DBMS > NSDb vs. Spark SQL vs. Transwarp Hippo vs. XTDB

System Properties Comparison NSDb vs. Spark SQL vs. Transwarp Hippo 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

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

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

Primary database model

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

nsdb.io

spark.apache.org/sql

www.transwarp.cn/en/subproduct/hippo

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

Technical documentation

nsdb.io/Architecture

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

www.xtdb.com/docs

Developer

Apache Software Foundation

Juxt Ltd.

Initial release

2017

2014

2023

2019

Current release

3.5.0 ( 2.13), September 2023

1.0, May 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

Open Source

MIT License

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

Java, Scala

Scala

C++

Clojure

Server operating systems

Linux
macOS

Linux
OS X
Windows

Linux
macOS

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

Vector, Numeric and String

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

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

gRPC
HTTP REST
WebSocket

JDBC
ODBC

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

Java
Scala

Java
Python
R
Scala

C++
Java
Python

Clojure
Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

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

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

Role based access control and fine grained access rights

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More resources

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