DBMS > Apache Impala vs. Google Cloud Firestore vs. QuestDB vs. SQLite

System Properties Comparison Apache Impala vs. Google Cloud Firestore vs. QuestDB vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

A high performance open source SQL database for time series data

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store

Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	2.70
Rank	#105	Overall
	#8	Time Series DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

impala.apache.org

firebase.google.com/products/firestore

questdb.io

www.sqlite.org

Technical documentation

impala.apache.org/impala-docs.html

firebase.google.com/docs/firestore

questdb.io/docs

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

QuestDB Technology Inc

Dwayne Richard Hipp

Initial release

2013

2017

2014

2000

Current release

4.1.0, June 2022

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

Open Source

Public Domain

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 (Zero-GC), C++, Rust

Server operating systems

Linux

hosted

Linux
macOS
Windows

server-less

Data scheme

yes

schema-free

yes

schema-free via InfluxDB Line Protocol

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

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

over HTTP

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, Firebase Rules & Cloud Functions

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by timestamps)

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

Source-replica replication with eventual consistency

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Using Cloud Dataflow

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

yes

ACID for single-table writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

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

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

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

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

News

Fluid real-time dashboards with Grafana and QuestDB
11 June 2024

QuestDB 8.0: Major Release
23 May 2024

QuestDB and Raspberry Pi 5 benchmark, a pocket-sized powerhouse
8 May 2024

Build your own resource monitor with QuestDB and Grafana
6 May 2024

Does "vpmovzxbd" Scare You? Here's Why it Doesn't Have To
12 April 2024

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