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DBMS > Hazelcast vs. Tarantool vs. Warp 10 vs. YottaDB

System Properties Comparison Hazelcast vs. Tarantool vs. Warp 10 vs. YottaDB

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Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonTarantool  Xexclude from comparisonWarp 10  Xexclude from comparisonYottaDB  Xexclude from comparison
DescriptionA widely adopted in-memory data gridIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsTimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBaseA fast and solid embedded Key-value store
Primary database modelKey-value storeDocument store
Key-value store
Relational DBMS
Time Series DBMSKey-value store
Secondary database modelsDocument store infoJSON support with IMDG 3.12Spatial DBMS infowith Tarantool/GIS extensionRelational DBMS infousing the Octo plugin
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.46
Rank#61  Overall
#7  Key-value stores
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Score0.14
Rank#344  Overall
#32  Time Series DBMS
Score0.28
Rank#306  Overall
#44  Key-value stores
Websitehazelcast.comwww.tarantool.iowww.warp10.ioyottadb.com
Technical documentationhazelcast.org/­imdg/­docswww.tarantool.io/­en/­docwww.warp10.io/­content/­02_Getting_startedyottadb.com/­resources/­documentation
DeveloperHazelcastVKSenXYottaDB, LLC
Initial release2008200820152001
Current release5.3.6, November 20232.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool EnterpriseOpen Source infoApache License 2.0Open Source infoAGPL 3.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++JavaC
Server operating systemsAll OS with a Java VMBSD
Linux
macOS
Linux
OS X
Windows
Docker
Linux
Data schemeschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsschema-freeschema-free
Typing infopredefined data types such as float or dateyesstring, double, decimal, uuid, integer, blob, boolean, datetimeyesno
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.yes infothe object must implement a serialization strategynonono
Secondary indexesyesyesnono
SQL infoSupport of SQLSQL-like query languageFull-featured ANSI SQL supportnoby using the Octo plugin
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
Open binary protocolHTTP API
Jupyter
WebSocket
PostgreSQL wire protocol infousing the Octo plugin
Proprietary protocol
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor ServicesLua, C and SQL stored proceduresyes infoWarpScript
Triggersyes infoEventsyes, before/after data modification events, on replication events, client session eventsno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.Sharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
selectable replication factor infobased on HBaseyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Immediate Consistency infobased on HBase
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitedACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesyes, write ahead loggingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes, full featured in-memory storage engine with persistenceyesyes
User concepts infoAccess controlRole-based access controlAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
Mandatory use of cryptographic tokens, containing fine-grained authorizationsUsers and groups based on OS-security mechanisms

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More resources
HazelcastTarantoolWarp 10YottaDB
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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