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DBMS > Apache Pinot vs. Google Cloud Bigtable vs. Tarantool vs. TinkerGraph

System Properties Comparison Apache Pinot vs. Google Cloud Bigtable vs. Tarantool vs. TinkerGraph

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonTarantool  Xexclude from comparisonTinkerGraph  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.In-memory computing platform with a flexible data schema for efficiently building high-performance applicationsA lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API
Primary database modelRelational DBMSKey-value store
Wide column store
Document store
Key-value store
Relational DBMS
Graph DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.40
Rank#270  Overall
#125  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Score0.08
Rank#348  Overall
#35  Graph DBMS
Websitepinot.apache.orgcloud.google.com/­bigtablewww.tarantool.iotinkerpop.apache.org/­docs/­current/­reference/­#tinkergraph-gremlin
Technical documentationdocs.pinot.apache.orgcloud.google.com/­bigtable/­docswww.tarantool.io/­en/­doc
DeveloperApache Software Foundation and contributorsGoogleVK
Initial release2015201520082009
Current release1.0.0, September 20232.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool EnterpriseOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++Java
Server operating systemsAll OS with a Java JDK11 or higherhostedBSD
Linux
macOS
Data schemeyesschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsschema-free
Typing infopredefined data types such as float or dateyesnostring, double, decimal, uuid, integer, blob, boolean, datetimeyes
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.nonono
Secondary indexesnoyesno
SQL infoSupport of SQLSQL-like query languagenoFull-featured ANSI SQL supportno
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Open binary protocolTinkerPop 3
Supported programming languagesGo
Java
Python
C#
C++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Groovy
Java
Server-side scripts infoStored proceduresnoLua, C and SQL stored proceduresno
Triggersnoyes, before/after data modification events, on replication events, client session eventsno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.none
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)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
none
Foreign keys infoReferential integritynoyesyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyes, cooperative multitaskingno
Durability infoSupport for making data persistentyesyes, write ahead loggingoptional
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, full featured in-memory storage engine with persistenceyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
no

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More resources
Apache PinotGoogle Cloud BigtableTarantoolTinkerGraph
DB-Engines blog posts

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

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