DBMS > Atos Standard Common Repository vs. InfinityDB vs. QuestDB vs. Spark SQL

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

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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

A Java embedded Key-Value Store which extends the Java Map interface

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

Document store
Key-value 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	0.02
Rank	#338	Overall
	#50	Key-value stores

Score	1.19
Rank	#161	Overall
	#12	Time Series DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

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

boilerbay.com

questdb.io

spark.apache.org/sql

Technical documentation

boilerbay.com/infinitydb/manual

questdb.io/docs/introduction

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

Developer

Atos Convergence Creators

Boiler Bay Inc.

QuestDB Limited

Apache Software Foundation

Initial release

2016

2002

2014

Current release

1703

4.0

3.2.0, October 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

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

Scala

Server operating systems

Linux

All OS with a Java VM

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

Schema and schema-less with LDAP views

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

schema-free via InfluxDB Line Protocol

yes

Typing

predefined data types such as float or date

optional

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

LDAP

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

Java

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

over HTTP

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

horizontal partitioning (by timestamps)

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Configurable consistency for N replicas

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

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

Atomic execution of specific operations

ACID

Optimistic locking for transactions; no isolation for bulk loads

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.

yes

through memory mapped files

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

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

QuestDB 6.3 Release Highlights
9 May 2022

Time Series Data Analytics with QuestDB and Cube
26 April 2022

Demo of live crypto data streamed with QuestDB and Grafana
12 April 2022

Calling on our community members to help us support Ukraine
7 March 2022

QuestDB 6.2 January release, SQL JIT compiler
27 January 2022

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