DBMS > Badger vs. HEAVY.AI vs. Splice Machine vs. Warp 10

System Properties Comparison Badger vs. HEAVY.AI vs. Splice Machine vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

github.com/dgraph-io/badger

github.com/heavyai/heavydb
www.heavy.ai

splicemachine.com

www.warp10.io

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.heavy.ai

splicemachine.com/how-it-works

www.warp10.io/content/02_Getting_started

Developer

DGraph Labs

HEAVY.AI, Inc.

Splice Machine

SenX

Initial release

2017

2016

2014

2015

Current release

5.10, January 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

AGPL 3.0, commercial license available

Open Source

Apache License 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.

Implementation language

C++ and CUDA

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
Native Spark Datasource
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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