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DBMS > Apache Impala vs. RavenDB vs. Tarantool vs. Transwarp KunDB

System Properties Comparison Apache Impala vs. RavenDB vs. Tarantool vs. Transwarp KunDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonRavenDB  Xexclude from comparisonTarantool  Xexclude from comparisonTranswarp KunDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopOpen Source Operational and Transactional Enterprise NoSQL Document DatabaseIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsOLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle
Primary database modelRelational DBMSDocument storeDocument store
Key-value store
Relational DBMS
Relational DBMS
Secondary database modelsDocument storeGraph 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
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score2.84
Rank#101  Overall
#18  Document stores
Score1.67
Rank#143  Overall
#25  Document stores
#25  Key-value stores
#65  Relational DBMS
Score0.14
Rank#341  Overall
#149  Relational DBMS
Websiteimpala.apache.orgravendb.netwww.tarantool.iowww.transwarp.cn/­en/­product/­kundb
Technical documentationimpala.apache.org/­impala-docs.htmlravendb.net/­docswww.tarantool.io/­en/­doc
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaHibernating RhinosVKTranswarp
Initial release201320102008
Current release4.1.0, June 20225.4, July 20222.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoAGPL version 3, commercial license availableOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C#C and C++
Server operating systemsLinuxLinux
macOS
Raspberry Pi
Windows
BSD
Linux
macOS
Data schemeyesschema-freeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
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.nono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query language (RQL)Full-featured ANSI SQL supportyes
APIs and other access methodsJDBC
ODBC
.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 languagesAll languages supporting JDBC/ODBC.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 proceduresyes infouser defined functions and integration of map-reduceyesLua, C and SQL stored proceduresyes
Triggersnoyesyes, before/after data modification events, on replication events, client session eventsyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.horizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-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 infoquery execution via MapReduceyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyDefault 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
Immediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID, Cluster-wide transaction availableACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesyesyes, write ahead loggingyes
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 persistence
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAuthorization 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
yes

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More resources
Apache ImpalaRavenDBTarantoolTranswarp KunDB
DB-Engines blog posts

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

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