DBMS > GeoSpock vs. QuestDB vs. RocksDB

System Properties Comparison GeoSpock vs. QuestDB vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Spatial and temporal data processing engine for extreme data scale

A high performance open source SQL database for time series data

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

Secondary database models

Time Series DBMS

Relational DBMS

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

Score	0.31
Rank	#310	Overall
	#140	Relational DBMS

Score	1.89
Rank	#146	Overall
	#11	Time Series DBMS

Score	4.57
Rank	#87	Overall
	#13	Key-value stores

Website

geospock.com

questdb.io

rocksdb.org

Technical documentation

docs.geospock.com/Content/Home.htm

questdb.io/docs/introduction

github.com/facebook/rocksdb/wiki

Developer

GeoSpock

QuestDB Limited

Facebook, Inc.

Initial release

2014

2013

Current release

2.0, September 2019

7.10.2, March 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

BSD

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, Javascript

Java

C++

Server operating systems

hosted

Linux
macOS
Windows

Linux

Data scheme

yes

schema-free via InfluxDB Line Protocol

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

temporal, categorical

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like query language

APIs and other access methods

JDBC

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC

C++ API
Java API

Supported programming languages

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

over HTTP

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

horizontal partitioning (by timestamps)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable consistency for N replicas

yes

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

Transaction concepts

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

ACID for single-table writes

yes

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

through memory mapped files

yes

User concepts

Access control

Access rights for users can be defined per table

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column based storage and time partitioned...
» more

Competitive advantages

Real-time data ingestion and istant SQL queries for time series High performance...
» more

Typical application scenarios

Application metrics Financial market data and algo trading DevOps monitoring Real-time...
» more

Licensing and pricing models

Apache 2.0.
» more

News

MongoDB Time Series Benchmark and Review
20 March 2023

Loading Pandas DataFrames into QuestDB
9 March 2023

How Reliable is Your Distributed Database?
3 March 2023

Running Databases on Kubernetes
2 March 2023

The Tale of Troubleshooting: Unstable Builds and Open Source Infrastructure
1 March 2023

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