DBMS > AlaSQL vs. Google BigQuery vs. Google Cloud Datastore vs. Microsoft Azure AI Search vs. SingleStore

System Properties Comparison AlaSQL vs. Google BigQuery vs. Google Cloud Datastore vs. Microsoft Azure AI Search vs. SingleStore

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Search-as-a-service for web and mobile app development

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Document store
Relational DBMS

Secondary database models

Vector DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Website

alasql.org

cloud.google.com/bigquery

cloud.google.com/datastore

azure.microsoft.com/en-us/services/search

www.singlestore.com

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

cloud.google.com/datastore/docs

learn.microsoft.com/en-us/azure/search

docs.singlestore.com

Developer

Andrey Gershun & Mathias R. Wulff

Google

Microsoft

SingleStore Inc.

Initial release

2014

2010

2008

2015

2013

Current release

8.5, January 2024

License

Commercial or Open Source

Open Source

MIT-License

commercial

free developer edition 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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

JavaScript

C++, Go

Server operating systems

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

hosted

Linux

64 bit version required

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

SQL-like query language (GQL)

yes

but no triggers and foreign keys

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

RESTful HTTP API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C#
Java
JavaScript
Python

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

using Google App Engine

yes

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

yes

only for local storage and DOM-storage

Since BigQuery is designed for querying data

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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)

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

yes

using Azure authentication

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
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