DBMS > Bangdb vs. JaguarDB vs. Solr vs. Sqrrl vs. XTDB

System Properties Comparison Bangdb vs. JaguarDB vs. Solr vs. Sqrrl vs. XTDB

Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

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

Performant, highly scalable DBMS for AI and IoT applications

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

Adaptable, secure NoSQL built on Apache Accumulo

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Graph DBMS
Time Series DBMS

Key-value store
Vector DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

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	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

bangdb.com

www.jaguardb.com

solr.apache.org

sqrrl.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.bangdb.com

www.jaguardb.com/support.html

solr.apache.org/resources.html

www.xtdb.com/docs

Developer

Sachin Sinha, BangDB

DataJaguar, Inc.

Apache Software Foundation

Amazon

originally Sqrrl Data, Inc.

Juxt Ltd.

Initial release

2012

2015

2006

2012

2019

Current release

BangDB 2.0, October 2021

3.3 July 2023

9.6.0, April 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

BSD 3

Open Source

GPL V3.0

Open Source

Apache Version 2

commercial

Open Source

MIT License

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, C++

C++

the server part. Clients available in other languages

Java

Clojure

Server operating systems

Linux

All OS with a Java VM

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

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

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

schema-free

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

yes

yes, extensible-data-notation format

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

SQL

Support of SQL

SQL like support with command line tool

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

Solr Parallel SQL Interface

limited SQL, making use of Apache Calcite

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

Java API
RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Java
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Clojure
Java

Server-side scripts

Stored procedures

Java plugins

Triggers

yes, Notifications (with Streaming only)

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

making use of Hadoop

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

yes

selectable replication factor

making use of Hadoop

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

Transaction concepts

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

ACID

optimistic locking

Atomic updates per row, document, or graph entity

ACID

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

yes (enterprise version only)

rights management via user accounts

yes

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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