DBMS > Apache IoTDB vs. CrateDB
System Properties Comparison Apache IoTDB vs. CrateDB
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|Editorial information provided by DB-Engines
|Apache IoTDB Xexclude from comparison
|CrateDB Xexclude from comparison
|An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink
|Distributed Database based on Lucene
|Primary database model
|Time Series DBMS
Time Series DBMS
|Secondary database models
|Apache Software Foundation
|1.1.0, April 2023
|License Commercial or Open Source
|Open Source Apache Version 2.0
|Cloud-based only Only available as a cloud service
|DBaaS offerings (sponsored links) Database 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.
|Server operating systems
|All OS with a Java VM (>= 1.8)
|All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support
|Flexible Schema (defined schema, partial schema, schema free)
|Typing predefined data types such as float or date
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.
|SQL Support of SQL
|SQL-like query language
|yes, but no triggers and constraints, and PostgreSQL compatibility
|APIs and other access methods
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
|Supported programming languages
Go community maintained client
Perl community maintained client
Ruby community maintained client
Scala community maintained client
|Server-side scripts Stored procedures
|Partitioning methods Methods for storing different data on different nodes
|horizontal partitioning (by time range) + vertical partitioning (by deviceId)
|Replication methods Methods for redundantly storing data on multiple nodes
|selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas
|Configurable replication on table/partition-level
|MapReduce Offers an API for user-defined Map/Reduce methods
|Integration with Hadoop and Spark
|Consistency concepts Methods to ensure consistency in a distributed system
Strong Consistency with Raft
Read-after-write consistency on record level
|Foreign keys Referential integrity
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data
|no unique row identifiers can be used for implementing an optimistic concurrency control strategy
|Concurrency Support for concurrent manipulation of data
|Durability Support for making data persistent
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.
|User concepts Access control
|rights management via user accounts
|More information provided by the system vendor
|CrateDB is an open-source distributed SQL database that spreads data and processing...
|Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
|Typical application scenarios
| IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
|Across all continents, CrateDB is used by companies of all sizes to meet the most...
|The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
|Licensing and pricing models
|See CrateDB pricing >
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|Conferences, events and webinars
CrateDB European Tour Amsterdam
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