DBMS > Databricks vs. HugeGraph vs. OpenSearch vs. Sphinx

System Properties Comparison Databricks vs. HugeGraph vs. OpenSearch vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

A fast-speed and highly-scalable Graph DBMS

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

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

Primary database model

Document store
Relational DBMS

Graph DBMS

Search engine

Secondary database models

Vector DBMS

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

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	16.03
Rank	#37	Overall
	#4	Search engines

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

www.databricks.com

github.com/hugegraph
hugegraph.apache.org

github.com/opensearch-project
opensearch.org

sphinxsearch.com

Technical documentation

docs.databricks.com

hugegraph.apache.org/docs

opensearch.org/docs/latest

sphinxsearch.com/docs

Developer

Databricks

Baidu

Amazon Web Services

Sphinx Technologies Inc.

Initial release

2013

2018

2021

2001

Current release

0.9

2.5.0, January 2023

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

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

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java

C++

Server operating systems

hosted

Linux
macOS
Unix

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

with Databricks SQL

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

Python
R
Scala

Groovy
Java
Python

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions and aggregates

asynchronous Gremlin script jobs

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Users, roles and permissions

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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OpenSearch is a highly scalable and extensible open-source software suite for search,...
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Competitive advantages

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
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Typical application scenarios

The OpenSearch platform is used across a broad range of search , analytics, and observability...
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Licensing and pricing models

The OpenSearch Project offers open source software licensed under Apache License...
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