DBMS > Apache Drill vs. BigchainDB vs. Google Cloud Bigtable vs. Oracle Berkeley DB vs. SurrealDB

System Properties Comparison Apache Drill vs. BigchainDB vs. Google Cloud Bigtable vs. Oracle Berkeley DB vs. SurrealDB

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Widely used in-process key-value store

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Document store
Relational DBMS

Document store

Key-value store
Wide column store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Graph DBMS

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

Score	2.28
Rank	#119	Overall
	#23	Document stores
	#56	Relational DBMS

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	3.58
Rank	#92	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	2.52
Rank	#114	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.91
Rank	#204	Overall
	#33	Document stores
	#18	Graph DBMS

Website

drill.apache.org

www.bigchaindb.com

cloud.google.com/bigtable

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

surrealdb.com

Technical documentation

drill.apache.org/docs

bigchaindb.readthedocs.io/en/latest

cloud.google.com/bigtable/docs

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

surrealdb.com/docs

Developer

Apache Software Foundation

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

SurrealDB Ltd

Initial release

2012

2016

2015

1994

2022

Current release

1.20.3, January 2023

18.1.40, May 2020

v1.1.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

AGPL v3

commercial

Open Source

commercial license available

Open Source

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

Python

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

Rust

Server operating systems

Linux
OS X
Windows

Linux

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

SQL interfaced based on SQLite is available

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

CLI Client
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C++

Go
Haskell
Java
JavaScript
Python
Ruby

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

.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

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes,with MongoDB ord RethinkDB

yes

In-memory capabilities

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

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

yes

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

yes, based on authentication and database rules

More information provided by the system vendor

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