DBMS > Google BigQuery vs. LMDB vs. Quasardb vs. Spark SQL vs. Transbase

System Properties Comparison Google BigQuery vs. LMDB vs. Quasardb vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

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

Distributed, high-performance timeseries database

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

cloud.google.com/bigquery

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

quasar.ai

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

Technical documentation

cloud.google.com/bigquery/docs

www.lmdb.tech/doc

doc.quasar.ai/master

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

www.transaction.de/en/products/transbase/features.html

Developer

Google

Symas

quasardb

Apache Software Foundation

Transaction Software GmbH

Initial release

2010

2011

2009

2014

1987

Current release

0.9.32, January 2024

3.14.1, January 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache 2.0

commercial

free development license

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

C++

Scala

C and C++

Server operating systems

hosted

Linux
Unix
Windows

BSD
Linux
OS X
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.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

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

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication with selectable replication factor

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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