DBMS > chDB vs. Couchbase vs. InfluxDB vs. Solr

System Properties Comparison chDB vs. Couchbase vs. InfluxDB vs. Solr

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Editorial information provided by DB-Engines

Name

Description

An embedded SQL OLAP Engine powered by ClickHouse

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

DBMS for storing time series, events and metrics

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Time Series DBMS

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Spatial DBMS

with GEO package

Spatial DBMS

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

Score	0.07
Rank	#376	Overall
	#158	Relational DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

github.com/chdb-io/chdb

www.couchbase.com

www.influxdata.com/products/influxdb-overview

solr.apache.org

Technical documentation

doc.chdb.io

docs.couchbase.com

docs.influxdata.com/influxdb

solr.apache.org/resources.html

Developer

Couchbase, Inc.

Apache Software Foundation

Initial release

2023

2011

2013

2006

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

2.7.6, April 2024

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache Version 2

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++, Go and Erlang

Java

Server operating systems

server-less

Linux
OS X
Windows

Linux
OS X

through Homebrew

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

HTTP API
JSON over UDP

Java API
RESTful HTTP/JSON API

Supported programming languages

Bun
C
C++
Go
JavaScript (Node.js)
Python
Rust

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Java plugins

Triggers

yes

via the TAP protocol

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

in enterprise version only

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

selectable replication factor

in enterprise version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Ephemeral buckets

yes

Depending on used storage engine

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

simple rights management via user accounts

yes

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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