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DBMS > atoti vs. CrateDB vs. Qdrant vs. Spark SQL

System Properties Comparison atoti vs. CrateDB vs. Qdrant vs. Spark SQL

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Editorial information provided by DB-Engines
Nameatoti  Xexclude from comparisonCrateDB  Xexclude from comparisonQdrant  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAn in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.Distributed Database based on LuceneA high-performance vector database with neural network or semantic-based matchingSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelObject oriented DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Vector DBMSRelational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.61
Rank#243  Overall
#10  Object oriented DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score1.28
Rank#167  Overall
#6  Vector DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteatoti.iocratedb.comgithub.com/­qdrant/­qdrant
qdrant.tech
spark.apache.org/­sql
Technical documentationdocs.atoti.iocratedb.com/­docsqdrant.tech/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperActiveViamCrateQdrantApache Software Foundation
Initial release201320212014
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercial infofree versions availableOpen SourceOpen Source infoApache Version 2.0Open 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 languageJavaJavaRustScala
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportDocker
Linux
macOS
Windows
Linux
OS X
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyes
Typing infopredefined data types such as float or dateyesNumbers, Strings, Geo, Booleanyes
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.nonono
Secondary indexesyesyes infoKeywords, numberic ranges, geo, full-textno
SQL infoSupport of SQLMultidimensional Expressions (MDX)yes, but no triggers and constraints, and PostgreSQL compatibilitynoSQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
gRPC
OpenAPI 3.0
RESTful HTTP/JSON API infoOpenAPI 3.0
JDBC
ODBC
Supported programming languages.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
.Net
Go
Java
JavaScript (Node.js)
Python
Rust
Java
Python
R
Scala
Server-side scripts infoStored proceduresPythonuser defined functions (Javascript)no
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesSharding, horizontal partitioningShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelCollection-level replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Eventual Consistency, tunable consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategyno
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlrights management via user accountsKey-based authenticationno
More information provided by the system vendor
atotiCrateDBQdrantSpark SQL
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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atotiCrateDBQdrantSpark SQL
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