.00.4, January 2022

GridGain 8.5.1

7.2.4, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free community version available

commercial

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

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.

Aiven for Redis: Fully managed in-memory key-value store for all your caching and speedy lookup needs.

Implementation language

C++

Scala

Java, C++, .Net

Server operating systems

Linux

Linux
Windows

hosted

Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

with RediSearch module

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

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

with RediSQL module

APIs and other access methods

JDBC
ODBC

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

RESTful HTTP API

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

proprietary protocol

RESP - REdis Serialization Protocol

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

user defined functions

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

Lua; Redis Functions coming in Redis 7 (slides and Github)

Triggers

yes (cache interceptors and events)

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

consistent hashing

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Multi-source replication

yes (replicated cache)

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

yes (compute grid and hadoop accelerator)

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Atomic execution of command blocks and scripts and optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

Administrators, Users, Groups

Identity management, authentication, and access control

Security Hooks for custom implementations

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

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