DBMS > BoltDB vs. Drizzle vs. MySQL vs. TimescaleDB vs. YDB

System Properties Comparison BoltDB vs. Drizzle vs. MySQL vs. TimescaleDB vs. YDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

An embedded key-value store for Go.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Widely used open source RDBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Key-value store

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Document store
Relational DBMS

Secondary database models

Document store
Spatial DBMS

Relational DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Score	0.35
Rank	#278	Overall
	#43	Document stores
	#127	Relational DBMS

Website

github.com/boltdb/bolt

www.mysql.com

www.timescale.com

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

dev.mysql.com/doc

docs.timescale.com

ydb.tech/en/docs

Developer

Drizzle project, originally started by Brian Aker

Oracle

since 2010, originally MySQL AB, then Sun

Timescale

Yandex

Initial release

2013

2008

1995

2017

2019

Current release

7.2.4, September 2012

8.4.0, April 2024

2.15.0, May 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

GNU GPL

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Open Source

Apache 2.0; commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C and C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

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

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

with proprietary extensions

yes

full PostgreSQL SQL syntax

SQL-like query language (YQL)

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

C
C++
Java
PHP

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, across time and space (hash partitioning) attributes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Source-replica replication with hot standby and reads on replicas

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

Access rights defined for Yandex Cloud users

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