Achieving Lakehouse Models with Spark 3.0
•
3 gostaram•636 visualizações
It’s very easy to be distracted by the latest and greatest approaches with technology, but sometimes there’s a reason old approaches stand the test of time. Star Schemas & Kimball is one of those things that isn’t going anywhere, but as we move towards the “Data Lakehouse” paradigm – how appropriate is this modelling technique, and how can we harness the Delta Engine & Spark 3.0 to maximise it’s performance?
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Semelhante a Achieving Lakehouse Models with Spark 3.0
Semelhante a Achieving Lakehouse Models with Spark 3.0 (20)
Mais de Databricks
Mais de Databricks (20)
Último
Último (20)
Achieving Lakehouse Models with Spark 3.0
- 1. Achieving Lakehouse Models with Spark 3.0 Simon Whiteley Director of Engineering, Advancing Analytics
- 2. Agenda Why Lakehouse? Kimball Problems Delta & Spark 3.0 ▪ SCD & SQL Merge ▪ Dynamic Partition Pruning ▪ Adaptive Query Execution Enabling the Lakehouse
- 6. The Modern Warehouse RAW BASE PARQUET
- 7. The Lakehouse RAW BASE ENRICHED DELTA DELTA
- 9. When you think Warehouse… We automatically think of Star Schemas and Kimball warehousing approaches. A large central fact table with smaller reference dimensions… some of which aren’t so small
- 10. Literally Everyone (All The time) “You can’t use Kimball in a Data Lake”
- 11. Three Historical Challenges ▪ Slowly Changing Dimensions ▪ Filtering Dimensions ▪ General SQL Performance
- 13. SCD - Enabling the Familiar PrimaryKey Address Current EffectiveDate EndDate 11 A new customer address TRUE 03/08/2020 null 58 Yet another address TRUE 03/08/2020 null 41 A different address TRUE 03/08/2020 null PrimaryKey Address Current EffectiveDate EndDate 11 A new customer address FALSE 03/08/2020 22/10/2020 11 An updated address TRUE 22/10/2020 null 58 Yet another address TRUE 03/08/2020 null 41 A different address TRUE 03/08/2020 null
- 14. SCD - Merge Commands MERGE INTO dataai.addresses as original USING updates ON original.primaryKey = updates.primaryKey WHEN MATCHED THEN UPDATE SET * WHEN NOT MATCHED THEN INSERT * Available in SQL, Scala and Python APIs - the merge command has made many complex warehousing jobs accessible to the wider Analytics community This is enabled by the Delta file format
- 16. Spark Partitioning SELECT * FROM Sales WHERE Month = 3 SQL Query Action Filtering performed by selectively reading files SALES Month=1 Month=2 Month=3 Month=4
- 17. Cross-Filter Spark 2.4 SELECT * FROM Sales JOIN Date WHERE DateMonth = 3 SQL Query Action SALES Month=1 Month=2 Month=3 Month=4 DimDATE Partition Keys not hit when filtering on joined tables
- 18. Cross-Filter Spark 3.0 SELECT * FROM Sales JOIN Date WHERE DateMonth = 3 SQL Query Action SALES Month=1 Month=2 Month=3 Month=4 DimDATE Dynamic Partition Pruning determines partition filters during runtime
- 20. AQE in Spark 3.0 AQE will speed up common queries in a number of ways: ▪ Coalescing Shuffle Partitions ▪ Switching Join Strategies ▪ Optimizing Skew Joins
- 21. Before AQE - Shuffle Coalescing Read RDDs Read RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Shuffle RDDs Write RDDs Write RDDs 2 Tasks 200 Tasks 2 Tasks
- 22. Using Spark 3.0 AQE - Shuffle Coalescing Read RDDs Read RDDs Write RDDs Write RDDs 2 Tasks 2 Tasks 2 Tasks Shuffle RDDs Shuffle RDDs
- 23. DEMO: Let’s see it in action
- 24. The Data Lakehouse Delta & Spark 3.0 enable the Lakehouse through: ▪ Enabling familiar (SQL) patterns ▪ Removing technical barriers ▪ Targeting performance of common warehousing activities
- 25. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.