DBMS > LMDB vs. Microsoft Azure Cosmos DB vs. Oracle Rdb vs. QuestDB vs. Solr

System Properties Comparison LMDB vs. Microsoft Azure Cosmos DB vs. Oracle Rdb vs. QuestDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

A high performant, light-weight, embedded key-value database library

Globally distributed, horizontally scalable, multi-model database service

A high performance open source SQL database for time series data

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

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Search engine

Secondary database models

Spatial DBMS

Relational DBMS

Spatial DBMS

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

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.symas.com/symas-embedded-database-lmdb

azure.microsoft.com/services/cosmos-db

www.oracle.com/database/technologies/related/rdb.html

questdb.io

solr.apache.org

Technical documentation

www.lmdb.tech/doc

learn.microsoft.com/azure/cosmos-db

www.oracle.com/database/technologies/related/rdb-doc.html

questdb.io/docs

solr.apache.org/resources.html

Developer

Symas

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

QuestDB Technology Inc

Apache Software Foundation

Initial release

2011

2014

1984

2014

2006

Current release

0.9.32, January 2024

7.4.1.1, 2021

9.6.0, April 2024

License

Commercial or Open Source

Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2

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.

Implementation language

Java (Zero-GC), C++, Rust

Java

Server operating systems

Linux
Unix
Windows

hosted

HP Open VMS

Linux
macOS
Windows

All OS with a Java VM

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

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free via InfluxDB Line Protocol

yes

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

Typing

predefined data types such as float or date

yes

JSON types

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 properties auto-indexed by default

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

SQL with time-series extensions

Solr Parallel SQL Interface

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

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

Server-side scripts

Stored procedures

JavaScript

Java plugins

Triggers

JavaScript

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

horizontal partitioning (by timestamps)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

Source-replica replication with eventual consistency

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

yes, on a single node

ACID for single-table writes

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

through memory mapped files

yes

User concepts

Access control

Access rights can be defined down to the item level

yes

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.