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DBMS > Tarantool vs. TigerGraph vs. TimescaleDB

System Properties Comparison Tarantool vs. TigerGraph vs. TimescaleDB

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Editorial information provided by DB-Engines
NameTarantool  Xexclude from comparisonTigerGraph  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-timeA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelDocument store
Key-value store
Relational DBMS
Graph DBMSTime Series DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extensionRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Score1.83
Rank#139  Overall
#13  Graph DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitewww.tarantool.iowww.tigergraph.comwww.timescale.com
Technical documentationwww.tarantool.io/­en/­docdocs.tigergraph.comdocs.timescale.com
DeveloperVKTimescale
Initial release200820172017
Current release2.10.0, May 20222.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool EnterprisecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++C++C
Server operating systemsBSD
Linux
macOS
LinuxLinux
OS X
Windows
Data schemeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyesyes
Typing infopredefined data types such as float or datestring, double, decimal, uuid, integer, blob, boolean, datetimeyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nonoyes
Secondary indexesyesyes
SQL infoSupport of SQLFull-featured ANSI SQL supportSQL-like query language (GSQL)yes infofull PostgreSQL SQL syntax
APIs and other access methodsOpen binary protocolGSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
C++
Java
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresLua, C and SQL stored proceduresyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyes, before/after data modification events, on replication events, client session eventsnoyes
Partitioning methods infoMethods for storing different data on different nodesSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.yes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
Source-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemCasual 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
Foreign keys infoReferential integrityyesyes infoRelationships in graphsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACIDACID
Concurrency infoSupport for concurrent manipulation of datayes, cooperative multitaskingyesyes
Durability infoSupport for making data persistentyes, write ahead loggingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, full featured in-memory storage engine with persistencenono
User concepts infoAccess 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
Role-based access controlfine grained access rights according to SQL-standard

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More resources
TarantoolTigerGraphTimescaleDB
DB-Engines blog posts

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

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Recent citations in the news

TaranHouse: New Big Data Warehouse Announced by Tarantool
4 April 2018, Newswire

In-Memory Showdown: Redis vs. Tarantool
1 September 2021, Хабр

Tarantool Announces New Enterprise Version With Enhanced Scaling and Monitoring Capabilities
18 May 2018, Newswire

Deploying Tarantool Cartridge applications with zero effort (Part 1)
16 December 2019, Хабр

Deploying Tarantool Cartridge applications with zero effort (Part 2)
13 April 2020, Хабр

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20 June 2023, SiliconANGLE News

TigerGraph Bolsters DB for Enterprise Graph Workloads
1 November 2023, Datanami

provided by Google News

TimescaleDB Is a Vector Database Now, Too
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4 April 2024, PR Newswire

Power IoT and time-series workloads with TimescaleDB for Azure Database for PostgreSQL
18 March 2019, Microsoft

Timescale Valuation Rockets to Over $1B with $110M Round, Marking the Explosive Rise of Time-Series Data
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