DBMS > BigchainDB vs. Datomic vs. Google BigQuery vs. Kinetica vs. LMDB

System Properties Comparison BigchainDB vs. Datomic vs. Google BigQuery vs. Kinetica vs. LMDB

Editorial information provided by DB-Engines

Name

Description

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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

A high performant, light-weight, embedded key-value database library

Primary database model

Document store

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Website

www.bigchaindb.com

www.datomic.com

cloud.google.com/bigquery

www.kinetica.com

www.symas.com/symas-embedded-database-lmdb

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.datomic.com

cloud.google.com/bigquery/docs

docs.kinetica.com

www.lmdb.tech/doc

Developer

Cognitect

Google

Kinetica

Symas

Initial release

2016

2012

2010

2012

2011

Current release

1.0.7075, December 2023

7.1, August 2021

0.9.32, January 2024

License

Commercial or Open Source

Open Source

AGPL v3

commercial

limited edition free

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

Java, Clojure

C, C++

Server operating systems

Linux

All OS with a Java VM

hosted

Linux

Linux
Unix
Windows

Data scheme

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
RESTful HTTP API

RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

in JavaScript

user defined functions

Triggers

By using transaction functions

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

But extensive use of caching in the application peers

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

GPU vRAM or System RAM

yes

User concepts

Access control

yes

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

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