DBMS > Axibase vs. Google Cloud Datastore vs. Hawkular Metrics vs. TimesTen vs. XTDB

System Properties Comparison Axibase vs. Google Cloud Datastore vs. Hawkular Metrics vs. TimesTen vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

In-Memory RDBMS compatible to Oracle

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

axibase.com/docs/atsd/finance

cloud.google.com/datastore

www.hawkular.org

www.oracle.com/database/technologies/related/timesten.html

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cloud.google.com/datastore/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

docs.oracle.com/database/timesten-18.1

www.xtdb.com/docs

Developer

Axibase Corporation

Google

Community supported by Red Hat

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Juxt Ltd.

Initial release

2013

2008

2014

1998

2019

Current release

15585

11 Release 2 (11.2.2.8.0)

1.19, September 2021

License

Commercial or Open Source

commercial

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

commercial

Open Source

Apache 2.0

commercial

Open Source

MIT License

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

Clojure

Server operating systems

Linux

hosted

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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

yes, details here

yes

yes, extensible-data-notation format

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

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

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

HTTP REST

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP REST
JDBC

Supported programming languages

Go
Java
PHP
Python
R
Ruby

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

Go
Java
Python
Ruby

C
C++
Java
PL/SQL

Clojure
Java

Server-side scripts

Stored procedures

yes

using Google App Engine

PL/SQL

Triggers

yes

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes, each node contains all data

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 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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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 Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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