DBMS > HarperDB vs. RocksDB vs. Solr vs. Sqrrl

System Properties Comparison HarperDB vs. RocksDB vs. Solr vs. Sqrrl

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Editorial information provided by DB-Engines

Name

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

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

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Document store

Key-value store

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.harperdb.io

rocksdb.org

solr.apache.org

sqrrl.com

Technical documentation

docs.harperdb.io/docs

github.com/facebook/rocksdb/wiki

solr.apache.org/resources.html

Developer

HarperDB

Facebook, Inc.

Apache Software Foundation

Amazon

originally Sqrrl Data, Inc.

Initial release

2017

2013

2006

2012

Current release

3.1, August 2021

9.2.1, May 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

BSD

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Node.js

C++

Java

Server operating systems

Linux
OS X

Linux

All OS with a Java VM

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

Linux

Data scheme

dynamic schema

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like data manipulation statements

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

C++ API
Java API

Java API
RESTful HTTP/JSON API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C
C++
Go
Java
Perl
Python
Ruby

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

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

horizontal partitioning

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

yes

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

yes

optimistic locking

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using LMDB

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 and roles

yes

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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