DBMS > Apache Impala vs. QuestDB vs. SAP HANA vs. TDengine

System Properties Comparison Apache Impala vs. QuestDB vs. SAP HANA vs. TDengine

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A high performance open source SQL database for time series data

In-memory, column based data store. Available as appliance or cloud service

Time Series DBMS and big data platform

Primary database model

Secondary database models

Document store

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	2.70
Rank	#105	Overall
	#8	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	2.68
Rank	#106	Overall
	#9	Time Series DBMS

Website

impala.apache.org

questdb.io

www.sap.com/products/hana.html

github.com/taosdata/TDengine
tdengine.com

Technical documentation

impala.apache.org/impala-docs.html

questdb.io/docs

help.sap.com/hana

docs.tdengine.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

QuestDB Technology Inc

SAP

TDEngine, previously Taos Data

Initial release

2013

2014

2010

2019

Current release

4.1.0, June 2022

2.0 SPS07 (April 4, 2023), April 2023

3.0, August 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

Open Source

AGPL V3, also commercial editions available

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java (Zero-GC), C++, Rust

Server operating systems

Linux

Linux
macOS
Windows

Appliance or cloud-service

Linux
Windows

Data scheme

yes

schema-free via InfluxDB Line Protocol

yes

Typing

predefined data types such as float or date

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

SQL with time-series extensions

yes

Standard SQL with extensions for time-series applications

APIs and other access methods

JDBC
ODBC

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

JDBC
ODBC

JDBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

SQLScript, R

Triggers

yes

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by timestamps)

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication with eventual consistency

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID for single-table writes

ACID

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

through memory mapped files

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
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Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more

News

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12 April 2024

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8 April 2024

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31 May 2024

Seamless Data Integration from MQTT and InfluxDB to TDengine
22 May 2024

Solving Long Query Performance Bottlenecks
22 May 2024

What Is Predictive Maintenance?
17 May 2024

Can Typical Time-Series Databases Replace Data Historians?
8 May 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.