feature that stores data similar to a column-oriented DBMS. While not a true column oriented database, this technique allows for the creation of a memory optimized index that groups and stores data for each column then and joins them together to complete the index. For certain types of queries, like aggregations, the query processor can take advantage of the column-store index to significantly improve execution times. Column store indexes can be used with partitioned tables providing a new way to think about how to design and process large datasets. The column-store index can be very useful on large fact tables in a Star schema improving overall performance, however the cost model approach utilized may choose the column-store index for a table when a row based index would have been better. Using the IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX query hint will work around this if it occurs. When data is stored with a column-store index, data can often be compressed more effectively over a row based index. This is accomplished as typically there is more redundancy within a column than within a row. Higher compression means less IO is required to retrieve data into memory which can significantly reduce response times. There are several restrictions and limitation in using a column-store index. For example, which data types are supported or not and that you can only create one column-store index on any table can be problematic. Become familiar with what it can do and where best to use it. Currently the column-store index is not supported on Microsoft Azure. Column-oriented databases provide significant advantages over traditional row oriented system applied correctly; In particular for data warehouse and business intelligence environments where aggregations prevail. It would not be fair however to ignore the disadvantages. Let’s look at these two: Column-Oriented Advantages Efficient storage and data compression Fast data loads Fast aggregation queries Simplified administration & configurations Disadvantages ‘Column-Oriented Transactions are to be avoided or just not supported Queries with joins can reduce high performance Record updates and deletes reduce storage efficiency
feature that stores data similar to a column-oriented DBMS. While not a true column oriented database, this technique allows for the creation of a memory optimized index that groups and stores data for each column then and joins them together to complete the index. For certain types of queries, like aggregations, the query processor can take advantage of the column-store index to significantly improve execution times. Column store indexes can be used with partitioned tables providing a new way to think about how to design and process large datasets. The column-store index can be very useful on large fact tables in a Star schema improving overall performance, however the cost model approach utilized may choose the column-store index for a table when a row based index would have been better. Using the IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX query hint will work around this if it occurs. When data is stored with a column-store index, data can often be compressed more effectively over a row based index. This is accomplished as typically there is more redundancy within a column than within a row. Higher compression means less IO is required to retrieve data into memory which can significantly reduce response times. There are several restrictions and limitation in using a column-store index. For example, which data types are supported or not and that you can only create one column-store index on any table can be problematic. Become familiar with what it can do and where best to use it. Currently the column-store index is not supported on Microsoft Azure. Column-oriented databases provide significant advantages over traditional row oriented system applied correctly; In particular for data warehouse and business intelligence environments where aggregations prevail. It would not be fair however to ignore the disadvantages. Let’s look at these two: Column-Oriented Advantages Efficient storage and data compression Fast data loads Fast aggregation queries Simplified administration & configurations ‘Column-Oriented Disadvantages Transactions are to be avoided or just not supported Queries with joins can reduce high performance Record updates and deletes reduce storage efficiency
Big-Data-Column-DBs-0011.docx