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Making Apache Spark Better with Delta Lake

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Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.

In this talk, we will cover:
* What data quality problems Delta helps address
* How to convert your existing application to Delta Lake
* How the Delta Lake transaction protocol works internally
* The Delta Lake roadmap for the next few releases
* How to get involved!

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Making Apache Spark Better with Delta Lake

  1. 1. Making Apache Spark™ Better with Delta Lake Michael Armbrust @michaelarmbrust
  2. 2. 1. Collect Everything • Recommendation Engines • Risk, Fraud Detection • IoT & Predictive Maintenance • Genomics & DNA Sequencing 3. Data Science & Machine Learning 2. Store it all in the Data Lake The Promise of the Data Lake Garbage In Garbage Stored Garbage Out 🔥 🔥 🔥 🔥🔥 🔥 🔥
  3. 3. What does a typical data lake project look like?
  4. 4. Evolution of a Cutting-Edge Data Lake Events ? AI & Reporting Streaming Analytics Data Lake
  5. 5. Evolution of a Cutting-Edge Data Lake Events AI & Reporting Streaming Analytics Data Lake
  6. 6. Challenge #1: Historical Queries? Data Lake λ-arch λ-arch Streaming Analytics AI & Reporting Events λ-arch1 1 1
  7. 7. Challenge #2: Messy Data? Data Lake λ-arch λ-arch Streaming Analytics AI & Reporting Events Validation λ-arch Validation 1 21 1 2
  8. 8. Reprocessing Challenge #3: Mistakes and Failures? Data Lake λ-arch λ-arch Streaming Analytics AI & Reporting Events Validation λ-arch Validation Reprocessing Partitioned 1 2 3 1 1 3 2
  9. 9. Reprocessing Challenge #4: Updates? Data Lake λ-arch λ-arch Streaming Analytics AI & Reporting Events Validation λ-arch Validation Reprocessing Updates Partitioned UPDATE & MERGE Scheduled to Avoid Modifications 1 2 3 1 1 3 4 4 4 2
  10. 10. Wasting Time & Money Solving Systems Problems Instead of Extracting Value From Data
  11. 11. Data Lake Distractions No atomicity means failed production jobs leave data in corrupt state requiring tedious recovery ✗ No quality enforcement creates inconsistent and unusable data No consistency / isolation makes it almost impossible to mix appends and reads, batch and streaming
  12. 12. Let’s try it instead with
  13. 13. Reprocessing Challenges of the Data Lake Data Lake λ-arch λ-arch Streaming Analytics AI & Reporting Events Validation λ-arch Validation Reprocessing Updates Partitioned UPDATE & MERGE Scheduled to Avoid Modifications 1 2 3 1 1 3 4 4 4 2
  14. 14. AI & Reporting Streaming Analytics The Architecture Data Lake CSV, JSON, TXT… Kinesis
  15. 15. AI & Reporting Streaming Analytics The Architecture Data Lake CSV, JSON, TXT… Kinesis Full ACID Transaction Focus on your data flow, instead of worrying about failures.
  16. 16. AI & Reporting Streaming Analytics The Architecture Data Lake CSV, JSON, TXT… Kinesis Open Standards, Open Source (Apache License) Store petabytes of data without worries of lock-in. Growing community including Presto, Spark and more.
  17. 17. AI & Reporting Streaming Analytics The Architecture Data Lake CSV, JSON, TXT… Kinesis Powered by Unifies Streaming / Batch. Convert existing jobs with minimal modifications.
  18. 18. Data Lake AI & Reporting Streaming Analytics Business-level Aggregates Filtered, Cleaned Augmented Raw Ingestion The Bronze Silver Gold CSV, JSON, TXT… Kinesis Quality Delta Lake allows you to incrementally improve the quality of your data until it is ready for consumption. *Data Quality Levels *
  19. 19. Data Lake AI & Reporting Streaming Analytics Business-level Aggregates Filtered, Cleaned Augmented Raw Ingestion The Bronze Silver Gold CSV, JSON, TXT… Kinesis • Dumping ground for raw data • Often with long retention (years) • Avoid error-prone parsing 🔥
  20. 20. Data Lake AI & Reporting Streaming Analytics Business-level Aggregates Filtered, Cleaned Augmented Raw Ingestion The Bronze Silver Gold CSV, JSON, TXT… Kinesis Intermediate data with some cleanup applied. Queryable for easy debugging!
  21. 21. Data Lake AI & Reporting Streaming Analytics Business-level Aggregates Filtered, Cleaned Augmented Raw Ingestion The Bronze Silver Gold CSV, JSON, TXT… Kinesis Clean data, ready for consumption. Read with Spark or Presto* *Coming Soon
  22. 22. Data Lake AI & Reporting Streaming Analytics Business-level Aggregates Filtered, Cleaned Augmented Raw Ingestion The Bronze Silver Gold CSV, JSON, TXT… Kinesis Streams move data through the Delta Lake • Low-latency or manually triggered • Eliminates management of schedules and jobs
  23. 23. Data Lake AI & Reporting Streaming Analytics Business-level Aggregates Filtered, Cleaned Augmented Raw Ingestion The Bronze Silver Gold CSV, JSON, TXT… Kinesis Delta Lake also supports batch jobs and standard DML UPDATE DELETE MERGE OVERWRITE • Retention • Corrections • GDPR • UPSERTS INSERT *DML Coming in 0.3.0
  24. 24. Data Lake AI & Reporting Streaming Analytics Business-level Aggregates Filtered, Cleaned Augmented Raw Ingestion The Bronze Silver Gold CSV, JSON, TXT… Kinesis Easy to recompute when business logic changes: • Clear tables • Restart streams DELETE DELETE
  25. 25. Who is using ?
  26. 26. Used by 1000s of organizations world wide > 1 exabyte processed last month alone
  27. 27. 27 Improved reliability: Petabyte-scale jobs 10x lower compute: 640 instances to 64! Simpler, faster ETL: 84 jobs → 3 jobs halved data latency
  28. 28. How do I use ?
  29. 29. dataframe .write .format("delta") .save("/data") Get Started with Delta using Spark APIs dataframe .write .format("parquet") .save("/data") Instead of parquet... … simply say delta Add Spark Package pyspark --packages io.delta:delta-core_2.12:0.1.0 bin/spark-shell --packages io.delta:delta-core_2.12:0.1.0 <dependency> <groupId>io.delta</groupId> <artifactId>delta-core_2.12</artifactId> <version>0.1.0</version> </dependency> Maven
  30. 30. Data Quality Enforce metadata, storage, and quality declaratively. table("warehouse") .location(…) // Location on DBFS .schema(…) // Optional strict schema checking .metastoreName(…) // Registration in Hive Metastore .description(…) // Human readable description for users .expect("validTimestamp", // Expectations on data quality "timestamp > 2012-01-01 AND …", "fail / alert / quarantine") *Coming Soon
  31. 31. Data Quality Enforce metadata, storage, and quality declaratively. table("warehouse") .location(…) // Location on DBFS .schema(…) // Optional strict schema checking .metastoreName(…) // Registration in Hive Metastore .description(…) // Human readable description for users .expect("validTimestamp", // Expectations on data quality "timestamp > 2012-01-01 AND …", "fail / alert / quarantine") *Coming Soon
  32. 32. How does work?
  33. 33. Delta On Disk my_table/ _delta_log/ 00000.json 00001.json date=2019-01-01/ file-1.parquet Transaction Log Table Versions (Optional) Partition Directories Data Files
  34. 34. Table = result of a set of actions Change Metadata – name, schema, partitioning, etc Add File – adds a file (with optional statistics) Remove File – removes a file Result: Current Metadata, List of Files, List of Txns, Version
  35. 35. Implementing Atomicity Changes to the table are stored as ordered, atomic units called commits Add 1.parquet Add 2.parquet Remove 1.parquet Remove 2.parquet Add 3.parquet 000000.json 000001.json …
  36. 36. Ensuring Serializablity Need to agree on the order of changes, even when there are multiple writers. 000000.json 000001.json 000002.json User 1 User 2
  37. 37. Solving Conflicts Optimistically 1. Record start version 2. Record reads/writes 3. Attempt commit 4. If someone else wins, check if anything you read has changed. 5. Try again. 000000.json 000001.json 000002.json User 1 User 2 Write: Append Read: Schema Write: Append Read: Schema
  38. 38. Handling Massive Metadata Large tables can have millions of files in them! How do we scale the metadata? Use Spark for scaling! Add 1.parquet Add 2.parquet Remove 1.parquet Remove 2.parquet Add 3.parquet Checkpoint
  39. 39. Road Map • 0.2.0 – Released! • S3 Support • Azure Blob Store and ADLS Support • 0.3.0 (~July) • UPDATE (Scala) • DELETE (Scala) • MERGE (Scala) • VACUUM (Scala) • Rest of Q3 • DDL Support / Hive Metastore • SQL DML Support
  40. 40. Build your own Delta Lake at https://delta.io

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