DBMS > Atos Standard Common Repository vs. Blueflood vs. QuestDB vs. Spark SQL vs. SQLite

System Properties Comparison Atos Standard Common Repository vs. Blueflood vs. QuestDB vs. Spark SQL vs. SQLite

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Scalable TimeSeries DBMS based on Cassandra

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

Widely used embeddable, in-process RDBMS

Primary database model

Document store
Key-value store

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

Technical documentation

github.com/rax-maas/blueflood/wiki

questdb.io/docs

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

www.sqlite.org/docs.html

Developer

Atos Convergence Creators

Rackspace

QuestDB Technology Inc

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2016

2013

2014

2000

Current release

1703

3.5.0 ( 2.13), September 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Public Domain

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

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

Scala

Server operating systems

Linux

Linux
OS X

Linux
macOS
Windows

Linux
OS X
Windows

server-less

Data scheme

Schema and schema-less with LDAP views

predefined scheme

yes

schema-free via InfluxDB Line Protocol

yes

dynamic column types

Typing

predefined data types such as float or date

optional

yes

not rigid because of 'dynamic typing' concept.

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 with time-series extensions

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

LDAP

HTTP REST

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

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages with LDAP bindings

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

over HTTP

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

horizontal partitioning (by timestamps)

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Source-replica replication with eventual consistency

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID for single-table writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

LDAP bind authentication

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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