DBMS > LevelDB vs. Pinecone vs. Sequoiadb vs. Vitess

System Properties Comparison LevelDB vs. Pinecone vs. Sequoiadb vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A managed, cloud-native vector database

NewSQL database with distributed OLTP and SQL

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Key-value store

Vector DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	2.33
Rank	#111	Overall
	#19	Key-value stores

Score	3.02
Rank	#87	Overall
	#3	Vector DBMS

Score	0.42
Rank	#263	Overall
	#41	Document stores
	#121	Relational DBMS

Score	0.86
Rank	#202	Overall
	#95	Relational DBMS

Website

github.com/google/leveldb

www.pinecone.io

www.sequoiadb.com

vitess.io

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

docs.pinecone.io/docs/overview

www.sequoiadb.com/en/index.php?m=Files&a=index

vitess.io/docs

Developer

Google

Pinecone Systems, Inc

Sequoiadb Ltd.

The Linux Foundation, PlanetScale

Initial release

2011

2019

2013

Current release

1.23, February 2021

15.0.2, December 2022

License

Commercial or Open Source

Open Source

BSD

commercial

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache Version 2.0, commercial licenses available

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

C++

Server operating systems

Illumos
Linux
OS X
Windows

hosted

Linux

Docker
Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

String, Number, Boolean

yes

oid, date, timestamp, binary, regex

yes

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

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

proprietary protocol using JSON

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Python

.Net
C++
Java
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Document is locked during a transaction

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

with automatic compression on writes

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

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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