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DBMS > CrateDB vs. H2 vs. Newts vs. Quasardb

System Properties Comparison CrateDB vs. H2 vs. Newts vs. Quasardb

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonH2  Xexclude from comparisonNewts  Xexclude from comparisonQuasardb  Xexclude from comparison
DescriptionDistributed Database based on LuceneFull-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.Time Series DBMS based on CassandraDistributed, high-performance timeseries database
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSTime Series DBMSTime Series DBMS
Secondary database modelsRelational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.73
Rank#229  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score8.22
Rank#50  Overall
#32  Relational DBMS
Score0.00
Rank#396  Overall
#42  Time Series DBMS
Score0.19
Rank#327  Overall
#30  Time Series DBMS
Websitecratedb.comwww.h2database.comopennms.github.io/­newtsquasar.ai
Technical documentationcratedb.com/­docswww.h2database.com/­html/­main.htmlgithub.com/­OpenNMS/­newts/­wikidoc.quasar.ai/­master
DeveloperCrateThomas MuellerOpenNMS Groupquasardb
Initial release2013200520142009
Current release2.2.220, July 20233.14.1, January 2024
License infoCommercial or Open SourceOpen SourceOpen Source infodual-licence (Mozilla public license, Eclipse public license)Open Source infoApache 2.0commercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licenses
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaJavaJavaC++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VMLinux
OS X
Windows
BSD
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyesyes infointeger and binary
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 indexesyesyesnoyes infowith tags
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilityyesnoSQL-like query language
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
HTTP REST
Java API
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
JavaJava.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Server-side scripts infoStored proceduresuser defined functions (Javascript)Java Stored Procedures and User-Defined Functionsnono
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infobased on CassandraSharding infoconsistent hashing
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelWith clustering: 2 database servers on different computers operate on identical copies of a databaseselectable replication factor infobased on CassandraSource-replica replication with selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononowith Hadoop integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integritynoyesnono
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 strategyACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyes
Durability infoSupport for making data persistentyesyesyesyes infoby using LevelDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes infoTransient mode
User concepts infoAccess controlrights management via user accountsfine grained access rights according to SQL-standardnoCryptographically strong user authentication and audit trail
More information provided by the system vendor
CrateDBH2NewtsQuasardb
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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CrateDBH2NewtsQuasardb
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