In-Memory DataBase

1. In Memory Database (IMDB) (old is new again) Kodamasimham Pridhvi MT2012066

2. Agenda Introduction Architecture of IMDB Practical Application Myths about IMDB’s IMDB vs. DRDB Impact of IMDB Challenges in IMDB IMDB Open Sources

3. Introduction What is In-Memory Data Base(IMDB)? An IMDB also called Main memory Database(MMDB) is a database whose primary data store is main memory.

4. History Is this a new idea? NO!!! Why now so important? Due to 4 factors

5. Factors: LOWER ING COSTS & GROWING SIZE (RAM): In early 2000, the cost of 64 MB RAM @ $71 But now , 8GB DDR3 @ $69.99 MULTICORE PROCESSORS parallel and faster computation 64 bit Computing multiple GB of main memory Faster responses to queries

6. Architecture Recovery Purpose Application In – Memory Database SQL Engine Secondary Storage Data Store Memory Address Query Optimizer Logs/Redo/ Ckpt Index and Data Manager Lock , Log Primary Storage of Data Base

7. Practical Application Applications that demand very fast data access, storage and manipulation In real-time embedded systems Music databases in MP3 players Programming data in set-top boxes e-commerce and social networking sites financial services and many more…

8. IMDB vs. DRDB(Disk Resident DB) Disk Resident Data Base In-Memory Data Base Carries File I/O burden No file I/O burden Extra memory For Cache No extra memory Algorithm optimized for disk Algorithms optimized for memory More CPU cycles Less CPU cycles Assumes Memory is abundant Uses memory more efficiently

9. Myths about IMDB’s Given the same amount of RAM, disk DBs can perform at the same speed as IMDBs (by using caching technology). If a RAM disk is created and a traditional disk DB is deployed on it, it delivers the same performance as an in-memory database.

10. Myths about IMDB’s In-memory database the same as an embedded database. Since RAM size is limited, sizes of IMDBs are also limited.

11. Impact of IMDB: Data Representation, Concurrency control, Data Access Methods, Query Processing, ACID Properties, Recovery.

12. Data Representation In Disk Resident DB , we use flat files and sequential access. In IMDB, Relational tuples with direct pointers. Space efficient. Shared between columns and relations

13. Domain Table Value 1 Value 2 Value 3 RollNo RollNo Age Age Age Relational Tuple Marks Age Relation Tuple Pointers To memory address Data Representation Diagram

14. Concurrency Control(lock based) In DRDB , locking granules are low level. To reduce contention In IMDB, due to fast processing it create coarser locks. locking granules like a relation or entire database. No need of hash table look up.

15. Data Access Methods In DRDB, B-tree index structure is used. ranges , exact match queries lies in hard disk In IMDB, T-tree index structure is explicitly designed reduces the CPU processing Eliminates index value compression and expansion

16. T-tree T-tree node consists ordered elements in the range min and max values two pointers to the left and right nodes …………….. Parent Key 1 Key.. Key n Control Left ptr Right ptr Minimum Element Maximum Element Pointer to parent

17. Query Processing In DRDB, main focus is on processing costs, and attempt to minimize disk access. In IMDB, main factors are Cardinality of table Presence of index Any ORDER BY clause Predicate evaluation Ex : TimesTen provides range, hash and bitmap indexes and support two types of join methods nested-loop and merge-join.

18. ACID Properties IMDBs can be said to lack support for the durability portion of the ACID Many MMDBs have added durability via the following mechanisms: Checkpoints Transaction logging NVRAM(Non-Volatile RAM)

19. Recovery Mechanisms for recovery are : Logging Checkpoints Reloading transactional durability is kept, by keeping two separate but synchronized copies of the database at all times as well as storing log files on-disk.

20. Challenges in IMDB Durability Query optimization Size of Data Base

21. IMDB Open Sources: CSQL HyperSQL VoltDB Mcobject MonetDB

22. Thank you