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DBMS > Apache Impala vs. Google Cloud Bigtable vs. Hypertable vs. KeyDB

System Properties Comparison Apache Impala vs. Google Cloud Bigtable vs. Hypertable vs. KeyDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHypertable  Xexclude from comparisonKeyDB  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.An open source BigTable implementation based on distributed file systems such as HadoopAn ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols
Primary database modelRelational DBMSKey-value store
Wide column store
Wide column storeKey-value store
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score10.63
Rank#40  Overall
#24  Relational DBMS
Score2.97
Rank#92  Overall
#15  Key-value stores
#8  Wide column stores
Score0.63
Rank#232  Overall
#32  Key-value stores
Websiteimpala.apache.orgcloud.google.com/­bigtablegithub.com/­Snapchat/­KeyDB
keydb.dev
Technical documentationimpala.apache.org/­impala-docs.htmlcloud.google.com/­bigtable/­docsdocs.keydb.dev
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaGoogleHypertable Inc.EQ Alpha Technology Ltd.
Initial release2013201520092019
Current release4.1.0, June 20220.9.8.11, March 2016
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoGNU version 3. Commercial license availableOpen Source infoBSD-3
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++C++
Server operating systemsLinuxhostedLinux
OS X
Windows infoan inofficial Windows port is available
Linux
Data schemeyesschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesnonopartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes
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 indexesyesnorestricted infoonly exact value or prefix value scansyes infoby using the Redis Search module
SQL infoSupport of SQLSQL-like DML and DDL statementsnonono
APIs and other access methodsJDBC
ODBC
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
C++ API
Thrift
Proprietary protocol infoRESP - REdis Serialization Protoco
Supported programming languagesAll languages supporting JDBC/ODBCC#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
Perl
PHP
Python
Ruby
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenonoLua
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorInternal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factor on file system levelMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyEventual Consistency
Strong eventual consistency with CRDTs
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic single-row operationsnoOptimistic locking, atomic execution of commands blocks and scripts
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logs
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nosimple password-based access control and ACL

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More resources
Apache ImpalaGoogle Cloud BigtableHypertableKeyDB
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