DBMS > BoltDB vs. Graph Engine vs. GridDB vs. Sqrrl vs. XTDB

System Properties Comparison BoltDB vs. Graph Engine vs. GridDB 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

An embedded key-value store for Go.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Scalable in-memory time series database optimized for IoT and Big Data

Adaptable, secure NoSQL built on Apache Accumulo

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

Primary database model

Key-value store

Graph DBMS
Key-value store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

Key-value store
Relational DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/boltdb/bolt

www.graphengine.io

griddb.net

sqrrl.com

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

Technical documentation

www.graphengine.io/docs/manual

docs.griddb.net

www.xtdb.com/docs

Developer

Microsoft

Toshiba Corporation

Amazon

originally Sqrrl Data, Inc.

Juxt Ltd.

Initial release

2013

2010

2013

2012

2019

Current release

5.1, August 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

MIT License

Open Source

MIT License

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

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

.NET and C

C++

Java

Clojure

Server operating systems

BSD
Linux
OS X
Solaris
Windows

.NET

Linux

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

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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.

Secondary indexes

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

HTTP REST
JDBC

Supported programming languages

C#
C++
F#
Visual Basic

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

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

making use of Hadoop

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

selectable replication factor

making use of Hadoop

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency within container, eventual consistency across containers

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

yes

ACID at container level

Atomic updates per row, document, or graph entity

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

User concepts

Access control

Access rights for users can be defined per database

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

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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