DBMS > Apache Impala vs. DolphinDB vs. Heroic vs. Riak KV vs. SingleStore

System Properties Comparison Apache Impala vs. DolphinDB vs. Heroic vs. Riak KV vs. SingleStore

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

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

Distributed, fault tolerant key-value store

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

Relational DBMS

Time Series DBMS
Vector DBMS

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Document store

Relational DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Website

impala.apache.org

www.dolphindb.com

github.com/spotify/heroic

www.singlestore.com

Technical documentation

impala.apache.org/impala-docs.html

docs.dolphindb.cn/en/help200/index.html

spotify.github.io/heroic

www.tiot.jp/riak-docs/riak/kv/latest

docs.singlestore.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DolphinDB, Inc

Spotify

OpenSource, formerly Basho Technologies

SingleStore Inc.

Initial release

2013

2018

2014

2009

2013

Current release

4.1.0, June 2022

v2.00.4, January 2022

3.2.0, December 2022

8.5, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free community version available

Open Source

Apache 2.0

Open Source

Apache version 2, commercial enterprise edition

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.

Implementation language

C++

Java

Erlang

C++, Go

Server operating systems

Linux

Linux
Windows

Linux
OS X

Linux

64 bit version required

Data scheme

yes

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

restricted

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

but no triggers and foreign keys

APIs and other access methods

JDBC
ODBC

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

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

HTTP API
Native Erlang Interface

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Erlang

yes

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

no "single point of failure"

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

selectable replication factor

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

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

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

Administrators, Users, Groups

yes, using Riak Security

Fine grained access control via users, groups and roles

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