DBMS > BigObject vs. Google BigQuery vs. LevelDB vs. RavenDB vs. TDengine

System Properties Comparison BigObject vs. Google BigQuery vs. LevelDB vs. RavenDB vs. TDengine

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Large scale data warehouse service with append-only tables

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Open Source Operational and Transactional Enterprise NoSQL Document Database

Time Series DBMS and big data platform

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Website

bigobject.io

cloud.google.com/bigquery

github.com/google/leveldb

ravendb.net

github.com/taosdata/TDengine
tdengine.com

Technical documentation

docs.bigobject.io

cloud.google.com/bigquery/docs

github.com/google/leveldb/blob/main/doc/index.md

ravendb.net/docs

docs.tdengine.com

Developer

BigObject, Inc.

Google

Hibernating Rhinos

TDEngine, previously Taos Data

Initial release

2015

2010

2011

2010

2019

Current release

1.23, February 2021

5.4, July 2022

3.0, August 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

BSD

Open Source

AGPL version 3, commercial license available

Open Source

AGPL V3, also commercial editions available

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++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Illumos
Linux
OS X
Windows

Linux
macOS
Raspberry Pi
Windows

Linux
Windows

Data scheme

yes

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

SQL-like DML and DDL statements

yes

SQL-like query language (RQL)

Standard SQL with extensions for time-series applications

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

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

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

yes

Triggers

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

Since BigQuery is designed for querying data

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Authorization levels configured per client per database

yes

More information provided by the system vendor

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Licensing and pricing models

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