DBMS > Google Cloud Datastore vs. Vertica vs. VoltDB vs. YugabyteDB

System Properties Comparison Google Cloud Datastore vs. Vertica vs. VoltDB vs. YugabyteDB

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Editorial information provided by DB-Engines

Name

Description

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.

Primary database model

Document store

Relational DBMS

Column oriented

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Wide column store

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Score	2.91
Rank	#102	Overall
	#51	Relational DBMS

Website

cloud.google.com/datastore

www.vertica.com

www.voltdb.com

www.yugabyte.com

Technical documentation

cloud.google.com/datastore/docs

vertica.com/documentation

docs.voltdb.com

docs.yugabyte.com
github.com/yugabyte/yugabyte-db

Developer

Google

OpenText

previously Micro Focus and Hewlett Packard

VoltDB Inc.

Yugabyte Inc.

Initial release

2008

2005

2010

2017

Current release

12.0.3, January 2023

11.3, April 2022

2.19, September 2023

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.

Implementation language

C++

Java, C++

C and C++

Server operating systems

hosted

Linux

Linux
OS X

for development

Linux
OS X

Data scheme

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

yes

depending on used data model

Typing

predefined data types such as float or date

yes, details here

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

SQL-like query language (GQL)

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

yes

only a subset of SQL 99

yes, PostgreSQL compatible

APIs and other access methods

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

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Java API
JDBC
RESTful HTTP/JSON API

JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL

Supported programming languages

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

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

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

Server-side scripts

Stored procedures

using Google App Engine

yes, PostgreSQL PL/pgSQL, with minor differences

Java

yes

sql, plpgsql, C

Triggers

Callbacks using the Google Apps Engine

yes, called Custom Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, hierarchical partitioning

Sharding

Hash and Range Sharding, row-level geo-partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

Multi-source replication
Source-replica replication

Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Strong consistency on writes and tunable consistency on reads

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

FOREIGN KEY constraints are not supported

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

Transactions are executed single-threaded within stored procedures

Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Snapshots and command logging

yes

based on RocksDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

Users and roles with access to stored procedures

yes

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

YugabyteDB is an open source distributed SQL database for cloud native transactional...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

PostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
» more

Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Systems of record and engagement for cloud native applications that require resilience,...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Market metrics

2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

Apache 2.0 license for the database
» more

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

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