DBMS > EJDB vs. Graph Engine vs. Hawkular Metrics vs. Heroic vs. Prometheus

System Properties Comparison EJDB vs. Graph Engine vs. Hawkular Metrics vs. Heroic vs. Prometheus

Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Open-source Time Series DBMS and monitoring system

Primary database model

Document store

Graph DBMS
Key-value store

Time Series DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Website

github.com/Softmotions/ejdb

www.graphengine.io

www.hawkular.org

github.com/spotify/heroic

prometheus.io

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

www.graphengine.io/docs/manual

www.hawkular.org/hawkular-metrics/docs/user-guide

spotify.github.io/heroic

prometheus.io/docs

Developer

Softmotions

Microsoft

Community supported by Red Hat

Spotify

Initial release

2012

2010

2014

2015

License

Commercial or Open Source

Open Source

GPLv2

Open Source

MIT License

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache 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

.NET and C

Java

Server operating systems

server-less

.NET

Linux
OS X
Windows

Linux
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

APIs and other access methods

in-process shared library

RESTful HTTP API

HTTP REST

HQL (Heroic Query Language, a JSON-based language)
HTTP API

RESTful HTTP/JSON API

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
C++
F#
Visual Basic

Go
Java
Python
Ruby

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

none

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

In-memory capabilities

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

yes

User concepts

Access control

More information provided by the system vendor

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Trinity
30 October 2010, Microsoft

Open source Microsoft Graph Engine takes on Neo4j
13 February 2017, InfoWorld

IBM releases Graph, a service that can outperform SQL databases
27 July 2016, GeekWire

The graph analytics landscape 2019 - DataScienceCentral.com
27 February 2019, Data Science Central

Aerospike Is Now a Graph Database, Too
21 June 2023, Datanami

provided by Google News

Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived… • DEVCLASS
5 June 2019, DevClass

provided by Google News

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

VTEX scales to 150 million metrics using Amazon Managed Service for Prometheus | Amazon Web Services
10 March 2024, AWS Blog

Exadata Real-Time Insight - Quick Start
3 April 2024, Oracle

OpenTelemetry vs. Prometheus: You can’t fix what you can’t see
29 March 2024, IBM

VictoriaMetrics Offers Prometheus Replacement for Time Series Monitoring
17 July 2023, The New Stack

Linux System Monitoring with Prometheus, Grafana, and collectd
1 February 2024, Linux Journal

provided by Google News

Share this page