DBMS > Spark SQL vs. TDSQL for MySQL vs. TerarkDB vs. Typesense

System Properties Comparison Spark SQL vs. TDSQL for MySQL vs. TerarkDB vs. Typesense

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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

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

Primary database model

Relational DBMS

Key-value store

Search engine

Secondary database models

Document store
Spatial DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.85
Rank	#205	Overall
	#95	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.79
Rank	#210	Overall
	#14	Search engines

Website

spark.apache.org/sql

www.tencentcloud.com/products/dcdb

github.com/bytedance/terarkdb

typesense.org

Technical documentation

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

www.tencentcloud.com/document/product/1042

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

typesense.org/docs

Developer

Apache Software Foundation

Tencent

ByteDance, originally Terark

Initial release

2014

2013

2016

2015

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted open source version available

Open Source

GPL V3

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

Scala

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux

Data scheme

yes

schema-free

pre-defined schema optional

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

C++ API
Java API

RESTful HTTP API

Supported programming languages

Java
Python
R
Scala

C
C#
C++
Java
PHP
Python
Ruby

C++
Java

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

none

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

Users with fine-grained authorization concept

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