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DBMS > atoti vs. CrateDB vs. Hazelcast vs. Sphinx

System Properties Comparison atoti vs. CrateDB vs. Hazelcast vs. Sphinx

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Editorial information provided by DB-Engines
Nameatoti  Xexclude from comparisonCrateDB  Xexclude from comparisonHazelcast  Xexclude from comparisonSphinx  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 widely adopted in-memory data gridOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelObject oriented DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Key-value storeSearch engine
Secondary database modelsRelational DBMSDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.56
Rank#245  Overall
#10  Object oriented DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score5.98
Rank#56  Overall
#5  Search engines
Websiteatoti.iocratedb.comhazelcast.comsphinxsearch.com
Technical documentationdocs.atoti.iocratedb.com/­docshazelcast.org/­imdg/­docssphinxsearch.com/­docs
DeveloperActiveViamCrateHazelcastSphinx Technologies Inc.
Initial release201320082001
Current release5.3.6, November 20233.5.1, February 2023
License infoCommercial or Open Sourcecommercial infofree versions availableOpen SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoGPL version 2, commercial licence 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 languageJavaJavaJavaC++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VMFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyes
Typing infopredefined data types such as float or dateyesyesno
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.noyes infothe object must implement a serialization strategy
Secondary indexesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLMultidimensional Expressions (MDX)yes, but no triggers and constraints, and PostgreSQL compatibilitySQL-like query languageSQL-like query language (SphinxQL)
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JCache
JPA
Memcached protocol
RESTful HTTP API
Proprietary protocol
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
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresPythonuser defined functions (Javascript)yes infoEvent Listeners, Executor Servicesno
Triggersnoyes infoEventsno
Partitioning methods infoMethods for storing different data on different nodesSharding, horizontal partitioningShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyes infoReplicated Mapnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus Algorithm
Foreign keys infoReferential integritynonono
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 strategyone or two-phase-commit; repeatable reads; read commitedno
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlrights management via user accountsRole-based access controlno
More information provided by the system vendor
atotiCrateDBHazelcastSphinx
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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atotiCrateDBHazelcastSphinx
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