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DBMS > Apache Druid vs. Apache Impala vs. CrateDB vs. H2GIS

System Properties Comparison Apache Druid vs. Apache Impala vs. CrateDB vs. H2GIS

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonApache Impala  Xexclude from comparisonCrateDB  Xexclude from comparisonH2GIS  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataAnalytic DBMS for HadoopDistributed Database based on LuceneSpatial extension of H2
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Spatial DBMS
Secondary database modelsDocument storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
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
Score0.00
Rank#383  Overall
#7  Spatial DBMS
Websitedruid.apache.orgimpala.apache.orgcratedb.comwww.h2gis.org
Technical documentationdruid.apache.org/­docs/­latest/­designimpala.apache.org/­impala-docs.htmlcratedb.com/­docswww.h2gis.org/­docs/­home
DeveloperApache Software Foundation and contributorsApache Software Foundation infoApache top-level project, originally developed by ClouderaCrateCNRS
Initial release2012201320132013
Current release29.0.1, April 20244.1.0, June 2022
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoApache Version 2Open SourceOpen Source infoLGPL 3.0
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 languageJavaC++JavaJava
Server operating systemsLinux
OS X
Unix
LinuxAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support
Data schemeyes infoschema-less columns are supportedyesFlexible Schema (defined schema, partial schema, schema free)yes
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 for queryingSQL-like DML and DDL statementsyes, but no triggers and constraints, and PostgreSQL compatibilityyes
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
All 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
Java
Server-side scripts infoStored proceduresnoyes infouser defined functions and integration of map-reduceuser defined functions (Javascript)yes infobased on H2
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesselectable replication factorConfigurable replication on table/partition-levelyes infobased on H2
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono infounique row identifiers can be used for implementing an optimistic concurrency control strategyACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
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.nononoyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosrights management via user accountsyes infobased on H2
More information provided by the system vendor
Apache DruidApache ImpalaCrateDBH2GIS
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 DruidApache ImpalaCrateDBH2GIS
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