DBMS > chDB vs. Google Cloud Firestore vs. LeanXcale vs. Teradata

System Properties Comparison chDB vs. Google Cloud Firestore vs. LeanXcale vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

An embedded SQL OLAP Engine powered by ClickHouse

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.05
Rank	#358	Overall
	#152	Relational DBMS

Score	6.63
Rank	#54	Overall
	#9	Document stores

Score	0.28
Rank	#286	Overall
	#41	Key-value stores
	#130	Relational DBMS

Score	41.47
Rank	#22	Overall
	#15	Relational DBMS

Website

github.com/chdb-io/chdb

firebase.google.com/products/firestore

www.leanxcale.com

www.teradata.com

Technical documentation

doc.chdb.io

firebase.google.com/docs/firestore

docs.teradata.com

Developer

Google

LeanXcale

Teradata

Initial release

2023

2017

2015

1984

Current release

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

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.

Server operating systems

server-less

hosted

hosted
Linux

Data 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.

yes

Secondary indexes

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

through Apache Derby

yes

SQL 2016 + extensions

APIs and other access methods

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

Bun
C
C++
Go
JavaScript (Node.js)
Python
Rust

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C
Java
Scala

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

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

yes

ACID

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

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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Teradata is the most popular data warehouse DBMS
2 April 2013, Paul Andlinger

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