DBMS > Apache Impala vs. Graphite vs. JanusGraph vs. VelocityDB

System Properties Comparison Apache Impala vs. Graphite vs. JanusGraph vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Time Series DBMS

Graph DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Document store

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	4.75
Rank	#75	Overall
	#5	Time Series DBMS

Score	1.91
Rank	#135	Overall
	#12	Graph DBMS

Score	0.11
Rank	#347	Overall
	#35	Graph DBMS
	#15	Object oriented DBMS

Website

impala.apache.org

github.com/graphite-project/graphite-web

janusgraph.org

velocitydb.com

Technical documentation

impala.apache.org/impala-docs.html

graphite.readthedocs.io

docs.janusgraph.org

velocitydb.com/UserGuide

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Chris Davis

Linux Foundation; originally developed as Titan by Aurelius

VelocityDB Inc

Initial release

2013

2006

2017

2011

Current release

4.1.0, June 2022

0.6.3, February 2023

7.x

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

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++

Python

Java

Server operating systems

Linux

Linux
Unix

Linux
OS X
Unix
Windows

Any that supports .NET

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

HTTP API
Sockets

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

.Net

Supported programming languages

All languages supporting JDBC/ODBC

JavaScript (Node.js)
Python

Clojure
Java
Python

.Net

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

User authentification and security via Rexster Graph Server

Based on Windows Authentication

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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Recent citations in the news

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

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

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The Billion Data Point Challenge: Building a Query Engine for High Cardinality Time Series Data
10 December 2018, Uber

Grafana Labs Announces Mimir Time Series Database
1 April 2022, Datanami

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23 January 2015, InfoQ.com

How Grafana made observability accessible
12 June 2023, InfoWorld

Grafana 4.0 Brings Major Enhancements to Leading Visualization Tool
30 November 2016, PR Newswire

provided by Google News

Simple Deployment of a Graph Database: JanusGraph | by Edward Elson Kosasih
12 October 2020, Towards Data Science

Database Deep Dives: JanusGraph
8 August 2019, IBM

JanusGraph Picks Up Where TitanDB Left Off
13 January 2017, Datanami

Compose for JanusGraph arrives on Bluemix
15 September 2017, IBM

Nordstrom Builds Flexible Backend Ops with Kubernetes, Spark and JanusGraph
3 October 2019, The New Stack

provided by Google News

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