DBMS > Apache Solr vs. Microsoft Azure Cosmos DB vs. SvectorDB vs. Tarantool

System Properties Comparison Apache Solr vs. Microsoft Azure Cosmos DB vs. SvectorDB vs. Tarantool

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Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

Serverless cloud-native vector database

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	32.79
Rank	#23	Overall
	#3	Search engines

Score	22.94
Rank	#26	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#2	Wide column stores

Score	0.00
Rank	#385	Overall
	#21	Vector DBMS

Score	1.42
Rank	#148	Overall
	#26	Document stores
	#24	Key-value stores
	#66	Relational DBMS

Website

solr.apache.org

azure.microsoft.com/services/cosmos-db

svectordb.com

www.tarantool.io

Technical documentation

solr.apache.org/resources.html

learn.microsoft.com/azure/cosmos-db

www.svectordb.com/docs

www.tarantool.io/en/doc

Developer

Apache Software Foundation

Microsoft

SvectorDB

Initial release

2006

2014

2023

2008

Current release

9.6.1, May 2024

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Server operating systems

All OS with a Java VM

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

hosted

server-less

BSD
Linux
macOS

Data scheme

yes

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

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes

JSON types

string, double, vector

string, double, decimal, uuid, integer, blob, boolean, datetime

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

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

Solr Parallel SQL Interface

SQL-like query language

Full-featured ANSI SQL support

APIs and other access methods

Java API
RESTful HTTP/JSON API

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

OpenAPI 3.0, RESTful HTTP API, Python SDK, JavaScript / TypeScript SDK

Open binary protocol

Supported programming languages

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

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

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

Java plugins

JavaScript

Lua, C and SQL stored procedures

Triggers

yes

User configurable commands triggered on index changes

JavaScript

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

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

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

Multi-item ACID transactions with snapshot isolation within a partition

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

yes

Access rights can be defined down to the item level

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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