DBMS > Axibase vs. EsgynDB vs. IBM Db2 Event Store vs. RocksDB vs. Solr

System Properties Comparison Axibase vs. EsgynDB vs. IBM Db2 Event Store vs. RocksDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Distributed Event Store optimized for Internet of Things use cases

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

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

Primary database model

Time Series DBMS

Relational DBMS

Event Store
Time Series DBMS

Key-value store

Search engine

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

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

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

axibase.com/docs/atsd/finance

www.esgyn.cn

www.ibm.com/products/db2-event-store

rocksdb.org

solr.apache.org

Technical documentation

www.ibm.com/docs/en/db2-event-store

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

Axibase Corporation

Esgyn

IBM

Facebook, Inc.

Apache Software Foundation

Initial release

2013

2015

2017

2013

2006

Current release

15585

2.0

9.2.1, May 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

free developer edition available

Open Source

BSD

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

Java

C++, Java

C and C++

C++

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux

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

short, integer, long, float, double, decimal, string

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

SQL-like query language

yes

through the embedded Spark runtime

Solr Parallel SQL Interface

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

C++ API
Java API

Java API
RESTful HTTP/JSON API

Supported programming languages

Go
Java
PHP
Python
R
Ruby

All languages supporting JDBC/ODBC/ADO.Net

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C
C++
Go
Java
Perl
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

yes

Java Stored Procedures

yes

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

Active-active shard replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

yes

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