[GitHub] [hudi] umehrot2 commented on a change in pull request #3547: Add Hudi 0.9.0 release page with highlights

[GitBox <gi...@apache.org>]

umehrot2 commented on a change in pull request #3547:
URL: https://github.com/apache/hudi/pull/3547#discussion_r697745362



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File path: website/releases/release-0.9.0.md
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@@ -0,0 +1,118 @@
+---
+title: "Release 0.9.0"
+sidebar_position: 2
+layout: releases
+toc: true
+last_modified_at: 2021-08-26T08:40:00-07:00
+---
+# [Release 0.9.0](https://github.com/apache/hudi/releases/tag/release-0.9.0) ([docs](/docs/quick-start-guide))
+
+## Download Information
+* Source Release : [Apache Hudi 0.9.0 Source Release](https://downloads.apache.org/hudi/0.9.0/hudi-0.9.0.src.tgz) ([asc](https://downloads.apache.org/hudi/0.9.0/hudi-0.9.0.src.tgz.asc), [sha512](https://downloads.apache.org/hudi/0.9.0/hudi-0.9.0.src.tgz.sha512))
+* Apache Hudi jars corresponding to this release is available [here](https://repository.apache.org/#nexus-search;quick~hudi)
+
+## Migration Guide for this release
+- If migrating from release older than 0.5.3, please also check the upgrade instructions for each subsequent release below.
+- Specifically check upgrade instructions for 0.6.0.
+- With 0.9.0 Hudi is adding more table properties to aid in using an existing hudi table with spark-sql. 
+  To smoothly aid this transition these properties added to `hoodie.properties` file. Whenever Hudi is launched with
+  newer table version i.e 2 (or moving from pre 0.9.0 to 0.9.0), an upgrade step will be executed automatically.
+  This automatic upgrade step will happen just once per Hudi table as the `hoodie.table.version` will be updated in 
+  property file after upgrade is completed.
+- Similarly, a command line tool for Downgrading (command - `downgrade`) is added if in case some users want to 
+  downgrade Hudi from table version 2 to 1 or move from Hudi 0.9.0 to pre 0.9.0
+- This release also replaces string representation of all configs to ConfigProperty. Older configs (strings) are deprecated
+and users are encouraged to use the new ConfigProperties. 
+
+## Release Highlights
+
+### Spark SQL DML and DDL Support
+
+0.9.0 brings the ability to perform data definition (create, modify and truncate) and data manipulation (inserts, 
+upserts and deletes) on Hudi tables using the familiar Structured Query Language (SQL) via Spark SQL.
+This is huge step towards making Hudi more easily accessible and operable by all personas (non-engineers, analysts etc).
+Users can now use `INSERT`, `UPDATE`, `MERGE INTO` and `DELETE`
+sql statements to manipulate data. In addition, `INSERT OVERWRITE` statement can be used to overwrite existing data in the table or partition
+with new values. 
+
+On the data definition side, customers can now use `CREATE TABLE....USING HUDI` statement, which creates and registers the
+table as Hudi Spark DataSource table, instead of the earlier approach of having to define Hudi Input Format and Parquet
+Serde. They can also use `CREATE TABLE....AS`, `ALTER TABLE` and `TRUNCATE TABLE` statements to create, modify or truncate table
+definitions.
+
+Please see [RFC-25](https://cwiki.apache.org/confluence/display/HUDI/RFC+-+25%3A+Spark+SQL+Extension+For+Hudi)
+for more implementation details and follow this [page](/docs/quick-start-guide) to get started with DML and DDL!
+
+### Query side improvements
+- Hudi tables can now be registered adn read as DataSource Table in spark. 
+- **Metadata based File Listing Improvements for Spark**: ?? 
+- Added support for timetravel query. Please check the [quick start page](http://localhost:3000/docs/next/quick-start-guide)
+  on how to invoke the same. 
+
+### Writer side improvements 
+- Virtual keys support has been added where users can avoid adding meta fields to hudi table and leverage existing 
+  fields to populate record keys and partition paths. One needs to disable [this](http://localhost:3000/docs/next/configurations#hoodiepopulatemetafields)
+  config to enable virtual keys. 
+- Clustering improvements:  
+    - Async Clustering support has been added to both DeltaStreamer and Spark Streaming. More on this can be found in this 
+      blog post and how to use the same.
+    - Incremental read works for clustered data as well. ? This is more of a fix right? 
+    - HoodieClusteringJob has been added to assist in building and executing a clustering plan together as a standalone job. 
+    - Added a config (`hoodie.clustering.plan.strategy.daybased.skipfromlatest.partitions`) to skip latest N partitions 
+      while creating the cluster plan.  
+- Bulk_Insert using row writer path has been enhanced and brought to feature parity with WriteClient's version of Bulk_insert. 
+Users can start to use the row writer path for better performance. 
+- Added support for HMS in HiveSyncTool. HMSDDLExecutor is a DDLExecutor implementation, based on HMS which uses HMS 
+  apis directly for all DDL tasks.
+- A pre commit validator framework has been added to spark engine. Users can leverage this to add validations like verifying all valid
+files are present for given instant, or all invalid files are removed, etc as part of this framework.
+- Users can choose to drop fields used to generate partition paths if need be. (`hoodie.datasource.write.drop.partition.columns`)
+- Added support for "delete_partition" operation to spark. Users can leverage this to delete older partitions as and when required.    
+- Disk based map improvements? 
+- Concurrency control: https://issues.apache.org/jira/browse/HUDI-944
+- ORC format support: ?
+- Support for Huawei Cloud Object Storage, BAIDU AFS storage format, Baidu BOS storage in Hudi: Should we expand more on this? 
+- Marker file management has been enhanced to use batch mode and is now centrally coordinate by timeline server. This benefits 
+cloud stores like S3 when large number of marker files are created or deleted during commits. You can read more on this blog.
+- Java engine now support HoodieBloomIndex.
+
+### Flink Integration Improvements
+- **Insert Overwrite Support**: HUDI-1788
+- **Bulk Insert Support**: HUDI-2209
+- **Non Partitioned Table Support**: HUDI-1814
+- **Streaming Read for COW Tables**: HUDI-1867
+- **Emit Deletes for MOR Streaming Read**: HUDI-1738
+- **Global Index Support**: HUDI-1908
+- Propagate CDC format for Hudi
+- Emit deletes for Flink MOR table streaming read 
+- Supports hive style partitioning for flink writer
+- Support Transformer for HoodieFlinkStreamer
+-  Metadata table for flink
+-  Support hive3 meta sync for flink writer
+-  Support hive1 metadata sync for flink writer
+-  Asynchronous Hive sync and commits cleaning for Flink writer
+-  Support flink hive sync in batch mode-
+-  Support Append only in Flink stream
+-  Add marker files for flink writer
+
+## Utilities
+### DeltaStreamer
+- Couple of sources have been added to deltastreamer. 
+  1. JDBC source can assist in reading data from RDBMS sources.
+  2. SQLSource can assist in backfill use-cases like adding a new column and backfilling just one column for the past 6 months.
+  3. S3EventsHoodieIncrSource and S3EventsSource assists in reading data from S3 reliably and efficiently ingesting data to Hudi. 
+     Existing approach of using DFSSource uses last modification time of files as checkpoint to pull in new files, but if large 
+     number of files have same modification time, might run into issues of missing some files to be read from source. 
+     These two sources (S3EventsHoodieIncrSource and S3EventsSource) together ensures data is reliably ingested from S3 into 
+     Hudi by leveraging AWS SNS and SQS services that subscribes to file events from the source bucket.
+- In addition to pulling data from kafka for kafka sources using DeltaStreamer using regular offset format 
+  (topic_name,partition_num:offset,partition_num:offset,....), we also added two new formats. Timestampd based and 
+  Group consumer offset. 
+- Added support to pass in basic auth credentials in schema registry provider url with schema provider in deltastreamer.   
+- Some improvements have been made to hudi-cli like `SCHEDULE COMPACTION` and `RUN COMPACTION` statements to easily schedule and run

Review comment:
       @nsivabalan I had added this in the SQL DML section, not for the CLI. I think you might have added this by mistake.




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