Forward Looking Statements This presentation contains “forward-looking statements” as defined under the Federal Securities Laws. Actual results could differ material y from those projected in the forward-looking statements as a result of certain risk factors, including but not limited to: (i) adverse changes in general economic or market conditions; (ii) delays or reductions in information technology spending; (ii ) the relative and varying rates of product price and component cost declines and the volume and mixture of product and services revenues; (iv) competitive factors, including but not limited to pricing pressures and new product introductions; (v) component and product quality and availability; (vi) fluctuations in VMware’s Inc.’s operating results and risks associated with trading of VMware stock; (vii) the transition to new products, the uncertainty of customer acceptance of new product offerings and rapid technological and market change; (vii ) risks associated with managing the growth of our business, including risks associated with acquisitions and investments and the chal enges and costs of integration, restructuring and achieving anticipated synergies; (ix) the ability to attract and retain highly qualified employees; (x) insufficient, excess or obsolete inventory; (xi) fluctuating currency exchange rates; (xii) threats and other disruptions to our secure data centers and networks; (xii ) our ability to protect our proprietary technology; (xiv) war or acts of terrorism; and (xv) other one-time events and other important factors disclosed previously and from time to time in the filings EMC Corporation, the parent company of Pivotal, with the U.S. Securities and Exchange Commission. EMC and Pivotal disclaim any obligation to update any such forward-looking statements after the date of this release.
MPP Shared Nothing Architecture Performance Through Segment Instance Paral elism SQL Master Host and Standby Master Host Master Standby Master coordinates work with Segment Hosts Host Master Segment Host with one or more Segment Instances Segment Instances process queries in paral el
Paral el Data Scans Across All Segments SELECT COUNT(*) FROM orders WHERE order_date >= ‘Oct 20 2007’ 4,423,323 AND order_date < ‘Oct 27 2007’ Seg Se me g n me t n t1 A 1 A Seg Se me g n me t n t1 B 1 B Seg Se me g n me t n t1 C 1 C Seg Se me g n me t n t1 D 1 D Seg Se me g n me t n t2 A 2 A Seg Se me g n me t n t2 B 2 B Seg Se me g n me t n t2 C 2 C Seg Se me g n me t n t2 D 2 D Master Seg Se me g n me t n t3 A 3 A Seg Se me g n me t n t3 B 3 B Seg Se me g n me t n t3 C 3 C Seg Se me g n me t n t3 D 3 D Se g me SeD nd e n R t e ve s t Pllu a o R n p e rn t t o Q u R rn e Se R sul g ry te Plsu s me n a lt n t s s
GPDB Data Loading Options Loading Method Common Uses Examples INSERTS • Operational Workloads INSERT INTO performers (name, specialty) • OBDC/JDBC Interfaces VALUES (‘Sinatra’, ‘Singer’);
• Quick and easy data in COPY performers FROM ‘/tmp/comedians.dat’ COPY • Legacy PostgreSQL applications WITH DELIMITER ‘|’; • Output sample results from SQL statements
• High speed bulk loads INSERT INTO craps_bets SELECT g.bet_type External Tables • Paral el loading using gpfdist protocol , g.bet_dttm • Local file, remote file, HTTP or HDFS based , g.bt_amt FROM x_al bets b sources JOIN games g ON ( g.id = b.game_id ) WHERE g.name = ‘CRAPS’;
ANALYZEDB Incremental ANALYZE • If a table/partition has not changed (DML, DDL) since last run of ANALYZEDB, it wil be skipped automatical y • ANALYZEDB keeps a record of which tables have up-to-state stats after a run on disk in $MASTER_DATA_DIRECTORY/db_analyze • ANALYZEDB compares the current catalog with the state files of last run to determine the incremental • ANALYZEDB captures statistics on root partition table required for the Pivotal Query Optimizer (PQO)