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DBMS > Amazon CloudSearch vs. CrateDB vs. Hazelcast vs. RisingWave

System Properties Comparison Amazon CloudSearch vs. CrateDB vs. Hazelcast vs. RisingWave

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Editorial information provided by DB-Engines
NameAmazon CloudSearch  Xexclude from comparisonCrateDB  Xexclude from comparisonHazelcast  Xexclude from comparisonRisingWave  Xexclude from comparison
DescriptionA hosted search engine service by Amazon with the data stored in Amazons cloudDistributed Database based on LuceneA widely adopted in-memory data gridA distributed RDBMS for stream processing, wire-compatible with PostgreSQL
Primary database modelSearch engineDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Key-value storeRelational DBMS
Secondary database modelsRelational DBMSDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.81
Rank#137  Overall
#12  Search engines
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score0.64
Rank#238  Overall
#110  Relational DBMS
Websiteaws.amazon.com/­cloudsearchcratedb.comhazelcast.comwww.risingwave.com/­database
Technical documentationdocs.aws.amazon.com/­cloudsearchcratedb.com/­docshazelcast.org/­imdg/­docsdocs.risingwave.com/­docs/­current/­intro
DeveloperAmazonCrateHazelcastRisingWave Labs
Initial release2012201320082022
Current release5.3.6, November 20231.2, September 2023
License infoCommercial or Open SourcecommercialOpen SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
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 languageJavaJavaRust
Server operating systemshostedAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VMDocker
Linux
macOS
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)schema-freeyes
Typing infopredefined data types such as float or dateyesyesyesStandard SQL-types and JSON
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.noyes infothe object must implement a serialization strategyno
Secondary indexesyes infoall search fields are automatically indexedyesyesyes
SQL infoSupport of SQLnoyes, but no triggers and constraints, and PostgreSQL compatibilitySQL-like query languageyes
APIs and other access methodsHTTP APIADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
PostgreSQL wire protocol
Supported programming languages.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
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnouser defined functions (Javascript)yes infoEvent Listeners, Executor ServicesUDFs in Python or Java
Triggersnonoyes infoEventsno
Partitioning methods infoMethods for storing different data on different nodesyes infoautomatic partitioning across Amazon Search Instance as requiredShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infomanaged transparently by AWSConfigurable replication on table/partition-levelyes infoReplicated Map
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus Algorithm
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infounique row identifiers can be used for implementing an optimistic concurrency control strategyone or two-phase-commit; repeatable reads; read commitedno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlauthentication via encrypted signaturesrights management via user accountsRole-based access controlUsers and Roles
More information provided by the system vendor
Amazon CloudSearchCrateDBHazelcastRisingWave
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 CloudSearchCrateDBHazelcastRisingWave
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