DBMS > Alibaba Cloud TSDB vs. Apache Kylin vs. Google Cloud Datastore vs. Oracle Berkeley DB vs. XTDB

System Properties Comparison Alibaba Cloud TSDB vs. Apache Kylin vs. Google Cloud Datastore vs. Oracle Berkeley DB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

A stable, reliable, and cost-effective online high-performance time series database service

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

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

Widely used in-process key-value store

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

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

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

Score	0.24
Rank	#306	Overall
	#26	Time Series DBMS

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.alibabacloud.com/product/hitsdb

kylin.apache.org

cloud.google.com/datastore

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

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

Technical documentation

www.alibabacloud.com/help/en/time-series-database/latest/what-is-tsdb

kylin.apache.org/docs

cloud.google.com/datastore/docs

docs.oracle.com/cd/E17076_05/html/index.html

www.xtdb.com/docs

Developer

Alibaba

Apache Software Foundation, originally contributed from eBay Inc

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Juxt Ltd.

Initial release

2015

2008

1994

2019

Current release

3.1.0, July 2020

18.1.40, May 2020

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

commercial license available

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

C, Java, C++ (depending on the Berkeley DB edition)

Clojure

Server operating systems

hosted

Linux

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

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

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

Numeric data and Strings

yes

yes, details here

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like query language (GQL)

yes

SQL interfaced based on SQLite is available

limited SQL, making use of Apache Calcite

APIs and other access methods

HTTP REST

JDBC
ODBC
RESTful HTTP API

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

HTTP REST
JDBC

Supported programming languages

Java

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Clojure
Java

Server-side scripts

Stored procedures

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

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

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.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

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

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 managed on TSDB instance level

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

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