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Which API Call Causes Most Tickets?
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Classroom Contents
Spark 2.0
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- 1 Intro
- 2 What is Apache Spark?
- 3 A Large Community
- 4 Apache Spark Users
- 5 Original Spark Vision
- 6 Motivation: Unification
- 7 Motivation: Concise API
- 8 How Did the Vision Hold Up?
- 9 Libraries Built on Spark
- 10 Which Libraries Do People Use?
- 11 Top Applications
- 12 Main Challenge: Functional API
- 13 Which API Call Causes Most Tickets?
- 14 Example Problem
- 15 Challenge: Data Representation
- 16 Why Structure?
- 17 DataFrames and Datasets
- 18 Execution Steps
- 19 DataFrame API
- 20 Why DataFrames?
- 21 What Structured APIs Enable
- 22 Performance
- 23 Dataset API Details
- 24 Data Sources
- 25 Data Source API
- 26 Examples
- 27 Hardware Trends
- 28 Project Tungsten
- 29 Tungsten's Compact Encoding
- 30 Space Efficiency
- 31 Runtime Code Generation
- 32 Long-Term Vision
- 33 Versioning in Spark
- 34 Major Features in 2.0
- 35 Background
- 36 Structured Streaming High-level streaming API built on DataFrames/Datasets
- 37 Structured Streaming API
- 38 Example: Batch Aggregation
- 39 Example: Continuous Aggregation
- 40 Incrementalized By Spark
- 41 Release Timeline
- 42 Conclusion
- 43 Want to Learn Apache Spark?
