DBMS > Ignite vs. Redis vs. Spark SQL vs. Splice Machine vs. Vertica

System Properties Comparison Ignite vs. Redis vs. Spark SQL vs. Splice Machine vs. Vertica

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.

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Key-value store
Relational DBMS

Key-value store

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Relational DBMS

Column oriented

Secondary database models

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

Spatial 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	157.80
Rank	#6	Overall
	#1	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

Technical documentation

apacheignite.readme.io/docs

docs.redis.com/latest/index.html
redis.io/docs

spark.apache.org/docs/latest/sql-programming-guide.html

splicemachine.com/how-it-works

vertica.com/documentation

Developer

Apache Software Foundation

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Apache Software Foundation

Splice Machine

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2015

2009

2014

2005

Current release

Apache Ignite 2.6

7.2.4, January 2024

3.5.0 ( 2.13), September 2023

3.1, March 2021

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Aiven for Redis: Fully managed in-memory key-value store for all your caching and speedy lookup needs.

Implementation language

C++, Java, .Net

Scala

Java

C++

Server operating systems

Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

with RediSearch module

yes

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

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

with RediSQL module

SQL-like DML and DDL statements

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

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

proprietary protocol

RESP - REdis Serialization Protocol

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Java
Python
R
Scala

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

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

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

Lua; Redis Functions coming in Redis 7 (slides and Github)

yes

Java

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes (cache interceptors and events)

publish/subscribe channels provide some trigger functionality; RedisGears

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

yes, utilizing Spark Core

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

none

Multi-source replication
Source-replica replication

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

through RedisGears

Yes, via Full Spark Integration

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts and optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

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

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Licensing and pricing models

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