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DBMS > Apache Phoenix vs. Geode vs. Heroic vs. Microsoft Azure Data Explorer vs. SingleStore

System Properties Comparison Apache Phoenix vs. Geode vs. Heroic vs. Microsoft Azure Data Explorer vs. SingleStore

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGeode  Xexclude from comparisonHeroic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSingleStore infoformer name was MemSQL  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseGeode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processesTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchFully managed big data interactive analytics platformMySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type
Primary database modelRelational DBMSKey-value storeTime Series DBMSRelational DBMS infocolumn orientedRelational DBMS
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Document store
Spatial DBMS
Time Series DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score1.92
Rank#131  Overall
#23  Key-value stores
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score5.60
Rank#62  Overall
#35  Relational DBMS
Websitephoenix.apache.orggeode.apache.orggithub.com/­spotify/­heroicazure.microsoft.com/­services/­data-explorerwww.singlestore.com
Technical documentationphoenix.apache.orggeode.apache.org/­docsspotify.github.io/­heroicdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.singlestore.com
DeveloperApache Software FoundationOriginally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.SpotifyMicrosoftSingleStore Inc.
Initial release20142002201420192013
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20191.1, February 2017cloud service with continuous releases8.5, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2; commercial licenses available as GemfireOpen Source infoApache 2.0commercialcommercial infofree developer edition available
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities
Implementation languageJavaJavaJavaC++, Go
Server operating systemsLinux
Unix
Windows
All OS with a Java VM infothe JDK (8 or later) is also requiredhostedLinux info64 bit version required
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.nononoyesno
Secondary indexesyesnoyes infovia Elasticsearchall fields are automatically indexedyes
SQL infoSupport of SQLyesSQL-like query language (OQL)noKusto Query Language (KQL), SQL subsetyes infobut no triggers and foreign keys
APIs and other access methodsJDBCJava Client API
Memcached protocol
RESTful HTTP API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Cluster Management API infoas HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
All JVM based languages
C++
Groovy
Java
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Bash
C
C#
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsnoYes, possible languages: KQL, Python, Ryes
Triggersnoyes infoCache Event Listenersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoImplicit feature of the cloud serviceSharding infohash partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replicationyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication infostores two copies of each physical data partition on two separate nodes
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno infocan define user-defined aggregate functions for map-reduce-style calculations
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyes, on a single nodenonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyesyes infoAll updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnonoyes
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights per client and object definableAzure Active Directory AuthenticationFine grained access control via users, groups and roles
More information provided by the system vendor
Apache PhoenixGeodeHeroicMicrosoft Azure Data ExplorerSingleStore infoformer name was MemSQL
Specific characteristicsSingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more
Competitive advantagesSingleStore’s competitive advantages include: Easy and Simplified Architecture with...
» more
Typical application scenariosDriving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more
Key customersIEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more
Market metricsCustomers in various industries worldwide including US and International Industry...
» more
Licensing and pricing modelsF ree Tier and Enterprise Edition
» more

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More resources
Apache PhoenixGeodeHeroicMicrosoft Azure Data ExplorerSingleStore infoformer name was MemSQL
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