DBMS > Apache Impala vs. HugeGraph vs. Infobright vs. YugabyteDB

System Properties Comparison Apache Impala vs. HugeGraph vs. Infobright vs. YugabyteDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A fast-speed and highly-scalable Graph DBMS

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store

Document store
Wide column store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	2.91
Rank	#102	Overall
	#51	Relational DBMS

Website

impala.apache.org

github.com/hugegraph
hugegraph.apache.org

ignitetech.com/softwarelibrary/infobrightdb

www.yugabyte.com

Technical documentation

impala.apache.org/impala-docs.html

hugegraph.apache.org/docs

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Baidu

Ignite Technologies Inc.; formerly InfoBright Inc.

Yugabyte Inc.

Initial release

2013

2018

2005

2017

Current release

4.1.0, June 2022

0.9

2.19, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.

Implementation language

C++

Java

C and C++

Server operating systems

Linux

Linux
macOS
Unix

Linux
Windows

Linux
OS X

Data scheme

yes

depending on used data model

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.

Secondary indexes

yes

also supports composite index and range index

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

yes, PostgreSQL compatible

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

Supported programming languages

All languages supporting JDBC/ODBC

Groovy
Java
Python

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

asynchronous Gremlin script jobs

yes

sql, plpgsql, C

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

none

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

Source-replica replication

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

based on RocksDB

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 rights for users, groups and roles

based on Apache Sentry and Kerberos

Users, roles and permissions

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

yes

More information provided by the system vendor

Specific characteristics

YugabyteDB is an open source distributed SQL database for cloud native transactional...
» more

Competitive advantages

PostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
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Typical application scenarios

Systems of record and engagement for cloud native applications that require resilience,...
» more

Market metrics

2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
» more

Licensing and pricing models

Apache 2.0 license for the database
» more

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More resources

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