DBMS > Apache Impala vs. Apache IoTDB vs. Google Cloud Datastore vs. STSdb

System Properties Comparison Apache Impala vs. Apache IoTDB vs. Google Cloud Datastore vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

impala.apache.org

iotdb.apache.org

cloud.google.com/datastore

github.com/STSSoft/STSdb4

Technical documentation

impala.apache.org/impala-docs.html

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

cloud.google.com/datastore/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Google

STS Soft SC

Initial release

2013

2018

2008

2011

Current release

4.1.0, June 2022

1.1.0, April 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

commercial

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Server operating systems

Linux

All OS with a Java VM (>= 1.8)

hosted

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

primitive types and user defined types (classes)

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

SQL-like query language

SQL-like query language (GQL)

APIs and other access methods

JDBC
ODBC

JDBC
Native API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Integration with Hadoop and Spark

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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