DBMS > Apache Impala vs. Google BigQuery vs. Hive vs. Ignite vs. Solr

System Properties Comparison Apache Impala vs. Google BigQuery vs. Hive vs. Ignite vs. Solr

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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.

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Search engine

Secondary database models

Document store

Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

impala.apache.org

cloud.google.com/bigquery

hive.apache.org

ignite.apache.org

solr.apache.org

Technical documentation

impala.apache.org/impala-docs.html

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

apacheignite.readme.io/docs

solr.apache.org/resources.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

Apache Software Foundation

initially developed by Facebook

Apache Software Foundation

Initial release

2013

2010

2012

2015

2006

Current release

4.1.0, June 2022

3.1.3, April 2022

Apache Ignite 2.6

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache Version 2

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++

Java

C++, Java, .Net

Java

Server operating systems

Linux

hosted

All OS with a Java VM

Linux
OS X
Solaris
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like DML and DDL statements

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

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

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

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

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

Java plugins

Triggers

yes (cache interceptors and events)

yes

User configurable commands triggered on index changes

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

yes (replicated cache)

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

query execution via MapReduce

yes (compute grid and hadoop accelerator)

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual 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

ACID

optimistic locking

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)

Access rights for users, groups and roles

Security Hooks for custom implementations

yes

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