DBMS > Blueflood vs. Google Cloud Datastore vs. LevelDB vs. Riak KV vs. Splice Machine

System Properties Comparison Blueflood vs. Google Cloud Datastore vs. LevelDB vs. Riak KV vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

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

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

Distributed, fault tolerant key-value store

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

Primary database model

Time Series DBMS

Document store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

blueflood.io

cloud.google.com/datastore

github.com/google/leveldb

splicemachine.com

Technical documentation

github.com/rax-maas/blueflood/wiki

cloud.google.com/datastore/docs

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

www.tiot.jp/riak-docs/riak/kv/latest

splicemachine.com/how-it-works

Developer

Rackspace

Google

OpenSource, formerly Basho Technologies

Splice Machine

Initial release

2013

2008

2011

2009

2014

Current release

1.23, February 2021

3.2.0, December 2022

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

BSD

Open Source

Apache version 2, commercial enterprise edition

Open Source

AGPL 3.0, commercial license available

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++

Erlang

Java

Server operating systems

Linux
OS X

hosted

Illumos
Linux
OS X
Windows

Linux
OS X

Linux
OS X
Solaris
Windows

Data scheme

predefined scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

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.

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

HTTP REST

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

HTTP API
Native Erlang Interface

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

Server-side scripts

Stored procedures

using Google App Engine

Erlang

yes

Java

Triggers

Callbacks using the Google Apps Engine

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

none

Sharding

no "single point of failure"

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication using Paxos

none

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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.

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

links between data sets can be stored

yes

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

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

with automatic compression on writes

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

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

yes, using Riak Security

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

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