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Posted to commits@hudi.apache.org by GitBox <gi...@apache.org> on 2022/04/21 21:21:21 UTC
[GitHub] [hudi] vinothchandar commented on a diff in pull request #5113: [HUDI-3625] [RFC-48] Optimized storage layout for Cloud Object Stores
vinothchandar commented on code in PR #5113:
URL: https://github.com/apache/hudi/pull/5113#discussion_r855589377
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
+re-trying of failed requests affecting throughput, and result in occasional failures if the retries are not able to
+succeed either and continue to be throttled.
+
+The high level proposal here is to introduce a new storage layout, where all files are distributed evenly across multiple
+randomly generated prefixes under the Amazon S3 bucket, instead of being stored under a common table path/prefix. This
+would help distribute the requests evenly across different prefixes, resulting in Amazon S3 to create partitions for
+the prefixes each with its own request limit. This significantly reduces the possibility of hitting the request limit
+for a specific prefix/partition.
+
+## Design
+
+### Generating file paths
+
+We want to distribute files evenly across multiple random prefixes, instead of following the traditional Hive storage
+layout of keeping them under a common table path/prefix. In addition to the `Table Path`, for this new layout user will
+configure another `Table Storage Path` under which the actual data files will be distributed. The original `Table Path` will
+be used to maintain the table/partitions Hudi metadata.
+
+For the purpose of this documentation lets assume:
+```
+Table Path => s3://<table_bucket>/<hudi_table_name>/
+
+Table Storage Path => s3://<table_storage_bucket>/
+```
+Note: `Table Storage Path` can be a path in the same Amazon S3 bucket or a different bucket. For best results,
+`Table Storage Path` should be a bucket instead of a prefix under the bucket as it allows for S3 to partition sooner.
+
+We will use a Hashing function on the `File Name` to map them to a prefix generated under `Table Storage Path`:
+```
+s3://<table_storage_bucket>/<hash_prefix>/..
+```
+
+In addition, under the hash prefix we will follow a folder structure by appending Hudi Table Name and Partition. This
+folder structuring would be useful if we ever have to do a file system listing to re-create the metadata file list for
+the table (discussed more in the next section). Here is how the final layout would look like for `partitioned` tables:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=usa/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=india/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+...
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=india/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=uk/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+For `non-partitioned` tables, this is how it would look:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+The original table path will continue to store the `metadata folder` and `partition metadata` files:
+```
+s3://<table_bucket>/<hudi_table_name>/.hoodie/...
+s3://<table_bucket>/<hudi_table_name>/country=usa/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=india/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=uk/.hoodie_partition_metadata
+...
+```
+
+#### Hashing
+
+To generate the prefixes we can use `Murmur 32 bit` hash on the `File Names`, which is known for being fast and provides
+good distribution guarantees. We might have to further do bucketing and re-hash it to reduce the number of possible hashes
+from 2^32 to a slightly lower number, as it may be overkill to have that many unique hashes, which might result in scenarios
+where each file is under a different prefix.
+
+### Maintain mapping to files
+
+In [RFC-15](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427331), we introduced an internal
+Metadata Table with a `files` partition that maintains mapping from partitions to list of files in the partition stored
+under `Table Path`. This mapping is kept up to date, as operations are performed on the original table. We will leverage
+the same to now maintain mappings to files stored at `Table Storage Path` under different prefixes.
+
+Here are some of the design considerations:
+
+1. Metadata table is a pre-requisite and will be automatically enabled if object storage layout is enabled.
+
+2. The object storage layout cannot be enabled on an existing table that is already bootstrapped with Hive storage layout.
+To switch to object storage layout, the table will need to be re-bootstrapped with the new layout.
+
+3. The Instant metadata (`HoodieCommitMetadata`,`HoodieCleanMetadata` etc.) will always act as the source of file listing
+for metadata table to be populated.
+
+4. `HoodieCommitMetadata` currently stores `file name` instead of complete `file path`. We will have to modify commit
Review Comment:
can we instead just derive the hash on the fly? i.e if the table has a storage path configured, then hash(file_name) and then look under `storage_path/hash/...`? Do we need to store it?
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
+re-trying of failed requests affecting throughput, and result in occasional failures if the retries are not able to
+succeed either and continue to be throttled.
+
+The high level proposal here is to introduce a new storage layout, where all files are distributed evenly across multiple
+randomly generated prefixes under the Amazon S3 bucket, instead of being stored under a common table path/prefix. This
+would help distribute the requests evenly across different prefixes, resulting in Amazon S3 to create partitions for
+the prefixes each with its own request limit. This significantly reduces the possibility of hitting the request limit
+for a specific prefix/partition.
+
+## Design
+
+### Generating file paths
+
+We want to distribute files evenly across multiple random prefixes, instead of following the traditional Hive storage
+layout of keeping them under a common table path/prefix. In addition to the `Table Path`, for this new layout user will
+configure another `Table Storage Path` under which the actual data files will be distributed. The original `Table Path` will
+be used to maintain the table/partitions Hudi metadata.
+
+For the purpose of this documentation lets assume:
+```
+Table Path => s3://<table_bucket>/<hudi_table_name>/
+
+Table Storage Path => s3://<table_storage_bucket>/
+```
+Note: `Table Storage Path` can be a path in the same Amazon S3 bucket or a different bucket. For best results,
+`Table Storage Path` should be a bucket instead of a prefix under the bucket as it allows for S3 to partition sooner.
+
+We will use a Hashing function on the `File Name` to map them to a prefix generated under `Table Storage Path`:
+```
+s3://<table_storage_bucket>/<hash_prefix>/..
+```
+
+In addition, under the hash prefix we will follow a folder structure by appending Hudi Table Name and Partition. This
+folder structuring would be useful if we ever have to do a file system listing to re-create the metadata file list for
+the table (discussed more in the next section). Here is how the final layout would look like for `partitioned` tables:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=usa/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=india/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+...
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=india/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=uk/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+For `non-partitioned` tables, this is how it would look:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+The original table path will continue to store the `metadata folder` and `partition metadata` files:
+```
+s3://<table_bucket>/<hudi_table_name>/.hoodie/...
+s3://<table_bucket>/<hudi_table_name>/country=usa/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=india/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=uk/.hoodie_partition_metadata
+...
+```
+
+#### Hashing
+
+To generate the prefixes we can use `Murmur 32 bit` hash on the `File Names`, which is known for being fast and provides
Review Comment:
See `HashID` class, which already has some fast hashing functions!
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
+re-trying of failed requests affecting throughput, and result in occasional failures if the retries are not able to
+succeed either and continue to be throttled.
+
+The high level proposal here is to introduce a new storage layout, where all files are distributed evenly across multiple
+randomly generated prefixes under the Amazon S3 bucket, instead of being stored under a common table path/prefix. This
+would help distribute the requests evenly across different prefixes, resulting in Amazon S3 to create partitions for
+the prefixes each with its own request limit. This significantly reduces the possibility of hitting the request limit
+for a specific prefix/partition.
+
+## Design
+
+### Generating file paths
+
+We want to distribute files evenly across multiple random prefixes, instead of following the traditional Hive storage
+layout of keeping them under a common table path/prefix. In addition to the `Table Path`, for this new layout user will
+configure another `Table Storage Path` under which the actual data files will be distributed. The original `Table Path` will
+be used to maintain the table/partitions Hudi metadata.
+
+For the purpose of this documentation lets assume:
+```
+Table Path => s3://<table_bucket>/<hudi_table_name>/
+
+Table Storage Path => s3://<table_storage_bucket>/
+```
+Note: `Table Storage Path` can be a path in the same Amazon S3 bucket or a different bucket. For best results,
+`Table Storage Path` should be a bucket instead of a prefix under the bucket as it allows for S3 to partition sooner.
+
+We will use a Hashing function on the `File Name` to map them to a prefix generated under `Table Storage Path`:
+```
+s3://<table_storage_bucket>/<hash_prefix>/..
+```
+
+In addition, under the hash prefix we will follow a folder structure by appending Hudi Table Name and Partition. This
+folder structuring would be useful if we ever have to do a file system listing to re-create the metadata file list for
+the table (discussed more in the next section). Here is how the final layout would look like for `partitioned` tables:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=usa/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=india/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+...
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=india/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=uk/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+For `non-partitioned` tables, this is how it would look:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+The original table path will continue to store the `metadata folder` and `partition metadata` files:
+```
+s3://<table_bucket>/<hudi_table_name>/.hoodie/...
+s3://<table_bucket>/<hudi_table_name>/country=usa/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=india/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=uk/.hoodie_partition_metadata
+...
+```
+
+#### Hashing
+
+To generate the prefixes we can use `Murmur 32 bit` hash on the `File Names`, which is known for being fast and provides
+good distribution guarantees. We might have to further do bucketing and re-hash it to reduce the number of possible hashes
+from 2^32 to a slightly lower number, as it may be overkill to have that many unique hashes, which might result in scenarios
+where each file is under a different prefix.
+
+### Maintain mapping to files
+
+In [RFC-15](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427331), we introduced an internal
+Metadata Table with a `files` partition that maintains mapping from partitions to list of files in the partition stored
+under `Table Path`. This mapping is kept up to date, as operations are performed on the original table. We will leverage
+the same to now maintain mappings to files stored at `Table Storage Path` under different prefixes.
+
+Here are some of the design considerations:
+
+1. Metadata table is a pre-requisite and will be automatically enabled if object storage layout is enabled.
+
+2. The object storage layout cannot be enabled on an existing table that is already bootstrapped with Hive storage layout.
+To switch to object storage layout, the table will need to be re-bootstrapped with the new layout.
+
+3. The Instant metadata (`HoodieCommitMetadata`,`HoodieCleanMetadata` etc.) will always act as the source of file listing
+for metadata table to be populated.
+
+4. `HoodieCommitMetadata` currently stores `file name` instead of complete `file path`. We will have to modify commit
+metadata to store the complete file path instead of just file name, as the files are now distributed across several random
+prefix paths instead of a derivable table/partition path.
+
+5. If there is an error reading from Metadata table, we will not fall back listing from file system.
+
+6. In case of metadata table getting corrupted or lost, we need to have a solution here to reconstruct metadata table
+from the files which distributed using object storage layout. We will likely have to implement a file system listing
+logic, that can get all the partition to files mapping by listing all the prefixes under the `Table Storage Path`.
+Following the folder structure of adding table name/partitions under the prefix will help in getting the listing and
+identifying the table/partition they belong to.
+
+### Query Side Integration
+
+Spark, Hive and [Presto](https://github.com/prestodb/presto/commit/ef1fd25c582631513ccdd097e0a654cda44ec3dc) are
+already integrated to use metadata based listing. Hudi connector for [Trino](https://github.com/prestodb/presto/commit/ef1fd25c582631513ccdd097e0a654cda44ec3dc)
+is currently in process of being contributed, which also integrates with metadata based listing. In general, since these
+query engines are able to use Hudi's metadata table there should ideally be no changes required in
+terms of making them work with Object storage layout. Here are some considerations:
+
+- Spark DataSource and Spark SQL queries have been integrated with metadata based listing via the Hudi's custom implementation
+of Spark's FileIndex interface. However, if Spark DataSource queries are used with globbed paths then the FileIndex path
Review Comment:
We can end support for globbed paths if need be. is that problematic?
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
+re-trying of failed requests affecting throughput, and result in occasional failures if the retries are not able to
+succeed either and continue to be throttled.
+
+The high level proposal here is to introduce a new storage layout, where all files are distributed evenly across multiple
+randomly generated prefixes under the Amazon S3 bucket, instead of being stored under a common table path/prefix. This
+would help distribute the requests evenly across different prefixes, resulting in Amazon S3 to create partitions for
+the prefixes each with its own request limit. This significantly reduces the possibility of hitting the request limit
+for a specific prefix/partition.
+
+## Design
+
+### Generating file paths
+
+We want to distribute files evenly across multiple random prefixes, instead of following the traditional Hive storage
+layout of keeping them under a common table path/prefix. In addition to the `Table Path`, for this new layout user will
+configure another `Table Storage Path` under which the actual data files will be distributed. The original `Table Path` will
+be used to maintain the table/partitions Hudi metadata.
+
+For the purpose of this documentation lets assume:
+```
+Table Path => s3://<table_bucket>/<hudi_table_name>/
+
+Table Storage Path => s3://<table_storage_bucket>/
+```
+Note: `Table Storage Path` can be a path in the same Amazon S3 bucket or a different bucket. For best results,
+`Table Storage Path` should be a bucket instead of a prefix under the bucket as it allows for S3 to partition sooner.
+
+We will use a Hashing function on the `File Name` to map them to a prefix generated under `Table Storage Path`:
+```
+s3://<table_storage_bucket>/<hash_prefix>/..
+```
+
+In addition, under the hash prefix we will follow a folder structure by appending Hudi Table Name and Partition. This
+folder structuring would be useful if we ever have to do a file system listing to re-create the metadata file list for
+the table (discussed more in the next section). Here is how the final layout would look like for `partitioned` tables:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=usa/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=india/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+...
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=india/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=uk/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+For `non-partitioned` tables, this is how it would look:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+The original table path will continue to store the `metadata folder` and `partition metadata` files:
+```
+s3://<table_bucket>/<hudi_table_name>/.hoodie/...
+s3://<table_bucket>/<hudi_table_name>/country=usa/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=india/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=uk/.hoodie_partition_metadata
+...
+```
+
+#### Hashing
+
+To generate the prefixes we can use `Murmur 32 bit` hash on the `File Names`, which is known for being fast and provides
+good distribution guarantees. We might have to further do bucketing and re-hash it to reduce the number of possible hashes
+from 2^32 to a slightly lower number, as it may be overkill to have that many unique hashes, which might result in scenarios
+where each file is under a different prefix.
+
+### Maintain mapping to files
+
+In [RFC-15](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427331), we introduced an internal
+Metadata Table with a `files` partition that maintains mapping from partitions to list of files in the partition stored
+under `Table Path`. This mapping is kept up to date, as operations are performed on the original table. We will leverage
+the same to now maintain mappings to files stored at `Table Storage Path` under different prefixes.
+
+Here are some of the design considerations:
+
+1. Metadata table is a pre-requisite and will be automatically enabled if object storage layout is enabled.
+
+2. The object storage layout cannot be enabled on an existing table that is already bootstrapped with Hive storage layout.
+To switch to object storage layout, the table will need to be re-bootstrapped with the new layout.
+
+3. The Instant metadata (`HoodieCommitMetadata`,`HoodieCleanMetadata` etc.) will always act as the source of file listing
+for metadata table to be populated.
+
+4. `HoodieCommitMetadata` currently stores `file name` instead of complete `file path`. We will have to modify commit
+metadata to store the complete file path instead of just file name, as the files are now distributed across several random
+prefix paths instead of a derivable table/partition path.
+
+5. If there is an error reading from Metadata table, we will not fall back listing from file system.
+
+6. In case of metadata table getting corrupted or lost, we need to have a solution here to reconstruct metadata table
+from the files which distributed using object storage layout. We will likely have to implement a file system listing
+logic, that can get all the partition to files mapping by listing all the prefixes under the `Table Storage Path`.
+Following the folder structure of adding table name/partitions under the prefix will help in getting the listing and
+identifying the table/partition they belong to.
+
+### Query Side Integration
+
+Spark, Hive and [Presto](https://github.com/prestodb/presto/commit/ef1fd25c582631513ccdd097e0a654cda44ec3dc) are
+already integrated to use metadata based listing. Hudi connector for [Trino](https://github.com/prestodb/presto/commit/ef1fd25c582631513ccdd097e0a654cda44ec3dc)
+is currently in process of being contributed, which also integrates with metadata based listing. In general, since these
+query engines are able to use Hudi's metadata table there should ideally be no changes required in
+terms of making them work with Object storage layout. Here are some considerations:
+
+- Spark DataSource and Spark SQL queries have been integrated with metadata based listing via the Hudi's custom implementation
+of Spark's FileIndex interface. However, if Spark DataSource queries are used with globbed paths then the FileIndex path
+does not kick in, and it would rely on Spark's `InMemoryFileIndex` to do the file listing with Hudi's path filter applied. Thus,
+these Spark DataSource queries with globbed paths would not work with object storage layout.
+
+- Query engines should be able to determine that Object storage layout is configured, and rely on metadata to list files. It
Review Comment:
yes we can add a check to fail if storagePath is configured, but metadata table is not
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
Review Comment:
the bigger problem is this thundering herd, bursty puts/gets from engines.
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
+re-trying of failed requests affecting throughput, and result in occasional failures if the retries are not able to
+succeed either and continue to be throttled.
+
+The high level proposal here is to introduce a new storage layout, where all files are distributed evenly across multiple
+randomly generated prefixes under the Amazon S3 bucket, instead of being stored under a common table path/prefix. This
+would help distribute the requests evenly across different prefixes, resulting in Amazon S3 to create partitions for
+the prefixes each with its own request limit. This significantly reduces the possibility of hitting the request limit
+for a specific prefix/partition.
+
+## Design
+
+### Generating file paths
+
+We want to distribute files evenly across multiple random prefixes, instead of following the traditional Hive storage
+layout of keeping them under a common table path/prefix. In addition to the `Table Path`, for this new layout user will
+configure another `Table Storage Path` under which the actual data files will be distributed. The original `Table Path` will
+be used to maintain the table/partitions Hudi metadata.
+
+For the purpose of this documentation lets assume:
+```
+Table Path => s3://<table_bucket>/<hudi_table_name>/
+
+Table Storage Path => s3://<table_storage_bucket>/
+```
+Note: `Table Storage Path` can be a path in the same Amazon S3 bucket or a different bucket. For best results,
+`Table Storage Path` should be a bucket instead of a prefix under the bucket as it allows for S3 to partition sooner.
+
+We will use a Hashing function on the `File Name` to map them to a prefix generated under `Table Storage Path`:
+```
+s3://<table_storage_bucket>/<hash_prefix>/..
+```
+
+In addition, under the hash prefix we will follow a folder structure by appending Hudi Table Name and Partition. This
+folder structuring would be useful if we ever have to do a file system listing to re-create the metadata file list for
+the table (discussed more in the next section). Here is how the final layout would look like for `partitioned` tables:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=usa/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=india/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+...
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=india/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=uk/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+For `non-partitioned` tables, this is how it would look:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+The original table path will continue to store the `metadata folder` and `partition metadata` files:
+```
+s3://<table_bucket>/<hudi_table_name>/.hoodie/...
+s3://<table_bucket>/<hudi_table_name>/country=usa/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=india/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=uk/.hoodie_partition_metadata
+...
+```
+
+#### Hashing
+
+To generate the prefixes we can use `Murmur 32 bit` hash on the `File Names`, which is known for being fast and provides
+good distribution guarantees. We might have to further do bucketing and re-hash it to reduce the number of possible hashes
+from 2^32 to a slightly lower number, as it may be overkill to have that many unique hashes, which might result in scenarios
+where each file is under a different prefix.
+
+### Maintain mapping to files
+
+In [RFC-15](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427331), we introduced an internal
+Metadata Table with a `files` partition that maintains mapping from partitions to list of files in the partition stored
+under `Table Path`. This mapping is kept up to date, as operations are performed on the original table. We will leverage
+the same to now maintain mappings to files stored at `Table Storage Path` under different prefixes.
+
+Here are some of the design considerations:
+
+1. Metadata table is a pre-requisite and will be automatically enabled if object storage layout is enabled.
+
+2. The object storage layout cannot be enabled on an existing table that is already bootstrapped with Hive storage layout.
+To switch to object storage layout, the table will need to be re-bootstrapped with the new layout.
+
+3. The Instant metadata (`HoodieCommitMetadata`,`HoodieCleanMetadata` etc.) will always act as the source of file listing
+for metadata table to be populated.
+
+4. `HoodieCommitMetadata` currently stores `file name` instead of complete `file path`. We will have to modify commit
+metadata to store the complete file path instead of just file name, as the files are now distributed across several random
+prefix paths instead of a derivable table/partition path.
+
+5. If there is an error reading from Metadata table, we will not fall back listing from file system.
Review Comment:
+1
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
+re-trying of failed requests affecting throughput, and result in occasional failures if the retries are not able to
+succeed either and continue to be throttled.
+
+The high level proposal here is to introduce a new storage layout, where all files are distributed evenly across multiple
+randomly generated prefixes under the Amazon S3 bucket, instead of being stored under a common table path/prefix. This
+would help distribute the requests evenly across different prefixes, resulting in Amazon S3 to create partitions for
+the prefixes each with its own request limit. This significantly reduces the possibility of hitting the request limit
+for a specific prefix/partition.
+
+## Design
+
+### Generating file paths
+
+We want to distribute files evenly across multiple random prefixes, instead of following the traditional Hive storage
+layout of keeping them under a common table path/prefix. In addition to the `Table Path`, for this new layout user will
+configure another `Table Storage Path` under which the actual data files will be distributed. The original `Table Path` will
+be used to maintain the table/partitions Hudi metadata.
+
+For the purpose of this documentation lets assume:
+```
+Table Path => s3://<table_bucket>/<hudi_table_name>/
+
+Table Storage Path => s3://<table_storage_bucket>/
+```
+Note: `Table Storage Path` can be a path in the same Amazon S3 bucket or a different bucket. For best results,
+`Table Storage Path` should be a bucket instead of a prefix under the bucket as it allows for S3 to partition sooner.
+
+We will use a Hashing function on the `File Name` to map them to a prefix generated under `Table Storage Path`:
+```
+s3://<table_storage_bucket>/<hash_prefix>/..
+```
+
+In addition, under the hash prefix we will follow a folder structure by appending Hudi Table Name and Partition. This
+folder structuring would be useful if we ever have to do a file system listing to re-create the metadata file list for
+the table (discussed more in the next section). Here is how the final layout would look like for `partitioned` tables:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=usa/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=india/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+...
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=india/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=uk/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+For `non-partitioned` tables, this is how it would look:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+The original table path will continue to store the `metadata folder` and `partition metadata` files:
+```
+s3://<table_bucket>/<hudi_table_name>/.hoodie/...
+s3://<table_bucket>/<hudi_table_name>/country=usa/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=india/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=uk/.hoodie_partition_metadata
+...
+```
+
+#### Hashing
+
+To generate the prefixes we can use `Murmur 32 bit` hash on the `File Names`, which is known for being fast and provides
+good distribution guarantees. We might have to further do bucketing and re-hash it to reduce the number of possible hashes
+from 2^32 to a slightly lower number, as it may be overkill to have that many unique hashes, which might result in scenarios
+where each file is under a different prefix.
+
+### Maintain mapping to files
+
+In [RFC-15](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427331), we introduced an internal
+Metadata Table with a `files` partition that maintains mapping from partitions to list of files in the partition stored
+under `Table Path`. This mapping is kept up to date, as operations are performed on the original table. We will leverage
+the same to now maintain mappings to files stored at `Table Storage Path` under different prefixes.
+
+Here are some of the design considerations:
+
+1. Metadata table is a pre-requisite and will be automatically enabled if object storage layout is enabled.
+
+2. The object storage layout cannot be enabled on an existing table that is already bootstrapped with Hive storage layout.
+To switch to object storage layout, the table will need to be re-bootstrapped with the new layout.
+
+3. The Instant metadata (`HoodieCommitMetadata`,`HoodieCleanMetadata` etc.) will always act as the source of file listing
+for metadata table to be populated.
+
+4. `HoodieCommitMetadata` currently stores `file name` instead of complete `file path`. We will have to modify commit
+metadata to store the complete file path instead of just file name, as the files are now distributed across several random
+prefix paths instead of a derivable table/partition path.
+
+5. If there is an error reading from Metadata table, we will not fall back listing from file system.
+
+6. In case of metadata table getting corrupted or lost, we need to have a solution here to reconstruct metadata table
+from the files which distributed using object storage layout. We will likely have to implement a file system listing
+logic, that can get all the partition to files mapping by listing all the prefixes under the `Table Storage Path`.
+Following the folder structure of adding table name/partitions under the prefix will help in getting the listing and
+identifying the table/partition they belong to.
+
+### Query Side Integration
Review Comment:
Thinking out loud, the main thing here is - even files under the same partition will not be spread across different prefixes. I think all the fileSystemView methods that fetch file slices for a given partition, can be made to work as-is.
Something to watch for is code that assumes all files under a logical partition now lives the same folder i.e using `partitionPath + fileName` as the path. Using `BaseFile#fullPath` consistently should help address this
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
Review Comment:
Good explanation. Looks like, in practice this is a soft limit. but this 60 min internal replication period can be very disruptive for big tables.
The other thing I would like to point out here. Hudi writes out well sized files. Assuming 100 MB files, read out fully in 10 seconds, someone must be reading 60 GB/sec or something in aggregate from a bucket to hit throttles for gets.
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
Review Comment:
do other cloud storage throttle as well?
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
+re-trying of failed requests affecting throughput, and result in occasional failures if the retries are not able to
+succeed either and continue to be throttled.
+
+The high level proposal here is to introduce a new storage layout, where all files are distributed evenly across multiple
+randomly generated prefixes under the Amazon S3 bucket, instead of being stored under a common table path/prefix. This
+would help distribute the requests evenly across different prefixes, resulting in Amazon S3 to create partitions for
+the prefixes each with its own request limit. This significantly reduces the possibility of hitting the request limit
+for a specific prefix/partition.
+
+## Design
+
+### Generating file paths
+
+We want to distribute files evenly across multiple random prefixes, instead of following the traditional Hive storage
+layout of keeping them under a common table path/prefix. In addition to the `Table Path`, for this new layout user will
+configure another `Table Storage Path` under which the actual data files will be distributed. The original `Table Path` will
+be used to maintain the table/partitions Hudi metadata.
+
+For the purpose of this documentation lets assume:
+```
+Table Path => s3://<table_bucket>/<hudi_table_name>/
+
+Table Storage Path => s3://<table_storage_bucket>/
+```
+Note: `Table Storage Path` can be a path in the same Amazon S3 bucket or a different bucket. For best results,
+`Table Storage Path` should be a bucket instead of a prefix under the bucket as it allows for S3 to partition sooner.
+
+We will use a Hashing function on the `File Name` to map them to a prefix generated under `Table Storage Path`:
+```
+s3://<table_storage_bucket>/<hash_prefix>/..
+```
+
+In addition, under the hash prefix we will follow a folder structure by appending Hudi Table Name and Partition. This
+folder structuring would be useful if we ever have to do a file system listing to re-create the metadata file list for
+the table (discussed more in the next section). Here is how the final layout would look like for `partitioned` tables:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=usa/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=india/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+...
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=india/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/country=uk/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+For `non-partitioned` tables, this is how it would look:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/4b0c6b40-2ac0-4a1c-a26f-6338aa4db22e-0_6-11-48_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/9320889c-8537-4aa7-a63e-ef088b9a21ce-0_9-11-51_20220301005056692.parquet
+s3://<table_storage_bucket>/0bfb3d6e/<hudi_table_name>/a62aa56b-d55e-4a2b-88a6-d603ef26775c-0_8-11-50_20220301005056692.parquet
+...
+```
+
+The original table path will continue to store the `metadata folder` and `partition metadata` files:
+```
+s3://<table_bucket>/<hudi_table_name>/.hoodie/...
+s3://<table_bucket>/<hudi_table_name>/country=usa/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=india/.hoodie_partition_metadata
+s3://<table_bucket>/<hudi_table_name>/country=uk/.hoodie_partition_metadata
+...
+```
+
+#### Hashing
+
+To generate the prefixes we can use `Murmur 32 bit` hash on the `File Names`, which is known for being fast and provides
+good distribution guarantees. We might have to further do bucketing and re-hash it to reduce the number of possible hashes
+from 2^32 to a slightly lower number, as it may be overkill to have that many unique hashes, which might result in scenarios
+where each file is under a different prefix.
+
+### Maintain mapping to files
+
+In [RFC-15](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427331), we introduced an internal
+Metadata Table with a `files` partition that maintains mapping from partitions to list of files in the partition stored
+under `Table Path`. This mapping is kept up to date, as operations are performed on the original table. We will leverage
+the same to now maintain mappings to files stored at `Table Storage Path` under different prefixes.
+
+Here are some of the design considerations:
+
+1. Metadata table is a pre-requisite and will be automatically enabled if object storage layout is enabled.
+
+2. The object storage layout cannot be enabled on an existing table that is already bootstrapped with Hive storage layout.
Review Comment:
I think its a fair call for the initial cut
##########
rfc/rfc-48/rfc-48.md:
##########
@@ -0,0 +1,171 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+
+# RFC-[48]: Optimized storage layout for Cloud Object Stores
+
+## Proposers
+- @umehrot2
+
+## Approvers
+- @vinoth
+- @shivnarayan
+
+## Status
+
+JIRA: [https://issues.apache.org/jira/browse/HUDI-3625](https://issues.apache.org/jira/browse/HUDI-3625)
+
+## Abstract
+
+As you scale your Apache Hudi workloads over Cloud object stores like Amazon S3, there is potential of hitting request
+throttling limits which in-turn impacts performance. In this RFC, we are proposing to support an alternate storage
+layout that is optimized for Amazon S3 and other cloud object stores, which helps achieve maximum throughput and
+significantly reduce throttling.
+
+## Background
+
+Apache Hudi follows the traditional Hive storage layout while writing files on storage:
+- Partitioned Tables: The files are distributed across multiple physical partition folders, under the table's base path.
+- Non Partitioned Tables: The files are stored directly under the table's base path.
+
+While this storage layout scales well for HDFS, it increases the probability of hitting request throttle limits when
+working with cloud object stores like Amazon S3 and others. This is because Amazon S3 and other cloud stores [throttle
+requests based on object prefix](https://aws.amazon.com/premiumsupport/knowledge-center/s3-request-limit-avoid-throttling/).
+Amazon S3 does scale based on request patterns for different prefixes and adds internal partitions (with their own request limits),
+but there can be a 30 - 60 minute wait time before new partitions are created. Thus, all files/objects stored under the
+same table path prefix could result in these request limits being hit for the table prefix, specially as workloads
+scale, and there are several thousands of files being written/updated concurrently. This hurts performance due to
+re-trying of failed requests affecting throughput, and result in occasional failures if the retries are not able to
+succeed either and continue to be throttled.
+
+The high level proposal here is to introduce a new storage layout, where all files are distributed evenly across multiple
+randomly generated prefixes under the Amazon S3 bucket, instead of being stored under a common table path/prefix. This
+would help distribute the requests evenly across different prefixes, resulting in Amazon S3 to create partitions for
+the prefixes each with its own request limit. This significantly reduces the possibility of hitting the request limit
+for a specific prefix/partition.
+
+## Design
+
+### Generating file paths
+
+We want to distribute files evenly across multiple random prefixes, instead of following the traditional Hive storage
+layout of keeping them under a common table path/prefix. In addition to the `Table Path`, for this new layout user will
+configure another `Table Storage Path` under which the actual data files will be distributed. The original `Table Path` will
+be used to maintain the table/partitions Hudi metadata.
+
+For the purpose of this documentation lets assume:
+```
+Table Path => s3://<table_bucket>/<hudi_table_name>/
+
+Table Storage Path => s3://<table_storage_bucket>/
+```
+Note: `Table Storage Path` can be a path in the same Amazon S3 bucket or a different bucket. For best results,
+`Table Storage Path` should be a bucket instead of a prefix under the bucket as it allows for S3 to partition sooner.
+
+We will use a Hashing function on the `File Name` to map them to a prefix generated under `Table Storage Path`:
+```
+s3://<table_storage_bucket>/<hash_prefix>/..
+```
+
+In addition, under the hash prefix we will follow a folder structure by appending Hudi Table Name and Partition. This
+folder structuring would be useful if we ever have to do a file system listing to re-create the metadata file list for
+the table (discussed more in the next section). Here is how the final layout would look like for `partitioned` tables:
+```
+s3://<table_storage_bucket>/01f50736/<hudi_table_name>/country=usa/075f3295-def8-4a42-a927-07fd2dd2976c-0_7-11-49_20220301005056692.parquet
Review Comment:
what about log files? Should keep it under the same prefix? Can we write the RFC/code with also MOR considered from the start
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