DBMS > EsgynDB vs. Google Cloud Bigtable vs. Heroic vs. InfluxDB vs. Microsoft Azure Cosmos DB

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. Heroic vs. InfluxDB vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

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

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

DBMS for storing time series, events and metrics

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

with GEO package

Spatial DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

www.esgyn.cn

cloud.google.com/bigtable

github.com/spotify/heroic

www.influxdata.com/products/influxdb-overview

azure.microsoft.com/services/cosmos-db

Technical documentation

cloud.google.com/bigtable/docs

spotify.github.io/heroic

docs.influxdata.com/influxdb

learn.microsoft.com/azure/cosmos-db

Developer

Esgyn

Google

Spotify

Microsoft

Initial release

2015

2014

2013

2014

Current release

2.7.6, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MIT-License; commercial enterprise version available

commercial

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

C++, Java

Java

Server operating systems

Linux

hosted

Linux
OS X

through Homebrew

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes

JSON types

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

via Elasticsearch

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

HTTP API
JSON over UDP

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

Java Stored Procedures

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

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

yes

selectable replication factor

in enterprise version only

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Eventual Consistency
Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

Multi-item ACID transactions with snapshot isolation within a partition

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

Depending on used storage engine

User concepts

Access control

fine grained access rights according to SQL-standard

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

simple rights management via user accounts

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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News

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