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DBMS > Amazon Neptune vs. DolphinDB vs. Google Cloud Bigtable vs. Hawkular Metrics vs. Memcached

System Properties Comparison Amazon Neptune vs. DolphinDB vs. Google Cloud Bigtable vs. Hawkular Metrics vs. Memcached

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonDolphinDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonMemcached  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudDolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.In-memory key-value store, originally intended for caching
Primary database modelGraph DBMS
RDF store
Time Series DBMSKey-value store
Wide column store
Time Series DBMSKey-value store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.20
Rank#119  Overall
#9  Graph DBMS
#5  RDF stores
Score4.13
Rank#80  Overall
#6  Time Series DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score19.42
Rank#32  Overall
#4  Key-value stores
Websiteaws.amazon.com/­neptunewww.dolphindb.comcloud.google.com/­bigtablewww.hawkular.orgwww.memcached.org
Technical documentationaws.amazon.com/­neptune/­developer-resourcesdocs.dolphindb.cn/­en/­help200/­index.htmlcloud.google.com/­bigtable/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guidegithub.com/­memcached/­memcached/­wiki
DeveloperAmazonDolphinDB, IncGoogleCommunity supported by Red HatDanga Interactive infooriginally developed by Brad Fitzpatrick for LiveJournal
Initial release20172018201520142003
Current releasev2.00.4, January 20221.6.27, May 2024
License infoCommercial or Open Sourcecommercialcommercial infofree community version availablecommercialOpen Source infoApache 2.0Open Source infoBSD license
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaC
Server operating systemshostedLinux
Windows
hostedLinux
OS X
Windows
FreeBSD
Linux
OS X
Unix
Windows
Data schemeschema-freeyesschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesnoyesno
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.nononono
Secondary indexesnoyesnonono
SQL infoSupport of SQLnoSQL-like query languagenonono
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HTTP RESTProprietary protocol
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust
C#
C++
Go
Java
JavaScript (Node.js)
Python
Go
Java
Python
Ruby
.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresnoyesnonono
Triggersnononoyes infovia Hawkular Alertingno
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioningShardingSharding infobased on Cassandranone
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.yesInternal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factor infobased on Cassandranone infoRepcached, a Memcached patch, provides this functionallity
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Foreign keys infoReferential integrityyes infoRelationships in graphsnononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesAtomic single-row operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyesno
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 roles can be defined via the AWS Identity and Access Management (IAM)Administrators, Users, GroupsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noyes infousing SASL (Simple Authentication and Security Layer) protocol

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More resources
Amazon NeptuneDolphinDBGoogle Cloud BigtableHawkular MetricsMemcached
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