DBMS > CrateDB vs. EsgynDB vs. Galaxybase vs. LevelDB vs. XTDB

System Properties Comparison CrateDB vs. EsgynDB vs. Galaxybase vs. LevelDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

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

Scalable, ACID-compliant native distributed parallel graph platform

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS

Score	2.33
Rank	#111	Overall
	#19	Key-value stores

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

cratedb.com

www.esgyn.cn

galaxybase.com

github.com/google/leveldb

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cratedb.com/docs

github.com/google/leveldb/blob/main/doc/index.md

www.xtdb.com/docs

Developer

Crate

Esgyn

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

Google

Juxt Ltd.

Initial release

2013

2015

2017

2011

2019

Current release

5.8.1, August 2024

Nov 20, November 2021

1.23, February 2021

1.19, September 2021

License

Commercial or Open Source

Open Source

commercial

Open Source

BSD

Open Source

MIT License

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 Java

C++

Clojure

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux

Illumos
Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

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

yes

Strong typed schema

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

ADO.NET
JDBC
ODBC

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

HTTP REST
JDBC

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

All languages supporting JDBC/ODBC/ADO.Net

Go
Java
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Clojure
Java

Server-side scripts

Stored procedures

user defined functions (Javascript)

Java Stored Procedures

user defined procedures and functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

Multi-source replication between multi datacenters

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

rights management via user accounts

fine grained access rights according to SQL-standard

Role-based access control

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