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DBMS > AgensGraph vs. Google Cloud Spanner vs. OpenMLDB vs. Tarantool

System Properties Comparison AgensGraph vs. Google Cloud Spanner vs. OpenMLDB vs. Tarantool

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Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonOpenMLDB  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLA horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.An open-source machine learning database that provides a feature platform for training and inferenceIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelGraph DBMS
Relational DBMS
Relational DBMSTime Series DBMSDocument store
Key-value store
Relational DBMS
Secondary database modelsRelational DBMSSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.17
Rank#320  Overall
#29  Graph DBMS
#143  Relational DBMS
Score2.69
Rank#101  Overall
#51  Relational DBMS
Score0.11
Rank#339  Overall
#30  Time Series DBMS
Score1.63
Rank#139  Overall
#25  Document stores
#24  Key-value stores
#65  Relational DBMS
Websitebitnine.net/­agensgraphcloud.google.com/­spanneropenmldb.aiwww.tarantool.io
Technical documentationbitnine.net/­documentationcloud.google.com/­spanner/­docsopenmldb.ai/­docs/­zh/­mainwww.tarantool.io/­en/­doc
DeveloperBitnine Global Inc.Google4 Paradigm Inc.VK
Initial release2016201720202008
Current release2.1, December 20182024-2 February 20242.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache License 2.0commercialOpen SourceOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC++, Java, ScalaC and C++
Server operating systemsLinux
OS X
Windows
hostedLinuxBSD
Linux
macOS
Data schemedepending on used data modelyesFixed schemaFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesyesyesstring, 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.nononono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesyes infoQuery statements complying to ANSI 2011yesFull-featured ANSI SQL support
APIs and other access methodsCypher Query Language
JDBC
gRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
JDBC
SQLAlchemy
Open binary protocol
Supported programming languagesC
Java
JavaScript
Python
Go
Java
JavaScript (Node.js)
Python
C++
Go
Java
Python
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresyesnonoLua, C and SQL stored procedures
Triggersnononoyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesno, but can be realized using table inheritanceShardinghorizontal 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 nodesSource-replica replicationMulti-source replication with 3 replicas for regional instances.Source-replica 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 methodsnoyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate 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 integrityyesyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoStrict serializable isolationnoACID, 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.nonoyesyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights 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
AgensGraphGoogle Cloud SpannerOpenMLDBTarantool
DB-Engines blog posts

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

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