DBMS > Badger vs. Datomic vs. Google Cloud Datastore vs. GreptimeDB vs. Quasardb

System Properties Comparison Badger vs. Datomic vs. Google Cloud Datastore vs. GreptimeDB vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

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

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Distributed, high-performance timeseries database

Primary database model

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

github.com/dgraph-io/badger

www.datomic.com

cloud.google.com/datastore

greptime.com

quasar.ai

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.datomic.com

cloud.google.com/datastore/docs

docs.greptime.com

doc.quasar.ai/master

Developer

DGraph Labs

Cognitect

Google

Greptime Inc.

quasardb

Initial release

2017

2012

2008

2022

2009

Current release

1.0.6735, June 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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, Clojure

Rust

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

Android
Docker
FreeBSD
Linux
macOS
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

integer and binary

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

with tags

SQL

Support of SQL

SQL-like query language (GQL)

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

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

gRPC
HTTP API
JDBC

HTTP API

Supported programming languages

Clojure
Java

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

C++
Erlang
Go
Java
JavaScript

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

Server-side scripts

Stored procedures

yes

Transaction Functions

using Google App Engine

Python

Triggers

By using transaction functions

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication using Paxos

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

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.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

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

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

yes

by using LevelDB

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

Transient mode

User concepts

Access control

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

Simple rights management via user accounts

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more

Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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