DBMS > Apache Impala vs. Hazelcast vs. SAP HANA vs. SurrealDB

System Properties Comparison Apache Impala vs. Hazelcast vs. SAP HANA vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A widely adopted in-memory data grid

In-memory, column based data store. Available as appliance or cloud service

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Document store
Graph DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

impala.apache.org

hazelcast.com

www.sap.com/products/hana.html

surrealdb.com

Technical documentation

impala.apache.org/impala-docs.html

hazelcast.org/imdg/docs

help.sap.com/hana

surrealdb.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Hazelcast

SAP

SurrealDB Ltd

Initial release

2013

2008

2010

2022

Current release

4.1.0, June 2022

5.3.6, November 2023

2.0 SPS07 (April 4, 2023), April 2023

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Rust

Server operating systems

Linux

All OS with a Java VM

Appliance or cloud-service

Linux
macOS
Windows

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Event Listeners, Executor Services

SQLScript, R

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Replicated Map

yes

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Role-based access control

yes

yes, based on authentication and database rules

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