DBMS > CockroachDB vs. Kinetica vs. Prometheus vs. Redis

System Properties Comparison CockroachDB vs. Kinetica vs. Prometheus vs. Redis

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Fully vectorized database across both GPUs and CPUs

Open-source Time Series DBMS and monitoring system

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

Key-value store

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

Secondary database models

Spatial DBMS
Time Series 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	4.74
Rank	#67	Overall
	#36	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	7.56
Rank	#49	Overall
	#3	Time Series DBMS

Score	149.43
Rank	#6	Overall
	#1	Key-value stores

Website

www.cockroachlabs.com

www.kinetica.com

prometheus.io

redis.com
redis.io

Technical documentation

www.cockroachlabs.com/docs

docs.kinetica.com

prometheus.io/docs

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

Developer

Cockroach Labs

Kinetica

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Initial release

2015

2012

2015

2009

Current release

24.1.0, May 2024

7.1, August 2021

7.2.5, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0, commercial license available

commercial

Open Source

Apache 2.0

Open Source

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

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.

Implementation language

C, C++

Server operating systems

Linux
macOS
Windows

Linux

Linux
Windows

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Data scheme

dynamic schema

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

with RediSearch module

SQL

Support of SQL

yes, wire compatible with PostgreSQL

SQL-like DML and DDL statements

with RediSQL module

APIs and other access methods

JDBC

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

proprietary protocol

RESP - REdis Serialization Protocol

Supported programming languages

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

C++
Java
JavaScript (Node.js)
Python

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

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

user defined functions

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

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using RAFT

Source-replica replication

yes

by Federation

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

Immediate Consistency or Eventual Consistency depending on configuration

none

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

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

GPU vRAM or System RAM

yes

User concepts

Access control

Role-based access control

Access rights for users and roles on table level

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.