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DBMS > Apache Impala vs. Datomic vs. Solr vs. YugabyteDB

System Properties Comparison Apache Impala vs. Datomic vs. Solr vs. YugabyteDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatomic  Xexclude from comparisonSolr  Xexclude from comparisonYugabyteDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA widely used distributed, scalable search engine based on Apache LuceneHigh-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL.
Primary database modelRelational DBMSRelational DBMSSearch engineRelational DBMS
Secondary database modelsDocument storeSpatial DBMSDocument store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Score2.63
Rank#109  Overall
#53  Relational DBMS
Websiteimpala.apache.orgwww.datomic.comsolr.apache.orgwww.yugabyte.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.datomic.comsolr.apache.org/­resources.htmldocs.yugabyte.com
github.com/­yugabyte/­yugabyte-db
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaCognitectApache Software FoundationYugabyte Inc.
Initial release2013201220062017
Current release4.1.0, June 20221.0.7075, December 20239.6.1, May 20242.19, September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infolimited edition freeOpen Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications.
Implementation languageC++Java, ClojureJavaC and C++
Server operating systemsLinuxAll OS with a Java VMAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Data schemeyesyesyes infoDynamic Fields enables on-the-fly addition of new fieldsdepending on used data model
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingyes
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.nonoyesno
Secondary indexesyesyesyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSolr Parallel SQL Interfaceyes, PostgreSQL compatible
APIs and other access methodsJDBC
ODBC
RESTful HTTP APIJava API
RESTful HTTP/JSON API
JDBC
YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language
YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL
Supported programming languagesAll languages supporting JDBC/ODBCClojure
Java
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infoTransaction FunctionsJava pluginsyes infosql, plpgsql, C
TriggersnoBy using transaction functionsyes infoUser configurable commands triggered on index changesyes
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersShardingHash and Range Sharding, row-level geo-partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornone infoBut extensive use of caching in the application peersyesBased on Raft distributed consensus protocol, minimum 3 replicas for continuous availability
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyEventual ConsistencyStrong consistency on writes and tunable consistency on reads
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDoptimistic lockingDistributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture.
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes infobased on RocksDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and developmentyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosnoyesyes
More information provided by the system vendor
Apache ImpalaDatomicSolrYugabyteDB
Specific characteristicsYugabyteDB is an open source distributed SQL database for cloud native transactional...
» more
Competitive advantagesPostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS....
» more
Typical application scenariosSystems of record and engagement for cloud native applications that require resilience,...
» more
Market metrics2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community...
» more
Licensing and pricing modelsApache 2.0 license for the database
» more

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Apache ImpalaDatomicSolrYugabyteDB
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