DBMS > Google BigQuery vs. HarperDB vs. Ignite vs. Spark SQL vs. Trafodion

System Properties Comparison Google BigQuery vs. HarperDB vs. Ignite vs. Spark SQL vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

Document store

Key-value store
Relational DBMS

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

cloud.google.com/bigquery

Technical documentation

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

apacheignite.readme.io/docs

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

trafodion.apache.org/documentation.html

Developer

Google

HarperDB

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Initial release

2010

2017

2015

2014

Current release

3.1, August 2021

Apache Ignite 2.6

3.5.0 ( 2.13), September 2023

2.3.0, February 2019

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Node.js

C++, Java, .Net

Scala

C++, Java

Server operating systems

hosted

Linux
OS X

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like data manipulation statements

ANSI-99 for query and DML statements, subset of DDL

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C#
C++
Java
PHP
Python
Ruby
Scala

Java
Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Custom Functions

since release 3.1

yes (compute grid and cache interceptors can be used instead)

Java Stored Procedures

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

A table resides as a whole on one (or more) nodes in a cluster

Sharding

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

yes (replicated cache)

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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