DBMS > Apache Impala vs. Google Cloud Datastore vs. MySQL vs. Rockset vs. SpatiaLite

System Properties Comparison Apache Impala vs. Google Cloud Datastore vs. MySQL vs. Rockset vs. SpatiaLite

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Widely used open source RDBMS

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Spatial extension of SQLite

Primary database model

Relational DBMS

Document store

Relational DBMS

Key/Value like access via memcached API

Document store

Spatial DBMS

Secondary database models

Document store

Document store
Spatial DBMS

Relational DBMS
Search engine

Relational DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

impala.apache.org

cloud.google.com/datastore

www.mysql.com

rockset.com

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/datastore/docs

dev.mysql.com/doc

docs.rockset.com

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Oracle

since 2010, originally MySQL AB, then Sun

Rockset

Alessandro Furieri

Initial release

2013

2008

1995

2019

2008

Current release

4.1.0, June 2022

8.4.0, April 2024

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C++

C and C++

C++

Server operating systems

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

hosted

server-less

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

dynamic typing

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

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (GQL)

yes

with proprietary extensions

Read-only SQL queries, including JOINs

yes

APIs and other access methods

JDBC
ODBC

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

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

using Google App Engine

yes

proprietary syntax

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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.

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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 (IAM)

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and organizations can be defined via Rockset console

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