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DBMS > BigObject vs. etcd vs. eXtremeDB vs. Pinecone vs. Solr

System Properties Comparison BigObject vs. etcd vs. eXtremeDB vs. Pinecone vs. Solr

Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonetcd  Xexclude from comparisoneXtremeDB  Xexclude from comparisonPinecone  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesA distributed reliable key-value storeNatively in-memory DBMS with options for persistency, high-availability and clusteringA managed, cloud-native vector databaseA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedKey-value storeRelational DBMS
Time Series DBMS
Vector DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#333  Overall
#147  Relational DBMS
Score7.25
Rank#54  Overall
#5  Key-value stores
Score0.74
Rank#223  Overall
#103  Relational DBMS
#18  Time Series DBMS
Score3.16
Rank#95  Overall
#2  Vector DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitebigobject.ioetcd.io
github.com/­etcd-io/­etcd
www.mcobject.comwww.pinecone.iosolr.apache.org
Technical documentationdocs.bigobject.ioetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
www.mcobject.com/­docs/­extremedb.htmdocs.pinecone.io/­docs/­overviewsolr.apache.org/­resources.html
DeveloperBigObject, Inc.McObjectPinecone Systems, IncApache Software Foundation
Initial release2015200120192006
Current release3.4, August 20198.2, 20219.6.0, April 2024
License infoCommercial or Open Sourcecommercial infofree community edition availableOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache Version 2
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.
Implementation languageGoC and C++Java
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
FreeBSD
Linux
Windows infoexperimental
AIX
HP-UX
Linux
macOS
Solaris
Windows
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-freeyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesnoyesString, Number, Booleanyes infosupports customizable data types and automatic typing
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.nonono infosupport of XML interfaces availablenoyes
Secondary indexesyesnoyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyes infowith the option: eXtremeSQLnoSolr Parallel SQL Interface
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
gRPC
JSON over HTTP
.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API
RESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languages.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
.Net
C
C#
C++
Java
Lua
Python
Scala
Python.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresLuanoyesJava plugins
Triggersnoyes, watching key changesyes infoby defining eventsyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning / shardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.Active Replication Fabric™ for IoT
Multi-source replication infoby means of eXtremeDB Cluster option
Source-replica replication infoby means of eXtremeDB High Availability option
yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyes infoOptimistic (MVCC) and pessimistic (locking) strategies availableyesyes
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.yesnoyesnoyes
User concepts infoAccess controlnonoyes
More information provided by the system vendor
BigObjectetcdeXtremeDBPineconeSolr
Specific characteristicseXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
» more
Competitive advantageseXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more
Typical application scenariosIoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more
Key customersSchneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more
Market metricsWith hundreds of customers and over 30 million devices/applications using the product...
» more
Licensing and pricing modelsFor server use cases, there is a simple per-server license irrespective of the number...
» more

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BigObjectetcdeXtremeDBPineconeSolr
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