[GitHub] [hudi] yihua commented on a diff in pull request #8093: [HUDI-5886][DOCS] Improve File Sizing, Timeline, and Flink docs

["yihua (via GitHub)" <gi...@apache.org>]

yihua commented on code in PR #8093:
URL: https://github.com/apache/hudi/pull/8093#discussion_r1128405129


##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.
+
+**Note**: the bulk_insert write operation does not have auto-sizing capabilities during ingestion
+
+If you need to customize the file sizing, i.e., increase the target file size or change how small files are identified, follow the instructions below for Copy-On-Write and Merge-On-Read.
+
+### Copy-On-Write (COW)​
+To tune the file sizing for a COW table, you can set the small file limit and the maximum Parquet file size. Hudi will try to add enough records to a small file at write time to get it to the configured maximum limit.
+
+ - For example, if the `hoodie.parquet.small.file.limit=104857600` (100MB) and `hoodie.parquet.max.file.size=125829120` (120MB), Hudi will pick all files < 100MB and try to get them up to 120MB.
+
+For creating a Hudi table initially, setting an accurate record size estimate is vital to ensure Hudi can adequately estimate how many records need to be bin-packed in a Parquet file for the first ingestion batch. Then, Hudi automatically uses the average record size for subsequent writes based on previous commits.
+
+Here are the important configuration of interest:
+
+## Merge-On-Read ​(MOR) 
+As a MOR table aims to reduce the write amplification, compared to a COW table, when writing to a MOR table, Hudi limits the number of Parquet base files to one for auto file sizing during insert and upsert operation. This limits the number of rewritten files. This can be configured through `hoodie.merge.small.file.group.candidates.limit`.
+
+In addition to file sizing Parquet base files for a MOR table, you can also tune the log files file-sizing with `hoodie.logfile.max.size`. 
+
+**NOTE**:  Small files in file groups included in the requested or inflight compaction or clustering under the active timeline, or small files with associated log files are not auto-sized with incoming inserts until the compaction or clustering is complete. For example: 
+
+- In case 1: If you had a log file and a compaction, C1, was scheduled to convert that log file to Parquet, no more inserts can go into the same file slice. 
+
+- In case 2: If the Hudi table has a file group with a Parquet base file and an associated log file from updates, or this file group is under a requested or inflight compaction, no more inserts can go into this file group to automatically size the Parquet file. Only after the compaction has been performed, and there are NO log files associated with the base Parquet file, can new inserts be sent to auto-size that parquet file.
+
+Here are the essential configurations:
+
+| Parameter Name | Default  | Description | Scope | Since Version                          |
+|----------------|--------|----------|---------------|--------------------------------------|
+| hoodie.parquet.small.file.limit | 104857600 (100MB) | During an insert and upsert operation, we opportunistically expand existing small files on storage instead of writing new files to keep the number of files optimum. This config sets the file size limit below which a storage file becomes a candidate to be selected as such a `small file`. By default, treat any file <= 100MB as a small file. Also note that if this is set to <= 0, Hudi will not try to get small files and directly write new files. | Write COW, MOR | 0.4.0 |

Review Comment:
   backticks for configs



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.

Review Comment:
   Add this to the end: "As the cloud storage like S3 enforces [rate-limiting](https://docs.aws.amazon.com/AmazonS3/latest/userguide/optimizing-performance.html) on how many requests can be processed per second per prefix in a bucket, a higher number of files, i.e., at least one request per file regardless of the file size, increases the chance of encountering the rate-limiting, causing the reader to slow down."



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.
+
+**Note**: the bulk_insert write operation does not have auto-sizing capabilities during ingestion
+
+If you need to customize the file sizing, i.e., increase the target file size or change how small files are identified, follow the instructions below for Copy-On-Write and Merge-On-Read.
+
+### Copy-On-Write (COW)​
+To tune the file sizing for a COW table, you can set the small file limit and the maximum Parquet file size. Hudi will try to add enough records to a small file at write time to get it to the configured maximum limit.
+
+ - For example, if the `hoodie.parquet.small.file.limit=104857600` (100MB) and `hoodie.parquet.max.file.size=125829120` (120MB), Hudi will pick all files < 100MB and try to get them up to 120MB.
+
+For creating a Hudi table initially, setting an accurate record size estimate is vital to ensure Hudi can adequately estimate how many records need to be bin-packed in a Parquet file for the first ingestion batch. Then, Hudi automatically uses the average record size for subsequent writes based on previous commits.
+
+Here are the important configuration of interest:
+
+## Merge-On-Read ​(MOR) 
+As a MOR table aims to reduce the write amplification, compared to a COW table, when writing to a MOR table, Hudi limits the number of Parquet base files to one for auto file sizing during insert and upsert operation. This limits the number of rewritten files. This can be configured through `hoodie.merge.small.file.group.candidates.limit`.
+
+In addition to file sizing Parquet base files for a MOR table, you can also tune the log files file-sizing with `hoodie.logfile.max.size`. 
+
+**NOTE**:  Small files in file groups included in the requested or inflight compaction or clustering under the active timeline, or small files with associated log files are not auto-sized with incoming inserts until the compaction or clustering is complete. For example: 
+
+- In case 1: If you had a log file and a compaction, C1, was scheduled to convert that log file to Parquet, no more inserts can go into the same file slice. 
+
+- In case 2: If the Hudi table has a file group with a Parquet base file and an associated log file from updates, or this file group is under a requested or inflight compaction, no more inserts can go into this file group to automatically size the Parquet file. Only after the compaction has been performed, and there are NO log files associated with the base Parquet file, can new inserts be sent to auto-size that parquet file.
+
+Here are the essential configurations:
+
+| Parameter Name | Default  | Description | Scope | Since Version                          |
+|----------------|--------|----------|---------------|--------------------------------------|
+| hoodie.parquet.small.file.limit | 104857600 (100MB) | During an insert and upsert operation, we opportunistically expand existing small files on storage instead of writing new files to keep the number of files optimum. This config sets the file size limit below which a storage file becomes a candidate to be selected as such a `small file`. By default, treat any file <= 100MB as a small file. Also note that if this is set to <= 0, Hudi will not try to get small files and directly write new files. | Write COW, MOR | 0.4.0 |

Review Comment:
   For scope, make them into two lines:
   ```
   Write
   COW, MOR
   ```



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.
+
+**Note**: the bulk_insert write operation does not have auto-sizing capabilities during ingestion
+
+If you need to customize the file sizing, i.e., increase the target file size or change how small files are identified, follow the instructions below for Copy-On-Write and Merge-On-Read.
+
+### Copy-On-Write (COW)​
+To tune the file sizing for a COW table, you can set the small file limit and the maximum Parquet file size. Hudi will try to add enough records to a small file at write time to get it to the configured maximum limit.
+
+ - For example, if the `hoodie.parquet.small.file.limit=104857600` (100MB) and `hoodie.parquet.max.file.size=125829120` (120MB), Hudi will pick all files < 100MB and try to get them up to 120MB.
+
+For creating a Hudi table initially, setting an accurate record size estimate is vital to ensure Hudi can adequately estimate how many records need to be bin-packed in a Parquet file for the first ingestion batch. Then, Hudi automatically uses the average record size for subsequent writes based on previous commits.
+
+Here are the important configuration of interest:
+
+## Merge-On-Read ​(MOR) 
+As a MOR table aims to reduce the write amplification, compared to a COW table, when writing to a MOR table, Hudi limits the number of Parquet base files to one for auto file sizing during insert and upsert operation. This limits the number of rewritten files. This can be configured through `hoodie.merge.small.file.group.candidates.limit`.
+
+In addition to file sizing Parquet base files for a MOR table, you can also tune the log files file-sizing with `hoodie.logfile.max.size`. 
+
+**NOTE**:  Small files in file groups included in the requested or inflight compaction or clustering under the active timeline, or small files with associated log files are not auto-sized with incoming inserts until the compaction or clustering is complete. For example: 
+
+- In case 1: If you had a log file and a compaction, C1, was scheduled to convert that log file to Parquet, no more inserts can go into the same file slice. 
+
+- In case 2: If the Hudi table has a file group with a Parquet base file and an associated log file from updates, or this file group is under a requested or inflight compaction, no more inserts can go into this file group to automatically size the Parquet file. Only after the compaction has been performed, and there are NO log files associated with the base Parquet file, can new inserts be sent to auto-size that parquet file.
+
+Here are the essential configurations:
+
+| Parameter Name | Default  | Description | Scope | Since Version                          |
+|----------------|--------|----------|---------------|--------------------------------------|
+| hoodie.parquet.small.file.limit | 104857600 (100MB) | During an insert and upsert operation, we opportunistically expand existing small files on storage instead of writing new files to keep the number of files optimum. This config sets the file size limit below which a storage file becomes a candidate to be selected as such a `small file`. By default, treat any file <= 100MB as a small file. Also note that if this is set to <= 0, Hudi will not try to get small files and directly write new files. | Write COW, MOR | 0.4.0 |
+| hoodie.parquet.max.file.size |125829120 (120MB) | This config is the target size in bytes for parquet files produced by the Hudi write phases. For DFS, this needs to be aligned with the underlying filesystem block size for optimal performance.  | Write COW, MOR  | 0.4.0 | 
+| hoodie.logfile.max.size | 1073741824 (1GB) | This is the log file max size in bytes. This is the maximum size allowed for a log file before it is rolled over to the next version. | Write MOR  | 0.4.0 | 
+| hoodie.merge.small.file.group.candidates.limit | 1 | This limits the number of file groups, whose base file satisfies the small-file limit to be considered for appending records during an upsert operation. This is only applicable for MOR tables. | Write MOR | 0.4.0 |
+
+
+## Auto-Sizing With Clustering​
+[Clustering](https://hudi.apache.org/docs/next/clustering) is a service that allows you to combine small files into larger ones while at the same time (optionally) changing the data layout by sorting or applying space-filling curves like Z-order or Hilbert curve. We won’t go into all the details about clustering here, but please refer to the [clustering section](https://hudi.apache.org/docs/next/clustering) for more details. 
+
+Clustering is very handy for file sizing so you can have faster queries. When you ingest data, you may still have a lot of small files (depending on your configurations and the data size from ingestion i.e., input batch). In this case, you will want to cluster all the small files to larger files in one write operation to improve query performance. Setting configs for this use case is unnecessary, other than running a job (i.e., Spark job). Optionally, you can customize the file sizing using the configs down below. 
+An example where clustering might be very useful is when a user has a Hudi table with many small files. Then, instead of waiting for multiple ingestion batches to gradually auto-size files, a user can use the clustering service to fix all the file sizes without ingesting any new data.
+
+Please note clustering in Hudi is not a blocking operation, and ingestion can continue concurrently as long as no files need to be updated while the clustering service is running. The writes will fail if files need to be updated while the clustering service runs.
+
+Here are the critical file sizing configurations:
+
+| Parameter Name | Default  | Description | Scope | Since Version                          |
+|----------------|--------|----------|---------------|--------------------------------------|
+| hoodie.clustering.plan.strategy.small.file.limit | 314572800 (300MB) | Files smaller than the size in bytes specified here are candidates for clustering. | Clustering | 0.7.0 |
+| target.file.max.bytes |1073741824 (1GB) | This configures the target file size in bytes for clustering.| Clustering  | 0.7.0 |
+
+*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will always create a newer version of the smaller file, resulting in 2 versions of the same file. The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*

Review Comment:
   move to note box



##########
website/docs/timeline.md:
##########
@@ -3,40 +3,384 @@ title: Timeline
 toc: true
 ---
 
-## Timeline
-At its core, Hudi maintains a `timeline` of all actions performed on the table at different `instants` of time that helps provide instantaneous views of the table,
-while also efficiently supporting retrieval of data in the order of arrival. A Hudi instant consists of the following components
+A Hudi table maintains all operations happened to the table in a single timeline comprised of two parts, an active timeline and an archived timeline. The active timeline stores all the recent instant, while the archive timeline stores the older instants. An instant is a transaction where all respective partitions within a base path have been successfully updated by either a writer or a table service. Instants that get older in the active timeline are moved to archived timeline at various times.
 
-* `Instant action` : Type of action performed on the table
-* `Instant time` : Instant time is typically a timestamp (e.g: 20190117010349), which monotonically increases in the order of action's begin time.
-* `state` : current state of the instant
+An instant can alter one or many partitions:
 
-Hudi guarantees that the actions performed on the timeline are atomic & timeline consistent based on the instant time.
+-   If you have one batch ingestion, you’ll see that as one commit in the active timeline. When you open that commit file, you’ll see a JSON object with metadata about how one or more partitions were altered.
+    
+-   If you’re ingesting streaming data, you might see multiple commits in the active timeline. In this case, when you open a commit file, you might see metadata about how one or more partition files were altered.

Review Comment:
   Could we combine this two?  The second sentence is almost the same.
   
   """
   If you have one batch ingestion, you’ll see that as one commit in the active timeline.  If you ingest streaming data, you might see multiple commits in the active timeline.  When you open one commit file, you’ll see a JSON object with metadata about how one or more partitions were altered.
   """



##########
website/docs/timeline.md:
##########
@@ -3,40 +3,384 @@ title: Timeline
 toc: true
 ---
 
-## Timeline
-At its core, Hudi maintains a `timeline` of all actions performed on the table at different `instants` of time that helps provide instantaneous views of the table,
-while also efficiently supporting retrieval of data in the order of arrival. A Hudi instant consists of the following components
+A Hudi table maintains all operations happened to the table in a single timeline comprised of two parts, an active timeline and an archived timeline. The active timeline stores all the recent instant, while the archive timeline stores the older instants. An instant is a transaction where all respective partitions within a base path have been successfully updated by either a writer or a table service. Instants that get older in the active timeline are moved to archived timeline at various times.
 
-* `Instant action` : Type of action performed on the table
-* `Instant time` : Instant time is typically a timestamp (e.g: 20190117010349), which monotonically increases in the order of action's begin time.
-* `state` : current state of the instant
+An instant can alter one or many partitions:
 
-Hudi guarantees that the actions performed on the timeline are atomic & timeline consistent based on the instant time.
+-   If you have one batch ingestion, you’ll see that as one commit in the active timeline. When you open that commit file, you’ll see a JSON object with metadata about how one or more partitions were altered.
+    
+-   If you’re ingesting streaming data, you might see multiple commits in the active timeline. In this case, when you open a commit file, you might see metadata about how one or more partition files were altered.
 
-Key actions performed include
+We’ll go over some details and concepts about the active and archived timeline below. All files in the timelines are immutable.
 
-* `COMMITS` - A commit denotes an **atomic write** of a batch of records into a table.
-* `CLEANS` - Background activity that gets rid of older versions of files in the table, that are no longer needed.
-* `DELTA_COMMIT` - A delta commit refers to an **atomic write** of a batch of records into a  MergeOnRead type table, where some/all of the data could be just written to delta logs.
-* `COMPACTION` - Background activity to reconcile differential data structures within Hudi e.g: moving updates from row based log files to columnar formats. Internally, compaction manifests as a special commit on the timeline
-* `ROLLBACK` - Indicates that a commit/delta commit was unsuccessful & rolled back, removing any partial files produced during such a write
-* `SAVEPOINT` - Marks certain file groups as "saved", such that cleaner will not delete them. It helps restore the table to a point on the timeline, in case of disaster/data recovery scenarios.
+**Note**: The user should never directly alter the timeline (i.e. manually delete the commits).
 
-Any given instant can be
-in one of the following states
+## Active Timeline
 
-* `REQUESTED` - Denotes an action has been scheduled, but has not initiated
-* `INFLIGHT` - Denotes that the action is currently being performed
-* `COMPLETED` - Denotes completion of an action on the timeline
+The active timeline is a source of truth for all write operations: when an action (described below) happens on a table, the timeline is responsible for recording it. This guarantees a good table state, and Hudi can provide read/write isolation based on the timeline. For example, when data is being written to a Hudi table (i.e., requested, inflight), any data being written as part of the transaction is not visible to a query engine until the write transaction is completed. The query engine can still read older data, but the data inflight won’t be exposed.
 
-<figure>
-    <img className="docimage" src={require("/assets/images/hudi_timeline.png").default} alt="hudi_timeline.png" />
-</figure>
+The active timeline is under the `.hoodie` metadata folder. For example, when you navigate to your Hudi project directory:
 
-Example above shows upserts happenings between 10:00 and 10:20 on a Hudi table, roughly every 5 mins, leaving commit metadata on the Hudi timeline, along
-with other background cleaning/compactions. One key observation to make is that the commit time indicates the `arrival time` of the data (10:20AM), while the actual data
-organization reflects the actual time or `event time`, the data was intended for (hourly buckets from 07:00). These are two key concepts when reasoning about tradeoffs between latency and completeness of data.
+```sh
+cd $YOUR_HUDI_PROJECT_DIRECTORY && ls -a 
+```
+
+You’ll see the `.hoodie` metadata folder:
+
+```sh
+ls -a
+.		..		.hoodie		americas	asia
+```
+
+When you navigate inside the `.hoodie` folder, you’ll see a lot of files with different suffixes and the archived timeline folder: 
+
+```sh
+cd .hoodie && ls
+2023021018095339.commit
+20230210180953939.commit.requested	
+20230210180953939.inflight
+archived
+```
+
+Before we go into what’s in the files or how the files are named, we’ll need to cover some broader concepts: 
+- actions
+-  states
+- instants

Review Comment:
   Add links to sections/subsections?



##########
website/docs/timeline.md:
##########
@@ -3,40 +3,384 @@ title: Timeline
 toc: true
 ---
 
-## Timeline
-At its core, Hudi maintains a `timeline` of all actions performed on the table at different `instants` of time that helps provide instantaneous views of the table,
-while also efficiently supporting retrieval of data in the order of arrival. A Hudi instant consists of the following components
+A Hudi table maintains all operations happened to the table in a single timeline comprised of two parts, an active timeline and an archived timeline. The active timeline stores all the recent instant, while the archive timeline stores the older instants. An instant is a transaction where all respective partitions within a base path have been successfully updated by either a writer or a table service. Instants that get older in the active timeline are moved to archived timeline at various times.
 
-* `Instant action` : Type of action performed on the table
-* `Instant time` : Instant time is typically a timestamp (e.g: 20190117010349), which monotonically increases in the order of action's begin time.
-* `state` : current state of the instant
+An instant can alter one or many partitions:
 
-Hudi guarantees that the actions performed on the timeline are atomic & timeline consistent based on the instant time.
+-   If you have one batch ingestion, you’ll see that as one commit in the active timeline. When you open that commit file, you’ll see a JSON object with metadata about how one or more partitions were altered.
+    
+-   If you’re ingesting streaming data, you might see multiple commits in the active timeline. In this case, when you open a commit file, you might see metadata about how one or more partition files were altered.
 
-Key actions performed include
+We’ll go over some details and concepts about the active and archived timeline below. All files in the timelines are immutable.
 
-* `COMMITS` - A commit denotes an **atomic write** of a batch of records into a table.
-* `CLEANS` - Background activity that gets rid of older versions of files in the table, that are no longer needed.
-* `DELTA_COMMIT` - A delta commit refers to an **atomic write** of a batch of records into a  MergeOnRead type table, where some/all of the data could be just written to delta logs.
-* `COMPACTION` - Background activity to reconcile differential data structures within Hudi e.g: moving updates from row based log files to columnar formats. Internally, compaction manifests as a special commit on the timeline
-* `ROLLBACK` - Indicates that a commit/delta commit was unsuccessful & rolled back, removing any partial files produced during such a write
-* `SAVEPOINT` - Marks certain file groups as "saved", such that cleaner will not delete them. It helps restore the table to a point on the timeline, in case of disaster/data recovery scenarios.
+**Note**: The user should never directly alter the timeline (i.e. manually delete the commits).
 
-Any given instant can be
-in one of the following states
+## Active Timeline
 
-* `REQUESTED` - Denotes an action has been scheduled, but has not initiated
-* `INFLIGHT` - Denotes that the action is currently being performed
-* `COMPLETED` - Denotes completion of an action on the timeline
+The active timeline is a source of truth for all write operations: when an action (described below) happens on a table, the timeline is responsible for recording it. This guarantees a good table state, and Hudi can provide read/write isolation based on the timeline. For example, when data is being written to a Hudi table (i.e., requested, inflight), any data being written as part of the transaction is not visible to a query engine until the write transaction is completed. The query engine can still read older data, but the data inflight won’t be exposed.
 
-<figure>
-    <img className="docimage" src={require("/assets/images/hudi_timeline.png").default} alt="hudi_timeline.png" />
-</figure>
+The active timeline is under the `.hoodie` metadata folder. For example, when you navigate to your Hudi project directory:
 
-Example above shows upserts happenings between 10:00 and 10:20 on a Hudi table, roughly every 5 mins, leaving commit metadata on the Hudi timeline, along
-with other background cleaning/compactions. One key observation to make is that the commit time indicates the `arrival time` of the data (10:20AM), while the actual data
-organization reflects the actual time or `event time`, the data was intended for (hourly buckets from 07:00). These are two key concepts when reasoning about tradeoffs between latency and completeness of data.
+```sh
+cd $YOUR_HUDI_PROJECT_DIRECTORY && ls -a 
+```
+
+You’ll see the `.hoodie` metadata folder:
+
+```sh
+ls -a
+.		..		.hoodie		americas	asia
+```
+
+When you navigate inside the `.hoodie` folder, you’ll see a lot of files with different suffixes and the archived timeline folder: 
+
+```sh
+cd .hoodie && ls
+2023021018095339.commit
+20230210180953939.commit.requested	
+20230210180953939.inflight
+archived
+```
+
+Before we go into what’s in the files or how the files are named, we’ll need to cover some broader concepts: 
+- actions
+-  states
+- instants
+
+## Actions
+An action describes what and how transactions were changed. Hudi guarantees that the actions performed on the timeline are atomic & consistent based on the instant time.

Review Comment:
   "&" -> "and"



##########
website/docs/flink_configuration.md:
##########
@@ -3,115 +3,179 @@ title: Flink Setup
 toc: true
 ---
 
-## Global Configurations
-When using Flink, you can set some global configurations in `$FLINK_HOME/conf/flink-conf.yaml`
+[Apache Flink](https://flink.apache.org/what-is-flink/flink-architecture/) is a powerful streaming-batch integrated engine that provides a stream processing framework. Flink can process events at an incredible speed with low latency. Along with Hudi, users can use streaming ingestion like with Kafka; streaming consumption like with Kafka; and also perform batch workloads like bulk ingest, snapshot queries and incremental queries. 
 
-### Parallelism
-
-|  Option Name  | Default | Type | Description |
-|  -----------  | -------  | ------- | ------- |
-| `taskmanager.numberOfTaskSlots` | `1` | `Integer` | The number of parallel operator or user function instances that a single TaskManager can run. We recommend setting this value > 4, and the actual value needs to be set according to the amount of data |
-| `parallelism.default` | `1` | `Integer` | The default parallelism used when no parallelism is specified anywhere (default: 1). For example, If the value of [`write.bucket_assign.tasks`](#parallelism-1) is not set, this value will be used |
+There are three executions modes a user can configure for Flink:
+- Streaming 

Review Comment:
   +1



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.
+
+**Note**: the bulk_insert write operation does not have auto-sizing capabilities during ingestion

Review Comment:
   use the following box for note:
   ```
   :::note
   The bulk_insert write operation does not have auto-sizing capabilities during ingestion.
   :::
   ```
   <img width="994" alt="Screenshot 2023-03-07 at 10 52 09" src="https://user-images.githubusercontent.com/2497195/223522409-83207266-9c0d-4aca-b24e-4821592a78e2.png">
   
   



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.

Review Comment:
   code block for `hoodie.parquet.max.file.size` by adding back ticks "`"



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.
+
+**Note**: the bulk_insert write operation does not have auto-sizing capabilities during ingestion
+
+If you need to customize the file sizing, i.e., increase the target file size or change how small files are identified, follow the instructions below for Copy-On-Write and Merge-On-Read.
+
+### Copy-On-Write (COW)​
+To tune the file sizing for a COW table, you can set the small file limit and the maximum Parquet file size. Hudi will try to add enough records to a small file at write time to get it to the configured maximum limit.
+
+ - For example, if the `hoodie.parquet.small.file.limit=104857600` (100MB) and `hoodie.parquet.max.file.size=125829120` (120MB), Hudi will pick all files < 100MB and try to get them up to 120MB.
+
+For creating a Hudi table initially, setting an accurate record size estimate is vital to ensure Hudi can adequately estimate how many records need to be bin-packed in a Parquet file for the first ingestion batch. Then, Hudi automatically uses the average record size for subsequent writes based on previous commits.
+
+Here are the important configuration of interest:
+
+## Merge-On-Read ​(MOR) 
+As a MOR table aims to reduce the write amplification, compared to a COW table, when writing to a MOR table, Hudi limits the number of Parquet base files to one for auto file sizing during insert and upsert operation. This limits the number of rewritten files. This can be configured through `hoodie.merge.small.file.group.candidates.limit`.
+
+In addition to file sizing Parquet base files for a MOR table, you can also tune the log files file-sizing with `hoodie.logfile.max.size`. 
+
+**NOTE**:  Small files in file groups included in the requested or inflight compaction or clustering under the active timeline, or small files with associated log files are not auto-sized with incoming inserts until the compaction or clustering is complete. For example: 
+
+- In case 1: If you had a log file and a compaction, C1, was scheduled to convert that log file to Parquet, no more inserts can go into the same file slice. 
+
+- In case 2: If the Hudi table has a file group with a Parquet base file and an associated log file from updates, or this file group is under a requested or inflight compaction, no more inserts can go into this file group to automatically size the Parquet file. Only after the compaction has been performed, and there are NO log files associated with the base Parquet file, can new inserts be sent to auto-size that parquet file.
+
+Here are the essential configurations:
+
+| Parameter Name | Default  | Description | Scope | Since Version                          |

Review Comment:
   "Parameter Name" -> "Property Name"



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.
+
+**Note**: the bulk_insert write operation does not have auto-sizing capabilities during ingestion
+
+If you need to customize the file sizing, i.e., increase the target file size or change how small files are identified, follow the instructions below for Copy-On-Write and Merge-On-Read.
+
+### Copy-On-Write (COW)​
+To tune the file sizing for a COW table, you can set the small file limit and the maximum Parquet file size. Hudi will try to add enough records to a small file at write time to get it to the configured maximum limit.
+
+ - For example, if the `hoodie.parquet.small.file.limit=104857600` (100MB) and `hoodie.parquet.max.file.size=125829120` (120MB), Hudi will pick all files < 100MB and try to get them up to 120MB.
+
+For creating a Hudi table initially, setting an accurate record size estimate is vital to ensure Hudi can adequately estimate how many records need to be bin-packed in a Parquet file for the first ingestion batch. Then, Hudi automatically uses the average record size for subsequent writes based on previous commits.
+
+Here are the important configuration of interest:
+
+## Merge-On-Read ​(MOR) 
+As a MOR table aims to reduce the write amplification, compared to a COW table, when writing to a MOR table, Hudi limits the number of Parquet base files to one for auto file sizing during insert and upsert operation. This limits the number of rewritten files. This can be configured through `hoodie.merge.small.file.group.candidates.limit`.
+
+In addition to file sizing Parquet base files for a MOR table, you can also tune the log files file-sizing with `hoodie.logfile.max.size`. 
+
+**NOTE**:  Small files in file groups included in the requested or inflight compaction or clustering under the active timeline, or small files with associated log files are not auto-sized with incoming inserts until the compaction or clustering is complete. For example: 
+
+- In case 1: If you had a log file and a compaction, C1, was scheduled to convert that log file to Parquet, no more inserts can go into the same file slice. 
+
+- In case 2: If the Hudi table has a file group with a Parquet base file and an associated log file from updates, or this file group is under a requested or inflight compaction, no more inserts can go into this file group to automatically size the Parquet file. Only after the compaction has been performed, and there are NO log files associated with the base Parquet file, can new inserts be sent to auto-size that parquet file.
+
+Here are the essential configurations:
+
+| Parameter Name | Default  | Description | Scope | Since Version                          |
+|----------------|--------|----------|---------------|--------------------------------------|
+| hoodie.parquet.small.file.limit | 104857600 (100MB) | During an insert and upsert operation, we opportunistically expand existing small files on storage instead of writing new files to keep the number of files optimum. This config sets the file size limit below which a storage file becomes a candidate to be selected as such a `small file`. By default, treat any file <= 100MB as a small file. Also note that if this is set to <= 0, Hudi will not try to get small files and directly write new files. | Write COW, MOR | 0.4.0 |
+| hoodie.parquet.max.file.size |125829120 (120MB) | This config is the target size in bytes for parquet files produced by the Hudi write phases. For DFS, this needs to be aligned with the underlying filesystem block size for optimal performance.  | Write COW, MOR  | 0.4.0 | 
+| hoodie.logfile.max.size | 1073741824 (1GB) | This is the log file max size in bytes. This is the maximum size allowed for a log file before it is rolled over to the next version. | Write MOR  | 0.4.0 | 
+| hoodie.merge.small.file.group.candidates.limit | 1 | This limits the number of file groups, whose base file satisfies the small-file limit to be considered for appending records during an upsert operation. This is only applicable for MOR tables. | Write MOR | 0.4.0 |
+
+
+## Auto-Sizing With Clustering​
+[Clustering](https://hudi.apache.org/docs/next/clustering) is a service that allows you to combine small files into larger ones while at the same time (optionally) changing the data layout by sorting or applying space-filling curves like Z-order or Hilbert curve. We won’t go into all the details about clustering here, but please refer to the [clustering section](https://hudi.apache.org/docs/next/clustering) for more details. 
+
+Clustering is very handy for file sizing so you can have faster queries. When you ingest data, you may still have a lot of small files (depending on your configurations and the data size from ingestion i.e., input batch). In this case, you will want to cluster all the small files to larger files in one write operation to improve query performance. Setting configs for this use case is unnecessary, other than running a job (i.e., Spark job). Optionally, you can customize the file sizing using the configs down below. 
+An example where clustering might be very useful is when a user has a Hudi table with many small files. Then, instead of waiting for multiple ingestion batches to gradually auto-size files, a user can use the clustering service to fix all the file sizes without ingesting any new data.
+
+Please note clustering in Hudi is not a blocking operation, and ingestion can continue concurrently as long as no files need to be updated while the clustering service is running. The writes will fail if files need to be updated while the clustering service runs.

Review Comment:
   "files need to be updated" -> "there are updates to the data to be clustered"



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.
+
+**Note**: the bulk_insert write operation does not have auto-sizing capabilities during ingestion
+
+If you need to customize the file sizing, i.e., increase the target file size or change how small files are identified, follow the instructions below for Copy-On-Write and Merge-On-Read.
+
+### Copy-On-Write (COW)​
+To tune the file sizing for a COW table, you can set the small file limit and the maximum Parquet file size. Hudi will try to add enough records to a small file at write time to get it to the configured maximum limit.
+
+ - For example, if the `hoodie.parquet.small.file.limit=104857600` (100MB) and `hoodie.parquet.max.file.size=125829120` (120MB), Hudi will pick all files < 100MB and try to get them up to 120MB.
+
+For creating a Hudi table initially, setting an accurate record size estimate is vital to ensure Hudi can adequately estimate how many records need to be bin-packed in a Parquet file for the first ingestion batch. Then, Hudi automatically uses the average record size for subsequent writes based on previous commits.
+
+Here are the important configuration of interest:
+
+## Merge-On-Read ​(MOR) 
+As a MOR table aims to reduce the write amplification, compared to a COW table, when writing to a MOR table, Hudi limits the number of Parquet base files to one for auto file sizing during insert and upsert operation. This limits the number of rewritten files. This can be configured through `hoodie.merge.small.file.group.candidates.limit`.
+
+In addition to file sizing Parquet base files for a MOR table, you can also tune the log files file-sizing with `hoodie.logfile.max.size`. 
+
+**NOTE**:  Small files in file groups included in the requested or inflight compaction or clustering under the active timeline, or small files with associated log files are not auto-sized with incoming inserts until the compaction or clustering is complete. For example: 
+
+- In case 1: If you had a log file and a compaction, C1, was scheduled to convert that log file to Parquet, no more inserts can go into the same file slice. 
+
+- In case 2: If the Hudi table has a file group with a Parquet base file and an associated log file from updates, or this file group is under a requested or inflight compaction, no more inserts can go into this file group to automatically size the Parquet file. Only after the compaction has been performed, and there are NO log files associated with the base Parquet file, can new inserts be sent to auto-size that parquet file.
+
+Here are the essential configurations:
+
+| Parameter Name | Default  | Description | Scope | Since Version                          |
+|----------------|--------|----------|---------------|--------------------------------------|
+| hoodie.parquet.small.file.limit | 104857600 (100MB) | During an insert and upsert operation, we opportunistically expand existing small files on storage instead of writing new files to keep the number of files optimum. This config sets the file size limit below which a storage file becomes a candidate to be selected as such a `small file`. By default, treat any file <= 100MB as a small file. Also note that if this is set to <= 0, Hudi will not try to get small files and directly write new files. | Write COW, MOR | 0.4.0 |
+| hoodie.parquet.max.file.size |125829120 (120MB) | This config is the target size in bytes for parquet files produced by the Hudi write phases. For DFS, this needs to be aligned with the underlying filesystem block size for optimal performance.  | Write COW, MOR  | 0.4.0 | 
+| hoodie.logfile.max.size | 1073741824 (1GB) | This is the log file max size in bytes. This is the maximum size allowed for a log file before it is rolled over to the next version. | Write MOR  | 0.4.0 | 
+| hoodie.merge.small.file.group.candidates.limit | 1 | This limits the number of file groups, whose base file satisfies the small-file limit to be considered for appending records during an upsert operation. This is only applicable for MOR tables. | Write MOR | 0.4.0 |
+
+
+## Auto-Sizing With Clustering​
+[Clustering](https://hudi.apache.org/docs/next/clustering) is a service that allows you to combine small files into larger ones while at the same time (optionally) changing the data layout by sorting or applying space-filling curves like Z-order or Hilbert curve. We won’t go into all the details about clustering here, but please refer to the [clustering section](https://hudi.apache.org/docs/next/clustering) for more details. 
+
+Clustering is very handy for file sizing so you can have faster queries. When you ingest data, you may still have a lot of small files (depending on your configurations and the data size from ingestion i.e., input batch). In this case, you will want to cluster all the small files to larger files in one write operation to improve query performance. Setting configs for this use case is unnecessary, other than running a job (i.e., Spark job). Optionally, you can customize the file sizing using the configs down below. 
+An example where clustering might be very useful is when a user has a Hudi table with many small files. Then, instead of waiting for multiple ingestion batches to gradually auto-size files, a user can use the clustering service to fix all the file sizes without ingesting any new data.
+
+Please note clustering in Hudi is not a blocking operation, and ingestion can continue concurrently as long as no files need to be updated while the clustering service is running. The writes will fail if files need to be updated while the clustering service runs.
+
+Here are the critical file sizing configurations:
+
+| Parameter Name | Default  | Description | Scope | Since Version                          |
+|----------------|--------|----------|---------------|--------------------------------------|
+| hoodie.clustering.plan.strategy.small.file.limit | 314572800 (300MB) | Files smaller than the size in bytes specified here are candidates for clustering. | Clustering | 0.7.0 |

Review Comment:
   similar for property name and backticks.  Fix these for all config tables.



##########
website/docs/timeline.md:
##########
@@ -3,40 +3,384 @@ title: Timeline
 toc: true
 ---
 
-## Timeline
-At its core, Hudi maintains a `timeline` of all actions performed on the table at different `instants` of time that helps provide instantaneous views of the table,
-while also efficiently supporting retrieval of data in the order of arrival. A Hudi instant consists of the following components
+A Hudi table maintains all operations happened to the table in a single timeline comprised of two parts, an active timeline and an archived timeline. The active timeline stores all the recent instant, while the archive timeline stores the older instants. An instant is a transaction where all respective partitions within a base path have been successfully updated by either a writer or a table service. Instants that get older in the active timeline are moved to archived timeline at various times.
 
-* `Instant action` : Type of action performed on the table
-* `Instant time` : Instant time is typically a timestamp (e.g: 20190117010349), which monotonically increases in the order of action's begin time.
-* `state` : current state of the instant
+An instant can alter one or many partitions:
 
-Hudi guarantees that the actions performed on the timeline are atomic & timeline consistent based on the instant time.
+-   If you have one batch ingestion, you’ll see that as one commit in the active timeline. When you open that commit file, you’ll see a JSON object with metadata about how one or more partitions were altered.
+    
+-   If you’re ingesting streaming data, you might see multiple commits in the active timeline. In this case, when you open a commit file, you might see metadata about how one or more partition files were altered.
 
-Key actions performed include
+We’ll go over some details and concepts about the active and archived timeline below. All files in the timelines are immutable.
 
-* `COMMITS` - A commit denotes an **atomic write** of a batch of records into a table.
-* `CLEANS` - Background activity that gets rid of older versions of files in the table, that are no longer needed.
-* `DELTA_COMMIT` - A delta commit refers to an **atomic write** of a batch of records into a  MergeOnRead type table, where some/all of the data could be just written to delta logs.
-* `COMPACTION` - Background activity to reconcile differential data structures within Hudi e.g: moving updates from row based log files to columnar formats. Internally, compaction manifests as a special commit on the timeline
-* `ROLLBACK` - Indicates that a commit/delta commit was unsuccessful & rolled back, removing any partial files produced during such a write
-* `SAVEPOINT` - Marks certain file groups as "saved", such that cleaner will not delete them. It helps restore the table to a point on the timeline, in case of disaster/data recovery scenarios.
+**Note**: The user should never directly alter the timeline (i.e. manually delete the commits).

Review Comment:
   use caution block:
   ```
   :::caution
   xyz
   :::
   ```



##########
website/docs/timeline.md:
##########
@@ -3,40 +3,384 @@ title: Timeline
 toc: true
 ---
 
-## Timeline
-At its core, Hudi maintains a `timeline` of all actions performed on the table at different `instants` of time that helps provide instantaneous views of the table,
-while also efficiently supporting retrieval of data in the order of arrival. A Hudi instant consists of the following components
+A Hudi table maintains all operations happened to the table in a single timeline comprised of two parts, an active timeline and an archived timeline. The active timeline stores all the recent instant, while the archive timeline stores the older instants. An instant is a transaction where all respective partitions within a base path have been successfully updated by either a writer or a table service. Instants that get older in the active timeline are moved to archived timeline at various times.
 
-* `Instant action` : Type of action performed on the table
-* `Instant time` : Instant time is typically a timestamp (e.g: 20190117010349), which monotonically increases in the order of action's begin time.
-* `state` : current state of the instant
+An instant can alter one or many partitions:
 
-Hudi guarantees that the actions performed on the timeline are atomic & timeline consistent based on the instant time.
+-   If you have one batch ingestion, you’ll see that as one commit in the active timeline. When you open that commit file, you’ll see a JSON object with metadata about how one or more partitions were altered.
+    
+-   If you’re ingesting streaming data, you might see multiple commits in the active timeline. In this case, when you open a commit file, you might see metadata about how one or more partition files were altered.
 
-Key actions performed include
+We’ll go over some details and concepts about the active and archived timeline below. All files in the timelines are immutable.
 
-* `COMMITS` - A commit denotes an **atomic write** of a batch of records into a table.
-* `CLEANS` - Background activity that gets rid of older versions of files in the table, that are no longer needed.
-* `DELTA_COMMIT` - A delta commit refers to an **atomic write** of a batch of records into a  MergeOnRead type table, where some/all of the data could be just written to delta logs.
-* `COMPACTION` - Background activity to reconcile differential data structures within Hudi e.g: moving updates from row based log files to columnar formats. Internally, compaction manifests as a special commit on the timeline
-* `ROLLBACK` - Indicates that a commit/delta commit was unsuccessful & rolled back, removing any partial files produced during such a write
-* `SAVEPOINT` - Marks certain file groups as "saved", such that cleaner will not delete them. It helps restore the table to a point on the timeline, in case of disaster/data recovery scenarios.
+**Note**: The user should never directly alter the timeline (i.e. manually delete the commits).
 
-Any given instant can be
-in one of the following states
+## Active Timeline
 
-* `REQUESTED` - Denotes an action has been scheduled, but has not initiated
-* `INFLIGHT` - Denotes that the action is currently being performed
-* `COMPLETED` - Denotes completion of an action on the timeline
+The active timeline is a source of truth for all write operations: when an action (described below) happens on a table, the timeline is responsible for recording it. This guarantees a good table state, and Hudi can provide read/write isolation based on the timeline. For example, when data is being written to a Hudi table (i.e., requested, inflight), any data being written as part of the transaction is not visible to a query engine until the write transaction is completed. The query engine can still read older data, but the data inflight won’t be exposed.
 
-<figure>
-    <img className="docimage" src={require("/assets/images/hudi_timeline.png").default} alt="hudi_timeline.png" />
-</figure>
+The active timeline is under the `.hoodie` metadata folder. For example, when you navigate to your Hudi project directory:
 
-Example above shows upserts happenings between 10:00 and 10:20 on a Hudi table, roughly every 5 mins, leaving commit metadata on the Hudi timeline, along
-with other background cleaning/compactions. One key observation to make is that the commit time indicates the `arrival time` of the data (10:20AM), while the actual data
-organization reflects the actual time or `event time`, the data was intended for (hourly buckets from 07:00). These are two key concepts when reasoning about tradeoffs between latency and completeness of data.
+```sh
+cd $YOUR_HUDI_PROJECT_DIRECTORY && ls -a 
+```
+
+You’ll see the `.hoodie` metadata folder:
+
+```sh
+ls -a
+.		..		.hoodie		americas	asia
+```
+
+When you navigate inside the `.hoodie` folder, you’ll see a lot of files with different suffixes and the archived timeline folder: 
+
+```sh
+cd .hoodie && ls
+2023021018095339.commit
+20230210180953939.commit.requested	
+20230210180953939.inflight
+archived
+```
+
+Before we go into what’s in the files or how the files are named, we’ll need to cover some broader concepts: 
+- actions
+-  states
+- instants
+
+## Actions

Review Comment:
   The concept of action, state, and instant is common across the active timeline and archived timeline.  Shall we move them up to level 1 before discussing the active timeline and archived timeline?
   
   Some descriptions on the layout, e.g., the filenames, are specific to active timeline.
   



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 
+
+All these challenges inevitably lead to stale analytics and scalability challenges:
+- Query performance slows down.
+- Jobs could be running faster.
+- You’re utilizing way more resources. 
+
+A critical design decision in the Hudi architecture is to avoid small file creation. Hudi is uniquely designed to write appropriately sized files automatically. This document will show you how Apache Hudi overcomes the dreaded small files problem. There are two ways to manage small files in Hudi: 
+
+- Auto-size during ingestion
+- Clustering
+
+Below, we will describe the advantages and trade-offs of each.
+
+## Auto-sizing during ingestion​
+
+You can manage file sizes through Hudi’s auto-sizing capability during ingestion. The default targeted file size for Parquet base files is 120MB, which can be configured by hoodie.parquet.max.file.size. Auto-sizing may add some data latency, but it ensures that the read queries are always efficient as soon as a write transaction is committed. It’s important to note that if you don’t manage file sizing as you write and,  instead, try to run clustering to fix your file sizing periodically, your queries might be slow until the point when the clustering finishes.
+
+**Note**: the bulk_insert write operation does not have auto-sizing capabilities during ingestion
+
+If you need to customize the file sizing, i.e., increase the target file size or change how small files are identified, follow the instructions below for Copy-On-Write and Merge-On-Read.
+
+### Copy-On-Write (COW)​
+To tune the file sizing for a COW table, you can set the small file limit and the maximum Parquet file size. Hudi will try to add enough records to a small file at write time to get it to the configured maximum limit.
+
+ - For example, if the `hoodie.parquet.small.file.limit=104857600` (100MB) and `hoodie.parquet.max.file.size=125829120` (120MB), Hudi will pick all files < 100MB and try to get them up to 120MB.
+
+For creating a Hudi table initially, setting an accurate record size estimate is vital to ensure Hudi can adequately estimate how many records need to be bin-packed in a Parquet file for the first ingestion batch. Then, Hudi automatically uses the average record size for subsequent writes based on previous commits.
+
+Here are the important configuration of interest:

Review Comment:
   @nfarah86 You should use the markdown to add the config tables.



##########
website/docs/file_sizing.md:
##########
@@ -3,51 +3,76 @@ title: "File Sizing"
 toc: true
 ---
 
-This doc will show you how Apache Hudi overcomes the dreaded small files problem. A key design decision in Hudi was to 
-avoid creating small files in the first place and always write properly sized files. 
-There are 2 ways to manage small files in Hudi and below will describe the advantages and trade-offs of each.
-
-## Auto-Size During ingestion
-
-You can automatically manage size of files during ingestion. This solution adds a little latency during ingestion, but
-it ensures that read queries are always efficient as soon as a write is committed. If you don't 
-manage file sizing as you write and instead try to periodically run a file-sizing clean-up, your queries will be slow until that resize cleanup is periodically performed.
- 
-(Note: [bulk_insert](/docs/next/write_operations) write operation does not provide auto-sizing during ingestion)
-
-### For Copy-On-Write 
-This is as simple as configuring the [maximum size for a base/parquet file](/docs/configurations#hoodieparquetmaxfilesize) 
-and the [soft limit](/docs/configurations#hoodieparquetsmallfilelimit) below which a file should 
-be considered a small file. For the initial bootstrap of a Hudi table, tuning record size estimate is also important to 
-ensure sufficient records are bin-packed in a parquet file. For subsequent writes, Hudi automatically uses average 
-record size based on previous commit. Hudi will try to add enough records to a small file at write time to get it to the 
-configured maximum limit. For e.g , with `compactionSmallFileSize=100MB` and limitFileSize=120MB, Hudi will pick all 
-files < 100MB and try to get them upto 120MB.
-
-### For Merge-On-Read 
-MergeOnRead works differently for different INDEX choices so there are few more configs to set:  
-
-- Indexes with **canIndexLogFiles = true** : Inserts of new data go directly to log files. In this case, you can 
-configure the [maximum log size](/docs/configurations#hoodielogfilemaxsize) and a 
-[factor](/docs/configurations#hoodielogfiletoparquetcompressionratio) that denotes reduction in 
-size when data moves from avro to parquet files.
-- Indexes with **canIndexLogFiles = false** : Inserts of new data go only to parquet files. In this case, the 
-same configurations as above for the COPY_ON_WRITE case applies.
-
-NOTE : In either case, small files will be auto sized only if there is no PENDING compaction or associated log file for 
-that particular file slice. For example, for case 1: If you had a log file and a compaction C1 was scheduled to convert 
-that log file to parquet, no more inserts can go into that log file. For case 2: If you had a parquet file and an update 
-ended up creating an associated delta log file, no more inserts can go into that parquet file. Only after the compaction 
-has been performed and there are NO log files associated with the base parquet file, can new inserts be sent to auto size that parquet file.
-
-## Auto-Size With Clustering
-**[Clustering](/docs/next/clustering)** is a feature in Hudi to group 
-small files into larger ones either synchronously or asynchronously. Since first solution of auto-sizing small files has 
-a tradeoff on ingestion speed (since the small files are sized during ingestion), if your use-case is very sensitive to 
-ingestion latency where you don't want to compromise on ingestion speed which may end up creating a lot of small files, 
-clustering comes to the rescue. Clustering can be scheduled through the ingestion job and an asynchronus job can stitch 
-small files together in the background to generate larger files. NOTE that during this, ingestion can continue to run concurrently.
-
-*Please note that Hudi always creates immutable files on disk. To be able to do auto-sizing or clustering, Hudi will 
-always create a newer version of the smaller file, resulting in 2 versions of the same file. 
-The [cleaner service](/docs/next/hoodie_cleaner) will later kick in and delete the older version small file and keep the latest one.*
\ No newline at end of file
+A fundamental problem when writing data to a source is having a lot of small files. This is also known as a small file problem. If you don’t size the files appropriately, you can slow down the query performance and work with stale analytics. Some of the issues you may encounter with small files include the following:
+- **Reads slow down**: You’ll have to scan through many small files to retrieve data for a query. It’s a very inefficient way of accessing and utilizing the data.
+
+- **Processes slow down**: You can slow down your i.e., Spark or Hive jobs; the more files you have, the more tasks you create for reading each file.
+
+- **Storage increases**: When working with a lot of data, you can be inefficient in using your storage. For example, many small files can have a lower compression ratio, leading to more data on storage. If you’re indexing the data, that also takes up more storage space inside the Parquet files. If you’re working with a small amount of data, you might not see a significant impact with storage. However, when dealing with petabyte and exabyte data, you’ll need to be efficient in managing storage resources. 

Review Comment:
   +1



-- 
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-unsubscribe@hudi.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org