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Improvement of no sql technology for relational databases v2
1. Improvement of NoSQL Technology for Relational Databases TsendsurenMunkhdalai twitter: @tsendeemts
2. Contents Limitation of relational database NoSQL technology Types of NoSQL database Conclusion
3. Nowadays, statement of data Large data Some data generators Facebook photos +25TB/week Twitter +7TB/day Flickr +21GB/hour Data size is repeatedly increased every year Not structured data New kinds of applications are growing up Such as Web 2.0, Enterprise applications and Cloud computing They needed not structured data There are many no structured data generators
4. Limitation of relational database Static, normalized data schema Have to store structured data There is complex join operation Not flexible datastore Data is centralized in one place Not distributed Data overflowing Nothing
5. NoSQL technology NoSQL: Not Only SQL Handle huge amount of data at full speed Distributed Natively support clustering Have Map/Reduce mechanism Support replication and sharding Schema free More flexible Have hashing and B-tree indexing There are four types of NoSQL databases
6. Distributed: NoSQL database Support replication and sharding Map/Reduce mechanism Similarity, parallel processing Sharding/Partitioned res res Big Result res job Big Job Data 1 2 3 4 5 6 job job replication 3 4 5 6 1 2
7. Types of NoSQL database 1/4 Key-Value database Stores value based on its key Designed to handle massive load Data model: Collection of Key-Value pairs Given key, get value Data hashing indexed Some systems do that automatically Good for Cashe aside Simple, id based interactions
8. Types of NoSQL database 2/4 Column oriented database Column oriented Relational database Tables similarly to RDBMS, but handles semi-structured Each row can have a different number of columns Table is sparse Columns are dynamic
9. Types of NoSQL database 3/4 Graph database These store data structure as graph Focus on modeling the structure of data Represent complex relation between objects as graph Data model: Nodes, relationships between theirs Each node can have key/value properties C P A
10. Types of NoSQL database 4/4 Document database Stores data as document More complex Key-Value database Data model: Collection of Key-Value, collections as JSON or XML types document { “name” : “Lady Gaga”, “ssn” : “213445”, “hobbies” : [“Dressing up”, “Singing”], “albums” : [{“name” : “The fame” “release_year” : “2008”}, {“name” : “Born this away” “release_year” : “2011”}] } { {….} } { {….} }
11. Some statistic Facebook search MySQL > 50 GB Data Writes Average : ~300 ms Reads Average : ~350 ms Rewritten with Cassandra (NoSQL) > 50 GB Data Writes Average : 0.12 ms Reads Average : 15 ms
13. Conclusion NoSQL databases Data process quite faster than relational database Distributed Dynamically determine new attributes Cheap Mostly, open source Have natively clustering, don’t need supercomputer (Expensive) Map/Reduce mechanism is provided Have B-tree and hashing indexing
Hello everyone, I’m tsendee from Database/Bioinformatics lab, Chungbuk national university. Welcome to today’s my presentation. I will try to talk improvement of NoSQL technology for relational databases. It is topic of my paper. Ok let’s begin.
-> I am going to introduce contents of my presentation-> First I will describe limitation of relational database, in this section we present what are there limitations of relational database for today’s data.-> NoSQL technology, this is our primary section. I will give you what is actually NoSQL what does NoSQL do that is better than relational databases.-> and there are a several types of nosql, that is presented in types of nosql section.-> Finally I will conclude to my presentation
-> We consider two things those would recently be features for data science-> Large volume of data was generated, there are a number of huge data generators Here showed some data generators, for example facebook photos are increased by twenty five terrabyteseveryweek , size of twitter database is increased by seven terrabytes per one day, so big data that is one special point for data science and data management system.-> Next thing is not structureddata there is not structured data everywhere
-> Now, I’m going to talk about limitation aspects of relational database.-> schema consists of tables and theirs relations, already we designed a schema , next time we difficulty modify the schema. So schema is hard. You have to store structured data, Your data must fit a table. After did that datastore can be more complex or not flexible-> data centralized in one place, not distributed depend on ACID property, join operation. There is one node failure. If the node fail, entire system That is big problem for developer.
-> So some problems fail in RDBMS,NoSQL technology aimed to improve relational database. That is one kind of database.-> NoSQL is standing Not Only SQL, -> Itcan handle huge amount of data at full speed. -> because such databases work well distributed over multiple nodes in a cluster.-> these are explained in next slide-> Schema free, At any time we can define new attribute for object in NoSQL database so it is more flexible-> There are four types of NoSQL databases according their data model. after some slide I will give you in more detailed
-> Sharding: big data is partitioned over individual nodes in cluster, those are connected in network-> replication: it means multiple write, same data, that is written on more than one nodes. There is not one node failure if a master computer failed then system automatically chooses another the data replicated computer.-> map/reduce mechanism consist of two phase first one is map next phase is reduce. In some case We need to process big job. We can easily do the big job by using map/reduce mechanism. This our big job Firstly, the big job is separated into a several small sections and distributed over nodes then these are processed on each nodes now we have small results finally bring to big result by combining to them, final process is called reduce the other one is map phase.
