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DBMS > GeoSpock vs. Hazelcast vs. HBase vs. QuestDB vs. Spark SQL

System Properties Comparison GeoSpock vs. Hazelcast vs. HBase vs. QuestDB vs. Spark SQL

Editorial information provided by DB-Engines
NameGeoSpock  Xexclude from comparisonHazelcast  Xexclude from comparisonHBase  Xexclude from comparisonQuestDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionSpatial and temporal data processing engine for extreme data scaleA widely adopted in-memory data gridWide-column store based on Apache Hadoop and on concepts of BigTableA high performance open source SQL database for time series dataSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value storeWide column storeTime Series DBMSRelational DBMS
Secondary database modelsTime Series DBMSDocument store infoJSON support with IMDG 3.12Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.97
Rank#57  Overall
#6  Key-value stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Score2.52
Rank#109  Overall
#9  Time Series DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitegeospock.comhazelcast.comhbase.apache.orgquestdb.iospark.apache.org/­sql
Technical documentationhazelcast.org/­imdg/­docshbase.apache.org/­book.htmlquestdb.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGeoSpockHazelcastApache Software Foundation infoApache top-level project, originally developed by PowersetQuestDB Technology IncApache Software Foundation
Initial release2008200820142014
Current release2.0, September 20195.3.6, November 20232.3.4, January 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache version 2Open Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, JavascriptJavaJavaJava (Zero-GC), C++, RustScala
Server operating systemshostedAll OS with a Java VMLinux
Unix
Windows infousing Cygwin
Linux
macOS
Windows
Linux
OS X
Windows
Data schemeyesschema-freeschema-free, schema definition possibleyes infoschema-free via InfluxDB Line Protocolyes
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVROyesyes
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.noyes infothe object must implement a serialization strategynonono
Secondary indexestemporal, categoricalyesnonono
SQL infoSupport of SQLANSI SQL for query only (using Presto)SQL-like query languagenoSQL with time-series extensionsSQL-like DML and DDL statements
APIs and other access methodsJDBCJCache
JPA
Memcached protocol
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
JDBC
ODBC
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor Servicesyes infoCoprocessors in Javanono
Triggersnoyes infoEventsyesnono
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingShardingShardinghorizontal partitioning (by timestamps)yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapMulti-source replication
Source-replica replication
Source-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoone or two-phase-commit; repeatable reads; read commitedSingle row ACID (across millions of columns)ACID for single-table writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesyes infothrough memory mapped filesno
User concepts infoAccess controlAccess rights for users can be defined per tableRole-based access controlAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACno
More information provided by the system vendor
GeoSpockHazelcastHBaseQuestDBSpark SQL
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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