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DBMS > Amazon DocumentDB vs. Hazelcast vs. Heroic vs. Vertica

System Properties Comparison Amazon DocumentDB vs. Hazelcast vs. Heroic vs. Vertica

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonHazelcast  Xexclude from comparisonHeroic  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceA widely adopted in-memory data gridTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelDocument storeKey-value storeTime Series DBMSRelational DBMS infoColumn oriented
Secondary database modelsDocument store infoJSON support with IMDG 3.12Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#124  Overall
#22  Document stores
Score5.72
Rank#59  Overall
#6  Key-value stores
Score0.13
Rank#335  Overall
#29  Time Series DBMS
Score9.62
Rank#42  Overall
#26  Relational DBMS
Websiteaws.amazon.com/­documentdbhazelcast.comgithub.com/­spotify/­heroicwww.vertica.com
Technical documentationaws.amazon.com/­documentdb/­resourceshazelcast.org/­imdg/­docsspotify.github.io/­heroicvertica.com/­documentation
DeveloperHazelcastSpotifyOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2019200820142005
Current release5.3.6, November 202312.0.3, January 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud serviceyesnonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++
Server operating systemshostedAll OS with a Java VMLinux
Data schemeschema-freeschema-freeschema-freeYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesyesyes
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 strategynono
Secondary indexesyesyesyes infovia ElasticsearchNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSQL-like query languagenoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JCache
JPA
Memcached protocol
RESTful HTTP API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor Servicesnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyes infoEventsnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasyes infoReplicated MapyesMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)yesnono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsone or two-phase-commit; repeatable reads; read commitednoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yesnono
User concepts infoAccess controlAccess rights for users and rolesRole-based access controlfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources
Amazon DocumentDBHazelcastHeroicVertica infoOpenText™ Vertica™
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