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DBMS > Apache Impala vs. GridGain vs. LeanXcale vs. Tarantool

System Properties Comparison Apache Impala vs. GridGain vs. LeanXcale vs. Tarantool

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonGridGain  Xexclude from comparisonLeanXcale  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopGridGain is an in-memory computing platform, built on Apache IgniteA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelRelational DBMSKey-value store
Relational DBMS
Key-value store
Relational DBMS
Document store
Key-value store
Relational DBMS
Secondary database modelsDocument storeSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websiteimpala.apache.orgwww.gridgain.comwww.leanxcale.comwww.tarantool.io
Technical documentationimpala.apache.org/­impala-docs.htmlwww.gridgain.com/­docs/­index.htmlwww.tarantool.io/­en/­doc
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaGridGain Systems, Inc.LeanXcaleVK
Initial release2013200720152008
Current release4.1.0, June 2022GridGain 8.5.12.10.0, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen 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

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Implementation languageC++Java, C++, .NetC and C++
Server operating systemsLinuxLinux
OS X
Solaris
Windows
BSD
Linux
macOS
Data schemeyesyesyesFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyesyesstring, 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.noyesno
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsANSI-99 for query and DML statements, subset of DDLyes infothrough Apache DerbyFull-featured ANSI SQL support
APIs and other access methodsJDBC
ODBC
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
Open binary protocol
Supported programming languagesAll languages supporting JDBC/ODBCC#
C++
Java
PHP
Python
Ruby
Scala
C
Java
Scala
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes (compute grid and cache interceptors can be used instead)Lua, C and SQL stored procedures
Triggersnoyes (cache interceptors and events)yes, before/after data modification events, on replication events, client session events
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.
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes (replicated cache)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 methodsyes infoquery execution via MapReduceyes (compute grid and hadoop accelerator)no
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual 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 integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID, 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.noyesyesyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosSecurity Hooks for custom implementationsAccess 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
Apache ImpalaGridGainLeanXcaleTarantool
DB-Engines blog posts

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

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