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DBMS > CrateDB vs. Google Cloud Datastore vs. Graph Engine vs. HBase

System Properties Comparison CrateDB vs. Google Cloud Datastore vs. Graph Engine vs. HBase

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonHBase  Xexclude from comparison
DescriptionDistributed Database based on LuceneAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineWide-column store based on Apache Hadoop and on concepts of BigTable
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document storeGraph DBMS
Key-value store
Wide column store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#10  Vector DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score0.67
Rank#232  Overall
#21  Graph DBMS
#34  Key-value stores
Score27.97
Rank#26  Overall
#2  Wide column stores
Websitecratedb.comcloud.google.com/­datastorewww.graphengine.iohbase.apache.org
Technical documentationcratedb.com/­docscloud.google.com/­datastore/­docswww.graphengine.io/­docs/­manualhbase.apache.org/­book.html
DeveloperCrateGoogleMicrosoftApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release2013200820102008
Current release2.3.4, January 2021
License infoCommercial or Open SourceOpen SourcecommercialOpen Source infoMIT LicenseOpen Source infoApache version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava.NET and CJava
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthosted.NETLinux
Unix
Windows infousing Cygwin
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesschema-free, schema definition possible
Typing infopredefined data types such as float or dateyesyes, details hereyesoptions to bring your own types, AVRO
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 indexesyesyesno
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitySQL-like query language (GQL)nono
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP APIJava API
RESTful HTTP API
Thrift
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
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
F#
Visual Basic
C
C#
C++
Groovy
Java
PHP
Python
Scala
Server-side scripts infoStored proceduresuser defined functions (Javascript)using Google App Engineyesyes infoCoprocessors in Java
TriggersnoCallbacks using the Google Apps Enginenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelMulti-source replication using PaxosMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate 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.Immediate Consistency or Eventual Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategyACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoSingle row ACID (across millions of columns)
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlrights management via user accountsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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More resources
CrateDBGoogle Cloud DatastoreGraph Engine infoformer name: TrinityHBase
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