DBMS > AlaSQL vs. Google BigQuery vs. HEAVY.AI vs. Manticore Search vs. Riak TS

System Properties Comparison AlaSQL vs. Google BigQuery vs. HEAVY.AI vs. Manticore Search vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

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

Multi-storage database for search, including full-text search.

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Document store
Relational DBMS

Relational DBMS

Search engine

Time Series DBMS

Secondary database models

Spatial DBMS

Time Series DBMS

using the Manticore Columnar Library

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	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

alasql.org

cloud.google.com/bigquery

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

manticoresearch.com

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

docs.heavy.ai

manual.manticoresearch.com

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Andrey Gershun & Mathias R. Wulff

Google

HEAVY.AI, Inc.

Manticore Software

Open Source, formerly Basho Technologies

Initial release

2014

2010

2016

2017

2015

Current release

5.10, January 2022

6.0, February 2023

3.0.0, September 2022

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

GPL version 2

Open Source

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

JavaScript

C++ and CUDA

C++

Erlang

Server operating systems

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

hosted

Linux

FreeBSD
Linux
macOS
Windows

Linux
OS X

Data scheme

schema-free

yes

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

restricted

SQL

Support of SQL

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

yes

SQL-like query language

yes, limited

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

HTTP API
Native Erlang Interface

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Synchronous replication based on Galera library

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

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

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

The original contents of fields are not stored in the Manticore index.

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)

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.