xushiyan commented on code in PR #5466: URL: https://github.com/apache/hudi/pull/5466#discussion_r862328929
########## website/releases/release-0.11.0.md: ########## @@ -0,0 +1,206 @@ +--- +title: "Release 0.11.0" +sidebar_position: 2 +layout: releases +toc: true +last_modified_at: 2022-01-27T22:07:00+08:00 +--- +# [Release 0.11.0](https://github.com/apache/hudi/releases/tag/release-0.11.0) ([docs](/docs/quick-start-guide)) + +## Release Highlights + +### Multi-Modal Index + +In 0.11.0, we enable the [metadata table](/docs/metadata) with synchronous updates and +metadata-table-based file listing by default with Spark engine, to improve the performance of partition and file listing +on large Hudi tables. The metadata table and related file listing functionality can still be turned off by setting +`hoodie.metadata.enable` to `false`. Please follow the upgrade steps in the migration guide to safely use this feature. + +We introduce a multi-modal index in metadata table to drastically improve the lookup performance in file index and query +latency with data skipping. Two new indices are added to the metadata table + +1. bloom filter index containing the file-level bloom filter to facilitate bloom filter key lookup and file pruning in + the writer +2. column stats index containing the statistics of all/interested columns to improve file pruning based on key and + column value range in both the writer and the reader, in query planning in Spark for example. + +They are disabled by default. You can enable them by setting `hoodie.metadata.index.bloom.filter.enable` +and `hoodie.metadata.index.column.stats.enable` to `true`, respectively. + +### Data Skipping with Metadata Table + +With the added support for Column Statistics in Metadata Table, Data Skipping is now relying on the Metadata Table's +Column Stats Index (CSI) instead of its own bespoke index implementation (comparing to Spatial Curves added in 0.10.0), +allowing to leverage Data Skipping for all datasets regardless of whether they execute layout optimization procedures ( +like clustering) or not. To enable Data Skipping make sure to set `hoodie.enable.data.skipping` to `true`, as well as +enable Metadata Table and Column Stats Index in Metadata table. + +Data Skipping supports standard functions (as well as some common expressions) allowing you to apply common standard +transformations onto the raw data in your columns within your query's filters. For example, if you have column "ts" that +stores timestamp as string, you can now query it using human-readable dates in your predicate like +following: `date_format(ts, "MM/dd/yyyy" ) < "04/01/2022"`. + +*Note: Currently Data Skipping is only supported in COW tables and MOR tables in read-optimized mode.* + +### Async Indexer + +In 0.11.0, we added a new asynchronous service for indexing to our rich set of table services. It allows users to create +different kinds of indices (e.g., files, bloom filters, and column stats) in the metadata table without blocking +ingestion. The indexer adds a new action `indexing` on the timeline. While most of the indexing process is lock-free, to +avoid any contention with the timeline, the indexer needs to take a lock while scheduling the indexing action. See the +migration guide for more details. + +### Spark DataSource Improvements + +Hudi's Spark low-level integration got considerable overhaul consolidating common flows to share the infrastructure and +bring both compute and data throughput efficiencies when querying the data. + +- Both COW and MOR (except for incremental queries) tables are now leveraging Vectorized Parquet reader while reading + the data, meaning that Parquet reader is now able to leverage modern processors vectorized instructions to further + speed up decoding of the data. Enabled by default. +- When standard Record Payload implementation is used (e.g., `OverwriteWithLatestAvroPayload`), MOR table will only + fetch *strictly necessary* columns (primary key, pre-combine key) on top of those referenced by the query, + substantially reducing wasted data throughput as well as compute spent on decompressing and decoding the data. This is + significantly beneficial to "wide" MOR tables with 1000s of columns, for example. + +### Schema-on-read for Spark + +In 0.11.0, users can now easily change the current schema of a Hudi table to adapt to the evolving data schema over +time. Spark SQL DDL support (experimental) was added for Spark 3.1.x and Spark 3.2.1 via `ALTER TABLE` syntax. Please +refer to the [schema evolution guide](/docs/schema_evolution) for more details. + +### Spark SQL Improvements + +- Users can update or delete records in Hudi tables using non-primary-key fields. +- Time travel query is now supported via `timestamp as of` syntax. (Spark 3.2+ only) +- `CALL` command is added to support invoking more actions on Hudi tables. + +Please refer to the Quick Start - Spark Guide for more details and examples. + +### Spark Versions and Bundles + +In 0.11.0, + +- Spark 3.2 support is added; users can use `hudi-spark3.2-bundle` or `hudi-spark3-bundle` with Spark 3.2. +- Spark 3.1 will continue to be supported via `hudi-spark3.1-bundle`. +- Spark 2.4 will continue to be supported via `hudi-spark2.4-bundle` or `hudi-spark-bundle`. +- Users are encouraged to use bundles with specific Spark version in the name: `hudi-sparkX.Y-bundle`. +- Spark bundle for 3.0.x is no longer officially supported. Users are encouraged to upgrade to Spark 3.2 or 3.1. +- `spark-avro` package is no longer required to work with Spark bundles. + +### Slim Utilities Bundle + +In 0.11.0, a new `hudi-utilities-slim-bundle` is added to exclude dependencies that could cause conflicts and +compatibility issues with other frameworks such as Spark. + +- `hudi-utilities-slim-bundle` works with Spark 3.1 and 2.4. +- `hudi-utilities-bundle` continues to work with Spark 3.1 as it does in Hudi 0.10. + +### BigQuery Integration + +In 0.11.0, Hudi tables can be queried from BigQuery as external tables. Users can +set `org.apache.hudi.gcp.bigquery.BigQuerySyncTool` as the sync tool implementation for `HoodieDeltaStreamer` and make +the target Hudi table discoverable in BigQuery. Please refer to [Google Cloud BigQuery](/docs/next/gcp_bigquery) guide +page for more details. + +*Note: this is an experimental feature and only works with hive-style partitioned Copy-On-Write tables.* + +### AWS Glue Meta Sync + +In 0.11.0, Hudi tables can be sync'ed to AWS Glue Data Catalog via AWS SDK directly. Users can +set `org.apache.hudi.aws.sync.AwsGlueCatalogSyncTool` as the sync tool implementation for `HoodieDeltaStreamer` and make +the target Hudi table discoverable in Glue catalog. Please refer +to [Sync to AWS Glue Data Catalog](/docs/next/syncing_aws_glue_data_catalog) guide page for more details. + +*Note: this is an experimental feature.* + +### DataHub Meta Sync + +In 0.11.0, Hudi table's metadata (specifically, schema and last sync commit time) can be sync'ed +to [DataHub](https://datahubproject.io/). Users can set `org.apache.hudi.sync.datahub.DataHubSyncTool` as the sync tool +implementation for `HoodieDeltaStreamer` and sync the target table as a Dataset in DataHub. Please refer +to [Sync to DataHub](/docs/next/syncing_datahub) guide page for more details. + +*Note: this is an experimental feature.* + +### Bucket Index + +Bucket index, an efficient and light-weight index type, is added in 0.11.0. It distributes records to buckets using a +hash function based on the record keys, where each bucket corresponds to a single file group. It has around ~3x +improvement on throughput compared to using Bloom index when upsert. To use this index, set the index type to `BUCKET` +and set `hoodie.storage.layout.partitioner.class` to `org.apache.hudi.table.action.commit.SparkBucketIndexPartitioner`. +Similar settings work for Flink. For more details, please refer to `HoodieIndexConfig` in +the [configurations](/docs/configurations) page. + +### CLI Savepoint & Restore + +Disaster recovery is a mission critical feature in any production deployment. Especially when it comes to systems that +store data. Hudi had savepoint and restore functionality right from the beginning for COW tables. In 0.11.0, we have +added support for MOR tables. More info about this feature can be found in [Disaster Recovery](/docs/next/disaster_recovery). + +### Write Commit Callback for Pulsar + +Hudi users can use `org.apache.hudi.callback.HoodieWriteCommitCallback` to invoke callback function upon successful +commits. In 0.11.0, we add`HoodieWriteCommitPulsarCallback` in addition to the existing HTTP callback and Kafka +callback. Please refer to `org.apache.hudi.utilities.callback.pulsar.HoodieWriteCommitPulsarCallbackConfig` for +configurations to set. + +### HiveSchemaProvider + +In 0.11.0, `org.apache.hudi.utilities.schema.HiveSchemaProvider` is added for getting schema from user-defined hive +tables. This is useful when tailing Hive tables in `HoodieDeltaStreamer` instead of having to provide avro schema files. + +## Migration Guide + +### Upgrade steps for enabling metadata table + +*Before the upgrade, stop all writers on the same table.* + +- Deployment Model A: If your current deployment model is single writer and all table services (cleaning, clustering, + compaction) are configured to be **inline**, such as Deltastreamer sync-once mode and Spark Datasource with default + configs, there is no additional configuration required. Restarting the Single Writer with 0.11.0 is sufficient to + safely enable metadata table. +- Deployment Model B: If your current deployment model is single writer along with async table services (such as + cleaning, clustering, compaction) running in the same process, such as `HoodieDeltaStreamer` continuous mode writing + MOR table, Spark streaming (where compaction is async by default), and your own job setup enabling async table + services inside the same writer, it is a must to have the optimistic concurrency control, the lock provider, lazy + failed write clean policy configured before enabling metadata table. More details can be found [here](/docs/metadata). + Failing to follow the configuration guide leads to loss of data. +- Deployment Model C: If your current deployment model is [multi-writer](/docs/concurrency_control) along + with [a lock provider](/docs/concurrency_control/#enabling-multi-writing) and other required configs set for every + writer, there is no additional configuration required. You can bring up the writers with 0.11.0 sequentially. Applying + the proper configurations to only partial writers leads to loss of data from the inconsistent writer. So, ensure you + enable metadata table across all writers. + +### Use async indexer + +Configuring a lock provider is a prerequisite for using async indexer. The implementation details were illustrated +in [RFC-45](https://github.com/apache/hudi/blob/master/rfc/rfc-45/rfc-45.md). At the minimum, users need to set the +following configurations to schedule and run the indexer: + +```shell +# enable async index +hoodie.metadata.index.async=true +# enable specific index type, column stats for example +hoodie.metadata.index.column.stats.enable=true +# set OCC concurrency mode +hoodie.write.concurrency.mode=optimistic_concurrency_control +# set lock provider configs +hoodie.write.lock.provider=<LockProviderClass> +``` + +Few points to note from deployment perspective: + +1. Files index is created by default as long as the metadata table is enabled. +2. If you intend to build any index asynchronously, say column stats, then be sure to enable the async index and column + stats index type on the regular ingestion writers as well. +3. In the case of multi-writers, enable async index and specific index config for all writers. +4. While an index can be created concurrently with ingestion, it cannot be dropped concurrently. Please stop all writers + before dropping an index. + +Some of these limitations will be overcome in the upcoming releases. Please +follow [HUDI-2488](https://issues.apache.org/jira/browse/HUDI-2488) for developments on this feature. + Review Comment: Added the highlights in the migration guide -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@hudi.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
