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Vinoth Chandar 85dd265b7b Improving out of box experience for data source
- Fixes #246
 - Bump up default parallelism to 1500, to handle large upserts
 - Add docs on s3 confuration & tuning tips with tested spark knobs
 - Fix bug to not duplicate hoodie metadata fields when input dataframe is another hoodie dataset
 - Improve speed of ROTablePathFilter by removing directory check
 - Move to spark-avro 4.0 to handle issue with nested fields with same name
 - Keep AvroConversionUtils in sync with spark-avro 4.0
2018-06-10 19:16:44 -07:00
2016-12-29 16:53:39 -08:00
2017-12-10 07:50:37 -08:00

Hudi

Hudi (pronounced Hoodie) stands for Hadoop Upserts anD Incrementals. Hudi manages storage of large analytical datasets on HDFS and serve them out via two types of tables

  • Read Optimized Table - Provides excellent query performance via purely columnar storage (e.g. Parquet)
  • Near-Real time Table (WIP) - Provides queries on real-time data, using a combination of columnar & row based storage (e.g Parquet + Avro)

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