DBMS > IBM Db2 Event Store vs. LevelDB vs. SingleStore vs. ToroDB

System Properties Comparison IBM Db2 Event Store vs. LevelDB vs. SingleStore vs. ToroDB

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Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Distributed Event Store optimized for Internet of Things use cases

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Event Store
Time Series DBMS

Key-value store

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Website

www.ibm.com/products/db2-event-store

github.com/google/leveldb

www.singlestore.com

github.com/torodb/server

Technical documentation

www.ibm.com/docs/en/db2-event-store

github.com/google/leveldb/blob/main/doc/index.md

docs.singlestore.com

Developer

IBM

Google

SingleStore Inc.

8Kdata

Initial release

2017

2011

2013

2016

Current release

2.0

1.23, February 2021

8.5, January 2024

License

Commercial or Open Source

commercial

free developer edition available

Open Source

BSD

commercial

free developer edition available

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C and C++

C++

C++, Go

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Illumos
Linux
OS X
Windows

Linux

64 bit version required

All OS with a Java 7 VM

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

yes

through the embedded Spark runtime

yes

but no triggers and foreign keys

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

hash partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

none

Source-replica replication

stores two copies of each physical data partition on two separate nodes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

with automatic compression on writes

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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

fine grained access rights according to SQL-standard

Fine grained access control via users, groups and roles

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more

Competitive advantages

SingleStore’s competitive advantages include: Easy and Simplified Architecture with...
» more

Typical application scenarios

Driving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more

Key customers

IEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more

Market metrics

Customers in various industries worldwide including US and International Industry...
» more

Licensing and pricing models

F ree Tier and Enterprise Edition
» more

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