DBMS > Apache Impala vs. Drizzle vs. gStore vs. Heroic vs. SingleStore

System Properties Comparison Apache Impala vs. Drizzle vs. gStore vs. Heroic vs. SingleStore

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A native Graph DBMS to store and maintain very large RDF datasets.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Secondary database models

Document store

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Website

impala.apache.org

en.gstore.cn

github.com/spotify/heroic

www.singlestore.com

Technical documentation

impala.apache.org/impala-docs.html

en.gstore.cn/#/enDocs

spotify.github.io/heroic

docs.singlestore.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Drizzle project, originally started by Brian Aker

Spotify

SingleStore Inc.

Initial release

2013

2008

2016

2014

2013

Current release

4.1.0, June 2022

7.2.4, September 2012

1.2, November 2023

8.5, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

GNU GPL

Open Source

BSD

Open Source

Apache 2.0

commercial

free developer edition available

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C++

Java

C++, Go

Server operating systems

Linux

FreeBSD
Linux
OS X

Linux

64 bit version required

Data scheme

yes

schema-free and OWL/RDFS-schema support

schema-free

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

but no triggers and foreign keys

APIs and other access methods

JDBC
ODBC

JDBC

HTTP API
SPARQL 1.1

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

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

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

yes

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Users, roles and permissions, Role-Based Access Control (RBAC) supported

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
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