Not Your Father's Database: How to Use Apache Spark Properly in Your Big Data Architecture
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The document discusses the appropriate use of Apache Spark within big data architecture, emphasizing the differences between traditional SQL databases and Spark's performance capabilities. It addresses various workload types, such as batch processing and ad hoc analysis, and highlights best practices for data storage and management while cautioning against Spark's limitations with random access and frequent updates. It also provides solutions for handling specific use cases, such as machine learning and external reporting.
Not Your Father's Database: How to Use Apache Spark Properly in Your Big Data Architecture
- 1. Not Your Father’s Database: How to Use Apache Spark Properly in Your Big Data Architecture Spark Summit East 2016
- 2. Not Your Father’s Database: How to Use Apache Spark Properly in Your Big Data Architecture Spark Summit East 2016
- 3. About Me 2005 Mobile Web & Voice Search 3
- 4. About Me 2005 Mobile Web & Voice Search 4 2012 Reporting & Analytics
- 5. About Me 2005 Mobile Web & Voice Search 5 2012 Reporting & Analytics 2014 Solutions Engineering
- 6. This system talks like a SQL Database… Is this your Spark infrastructure? 6 HDFS SQL
- 7. But the performance is very different… Is this your Spark infrastructure? 7 SQL HDFS
- 8. Just in Time Data Warehouse w/ Spark HDFS
- 9. Just in Time Data Warehouse w/ Spark HDFS
- 10. Just in Time Data Warehouse w/ Spark and more… HDFS
- 11. 11 Know when to use other data stores besides file systems Today’s Goal
- 12. Good: General Purpose Processing Types of Data Sets to Store in File Systems: • Archival Data • Unstructured Data • Social Media and other web datasets • Backup copies of data stores 12
- 13. Types of workloads • Batch Workloads • Ad Hoc Analysis – Best Practice: Use in memory caching • Multi-step Pipelines • Iterative Workloads 13 Good: General Purpose Processing
- 14. Benefits: • Inexpensive Storage • Incredibly flexible processing • Speed and Scale 14 Good: General Purpose Processing
- 15. Bad: Random Access sqlContext.sql( “select * from my_large_table where id=2I34823”) Will this command run in Spark? 15
- 16. Bad: Random Access sqlContext.sql( “select * from my_large_table where id=2I34823”) Will this command run in Spark? Yes, but it’s not very efficient — Spark may have to go through all your files to find your row. 16
- 17. Bad: Random Access Solution: If you frequently randomly access your data, use a database. • For traditional SQL databases, create an index on your key column. • Key-Value NOSQL stores retrieves the value of a key efficiently out of the box. 17
- 18. Bad: Frequent Inserts sqlContext.sql(“insert into TABLE myTable select fields from my2ndTable”) Each insert creates a new file: • Inserts are reasonably fast. • But querying will be slow… 18
- 19. Bad: Frequent Inserts Solution: • Option 1: Use a database to support the inserts. • Option 2: Routinely compact your Spark SQL table files. 19
- 20. Good: Data Transformation/ETL Use Spark to splice and dice your data files any way: File storage is cheap: Not an “Anti-pattern” to duplicately store your data. 20
- 21. Bad: Frequent/Incremental Updates Update statements — not supported yet. Why not? • Random Access: Locate the row(s) in the files. • Delete & Insert: Delete the old row and insert a new one. • Update: File formats aren’t optimized for updating rows. Solution: Many databases support efficient update operations. 21
- 22. Use Case: Up-to-date, live views of your SQL tables. Tip: Use ClusterBy for fast joins or Bucketing with 2.0. Bad: Frequent/Incremental Updates 22 Incremental SQL Query Database Snapshot +
- 23. Good: Connecting BI Tools Tip: Cache your tables for optimal performance. 23 HDFS
- 24. Bad: External Reporting w/ load Too many concurrent requests will overload Spark. 24 HDFS
- 25. Solution: Write out to a DB to handle load. Bad: External Reporting w/ load 25 HDFS DB
- 26. Good: Machine Learning & Data Science Use MLlib, GraphX and Spark packages for machine learning and data science. Benefits: • Built in distributed algorithms. • In memory capabilities for iterative workloads. • Data cleansing, featurization, training, testing, etc. 26
- 27. Bad: Searching Content w/ load sqlContext.sql(“select * from mytable where name like '%xyz%'”) Spark will go through each row to find results. 27
- 28. Thank you