Optimizing Your BI Semantic Model for Performance and Scale Dave Wickert (AE7TD) Principal Program Manager SQL Server Business Intelligence Microsoft
Main rig: ICOM w HF into a 33’ Buddipole
QRP rig: (on order ) Elecraft KX3 10w HF into a 17M 28’ PAR EndFedz wire thrown up into a tree
SDR ‘toy’: Funcube Dongle
DStar digital radio in my office: DV Dongle
Data model Business logic and queries Data access ROLAP MOLAP xVelocity VertiPaq Direct Query MDXDAX Multi- dimensional Tabular Third-party applications Reporting Services ExcelPowerPivot DatabasesLOB ApplicationsFilesOData FeedsCloud Services SharePoint Insights
Encoding (per column) Compression (per segment) valuesdataID (int) value hash
Sales 1 2,000,000,000 Sales 1 2 Hash Encoding Value Encoding Defined automatically to conserve space, discoverable through DMVs
P2 Partition 1 Partition 2 Dictionary Col. Segment
demo Inspecting VertiPaq Storage
Processing Architecture
Hands On
t
t
Split
DB1 FE VertiPaq caches DB2 DB1~ New, not committed part of the database DB1~ New, not committed part of the database
demo Inspecting Processing Sequence
Query Processing
VertiPaq Query Performance 5B+ rows/s 1B+ rows of data 20B+ rows/s 10B+ rows of data
demo Inspecting Query Evaluation
DAX/MDXVertiPaq Query
Vertipaq Performance, Performance, Performance Amdahl's law: Establishes the maximum expected improvement to an overall system when only part of the system is improved. The speedup of a program using multiple processors in parallel computing is limited by the time needed for the sequential fraction of the program.
DBI61-HOL: Developing a Tabular BISM Using SQL Server Data Tools Microsoft SQL Server: Breakthrough Insights - Credible, Consistent Data
Available here: /what-is-using-all-that-memory-on-my-analysis-server-instance /bismservermemoryreport /uploads/2012/02/BISMServerMemoryReport.xlsx
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