DBMS > GeoMesa vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. QuestDB vs. Spark SQL

System Properties Comparison GeoMesa vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. QuestDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

A Wide Column Store for rapid development using massive semi-structured datasets

Microsofts flagship relational DBMS

A high performance open source SQL database for time series data

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.geomesa.org

azure.microsoft.com/en-us/services/storage/tables

www.microsoft.com/en-us/sql-server

questdb.io

spark.apache.org/sql

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

learn.microsoft.com/en-US/sql/sql-server

questdb.io/docs

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

Developer

CCRi and others

Microsoft

QuestDB Technology Inc

Apache Software Foundation

Initial release

2014

2012

1989

2014

Current release

4.0.5, February 2024

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

restricted free version is available

Open Source

Apache 2.0

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

Scala

C++

Java (Zero-GC), C++, Rust

Scala

Server operating systems

hosted

Linux
Windows

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free via InfluxDB Line Protocol

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL with time-series extensions

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

JDBC
ODBC

Supported programming languages

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

over HTTP

Java
Python
R
Scala

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

Implicit feature of the cloud service

tables can be distributed across several files (horizontal partitioning); sharding through federation

horizontal partitioning (by timestamps)

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

Source-replica replication with eventual consistency

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

ACID

ACID for single-table writes

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.

depending on storage layer

yes

through memory mapped files

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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