DBMS > Spark SQL vs. Tarantool vs. Transbase vs. YottaDB

System Properties Comparison Spark SQL vs. Tarantool vs. Transbase vs. YottaDB

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Editorial information provided by DB-Engines

Name

Description

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

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

A resource-optimized, high-performance, universally applicable RDBMS

A fast and solid embedded Key-value store

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

Relational DBMS

using the Octo plugin

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

spark.apache.org/sql

www.tarantool.io

www.transaction.de/en/products/transbase.html

yottadb.com

Technical documentation

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

www.tarantool.io/en/doc

www.transaction.de/en/products/transbase/features.html

yottadb.com/resources/documentation

Developer

Apache Software Foundation

Transaction Software GmbH

YottaDB, LLC

Initial release

2014

2008

1987

2001

Current release

3.5.0 ( 2.13), September 2023

2.10.0, May 2022

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

commercial

free development license

Open Source

AGPL 3.0

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.

Implementation language

Scala

C and C++

Server operating systems

Linux
OS X
Windows

BSD
Linux
macOS

FreeBSD
Linux
macOS
Solaris
Windows

Docker
Linux

Data scheme

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes

schema-free

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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

SQL

Support of SQL

SQL-like DML and DDL statements

Full-featured ANSI SQL support

yes

by using the Octo plugin

APIs and other access methods

JDBC
ODBC

Open binary protocol

ADO.NET
JDBC
ODBC
Proprietary native API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

Java
Python
R
Scala

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

Lua, C and SQL stored procedures

yes

Triggers

yes, before/after data modification events, on replication events, client session events

yes

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

none

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

yes

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, full featured in-memory storage engine with persistence

yes

User concepts

Access control

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

fine grained access rights according to SQL-standard

Users and groups based on OS-security mechanisms

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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