DBMS > Axibase vs. Coveo vs. Google Cloud Datastore vs. SQLite vs. WakandaDB

System Properties Comparison Axibase vs. Coveo vs. Google Cloud Datastore vs. SQLite vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

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

Widely used embeddable, in-process RDBMS

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

axibase.com/docs/atsd/finance

www.coveo.com

cloud.google.com/datastore

www.sqlite.org

wakanda.github.io

Technical documentation

docs.coveo.com

cloud.google.com/datastore/docs

www.sqlite.org/docs.html

wakanda.github.io/doc

Developer

Axibase Corporation

Coveo

Google

Dwayne Richard Hipp

Wakanda SAS

Initial release

2013

2012

2008

2000

2012

Current release

15585

3.45.3 (15 April 2024), April 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Open Source

Public Domain

Open Source

AGPLv3, extended commercial license available

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

Java

C++, JavaScript

Server operating systems

Linux

hosted

server-less

Linux
OS X
Windows

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

yes, details here

yes

not rigid because of 'dynamic typing' concept.

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 query language

SQL-like query language (GQL)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

RESTful HTTP API

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

RESTful HTTP API

Supported programming languages

Go
Java
PHP
Python
R
Ruby

C#
Java
JavaScript
Python

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

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

JavaScript

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

yes

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

granular access controls, API key management, content filters

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

yes

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