DBMS > Apache IoTDB vs. Google BigQuery vs. Graphite vs. HugeGraph vs. Ignite

System Properties Comparison Apache IoTDB vs. Google BigQuery vs. Graphite vs. HugeGraph vs. Ignite

Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Large scale data warehouse service with append-only tables

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

A fast-speed and highly-scalable Graph DBMS

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Primary database model

Key-value store
Relational DBMS

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	4.75
Rank	#75	Overall
	#5	Time Series DBMS

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Score	3.64
Rank	#89	Overall
	#13	Key-value stores
	#48	Relational DBMS

Website

iotdb.apache.org

cloud.google.com/bigquery

github.com/graphite-project/graphite-web

github.com/hugegraph
hugegraph.apache.org

ignite.apache.org

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

cloud.google.com/bigquery/docs

graphite.readthedocs.io

hugegraph.apache.org/docs

apacheignite.readme.io/docs

Developer

Apache Software Foundation

Google

Chris Davis

Baidu

Apache Software Foundation

Initial release

2018

2010

2006

2018

2015

Current release

1.1.0, April 2023

0.9

Apache Ignite 2.6

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Python

Java

C++, Java, .Net

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
Unix

Linux
macOS
Unix

Linux
OS X
Solaris
Windows

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-like query language

yes

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

JDBC
Native API

RESTful HTTP/JSON API

HTTP API
Sockets

Java API
RESTful HTTP API
TinkerPop Gremlin

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

Groovy
Java
Python

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

asynchronous Gremlin script jobs

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

none

yes

depending on used storage backend, e.g. Cassandra and HBase

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

via hugegraph-spark

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

none

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

yes

Durability

Support for making data persistent

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

yes

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

Security Hooks for custom implementations

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