Column Oriented Database By: Deepak Sood Garima Chhikara Neha Rani Vijayita Gumber

Columnar Database Systems Stores data by column. Keeps all attribute information together. Handles fixed length data. 2-D data represented at conceptual level is mapped to 1-D data structure at physical level.

In row store data are stored in the disk tuple by tuple. In Column Store data is stored in disk column by column. Row StoreColumn Store (+) Easy to add/modify a record(+) Only need to read in relevant data (-) Might read in unnecessary data(-) Tuple writes require multiple accesses

Row Store and Column Store Most of the queries does not process all the attributes of a particular relation. For example the query Select c.name and c.address From CUSTOMES as c Where c.region=Mumbai; Only process three attributes of the relation CUSTOMER. But the customer relation can have more than three attributes. Column-stores are more I/O efficient for read-only queries as they read, only those attributes which are accessed by a query.

Why Column Store ? Faster. Fetch only required columns for a query. Better cache effects. Better Compresssion. Data Warehousing applications make more read operation. Row oriented have an overhead of seeking through all columns. Can be slower for some applications like OLTP with many row inserts.

Query Execution - Operators Select : Same as relational algebra, but produces a bit string Project : Same as relational algebra Join : Joins projections according to predicates Aggregation : SQL like aggregates Sort : Sort all columns of a projection Decompress: Converts compressed column to uncompressed representation

Query Execution - Operators Mask(Bitstring B, Projection Cs) => emit only those values whose corresponding bits are 1 Concat: Combines one or more projections sorted in the same order into a single projection Permute: Permutes a projection according to the ordering defined by a join index Bitstring operators: Band – Bitwise AND, Bor – Bitwise OR, Bnot – complement

Column-store simulation in a row-store 1.Vertical Partitioning: Each column is a relation. 2.Index-Only: B+ Tree on each columns. 3.Materialized Views: Optimal set of views for every query.

Column-Oriented Execution Four techniques are being introduced for Optimization in order to improve the performance of column-stores: Compression Late Materialization Block Iteration Invisible Join

Find Total revenue from Asian customers who purchase a product supplied by an Asian supplier between 1992 and 1997 grouped by nation of the customer, supplier and year of transaction

Phase 1 Invisible Join

Phase 2 Invisible Join

Phase 3 Invisible Join

Applications Analyzing unorganized BIG DATA with improved granularity. Data Warehouses and Business Intelligence. Online Analytical Processing. Data Marts Development. Data Mining.

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