Intro to Parquet (June 2015)

1. Sam BESSALAH @samklr http://parquet.apache.org
2. Typical Data workflow
3. Typical Data workflow
4. Typical Data workflow
5. Typical Data workflow
6. Multiple Data Format
7. Big Data Data Format Zoo - Sequence Files
8. these formats provide
10. Binary, columnar storage format for big data analytics workloads, inspired by the Google Dremel Paper. - Language independent - Processing framework independent - Formally specified - More than a columnar storage : Dynamic partionning, automatic predicate and projections push down - Awesome performance
11. Columnar Storage 101
12. Columnar Storage 101
13. Columnar Storage 101
14. Columnar Storage 101 Advantages : - Limits I/O to the data only needed - Big Space savings, better compression, and faster and low overhead encodings - Enables vectorized engine
15. Columnar Storage 101
17. Parquet Model
18. Example Parquet Schema
21. Definition and Repetition Levels Definition Level : Stores the level for which the field is null Repetition Level : Store levels when new lists are starting in column values.
28. Numbers Example: Appnexus 2 MM Logs of Ads impressions 270 TB of Log Data in Protobuf on HDFS http://techblog.appnexus.com/blog/2015/03/31/parquet-columnar-storage-for-hadoop-data/
29. simple bench with HIVE
32. Disk Space usage on HDFS with 128 MB blocks
39. Slides shamelessly cloned from Julien Le Dem(@J_) , Lead of the Apache Parquet Project
40. BACKUP SLIDES