DBMS > BoltDB vs. EJDB vs. Spark SQL vs. Transbase vs. YTsaurus

System Properties Comparison BoltDB vs. EJDB vs. Spark SQL vs. Transbase vs. YTsaurus

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Spark SQL is a component on top of 'Spark Core' for structured data processing

A resource-optimized, high-performance, universally applicable RDBMS

YTsaurus is an open source platform for distributed storage and processing.

Primary database model

Key-value store

Document store

Relational DBMS

Document store
Key-value store

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

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Score	0.11
Rank	#340	Overall
	#45	Document stores
	#50	Key-value stores

Website

github.com/boltdb/bolt

github.com/Softmotions/ejdb

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

ytsaurus.tech

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

spark.apache.org/docs/latest/sql-programming-guide.html

www.transaction.de/en/products/transbase/features.html

ytsaurus.tech/docs/en

Developer

Softmotions

Apache Software Foundation

Transaction Software GmbH

Yandex

Initial release

2013

2012

2014

1987

2023

Current release

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

MIT License

Open Source

GPLv2

Open Source

Apache 2.0

commercial

free development license

Open Source

Apache License, Version 2.0

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

Scala

C and C++

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

server-less

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Ubuntu

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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 DML and DDL statements

yes

YQL, an SQL-based language, is supported

APIs and other access methods

in-process shared library

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

C++
Go
Java
JavaScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access Control Lists

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