DBMS > Apache Druid vs. BoltDB vs. Milvus vs. Redis vs. SWC-DB

System Properties Comparison Apache Druid vs. BoltDB vs. Milvus vs. Redis vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

A DBMS designed for efficient storage of vector data and vector similarity searches

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.

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS
Time Series DBMS

Key-value store

Vector DBMS

Key-value store

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

Wide column store

Secondary database models

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

Time Series DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

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

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

druid.apache.org

github.com/boltdb/bolt

milvus.io

redis.com
redis.io

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

druid.apache.org/docs/latest/design

milvus.io/docs/overview.md

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

Developer

Apache Software Foundation and contributors

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Alex Kashirin

Initial release

2012

2013

2019

2009

2020

Current release

29.0.1, April 2024

2.3.4, January 2024

7.2.4, January 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

MIT License

Open Source

Apache Version 2.0

Open Source

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

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Zilliz Cloud – Cloud-native service for Milvus

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

Implementation language

Java

C++, Go

C++

Server operating systems

Linux
OS X
Unix

BSD
Linux
OS X
Solaris
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux

Data scheme

yes

schema-less columns are supported

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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.

Secondary indexes

yes

with RediSearch module

SQL

Support of SQL

SQL for querying

with RediSQL module

SQL-like query language

APIs and other access methods

JDBC
RESTful HTTP/JSON API

RESTful HTTP API

proprietary protocol

RESP - REdis Serialization Protocol

Proprietary protocol
Thrift

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++
Go
Java
JavaScript (Node.js)
Python

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

C++

Server-side scripts

Stored procedures

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

Triggers

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

none

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

none

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

MapReduce

Offers an API for user-defined Map/Reduce methods

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

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

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Role based access control and fine grained access rights

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

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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