DBMS > Apache Impala vs. Badger vs. Google BigQuery vs. Hypertable

System Properties Comparison Apache Impala vs. Badger vs. Google BigQuery vs. Hypertable

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Large scale data warehouse service with append-only tables

An open source BigTable implementation based on distributed file systems such as Hadoop

Primary database model

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

impala.apache.org

github.com/dgraph-io/badger

cloud.google.com/bigquery

Technical documentation

impala.apache.org/impala-docs.html

godoc.org/github.com/dgraph-io/badger

cloud.google.com/bigquery/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

DGraph Labs

Google

Hypertable Inc.

Initial release

2013

2017

2010

2009

Current release

4.1.0, June 2022

0.9.8.11, March 2016

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

Open Source

GNU version 3. Commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

restricted

only exact value or prefix value scans

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

C++ API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

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

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.

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)

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