[HUDI-3997] Add 0.11.0 release notes #5466
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| --- | ||
| 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)) | ||
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| ## Release Highlights | ||
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| ### Multi-Modal Index | ||
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| In 0.11.0, we enable the [metadata table](/docs/metadata) with synchronous updates and metadata-table-based file listing | ||
| by default for Spark writers, to improve the performance of partition and file listing on large Hudi tables. On the | ||
| reader side, users need to set it to `true` benefit from it. The metadata table and related file listing functionality | ||
| can still be turned off by setting `hoodie.metadata.enable=false`. Due to this, users deploying Hudi with async table | ||
| services need to configure a locking service. If this feature is not relevant for you, you can set | ||
| `hoodie.metadata.enable=false` additionally and use Hudi as before. | ||
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| 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 | ||
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| 1. bloom filter index containing the file-level bloom filter to facilitate key lookup and file pruning as a part of | ||
| bloom index during upserts by the writers | ||
| 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. | ||
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| 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. | ||
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| *Refer to the [metadata table guide](/docs/metadata#deployment-considerations) for detailed instructions on upgrade and | ||
| deployment.* | ||
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| ### Data Skipping with Metadata Table | ||
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| 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 benefit from Data Skipping, make sure to set `hoodie.enable.data.skipping=true` on both | ||
| writer and reader, as well as enable metadata table and Column Stats Index in the metadata table. | ||
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| 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"`. | ||
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| *Note: Currently Data Skipping is only supported in COW tables and MOR tables in read-optimized mode. The work of full | ||
| support for MOR tables is tracked in [HUDI-3866](https://issues.apache.org/jira/browse/HUDI-3866)* | ||
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| ### Async Indexer | ||
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| 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 the indexing process itself is asynchronous | ||
| and non-blocking to writers, a lock provider needs to be configured to safely co-ordinate the process with the inflight | ||
| writers. | ||
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| *See the [migration guide](#migration-guide) for more details.* | ||
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| ### Spark DataSource Improvements | ||
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| 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. | ||
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| - 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. | ||
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| *See the [migration guide](#migration-guide) for the relevant configuration updates.* | ||
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| ### Schema-on-read for Spark | ||
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| 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. | ||
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| *Please refer to the [schema evolution guide](/docs/schema_evolution) for more details.* | ||
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| ### Spark SQL Improvements | ||
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| - 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. | ||
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| *Please refer to the [Quick Start - Spark Guide](/docs/quick-start-guide) for more details and examples.* | ||
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| ### Spark Versions and Bundles | ||
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| In 0.11.0, | ||
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| - 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. | ||
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| ### Slim Utilities Bundle | ||
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| 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. | ||
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| - `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.x. | ||
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| ### Flink Integration Improvements | ||
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| - In 0.11.0, both Flink 1.13.x and 1.14.x are supported. | ||
| - Complex data types such as `Map` and `Array` are supported. Complex data types can be nested in another component data | ||
| type. | ||
| - A DFS-based Flink catalog is added with catalog identifier as `hudi`. You can instantiate the catalog through API | ||
| directly or use the `CREATE CATALOG` syntax to create it. | ||
| - Flink supports [Bucket Index](#bucket-index) in normal `UPSERT` and `BULK_INSERT` operations. Different from the | ||
| default Flink state-based index, bucket index is in constant number of buckets. Specify SQL option `index.type` | ||
| as `BUCKET` to enable it. | ||
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| ### BigQuery Integration | ||
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| 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. | ||
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| *Note: this is an experimental feature and only works with hive-style partitioned Copy-On-Write tables.* | ||
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| ### AWS Glue Meta Sync | ||
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| 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. | ||
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| *Note: this is an experimental feature.* | ||
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| ### DataHub Meta Sync | ||
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| 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. | ||
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| *Note: this is an experimental feature.* | ||
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| ### Bucket Index | ||
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| 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. 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`. | ||
| For Flink, set `index.type=BUCKET`. | ||
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| *For more details, please refer to `HoodieIndexConfig` in the [configurations](/docs/configurations) page.* | ||
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| ### Savepoint & Restore | ||
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| 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. | ||
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| *More info about this feature can be found in [Disaster Recovery](/docs/next/disaster_recovery).* | ||
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| ### Write Commit Callback for Pulsar | ||
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| 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 | ||
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| callback. Please refer to `org.apache.hudi.utilities.callback.pulsar.HoodieWriteCommitPulsarCallbackConfig` for | ||
| configurations to set. | ||
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| ### HiveSchemaProvider | ||
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| 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. | ||
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| ## Migration Guide | ||
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| ### Use async indexer | ||
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| Enabling metadata table and configuring a lock provider are the prerequisites 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: | ||
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| ```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> | ||
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| ``` | ||
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| Few points to note from deployment perspective: | ||
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| 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. | ||
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| 4. While an index can be created concurrently with ingestion, it cannot be dropped concurrently. Please stop all writers | ||
| before dropping an index. | ||
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| 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. | ||
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| ### Bundle usage | ||
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| As we relax the requirement of adding `spark-avro` package in 0.11.0 to work with Spark and Utilities bundle, | ||
| the option `--package org.apache.spark:spark-avro_2.1*:*` can be dropped. | ||
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| ### Configuration updates | ||
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| - For MOR tables, `hoodie.datasource.write.precombine.field` is required for both write and read. | ||
| - Only set `hoodie.datasource.write.drop.partition.columns=true` when work | ||
| with [BigQuery integration](/docs/next/gcp_bigquery). | ||
| - For Spark readers that rely on extracting physical partition path, | ||
| set `hoodie.datasource.read.extract.partition.values.from.path=true` to stay compatible with existing behaviors. | ||
| - Default index type for Spark was change from `BLOOM` | ||
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| to `SIMPLE` ([HUDI-3091](https://issues.apache.org/jira/browse/HUDI-3091)). If you currently rely on the default `BLOOM` | ||
| index type, please update your configuration accordingly. | ||
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| ## Raw Release Notes | ||
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| The raw release notes are available [here](https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12322822&version=12350673) | ||
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@xushiyan we should explicitly call out that using slim bundle requires also adding spark-bundle into the mix