DBMS > Cloudflare Workers KV vs. GeoSpock vs. Heroic vs. Spark SQL vs. WakandaDB

System Properties Comparison Cloudflare Workers KV vs. GeoSpock vs. Heroic vs. Spark SQL vs. WakandaDB

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

A global, low-latency, key-value store for applications on Cloudflare with exceptionally high read volumes and low-latency.

Spatial and temporal data processing engine for extreme data scale

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.39
Rank	#274	Overall
	#39	Key-value stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

www.cloudflare.com/developer-platform/workers-kv

geospock.com

github.com/spotify/heroic

spark.apache.org/sql

wakanda.github.io

Technical documentation

developers.cloudflare.com/kv/api

spotify.github.io/heroic

spark.apache.org/docs/latest/sql-programming-guide.html

wakanda.github.io/doc

Developer

Cloudflare

GeoSpock

Spotify

Apache Software Foundation

Wakanda SAS

Initial release

2018

2014

2012

Current release

2.0, September 2019

3.5.0 ( 2.13), September 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

AGPLv3, extended commercial license available

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

Scala

C++, JavaScript

Server operating systems

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

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

yes

via Elasticsearch

SQL

Support of SQL

ANSI SQL for query only (using Presto)

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST
Proprietary protocol

JDBC

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

JDBC
ODBC

RESTful HTTP API

Supported programming languages

C
C++
Dart
JavaScript
Kotlin
Python
Rust
Scala

Java
Python
R
Scala

JavaScript

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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.

User concepts

Access control

Access rights for users can be defined per table

yes

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