DBMS > NSDb vs. PlanetScale vs. Spark SQL vs. XTDB

System Properties Comparison NSDb vs. PlanetScale vs. Spark SQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Scalable, distributed, serverless MySQL database platform built on top of Vitess

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Time Series DBMS

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

nsdb.io

planetscale.com

spark.apache.org/sql

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

nsdb.io/Architecture

planetscale.com/docs

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

www.xtdb.com/docs

Developer

PlanetScale

Apache Software Foundation

Juxt Ltd.

Initial release

2017

2020

2014

2019

Current release

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Scala

Scala

Clojure

Server operating systems

Linux
macOS

Docker
Linux
macOS

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

yes

yes, extensible-data-notation format

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

gRPC
HTTP REST
WebSocket

ADO.NET
JDBC
MySQL protocol
ODBC

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

Java
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

Using Apache Lucene

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

no user groups or roles

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More resources

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