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DBMS > etcd vs. EventStoreDB vs. Splice Machine vs. Tarantool

System Properties Comparison etcd vs. EventStoreDB vs. Splice Machine vs. Tarantool

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Editorial information provided by DB-Engines
Nameetcd  Xexclude from comparisonEventStoreDB  Xexclude from comparisonSplice Machine  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionA distributed reliable key-value storeIndustrial-strength, open-source database solution built from the ground up for event sourcing.Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelKey-value storeEvent StoreRelational DBMSDocument store
Key-value store
Relational DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.03
Rank#54  Overall
#5  Key-value stores
Score1.19
Rank#173  Overall
#1  Event Stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Websiteetcd.io
github.com/­etcd-io/­etcd
www.eventstore.comsplicemachine.comwww.tarantool.io
Technical documentationetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
developers.eventstore.comsplicemachine.com/­how-it-workswww.tarantool.io/­en/­doc
DeveloperEvent Store LimitedSplice MachineVK
Initial release201220142008
Current release3.4, August 201921.2, February 20213.1, March 20212.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open SourceOpen Source infoAGPL 3.0, 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

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoJavaC and C++
Server operating systemsFreeBSD
Linux
Windows infoexperimental
Linux
Windows
Linux
OS X
Solaris
Windows
BSD
Linux
macOS
Data schemeschema-freeyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or datenoyesstring, 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.nono
Secondary indexesnoyesyes
SQL infoSupport of SQLnoyesFull-featured ANSI SQL support
APIs and other access methodsgRPC
JSON over HTTP
JDBC
Native Spark Datasource
ODBC
Open binary protocol
Supported programming languages.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresnoyes infoJavaLua, C and SQL stored procedures
Triggersyes, watching key changesyesyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesShared Nothhing Auto-Sharding, Columnar PartitioningSharding, 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 nodesUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.Multi-source replication
Source-replica replication
Asynchronous 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 methodsnoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyCasual 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 integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlnoAccess rights for users, groups and roles according to SQL-standardAccess 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
etcdEventStoreDBSplice MachineTarantool
DB-Engines blog posts

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

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