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DBMS > Amazon Neptune vs. CrateDB vs. Solr

System Properties Comparison Amazon Neptune vs. CrateDB vs. Solr

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonCrateDB  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudDistributed Database based on LuceneA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelGraph DBMS
RDF store
Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Search engine
Secondary database modelsRelational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.58
Rank#112  Overall
#9  Graph DBMS
#5  RDF stores
Score0.73
Rank#229  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score44.28
Rank#24  Overall
#3  Search engines
Websiteaws.amazon.com/­neptunecratedb.comsolr.apache.org
Technical documentationaws.amazon.com/­neptune/­developer-resourcescratedb.com/­docssolr.apache.org/­resources.html
DeveloperAmazonCrateApache Software Foundation
Initial release201720132006
Current release9.5.0, February 2024
License infoCommercial or Open SourcecommercialOpen SourceOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaJava
Server operating systemshostedAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typing
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonoyes
Secondary indexesnoyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoyes, but no triggers and constraints, and PostgreSQL compatibilitySolr Parallel SQL Interface
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnouser defined functions (Javascript)Java plugins
Triggersnonoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.Configurable replication on table/partition-levelyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Eventual Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infounique row identifiers can be used for implementing an optimistic concurrency control strategyoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)rights management via user accountsyes
More information provided by the system vendor
Amazon NeptuneCrateDBSolr
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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More resources
Amazon NeptuneCrateDBSolr
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