DBMS > Apache Impala vs. Google Cloud Firestore vs. LeanXcale vs. Yaacomo

System Properties Comparison Apache Impala vs. Google Cloud Firestore vs. LeanXcale vs. Yaacomo

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

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 highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store

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	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Website

impala.apache.org

firebase.google.com/products/firestore

www.leanxcale.com

yaacomo.com

Technical documentation

impala.apache.org/impala-docs.html

firebase.google.com/docs/firestore

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

LeanXcale

Q2WEB GmbH

Initial release

2013

2017

2015

2009

Current release

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

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++

Server operating systems

Linux

hosted

Android
Linux
Windows

Data scheme

yes

schema-free

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

yes

through Apache Derby

yes

APIs and other access methods

JDBC
ODBC

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

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C
Java
Scala

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

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

Source-replica replication

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

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

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

show all

Recent citations in the news

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

Updates & Upserts in Hadoop Ecosystem with Apache Kudu
27 October 2017, KDnuggets

provided by Google News

Google's AI-First Strategy Brings Vector Support To Cloud Databases
1 March 2024, Forbes

Realtime vs Cloud Firestore: Which Firebase Database to go?
8 March 2024, Appinventiv

Google launches Cloud Firestore, a new document database for app developers
3 October 2017, TechCrunch

Google's Cloud-Native NoSQL Database Cloud Firestore Is Now Generally Available
8 February 2019, InfoQ.com

Firestore and Python | NoSQL on Google Cloud
7 August 2020, Towards Data Science

provided by Google News

Share this page