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DBMS > Apache Impala vs. CrateDB vs. EsgynDB vs. JanusGraph

System Properties Comparison Apache Impala vs. CrateDB vs. EsgynDB vs. JanusGraph

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonCrateDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDistributed Database based on LuceneEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionA Graph DBMS optimized for distributed clusters infoIt was forked from the latest code base of Titan in January 2017
Primary database modelRelational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSGraph DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score1.94
Rank#129  Overall
#12  Graph DBMS
Websiteimpala.apache.orgcratedb.comwww.esgyn.cnjanusgraph.org
Technical documentationimpala.apache.org/­impala-docs.htmlcratedb.com/­docsdocs.janusgraph.org
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaCrateEsgynLinux Foundation; originally developed as Titan by Aurelius
Initial release2013201320152017
Current release4.1.0, June 20220.6.3, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open SourcecommercialOpen Source infoApache 2.0
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 languageC++JavaC++, JavaJava
Server operating systemsLinuxAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinuxLinux
OS X
Unix
Windows
Data schemeyesFlexible 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.nononono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes, but no triggers and constraints, and PostgreSQL compatibilityyesno
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
ADO.NET
JDBC
ODBC
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Supported programming languagesAll languages supporting JDBC/ODBC.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
All languages supporting JDBC/ODBC/ADO.NetClojure
Java
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions (Javascript)Java Stored Proceduresyes
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorConfigurable replication on table/partition-levelMulti-source replication between multi datacentersyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyesyes infovia Faunus, a graph analytics engine
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyesyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono 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 infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosrights management via user accountsfine grained access rights according to SQL-standardUser authentification and security via Rexster Graph Server
More information provided by the system vendor
Apache ImpalaCrateDBEsgynDBJanusGraph infosuccessor of Titan
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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Apache ImpalaCrateDBEsgynDBJanusGraph infosuccessor of Titan
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