DBMS > BoltDB vs. ClickHouse vs. Greenplum vs. jBASE vs. Spark SQL

System Properties Comparison BoltDB vs. ClickHouse vs. Greenplum vs. jBASE vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

A robust multi-value DBMS comprising development tools and middleware

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

Primary database model

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/boltdb/bolt

clickhouse.com

greenplum.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

spark.apache.org/sql

Technical documentation

clickhouse.com/docs

docs.greenplum.org

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

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

Developer

Clickhouse Inc.

Pivotal Software Inc.

Rocket Software (formerly Zumasys)

Apache Software Foundation

Initial release

2013

2016

2005

1991

2014

Current release

v24.4.1.2088-stable, May 2024

7.0.0, September 2023

5.7

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Open Source

Apache 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.

ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C++

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS

Linux

AIX
Linux
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

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

since Version 4.2

yes

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

Embedded SQL for jBASE in BASIC

SQL-like DML and DDL statements

APIs and other access methods

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

JDBC
ODBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

JDBC
ODBC

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

C
Java
Perl
Python
R

.Net
Basic
Jabbascript
Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

key based and custom

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Source-replica replication

yes

none

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

yes

Transaction concepts

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

yes

ACID

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

User concepts

Access control

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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