DBMS > Apache Impala vs. Couchbase vs. HugeGraph vs. Snowflake

System Properties Comparison Apache Impala vs. Couchbase vs. HugeGraph vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

A fast-speed and highly-scalable Graph DBMS

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

Document store

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

impala.apache.org

www.couchbase.com

github.com/hugegraph
hugegraph.apache.org

www.snowflake.com

Technical documentation

impala.apache.org/impala-docs.html

docs.couchbase.com

hugegraph.apache.org/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Couchbase, Inc.

Baidu

Snowflake Computing Inc.

Initial release

2013

2011

2018

2014

Current release

4.1.0, June 2022

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

0.9

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache Version 2.0

commercial

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++

C, C++, Go and Erlang

Java

Server operating systems

Linux

Linux
OS X
Windows

Linux
macOS
Unix

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

also supports composite index and range index

SQL

Support of SQL

SQL-like DML and DDL statements

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

JDBC
ODBC

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

Java API
RESTful HTTP API
TinkerPop Gremlin

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Groovy
Java
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

asynchronous Gremlin script jobs

user defined functions

Triggers

yes

via the TAP protocol

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

including cross data center replication
Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

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

Ephemeral buckets

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Users, roles and permissions

Users with fine-grained authorization concept, user roles and pluggable authentication

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