DBMS > Apache Impala vs. BoltDB vs. Google BigQuery vs. RisingWave

System Properties Comparison Apache Impala vs. BoltDB vs. Google BigQuery vs. RisingWave

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

An embedded key-value store for Go.

Large scale data warehouse service with append-only tables

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	0.70
Rank	#225	Overall
	#31	Key-value stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#237	Overall
	#111	Relational DBMS

Website

impala.apache.org

github.com/boltdb/bolt

cloud.google.com/bigquery

www.risingwave.com/database

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

docs.risingwave.com/docs/current/intro

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

RisingWave Labs

Initial release

2013

2010

2022

Current release

4.1.0, June 2022

1.2, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

MIT License

commercial

Open Source

Apache Version 2.0

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

C++

Rust

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

hosted

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Standard SQL-types and JSON

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

RESTful HTTP/JSON API

JDBC
PostgreSQL wire protocol

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

UDFs in Python or Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Since BigQuery is designed for querying data

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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

Google Cloud Identity & Access Management (IAM)

Users and Roles

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