DBMS > Databend vs. Kinetica vs. RocksDB vs. SAP SQL Anywhere vs. Solr

System Properties Comparison Databend vs. Kinetica vs. RocksDB vs. SAP SQL Anywhere vs. Solr

Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Fully vectorized database across both GPUs and CPUs

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

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

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.30
Rank	#287	Overall
	#130	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

github.com/datafuselabs/databend
www.databend.com

www.kinetica.com

rocksdb.org

www.sap.com/products/technology-platform/sql-anywhere.html

solr.apache.org

Technical documentation

docs.databend.com

docs.kinetica.com

github.com/facebook/rocksdb/wiki

help.sap.com/docs/SAP_SQL_Anywhere

solr.apache.org/resources.html

Developer

Databend Labs

Kinetica

Facebook, Inc.

SAP

formerly Sybase

Apache Software Foundation

Initial release

2021

2012

2013

1992

2006

Current release

1.0.59, April 2023

7.1, August 2021

8.11.4, April 2024

17, July 2015

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

BSD

commercial

Open Source

Apache Version 2

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

Rust

C, C++

C++

Java

Server operating systems

hosted
Linux
macOS

Linux

AIX
HP-UX
Linux
OS X
Solaris
Windows

All OS with a Java VM

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

Data scheme

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

Solr Parallel SQL Interface

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

ADO.NET
HTTP API
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

C++
Java
JavaScript (Node.js)
Python

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

yes, in C/C++, Java, .Net or Perl

Java plugins

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

Source-replica replication

Database mirroring

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

optimistic locking

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

GPU vRAM or System RAM

yes

User concepts

Access control

Users with fine-grained authorization concept, user roles

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

yes

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