DBMS > Bangdb vs. Elasticsearch vs. SAP HANA vs. Solr

System Properties Comparison Bangdb vs. Elasticsearch vs. SAP HANA vs. Solr

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Editorial information provided by DB-Engines

Name

Description

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

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

In-memory, column based data store. Available as appliance or cloud service

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Graph DBMS
Time Series DBMS

Search engine

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

Document store
Spatial DBMS
Vector DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

bangdb.com

www.elastic.co/elasticsearch

www.sap.com/products/hana.html

solr.apache.org

Technical documentation

docs.bangdb.com

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

help.sap.com/hana

solr.apache.org/resources.html

Developer

Sachin Sinha, BangDB

Elastic

SAP

Apache Software Foundation

Initial release

2012

2010

2006

Current release

BangDB 2.0, October 2021

8.6, January 2023

2.0 SPS07 (April 4, 2023), April 2023

9.6.0, April 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Elastic License

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++

Java

Server operating systems

Linux

All OS with a Java VM

Appliance or cloud-service

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

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

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

All search fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

yes

Solr Parallel SQL Interface

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
Python

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

SQLScript, R

Java plugins

Triggers

yes, Notifications (with Streaming only)

yes

by using the 'percolation' feature

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

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

Memcached and Redis integration

yes

User concepts

Access control

yes (enterprise version only)

yes

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