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DBMS > Apache Impala vs. Datomic vs. Google Cloud Spanner vs. Solr

System Properties Comparison Apache Impala vs. Datomic vs. Google Cloud Spanner vs. Solr

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatomic  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.A widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSRelational DBMSRelational DBMSSearch engine
Secondary database modelsDocument storeSpatial DBMS
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
Score2.84
Rank#100  Overall
#51  Relational DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websiteimpala.apache.orgwww.datomic.comcloud.google.com/­spannersolr.apache.org
Technical documentationimpala.apache.org/­impala-docs.htmldocs.datomic.comcloud.google.com/­spanner/­docssolr.apache.org/­resources.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaCognitectGoogleApache Software Foundation
Initial release2013201220172006
Current release4.1.0, June 20221.0.7075, December 20239.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infolimited edition freecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java, ClojureJava
Server operating systemsLinuxAll OS with a Java VMhostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes 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.nononoyes
Secondary indexesyesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyes infoQuery statements complying to ANSI 2011Solr Parallel SQL Interface
APIs and other access methodsJDBC
ODBC
RESTful HTTP APIgRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesAll languages supporting JDBC/ODBCClojure
Java
Go
Java
JavaScript (Node.js)
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infoTransaction FunctionsnoJava plugins
TriggersnoBy using transaction functionsnoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornone infoBut extensive use of caching in the application peersMulti-source replication with 3 replicas for regional instances.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyes infousing Google Cloud Dataflowspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoStrict serializable isolationoptimistic locking
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
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 developmentnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes

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Apache ImpalaDatomicGoogle Cloud SpannerSolr
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