DBMS > Apache Impala vs. Bangdb vs. Splice Machine

System Properties Comparison Apache Impala vs. Bangdb vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Converged and high performance database for device data, events, time series, document and graph

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

impala.apache.org

bangdb.com

splicemachine.com

Technical documentation

impala.apache.org/impala-docs.html

docs.bangdb.com

splicemachine.com/how-it-works

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Sachin Sinha, BangDB

Splice Machine

Initial release

2013

2012

2014

Current release

4.1.0, June 2022

BangDB 2.0, October 2021

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD 3

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C, C++

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL like support with command line tool

yes

APIs and other access methods

JDBC
ODBC

Proprietary protocol
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Java

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency

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, optimistic concurrency control

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes (enterprise version only)

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

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More resources

Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

Real-time machine learning with Splice Machine's ML Manager
17 April 2019, TechTarget

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10 March 2020, Towards Data Science

provided by Google News

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