DBMS > Apache Phoenix vs. GigaSpaces vs. Google BigQuery vs. Milvus

System Properties Comparison Apache Phoenix vs. GigaSpaces vs. Google BigQuery vs. Milvus

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Large scale data warehouse service with append-only tables

A DBMS designed for efficient storage of vector data and vector similarity searches

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Vector DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Website

phoenix.apache.org

www.gigaspaces.com

cloud.google.com/bigquery

milvus.io

Technical documentation

phoenix.apache.org

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

milvus.io/docs/overview.md

Developer

Apache Software Foundation

Gigaspaces Technologies

Google

Initial release

2014

2000

2010

2019

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

15.5, September 2020

2.3.4, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

Apache Version 2.0

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

Java

Java, C++, .Net

C++, Go

Server operating systems

Linux
Unix
Windows

Linux
macOS
Solaris
Windows

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-99 for query and DML statements

yes

APIs and other access methods

JDBC

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

in JavaScript

Triggers

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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