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DBMS > Alibaba Cloud MaxCompute vs. Google Cloud Datastore vs. Hawkular Metrics vs. Kinetica vs. Tkrzw

System Properties Comparison Alibaba Cloud MaxCompute vs. Google Cloud Datastore vs. Hawkular Metrics vs. Kinetica vs. Tkrzw

Editorial information provided by DB-Engines
NameAlibaba Cloud MaxCompute  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonKinetica  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionMaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousingAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Fully vectorized database across both GPUs and CPUsA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMS infoa graph-processing framework is available with the graphs being stored in tablesDocument storeTime Series DBMSRelational DBMSKey-value store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.78
Rank#221  Overall
#102  Relational DBMS
Score4.49
Rank#79  Overall
#12  Document stores
Score0.04
Rank#374  Overall
#38  Time Series DBMS
Score0.69
Rank#234  Overall
#107  Relational DBMS
Score0.09
Rank#354  Overall
#51  Key-value stores
Websitewww.alibabacloud.com/­product/­maxcomputecloud.google.com/­datastorewww.hawkular.orgwww.kinetica.comdbmx.net/­tkrzw
Technical documentationwww.alibabacloud.com/­help/­en/­maxcomputecloud.google.com/­datastore/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.kinetica.com
DeveloperAlibabaGoogleCommunity supported by Red HatKineticaMikio Hirabayashi
Initial release20162008201420122020
Current release7.1, August 20210.9.3, August 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0commercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC, C++C++
Server operating systemshostedhostedLinux
OS X
Windows
LinuxLinux
macOS
Data schemeyesschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details hereyesyesno
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.nonononono
Secondary indexesnoyesnoyes
SQL infoSupport of SQLSQL-like query languageSQL-like query language (GQL)noSQL-like DML and DDL statementsno
APIs and other access methodsFluentd
Flume
MaxCompute Console
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP RESTJDBC
ODBC
RESTful HTTP API
Supported programming languagesJava.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Go
Java
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions infoin Javausing Google App Enginenouser defined functionsno
TriggersnoCallbacks using the Google Apps Engineyes infovia Hawkular Alertingyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceShardingSharding infobased on CassandraShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud serviceMulti-source replication using Paxosselectable replication factor infobased on CassandraSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infousing Google Cloud Dataflownonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes infoGPU vRAM or System RAMyes infousing specific database classes
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noAccess rights for users and roles on table levelno

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More resources
Alibaba Cloud MaxComputeGoogle Cloud DatastoreHawkular MetricsKineticaTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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