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DBMS > EXASOL vs. Hazelcast vs. RavenDB vs. Tarantool

System Properties Comparison EXASOL vs. Hazelcast vs. RavenDB vs. Tarantool

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Editorial information provided by DB-Engines
NameEXASOL  Xexclude from comparisonHazelcast  Xexclude from comparisonRavenDB  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.A widely adopted in-memory data gridOpen Source Operational and Transactional Enterprise NoSQL Document DatabaseIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelRelational DBMSKey-value storeDocument storeDocument store
Key-value store
Relational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12Graph DBMS
Spatial DBMS
Time Series DBMS
Spatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.99
Rank#124  Overall
#58  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score2.92
Rank#101  Overall
#18  Document stores
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websitewww.exasol.comhazelcast.comravendb.netwww.tarantool.io
Technical documentationwww.exasol.com/­resourceshazelcast.org/­imdg/­docsravendb.net/­docswww.tarantool.io/­en/­doc
DeveloperExasolHazelcastHibernating RhinosVK
Initial release2000200820102008
Current release5.3.6, November 20235.4, July 20222.10.0, May 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoAGPL version 3, commercial license availableOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC#C and C++
Server operating systemsAll OS with a Java VMLinux
macOS
Raspberry Pi
Windows
BSD
Linux
macOS
Data schemeyesschema-freeschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesyesnostring, double, decimal, uuid, integer, blob, boolean, datetime
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 strategyno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesSQL-like query languageSQL-like query language (RQL)Full-featured ANSI SQL support
APIs and other access methods.Net
JDBC
ODBC
WebSocket
JCache
JPA
Memcached protocol
RESTful HTTP API
.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
Open binary protocol
Supported programming languagesJava
Lua
Python
R
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresuser defined functionsyes infoEvent Listeners, Executor ServicesyesLua, C and SQL stored procedures
Triggersyesyes infoEventsyesyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapMulti-source replicationAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoHadoop integrationyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmDefault ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.Casual 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
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitedACID, Cluster-wide transaction availableACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlAccess rights for users, groups and roles according to SQL-standardRole-based access controlAuthorization levels configured per client per databaseAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources
EXASOLHazelcastRavenDBTarantool
DB-Engines blog posts

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

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