DBMS > etcd vs. H2 vs. InfluxDB vs. Snowflake vs. TimescaleDB

System Properties Comparison etcd vs. H2 vs. InfluxDB vs. Snowflake vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

A distributed reliable key-value store

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

DBMS for storing time series, events and metrics

Cloud-based data warehousing service for structured and semi-structured data

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Spatial DBMS

with GEO package

Relational DBMS

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

etcd.io
github.com/etcd-io/etcd

www.h2database.com

www.influxdata.com/products/influxdb-overview

www.snowflake.com

www.timescale.com

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

www.h2database.com/html/main.html

docs.influxdata.com/influxdb

docs.snowflake.net/manuals/index.html

docs.timescale.com

Developer

Thomas Mueller

Snowflake Computing Inc.

Timescale

Initial release

2005

2013

2014

2017

Current release

3.4, August 2019

2.2.220, July 2023

2.7.6, April 2024

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

MIT-License; commercial enterprise version available

commercial

Open Source

Apache 2.0

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

Server operating systems

FreeBSD
Linux
Windows

experimental

All OS with a Java VM

Linux
OS X

through Homebrew

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

gRPC
JSON over HTTP

JDBC
ODBC

HTTP API
JSON over UDP

CLI Client
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

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

Java

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

JavaScript (Node.js)
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

user defined functions

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes, watching key changes

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

in enterprise version only

yes

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

With clustering: 2 database servers on different computers operate on identical copies of a database

selectable replication factor

in enterprise version only

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

simple rights management via user accounts

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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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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