DBMS > Google BigQuery vs. Graph Engine vs. Manticore Search vs. Spark SQL vs. STSdb

System Properties Comparison Google BigQuery vs. Graph Engine vs. Manticore Search vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Search engine

Relational DBMS

Key-value store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

cloud.google.com/bigquery

www.graphengine.io

manticoresearch.com

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

manual.manticoresearch.com

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Google

Microsoft

Manticore Software

Apache Software Foundation

STS Soft SC

Initial release

2010

2017

2014

2011

Current release

6.0, February 2023

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

GPL version 2

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

.NET and C

C++

Scala

Server operating systems

hosted

.NET

FreeBSD
Linux
macOS
Windows

Linux
OS X
Windows

Windows

Data scheme

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

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

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

yes

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

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

JDBC
ODBC

.NET Client API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

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

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

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

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication based on Galera library

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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)

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