DBMS > CrateDB vs. Dgraph vs. Google Cloud Datastore vs. Oracle Berkeley DB

System Properties Comparison CrateDB vs. Dgraph vs. Google Cloud Datastore vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

Distributed and scalable native Graph DBMS

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Widely used in-process key-value store

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Graph DBMS

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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	1.39
Rank	#155	Overall
	#15	Graph DBMS

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Website

cratedb.com

dgraph.io

cloud.google.com/datastore

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

cratedb.com/docs

dgraph.io/docs

cloud.google.com/datastore/docs

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Crate

Dgraph Labs, Inc.

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2013

2016

2008

1994

Current release

5.8.1, August 2024

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X
Windows

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

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

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like query language (GQL)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

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

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions (Javascript)

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

Synchronous replication via Raft

Multi-source replication using Paxos

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

rights management via user accounts

Planned for future releases

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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