DBMS > Apache Druid vs. openGemini vs. Tarantool vs. TimescaleDB

System Properties Comparison Apache Druid vs. openGemini vs. Tarantool vs. TimescaleDB

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

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

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

Document store
Key-value store
Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

Relational DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.01
Rank	#375	Overall
	#39	Time Series DBMS

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

druid.apache.org

www.opengemini.org
github.com/openGemini

www.tarantool.io

www.timescale.com

Technical documentation

druid.apache.org/docs/latest/design

docs.opengemini.org/guide

www.tarantool.io/en/doc

docs.timescale.com

Developer

Apache Software Foundation and contributors

Huawei and openGemini community

Timescale

Initial release

2012

2022

2008

2017

Current release

29.0.1, April 2024

1.1, July 2023

2.10.0, May 2022

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Unix

Linux
Windows

BSD
Linux
macOS

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

string, double, decimal, uuid, integer, blob, boolean, datetime

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

SQL for querying

SQL-like query language

Full-featured ANSI SQL support

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP REST

Open binary protocol

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

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Lua, C and SQL stored procedures

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, before/after data modification events, on replication events, client session events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

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

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Administrators and common users accounts

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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