DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Google BigQuery vs. HBase vs. LevelDB vs. Sphinx

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Google BigQuery vs. HBase vs. LevelDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

Large scale data warehouse service with append-only tables

Wide-column store based on Apache Hadoop and on concepts of BigTable

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

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

Score	0.68
Rank	#231	Overall
	#105	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

www.alibabacloud.com/product/hybriddb-postgresql

cloud.google.com/bigquery

hbase.apache.org

github.com/google/leveldb

sphinxsearch.com

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

github.com/google/leveldb/blob/main/doc/index.md

sphinxsearch.com/docs

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Google

Sphinx Technologies Inc.

Initial release

2016

2010

2008

2011

2001

Current release

2.3.4, January 2021

1.23, February 2021

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

BSD

Open Source

GPL version 2, commercial licence 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

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Illumos
Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

Proprietary protocol

Supported programming languages

C
Java
Perl
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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