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DBMS > Apache Phoenix vs. CrateDB vs. Greenplum vs. HugeGraph

System Properties Comparison Apache Phoenix vs. CrateDB vs. Greenplum vs. HugeGraph

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonCrateDB  Xexclude from comparisonGreenplum  Xexclude from comparisonHugeGraph  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseDistributed Database based on LuceneAnalytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database infoA logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.A fast-speed and highly-scalable Graph DBMS
Primary database modelRelational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSGraph DBMS
Secondary database modelsRelational DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score8.37
Rank#48  Overall
#30  Relational DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Websitephoenix.apache.orgcratedb.comgreenplum.orggithub.com/­hugegraph
hugegraph.apache.org
Technical documentationphoenix.apache.orgcratedb.com/­docsdocs.greenplum.orghugegraph.apache.org/­docs
DeveloperApache Software FoundationCratePivotal Software Inc.Baidu
Initial release2014201320052018
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20197.0.0, September 20230.9
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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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 languageJavaJavaJava
Server operating systemsLinux
Unix
Windows
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinuxLinux
macOS
Unix
Data schemeyes infolate-bound, schema-on-read capabilitiesFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nonoyes infosince Version 4.2no
Secondary indexesyesyesyesyes infoalso supports composite index and range index
SQL infoSupport of SQLyesyes, but no triggers and constraints, and PostgreSQL compatibilityyesno
APIs and other access methodsJDBCADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
Java API
RESTful HTTP API
TinkerPop Gremlin
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.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
Java
Perl
Python
R
Groovy
Java
Python
Server-side scripts infoStored proceduresuser defined functionsuser defined functions (Javascript)yesasynchronous Gremlin script jobs
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes infodepending on used storage backend, e.g. Cassandra and HBase
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Configurable replication on table/partition-levelSource-replica replicationyes infodepending on used storage backend, e.g. Cassandra and HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoyesvia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyesyes infoedges in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDACID
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.yesnonoyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyrights management via user accountsfine grained access rights according to SQL-standardUsers, roles and permissions
More information provided by the system vendor
Apache PhoenixCrateDBGreenplumHugeGraph
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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More resources
Apache PhoenixCrateDBGreenplumHugeGraph
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