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DBMS > Apache Impala vs. EsgynDB vs. Google Cloud Datastore vs. Sphinx

System Properties Comparison Apache Impala vs. EsgynDB vs. Google Cloud Datastore vs. Sphinx

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSRelational DBMSDocument storeSearch engine
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score0.23
Rank#319  Overall
#141  Relational DBMS
Score4.49
Rank#79  Overall
#12  Document stores
Score6.03
Rank#60  Overall
#6  Search engines
Websiteimpala.apache.orgwww.esgyn.cncloud.google.com/­datastoresphinxsearch.com
Technical documentationimpala.apache.org/­impala-docs.htmlcloud.google.com/­datastore/­docssphinxsearch.com/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaEsgynGoogleSphinx Technologies Inc.
Initial release2013201520082001
Current release4.1.0, June 20223.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C++, JavaC++
Server operating systemsLinuxLinuxhostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes, details hereno
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
Secondary indexesyesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL-like query language (GQL)SQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocol
Supported programming languagesAll languages supporting JDBC/ODBCAll languages supporting JDBC/ODBC/ADO.Net.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceJava Stored Proceduresusing Google App Engineno
TriggersnonoCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication between multi datacentersMulti-source replication using Paxosnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.
Foreign keys infoReferential integritynoyesyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no

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More resources
Apache ImpalaEsgynDBGoogle Cloud DatastoreSphinx
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