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DBMS > Apache Impala vs. EXASOL vs. GridDB vs. Hazelcast

System Properties Comparison Apache Impala vs. EXASOL vs. GridDB vs. Hazelcast

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonEXASOL  Xexclude from comparisonGridDB  Xexclude from comparisonHazelcast  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopHigh-performance, in-memory, MPP database specifically designed for in-memory analytics.Scalable in-memory time series database optimized for IoT and Big DataA widely adopted in-memory data grid
Primary database modelRelational DBMSRelational DBMSTime Series DBMSKey-value store
Secondary database modelsDocument storeKey-value store
Relational DBMS
Document store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.99
Rank#124  Overall
#58  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Websiteimpala.apache.orgwww.exasol.comgriddb.nethazelcast.com
Technical documentationimpala.apache.org/­impala-docs.htmlwww.exasol.com/­resourcesdocs.griddb.nethazelcast.org/­imdg/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaExasolToshiba CorporationHazelcast
Initial release2013200020132008
Current release4.1.0, June 20225.1, August 20225.3.6, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2; commercial licenses available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++Java
Server operating systemsLinuxLinuxAll OS with a Java VM
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampyes
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.nononoyes infothe object must implement a serialization strategy
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL92, SQL-like TQL (Toshiba Query Language)SQL-like query language
APIs and other access methodsJDBC
ODBC
.Net
JDBC
ODBC
WebSocket
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JCache
JPA
Memcached protocol
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCJava
Lua
Python
R
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsnoyes infoEvent Listeners, Executor Services
Triggersnoyesyesyes infoEvents
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationyes infoReplicated Map
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes infoHadoop integrationConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus Algorithm
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at container levelone or two-phase-commit; repeatable reads; read commited
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users, groups and roles according to SQL-standardAccess rights for users can be defined per databaseRole-based access control
More information provided by the system vendor
Apache ImpalaEXASOLGridDBHazelcast
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
Apache ImpalaEXASOLGridDBHazelcast
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