DBMS > Google Cloud Bigtable vs. RocksDB vs. Solr vs. TDengine vs. TerminusDB

System Properties Comparison Google Cloud Bigtable vs. RocksDB vs. Solr vs. TDengine vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A widely used distributed, scalable search engine based on Apache Lucene

Time Series DBMS and big data platform

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Key-value store
Wide column store

Secondary database models

Spatial DBMS

Relational DBMS

Document store
RDF store

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

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	2.68
Rank	#106	Overall
	#9	Time Series DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

cloud.google.com/bigtable

rocksdb.org

solr.apache.org

github.com/taosdata/TDengine
tdengine.com

terminusdb.com

Technical documentation

cloud.google.com/bigtable/docs

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

docs.tdengine.com

terminusdb.github.io/terminusdb/#

Developer

Google

Facebook, Inc.

Apache Software Foundation

TDEngine, previously Taos Data

DataChemist Ltd.

Initial release

2015

2013

2006

2019

2018

Current release

9.2.1, May 2024

9.6.1, May 2024

3.0, August 2022

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache Version 2

Open Source

AGPL V3, also commercial editions available

Open Source

GPL V3

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++

Java

Prolog, Rust

Server operating systems

hosted

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
Windows

Linux

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

Solr Parallel SQL Interface

Standard SQL with extensions for time-series applications

SQL-like query language (WOQL)

APIs and other access methods

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

C++ API
Java API

Java API
RESTful HTTP/JSON API

JDBC
RESTful HTTP API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

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

C
C++
Go
Java
Perl
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

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

JavaScript
Python

Server-side scripts

Stored procedures

Java plugins

yes

Triggers

yes

User configurable commands triggered on index changes

yes, via alarm monitoring

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

yes

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

Role-based access control

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Market metrics

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Licensing and pricing models

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