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DBMS > AnzoGraph DB vs. Apache IoTDB vs. Apache Phoenix vs. Hawkular Metrics vs. TimescaleDB

System Properties Comparison AnzoGraph DB vs. Apache IoTDB vs. Apache Phoenix vs. Hawkular Metrics vs. TimescaleDB

Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonApache IoTDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA scale-out RDBMS with evolutionary schema built on Apache HBaseHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelGraph DBMS
RDF store
Time Series DBMSRelational DBMSTime Series DBMSTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#307  Overall
#24  Graph DBMS
#13  RDF stores
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitecambridgesemantics.com/­anzographiotdb.apache.orgphoenix.apache.orgwww.hawkular.orgwww.timescale.com
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlphoenix.apache.orgwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.timescale.com
DeveloperCambridge SemanticsApache Software FoundationApache Software FoundationCommunity supported by Red HatTimescale
Initial release20182018201420142017
Current release2.3, January 20211.1.0, April 20235.0-HBase2, July 2018 and 4.15-HBase1, December 20192.13.0, November 2023
License infoCommercial or Open Sourcecommercial infofree trial version availableOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaJavaC
Server operating systemsLinuxAll OS with a Java VM (>= 1.8)Linux
Unix
Windows
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeSchema-free and OWL/RDFS-schema supportyesyes infolate-bound, schema-on-read capabilitiesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nonononoyes
Secondary indexesnoyesyesnoyes
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.SQL-like query languageyesnoyes infofull PostgreSQL SQL syntax
APIs and other access methodsApache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
JDBC
Native API
JDBCHTTP RESTADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC++
Java
Python
C
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Go
Java
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresuser defined functions and aggregatesyesuser defined functionsnouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyesnoyes infovia Hawkular Alertingyes
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardinghorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding infobased on Cassandrayes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication in MPP-Clusterselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
selectable replication factor infobased on CassandraSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsKerberos/HDFS data loadingIntegration with Hadoop and SparkHadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integrityno infonot needed in graphsnononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDnoACID
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.yesyesyesnono
User concepts infoAccess controlAccess rights for users and rolesyesAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynofine grained access rights according to SQL-standard

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More resources
AnzoGraph DBApache IoTDBApache PhoenixHawkular MetricsTimescaleDB
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