DBMS > Apache Impala vs. BoltDB vs. CouchDB vs. Google Cloud Spanner vs. RocksDB

System Properties Comparison Apache Impala vs. BoltDB vs. CouchDB vs. Google Cloud Spanner vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Secondary database models

Document store

Spatial DBMS

using the Geocouch extension

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

impala.apache.org

github.com/boltdb/bolt

couchdb.apache.org

cloud.google.com/spanner

rocksdb.org

Technical documentation

impala.apache.org/impala-docs.html

docs.couchdb.org/en/stable

cloud.google.com/spanner/docs

github.com/facebook/rocksdb/wiki

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Facebook, Inc.

Initial release

2013

2005

2017

2013

Current release

4.1.0, June 2022

3.3.3, December 2023

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

MIT License

Open Source

Apache version 2

commercial

Open Source

BSD

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++

Erlang

C++

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux

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

via views

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Query statements complying to ANSI 2011

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

Go
Java
JavaScript (Node.js)
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

improved architecture with release 2.0

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Multi-source replication
Source-replica replication

Multi-source replication with 3 replicas for regional instances.

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

yes

atomic operations within a single document possible

ACID

Strict serializable isolation

yes

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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 rights for users can be defined per database

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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