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DBMS > AgensGraph vs. CrateDB vs. EXASOL vs. Hazelcast

System Properties Comparison AgensGraph vs. CrateDB vs. EXASOL vs. Hazelcast

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Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonCrateDB  Xexclude from comparisonEXASOL  Xexclude from comparisonHazelcast  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLDistributed Database based on LuceneHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.A widely adopted in-memory data grid
Primary database modelGraph DBMS
Relational DBMS
Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSKey-value store
Secondary database modelsRelational DBMSDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.20
Rank#321  Overall
#27  Graph DBMS
#143  Relational DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score1.99
Rank#124  Overall
#58  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Websitebitnine.net/­agensgraphcratedb.comwww.exasol.comhazelcast.com
Technical documentationbitnine.net/­documentationcratedb.com/­docswww.exasol.com/­resourceshazelcast.org/­imdg/­docs
DeveloperBitnine Global Inc.CrateExasolHazelcast
Initial release2016201320002008
Current release2.1, December 20185.3.6, November 2023
License infoCommercial or Open SourceOpen Source infoApache License 2.0Open SourcecommercialOpen Source infoApache Version 2; commercial licenses available
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 languageCJavaJava
Server operating systemsLinux
OS X
Windows
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VM
Data schemedepending on used data modelFlexible Schema (defined schema, partial schema, schema free)yesschema-free
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.nononoyes infothe object must implement a serialization strategy
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesyes, but no triggers and constraints, and PostgreSQL compatibilityyesSQL-like query language
APIs and other access methodsCypher Query Language
JDBC
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
.Net
JDBC
ODBC
WebSocket
JCache
JPA
Memcached protocol
RESTful HTTP API
Supported programming languagesC
Java
JavaScript
Python
.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
Java
Lua
Python
R
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Server-side scripts infoStored proceduresyesuser defined functions (Javascript)user defined functionsyes infoEvent Listeners, Executor Services
Triggersnonoyesyes infoEvents
Partitioning methods infoMethods for storing different data on different nodesno, but can be realized using table inheritanceShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationConfigurable replication on table/partition-levelyes infoReplicated Map
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infoHadoop integrationyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus Algorithm
Foreign keys infoReferential integrityyesnoyesno
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 strategyACIDone or two-phase-commit; repeatable reads; read commited
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.nonoyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardrights management via user accountsAccess rights for users, groups and roles according to SQL-standardRole-based access control
More information provided by the system vendor
AgensGraphCrateDBEXASOLHazelcast
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
AgensGraphCrateDBEXASOLHazelcast
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