DBMS > Blueflood vs. LMDB vs. Microsoft Azure Synapse Analytics vs. TimesTen

System Properties Comparison Blueflood vs. LMDB vs. Microsoft Azure Synapse Analytics vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

A high performant, light-weight, embedded key-value database library

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

In-Memory RDBMS compatible to Oracle

Primary database model

Time Series DBMS

Key-value store

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	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

blueflood.io

www.symas.com/symas-embedded-database-lmdb

azure.microsoft.com/services/synapse-analytics

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

github.com/rax-maas/blueflood/wiki

www.lmdb.tech/doc

docs.microsoft.com/azure/synapse-analytics

docs.oracle.com/database/timesten-18.1

Developer

Rackspace

Symas

Microsoft

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2013

2011

2016

1998

Current release

0.9.32, January 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial

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

C++

Server operating systems

Linux
OS X

Linux
Unix
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

predefined scheme

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

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP REST

ADO.NET
JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C#
Java
PHP

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

Transact SQL

PL/SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

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

by means of logfiles and checkpoints

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

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