DBMS > Google BigQuery vs. HBase vs. JSqlDb vs. SingleStore

System Properties Comparison Google BigQuery vs. HBase vs. JSqlDb vs. SingleStore

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Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service with append-only tables

Wide-column store based on Apache Hadoop and on concepts of BigTable

JavaScript Query Language Database, stores JavaScript objects and primitives

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Relational DBMS

Wide column store

Document store
Object oriented DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Website

cloud.google.com/bigquery

hbase.apache.org

jsqldb.org (offline)

www.singlestore.com

Technical documentation

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

docs.singlestore.com

Developer

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Konrad von Backstrom

SingleStore Inc.

Initial release

2010

2008

2018

2013

Current release

2.3.4, January 2021

0.8, December 2018

8.5, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

Java

C++, Go

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Linux
macOS
Windows

Linux

64 bit version required

Data scheme

yes

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

yes

but no triggers and foreign keys

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

functions in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

using RocksDB

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

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Key customers

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Market metrics

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Licensing and pricing models

F ree Tier and Enterprise Edition
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