DBMS > Google Cloud Bigtable vs. HEAVY.AI vs. Lovefield vs. Microsoft SQL Server vs. XTDB

System Properties Comparison Google Cloud Bigtable vs. HEAVY.AI vs. Lovefield vs. Microsoft SQL Server vs. XTDB

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.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Embeddable relational database for web apps written in pure JavaScript

Microsofts flagship relational DBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store
Wide column store

Relational DBMS

Document store

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

cloud.google.com/bigtable

github.com/heavyai/heavydb
www.heavy.ai

google.github.io/lovefield

www.microsoft.com/en-us/sql-server

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cloud.google.com/bigtable/docs

docs.heavy.ai

github.com/google/lovefield/blob/master/docs/spec_index.md

learn.microsoft.com/en-US/sql/sql-server

www.xtdb.com/docs

Developer

Google

HEAVY.AI, Inc.

Google

Microsoft

Juxt Ltd.

Initial release

2015

2016

2014

1989

2019

Current release

5.10, January 2022

2.1.12, February 2017

SQL Server 2022, November 2022

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++ and CUDA

JavaScript

C++

Clojure

Server operating systems

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

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

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST
JDBC

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Clojure
Java

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

none

yes, but depending on the SQL-Server Edition

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

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