DBMS > Ignite vs. searchxml vs. Snowflake vs. Splice Machine vs. VictoriaMetrics

System Properties Comparison Ignite vs. searchxml vs. Snowflake vs. Splice Machine vs. VictoriaMetrics

Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

DBMS for structured and unstructured content wrapped with an application server

Cloud-based data warehousing service for structured and semi-structured data

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

A fast, cost-effective and scalable Time Series DBMS and monitoring solution

Primary database model

Key-value store
Relational DBMS

Native XML DBMS
Search engine

Relational DBMS

Time Series DBMS

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

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	1.32
Rank	#162	Overall
	#14	Time Series DBMS

Website

ignite.apache.org

www.searchxml.net/category/products

www.snowflake.com

splicemachine.com

victoriametrics.com

Technical documentation

apacheignite.readme.io/docs

www.searchxml.net/support/handouts

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

docs.victoriametrics.com
github.com/VictoriaMetrics/VictoriaMetrics/wiki

Developer

Apache Software Foundation

informationpartners gmbh

Snowflake Computing Inc.

Splice Machine

VictoriaMetrics

Initial release

2015

2014

2018

Current release

Apache Ignite 2.6

1.0

3.1, March 2021

v1.91, May 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 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, .Net

C++

Java

Server operating systems

Linux
OS X
Solaris
Windows

Windows

hosted

Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS
OpenBSD

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

RESTful HTTP API
WebDAV
XQuery
XSLT

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++

most other programming languages supported via APIs

JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

on the application server

user defined functions

yes

Java

Triggers

yes (cache interceptors and events)

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

sychronisation to multiple collections

yes

Multi-source replication
Source-replica replication

Synchronous replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Security Hooks for custom implementations

Domain, group and role-based access control at the document level and for application services

Users with fine-grained authorization concept, user roles and pluggable authentication

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

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