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DBMS > Apache Impala vs. Atos Standard Common Repository vs. Google Cloud Spanner vs. LeanXcale vs. RavenDB

System Properties Comparison Apache Impala vs. Atos Standard Common Repository vs. Google Cloud Spanner vs. LeanXcale vs. RavenDB

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonLeanXcale  Xexclude from comparisonRavenDB  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksA 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 highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesOpen Source Operational and Transactional Enterprise NoSQL Document Database
Primary database modelRelational DBMSDocument store
Key-value store
Relational DBMSKey-value store
Relational DBMS
Document store
Secondary database modelsDocument storeGraph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score2.89
Rank#103  Overall
#52  Relational DBMS
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score2.92
Rank#101  Overall
#18  Document stores
Websiteimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositorycloud.google.com/­spannerwww.leanxcale.comravendb.net
Technical documentationimpala.apache.org/­impala-docs.htmlcloud.google.com/­spanner/­docsravendb.net/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsGoogleLeanXcaleHibernating Rhinos
Initial release20132016201720152010
Current release4.1.0, June 202217035.4, July 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialcommercialOpen Source infoAGPL version 3, commercial license available
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC#
Server operating systemsLinuxLinuxhostedLinux
macOS
Raspberry Pi
Windows
Data schemeyesSchema and schema-less with LDAP viewsyesyesschema-free
Typing infopredefined data types such as float or dateyesoptionalyesno
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.noyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoyes infoQuery statements complying to ANSI 2011yes infothrough Apache DerbySQL-like query language (RQL)
APIs and other access methodsJDBC
ODBC
LDAPgRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCAll languages with LDAP bindingsGo
Java
JavaScript (Node.js)
Python
C
Java
Scala
.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenonoyes
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infocell divisionShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryesMulti-source replication with 3 replicas for regional instances.Multi-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes infousing Google Cloud Dataflownoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate ConsistencyDefault ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.
Foreign keys infoReferential integritynonoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsACID infoStrict serializable isolationACIDACID, Cluster-wide transaction available
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.noyesnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosLDAP bind authenticationAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Authorization levels configured per client per database

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More resources
Apache ImpalaAtos Standard Common RepositoryGoogle Cloud SpannerLeanXcaleRavenDB
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