Clusterpoint Margarita Sudņika ms11077

RDBMS & NoSQL Databases & tables → Document stores Columns, rows → Schemaless documents Scales UP → Scales UP & OUT Replications → Sharding & Replications For table like data → Unstructured data Legacy & mature → New Jan-16

Clusterpoint A scalable high-speed NoSQL database technology with Google-like search Manually ranking (svara piešķiršana) Solves 2 big data access problems: Long time waiting for query execution Querry execution 0,005-0,5 seconds Loads of information Jan-16

Arhitecture Jan-16 Application Clusterpoint server software HTTP DOCUMENTS MAILS CONTACTS PROJECTS CUSTOMERS EMPLOYEES CLUSTER NODES (multi-server hardware) STAND-ALONE SERVER

Clusterpoint Data storage model xml Supported formats Json Xml HTML Text Jan-16

Features Full context search Unlimted database size Guaranteed querry size <0,5 s Clustering as default feature Scallable database mirroring Snippets with search hits Web friendly api Flexible data relevancy rules Jan-16

Access Jan-16

Search Free text Phrase Wildcards Patterns matches by lookup John Smith In XML database structure Did you mean “...?” feature Faceted search and navigation Full data index for xml data Jan-16

API Simple, robust XML messaging XML request/response similar to SOAP Transport – http, https (post, get) – tcp – unix domain socket > 20 API commands Libraries: PHP,.NET (web service) Jan-16

API message Jan-16 storage name command name message date and time message number creator of message user name user password reply encoding Search search query number of documents intend from the beginning boolean type parameter maximum from one group FROM value TO value Insert document id document title document rate meta data document id textual information information that is not shown Lookup document id

Platform Runs on *nix (tested on Linux and FreeBSD) Written in C/C++ Optimized for multi-core processors Source code is IP of Clusterpoint written from the scratch PORTS Data tcp: 5550, 80 Unix domains sockets Cluster discovery UDP: :5550 Jan-16

Parameters Disk space 1.,5-2 times more than disk space Data of 100 GB = GB The amount doesn’t include space for log files, as its possible rotate and backup files, While file load and indexing size can increase 3-4 times, then return to normal size RAM more RAM - more cached data –better performance usually recomended >4 GB Jan-16

Use Complementary Solving performance issues and bottlenecks of existing database systems Standalone Application is implemented using Clusterpoint DBMS Jan-16 APP server SQL USERS Cluste rpoint XML DBMS XML APP server USERS

THANK YOU Jan-16