DBMS > Hyprcubd vs. Sphinx vs. Splice Machine vs. TimescaleDB

System Properties Comparison Hyprcubd vs. Sphinx vs. Splice Machine vs. TimescaleDB

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Editorial information provided by DB-Engines

Name

Description

Open source search engine for searching in data from different sources, e.g. relational databases

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

Score	0.00
Rank	#335	Overall
	#31	Time Series DBMS

Score	6.43
Rank	#60	Overall
	#7	Search engines

Score	0.88
Rank	#181	Overall
	#92	Relational DBMS

Score	2.93
Rank	#102	Overall
	#6	Time Series DBMS

Website

Technical documentation

sphinxsearch.com/docs

doc.splicemachine.com

docs.timescale.com

Developer

Hyprcubd, Inc.

Sphinx Technologies Inc.

Splice Machine

Timescale

Initial release

2001

2014

2017

Current release

3.3.1, July 2020

2.0

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

Open Source

AGPL 3.0, commercial license available

Open Source

Apache 2.0

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

C++

Java

Server operating systems

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

yes

full PostgreSQL SQL syntax

APIs and other access methods

gRPC (https)

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Java

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Shared Nothhing Auto-Sharding, Columnar Partitioning

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

token access

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

THE ONLY SCALE-OUT SQL RDBMS WITH BUILT-IN MACHINE LEARNING For data-rich, real-time...
» more

Timescale is the creator of TimescaleDB , the first open-source relational database...
» more

Competitive advantages

Operational Workloads: Full ACID support with high performance at scale Multi-row...
» more

TimescaleDB is purpose-built to scale and handle time-series data workloads and is...
» more

Typical application scenarios

New AI/ML and IoT Applications Data Warehouse Offloads Application Modernization...
» more

DevOps and Infrastructure Monitoring All monitoring data is time-series data. Efficiently...
» more

Key customers

TransferWise, Maersk, AppDynamics, Walmart, Comcast, LaunchDarkly, IBM, NOV, Cisco,...
» more

Market metrics

With over 9,000 GitHub stars and growing, 600,000 active databases, and an active...
» more

Licensing and pricing models

Kubernetes Community Edition: https://hub.helm.sh/charts/splicemachine/splice-helm...
» more

TimescaleDB comes in 3 versions, including fully managed Cloud-hosted solutions*,...
» more

News

Three Ways to Talk Machine Learning with Splice Machine in January
6 January 2021

How To Get Real-Time Analytics By Consolidating Databases
21 May 2020

How to Cut Costs and Digitally Transform
1 May 2020

How To Migrate A Legacy SQL Application To The Cloud With AI
10 March 2020

Building Production-Ready Machine Learning Models
4 March 2020

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