Columnstore Indexes in SQL Server 2012 Conor Cunningham Principal Architect, Microsoft SQL Server Representing Microsoft Development Center Serbia

What This Talk Covers SQL Server’s upcoming “Denali” release contains a new feature for Data Warehouses to speed up Data Warehouse queries This talk provides an overview of the new surface area and some details about how it works

Who am I? I’ve worked at Microsoft for the SQL Core Engine team as an Architect for many years I work mostly on Query Processors I wrote the SQL 2008 Internals book on how the Query Optimizer works I blog at “Conor vs. SQL” I like to talk to customers about how they use the product so that I improve things in future releases

Agenda Data Warehouse Introduction New Feature and Demo How the Feature Works Restrictions in this release

Data Warehouse Introduction Data Warehouses support reporting and business intelligence operations in organizations Store facts that can be aggregated over different dimensions They often store lots and lots of facts (rows) –This leads to a design pattern called a star schema where fact tables are “over”-normalized to reduce row width –Dimension tables are frequently joined –Results are very often aggregated Example: Show me the sales totals for each department by month for the past 3 years

Data Warehouse Challenges These kinds of databases become difficult once they get big. –Query latency –ETL load times –Backup time and size –Index rebuilding –Finding time to load new data –Query plan selection issues/limitations 6

Opportunity What If… –We made DW queries 10+ times faster? Example – Business Analyst does ROLAP reports against SQL Server 2008 –Click to drill down into a report –Go get some coffee –Click again –Go get more coffee We aim to make that experience interactive –(However, coffee shop profits may plunge!)

Demo 8

How Does It Work? 9 New Index Type – ColumnStore New Query Execution Algorithms – “Batch” mode Specifically Target Star Join Queries ▫ Not all queries are faster in the initial release ▫ Customers will want to consider this in their application design Supported Pattern: SELECT SUM(…), cols FROM FactTbl JOIN DimTbl1 JOIN DimTbl2 … WHERE … GROUP BY cols

Index Storage Design Column-Orientation –Store data vertically instead of per-row –String Dictionaries for variable-length data Segment data into groups (1 million rows/group) Benefits –DW queries usually pick only a subset of columns –You can do the IO only for those columns –We can also compress that data effectively since it often has lots of duplicates –Space savings of 1.5x-2x vs. a row-based page-compressed equivalent IO Patterns for (CI Scan, Column-based scan of 3 cols, Column-based w/Compression)

Speedup from the Index If the IO required is cut in half… –We don’t get to 10 times faster (yet) –We need to improve the memory utilization and CPU utilization to get the rest of the speedup So how do we improve Query Execution 10x??? 11

What takes time in a CPU? Memory IO takes time –Cache Misses stall the CPU –L2 cache misses stall the CPU even more –So we reduced cache misses Instructions take time –Instructions also go through the caches –So we reduced instructions Disk Access takes time –So we biased the memory policies for this index to work best when in memory Over time, CPU speed has increased faster than memory speed, making all of these worse 12

Query Execution Row Mode Changes Each operator calls child for each row This works fine for smaller numbers of rows, poorly for batches In bigger queries, CPU cycles instructions in and out of the CPU (L2 cache misses) So this model suffers in DW with too many instructions, too many cache misses 13

Batch Format Column-Oriented Sized to fit within L2 cache Multiple Operators work on a batch sequentially Goal: Reduce avg. per-tuple cost –Compression –Reducing L2 data and instruction cache misses –Probabilistic data representations –Probabilistic operator execution algorithms This gets us to 10x faster (avg)

SQL 2012 Restrictions Create index: –Only on common business data types Maintain table: limited operations –Can read but not update the data –However: One can switch partitions in and out Process queries: all read-only T-SQL queries run –Some queries are accelerated more than others Yesint, real, string, money, datetime, decimal <= 18 digits Nodecimal > 18 digits, binary, BLOB, CLR, (n)varchar(max), uniqueidentifier, datetimeoffset with precision > 2 15

Using Apollo: Loading new data Table with columnstore index can be read, not updated –Partition switching is allowed –INSERT, UPDATE, DELETE, and MERGE not allowed Three possible methods for loading data –Disable, update, rebuild –Partition switching –UNION ALL between large table with columnstore and smaller updateable table 16

Query performance issues Not all operators are batch-mode enabled –Scan, Filter, Project –Local hash partial aggregation –Hash inner join, hash table build Only parallel queries can use batch mode If hash tables don’t fit into memory, fall back to row-mode processing –Memory grant request depends on cardinality est. –Falling back to row-mode is slow 17

Revisiting Our Example Scenario For SQL Server 2012, our customer will be able to: –Have specific queries go very fast (with less coffee) –DW Application developers Must design their code to load/unload data online Can use hints to control user experience for the fast and slow cases –Hint index – if it fails to get a plan, then you can present UI to the user to “maybe go get coffee” and then run in row mode This story will continue to improve as we add more capabilities to Batch processing 18

Summary New Index and Execution Algorithms for DW Significant speedup for conforming applications Opportunities for customers who can build their code to leverage the benefits 19

Thank You! Questions? 20

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