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DBMS > CrateDB vs. HBase vs. Microsoft Azure Table Storage vs. Typesense

System Properties Comparison CrateDB vs. HBase vs. Microsoft Azure Table Storage vs. Typesense

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonHBase  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonTypesense  Xexclude from comparison
DescriptionDistributed Database based on LuceneWide-column store based on Apache Hadoop and on concepts of BigTableA Wide Column Store for rapid development using massive semi-structured datasetsA typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Wide column storeWide column storeSearch engine
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
Score27.97
Rank#26  Overall
#2  Wide column stores
Score4.04
Rank#77  Overall
#6  Wide column stores
Score0.76
Rank#219  Overall
#14  Search engines
Websitecratedb.comhbase.apache.orgazure.microsoft.com/­en-us/­services/­storage/­tablestypesense.org
Technical documentationcratedb.com/­docshbase.apache.org/­book.htmltypesense.org/­docs
DeveloperCrateApache Software Foundation infoApache top-level project, originally developed by PowersetMicrosoft
Initial release2013200820122015
Current release2.3.4, January 2021
License infoCommercial or Open SourceOpen SourceOpen Source infoApache version 2commercialOpen Source infoGPL V3
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
Unix
Windows infousing Cygwin
hostedLinux
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-free, schema definition possibleschema-freeschema-free infopre-defined schema optional
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROyesyes
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.nonono
Secondary indexesyesnonoyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitynonono
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
RESTful HTTP APIRESTful HTTP API
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
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net infocommunity maintained
Clojure infocommunity maintained
Dart infocommunity maintained
Go infocommunity maintained
Java infocommunity maintained
JavaScript
Perl infocommunity maintained
PHP
Python
Ruby
Rust infocommunity maintained
Swift infocommunity maintained
Server-side scripts infoStored proceduresuser defined functions (Javascript)yes infoCoprocessors in Javanono
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelMulti-source replication
Source-replica replication
yes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication using RAFT
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
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 strategySingle row ACID (across millions of columns)optimistic lockingno
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.noyesnoyes
User concepts infoAccess controlrights management via user accountsAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAccess rights based on private key authentication or shared access signatures

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More resources
CrateDBHBaseMicrosoft Azure Table StorageTypesense
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