Vertica the convertro way
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How we load into Vertica How we query Vertica. Check our Hadoop Loader and Hydro . Get couple of Tips from our experience.
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Vertica the convertro way
- 1. 1
- 2. Vertica The Convertro way Zvika Gutkin Big data dbOps zvika.gutkin@convertro.com
- 3. Agenda Why Vertica How we Load How we query Not all good Vertica The Convertro waConvertro Who We are Tips
- 4. 0% 0% 0% 0% 0% 100% Last click model Convertro Vertica The Convertro wa
- 5. 100% 15% 10% 5% 10% 20% 40% Multi attribution Convertro Vertica The Convertro wa
- 6. Convertro Reports Analytics Dashboard Batch Dashboard Aggregation Regression R Vertica The Convertro wa
- 7. Convertro Batch Dashboard Active active Copy aggregated data Predictable customer experience Vertica The Convertro wa Client facing
- 8. How we Load Convertro Vertica The Convertro wa
- 9. Pro Tip HDFS Vs. Hadoop Vs. Application Loader Big Bulks Bring your files to your cluster Load from Several Nodes Convertro Pro Tip Pro Tip Vertica The Convertro wa
- 10. Unified Temp Table Target Table/Partition Stream COPY Number of parallel loads1 Number of parallel nodes2 Chunk size per loads3 TEMP TEMP TEMP MOVE PARTITIONS MOVE PARTITIONS Vertica The Convertro waConvertro
- 11. How we query Vertica The Convertro waConvertro
- 12. Denormalize Use DBD for encoding Events DBD Check query_events system table Vertica The Convertro waConvertro
- 13. MMM HydroImprovements Out of the box improvements Conversion Table ~2B/Month Oracle (raw store) 418GB / 147GB Vertica (column store) 21 GB Vertica The Convertro waConvertro
- 14. MMM HydroImprovements Measure, Measure, Measure! Queries Locks Resources … … Input Size Resources Errors Query … Trends Anomalies
- 15. Measure, Measure, Measure! Select M1,.. From… Where filter1 = ‘a’ and filter2 = ‘b’ Select M1,.. From… Where filter1 = ‘c’ and filter 2 = ‘b’ Select M1,.. From… Where filter1 = “” and filter2 = “” MMM HydroImprovements
- 16. MMM HydroImprovements Real Time ETR select A from B where C=‘D’ Business Logic Topology Sampling Lookup Aggregate Hydro Web Service
- 17. Not all good Vertica The Convertro waConvertro
- 18. High concurrency Deletes Updates Short queries with high concurrency. (Routable queries) Vertica The Convertro waConvertro
- 19. Vertica The Convertro waConvertro Tips
- 20. ROS CONTAINERS Vertica The Convertro waConvertro
- 21. ROS CONTAINERS Vertica The Convertro waConvertro
- 22. ROS CONTAINERS Vertica The Convertro waConvertro
- 23. ROS CONTAINERS Convertro Vertica The Convertro wa
- 24. ROS CONTAINERS Convertro Vertica The Convertro wa
- 25. ROS CONTAINERS Convertro Vertica The Convertro wa
- 26. ROS CONTAINERS New Table Convertro Vertica The Convertro wa
- 27. ROS CONTAINERS New Table Convertro Vertica The Convertro wa
- 28. ROS CONTAINERS Convertro Vertica The Convertro wa
- 29. Vertica The Convertro waConvertro Tips
- 30. Convertro Node Crash Slow Recover Process Checking Recovery Status Incremental recovery replay- delete Vertica The Convertro wa Many Deletes / Updates
- 31. Convertro Node Crash Slow Recover Process Checking Recovery Status Incremental recovery replay- delete Solution 1 Wait Vertica The Convertro wa Many Deletes / Updates
- 32. Convertro Node Crash Slow Recover Process Checking Recovery Status Incremental recovery replay- delete Solution 2 Set make_ahm_now Incremental By Containers Vertica The Convertro wa Many Deletes / Updates
- 33. Convertro Many Deletes / Updates Node Crash Slow Recover Process Checking Recovery Status Incremental recovery replay- delete Solution 3 Delete only one file Incremental By Containers Vertica The Convertro wa
- 34. Great Database Even Steph Curry can’t Do it all Keep It Simple Convertro
- 35. Great Database Even Lebron can’t Do it all Keep It Simple Convertro
- 36. Convertro Great Database Even Lebron can’t Do it all Keep It Simple
Hinweis der Redaktion
- The data for the model is in vertica Process in R Attribution run on hadoop based on the model that R calculated .
- Vertica : Raw : 150TB DB Size : 60 TB Top Clients: (top 8 above 1 TB ) Intuit 3 TB qvc 2 TB Dashboard : qvc => 260 GB => 8 Billion rows intuit => 200 GB => less than 8 Billion rows Vertica scan it and fetches a result in less than a second Complex analytics on a year takes about 5 seconds.
- Facebook 35 TB per hour 2 years ago !!
- 150K rows per sec Regular days 10 – 15 billion rows per day 40 billion rows per day New vertica feature => Vhash
- out of the box improvements => denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins
- out of the box improvements => denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins MMM => measure measure measure => data collector tables .
- out of the box improvements => denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins MMM => measure measure measure => data collector tables .
- out of the box improvements => denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins MMM => measure measure measure => data collector tables .
- out of the box improvements => denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins MMM => measure measure measure => data collector tables .
- out of the box improvements => denormalize => data model changes are Game changer !!!.Vertica can handle big joins => merge joins MMM => measure measure measure => data collector tables .
- great database => out of the box performance even lebron => don’t put billing on it right tool => hadoop loader,extended analytics ,flex table,udx Keep it simple => easy to debug easy to maintain
- great database => out of the box performance even lebron => don’t put billing on it right tool => hadoop loader,extended analytics ,flex table,udx Keep it simple => easy to debug easy to maintain
- great database => out of the box performance even lebron => don’t put billing on it right tool => hadoop loader,extended analytics ,flex table,udx Keep it simple => easy to debug easy to maintain