DBMS > Firebird vs. Google Cloud Datastore vs. QuestDB vs. SAP HANA vs. Sphinx

System Properties Comparison Firebird vs. Google Cloud Datastore vs. QuestDB vs. SAP HANA vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

Firebird is an open source RDBMS forked from Borland's InterBase

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A high performance open source SQL database for time series data

In-memory, column based data store. Available as appliance or cloud service

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Secondary database models

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.firebirdsql.org

cloud.google.com/datastore

questdb.io

www.sap.com/products/hana.html

sphinxsearch.com

Technical documentation

www.firebirdsql.org/en/reference-manuals

cloud.google.com/datastore/docs

questdb.io/docs

help.sap.com/hana

sphinxsearch.com/docs

Developer

Firebird Foundation

Google

QuestDB Technology Inc

SAP

Sphinx Technologies Inc.

Initial release

2000

As fork of Borland's InterBase

2008

2014

2010

2001

Current release

5.0.0, January 2024

2.0 SPS07 (April 4, 2023), April 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Initial Developer's Public License

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and C++

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

C++

Server operating systems

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

hosted

Linux
macOS
Windows

Appliance or cloud-service

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

schema-free via InfluxDB Line Protocol

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

yes

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language (GQL)

SQL with time-series extensions

yes

SQL-like query language (SphinxQL)

APIs and other access methods

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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

JDBC
ODBC

Proprietary protocol

Supported programming languages

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

over HTTP

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

PSQL

using Google App Engine

SQLScript, R

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning (by timestamps)

yes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

Source-replica replication with eventual consistency

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID for single-table writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

Users with fine-grained authorization concept

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

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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