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Posted to commits@hudi.apache.org by GitBox <gi...@apache.org> on 2021/08/24 16:20:49 UTC
[GitHub] [hudi] nsivabalan commented on a change in pull request #3527: [HUDI-2347] Blog on improving marker mechanism
nsivabalan commented on a change in pull request #3527:
URL: https://github.com/apache/hudi/pull/3527#discussion_r694973697
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File path: website/blog/2021-08-18-improving-marker-mechanism.md
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@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
Review comment:
We can just have the title as "Marker Files". Its implicit that this section talks about what is marker file and its purpose. Current title is too big.
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File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
Review comment:
may be we can reword a bit.
Hudi creates a markers for every data file created during the lifecycle of every commit. Each marker entry .....
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File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
+
+Next, we dive into the existing marker mechanism, explain its performance problem, and demonstrate the new timeline-server-based marker mechanism to address the problem.
+
+## Existing direct marker file mechanism and its limitations
+
+The **existing marker mechanism** simply creates a new marker file corresponding to each data file, with the marker filename as described above. Each marker file is written to the file system in the same directory hierarchy, i.e., commit instant and partition path, under a temporary folder `.hoodie/.temp` under the base path of the Hudi table. For example, the figure below shows one example of the marker files created and the corresponding data files when writing data to the Hudi table. When getting or deleting all the marker file paths, the mechanism first lists all the paths under the temporary folder, `.hoodie/.temp/<commit_instant>`, and then does the operation.
+
+
+
+As the number of data files to write increases, so does the number of marker files to create. This can create performance bottlenecks for cloud storage such as AWS S3. In AWS S3, each file create and delete call triggers an HTTP request and there is [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. When the number of data files to write concurrently and the number of marker files is huge, the marker file operations become the performance bottleneck. In one case, the marker file deletion takes an hour to finish due to S3 rate-limiting for a bulk insert operation running for a few hours. Such behavior degrades the performance of the write.
+
+## Timeline-server-based marker mechanism improving write performance
+
+To address the performance bottleneck due to rate-limiting of AWS S3 explained above, we introduce a **new marker mechanism leveraging the timeline server**, which optimizes the marker-related latency for file systems with non-trivial file I/O latency. The **timeline server** in Hudi serves as a centralized place for providing the file system and timeline views. As shown below, the new timeline-server-based marker mechanism delegates the marker creation and other marker-related operations from individual executors to the timeline server for centralized processing. The timeline server maintains the created markers in memory for corresponding marker requests. The timeline server achieves consistency by periodically flushing the in-memory markers to a limited number of underlying files in the file system. In such a way, the number of actual file operations and latency related to markers can be significantly reduced even with a huge number of data files, thus improving the performan
ce of the writes.
Review comment:
"The timeline server maintains the created markers in memory for corresponding marker requests. The timeline server achieves consistency by periodically flushing the in-memory markers to a limited number of underlying files in the file system." ->
"The timeline server batches the marker creation requests and writes to a bounded set of marker files at regular intervals."
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
+
+Next, we dive into the existing marker mechanism, explain its performance problem, and demonstrate the new timeline-server-based marker mechanism to address the problem.
+
+## Existing direct marker file mechanism and its limitations
+
+The **existing marker mechanism** simply creates a new marker file corresponding to each data file, with the marker filename as described above. Each marker file is written to the file system in the same directory hierarchy, i.e., commit instant and partition path, under a temporary folder `.hoodie/.temp` under the base path of the Hudi table. For example, the figure below shows one example of the marker files created and the corresponding data files when writing data to the Hudi table. When getting or deleting all the marker file paths, the mechanism first lists all the paths under the temporary folder, `.hoodie/.temp/<commit_instant>`, and then does the operation.
+
+
+
+As the number of data files to write increases, so does the number of marker files to create. This can create performance bottlenecks for cloud storage such as AWS S3. In AWS S3, each file create and delete call triggers an HTTP request and there is [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. When the number of data files to write concurrently and the number of marker files is huge, the marker file operations become the performance bottleneck. In one case, the marker file deletion takes an hour to finish due to S3 rate-limiting for a bulk insert operation running for a few hours. Such behavior degrades the performance of the write.
+
+## Timeline-server-based marker mechanism improving write performance
+
+To address the performance bottleneck due to rate-limiting of AWS S3 explained above, we introduce a **new marker mechanism leveraging the timeline server**, which optimizes the marker-related latency for file systems with non-trivial file I/O latency. The **timeline server** in Hudi serves as a centralized place for providing the file system and timeline views. As shown below, the new timeline-server-based marker mechanism delegates the marker creation and other marker-related operations from individual executors to the timeline server for centralized processing. The timeline server maintains the created markers in memory for corresponding marker requests. The timeline server achieves consistency by periodically flushing the in-memory markers to a limited number of underlying files in the file system. In such a way, the number of actual file operations and latency related to markers can be significantly reduced even with a huge number of data files, thus improving the performan
ce of the writes.
+
+
+
+To improve the efficiency of processing marker creation requests, we design the batch processing in the handler of marker requests at the timeline server. Each marker creation request is handled asynchronously in the Javalin timeline server and queued before processing. For every batch interval, e.g., 20ms, a dispatching thread pulls the pending requests from the queue and sends them to the worker thread for processing. Each worker thread processes the marker creation requests, sets the responses, and flushes the new markers by overwriting the underlying file storing the markers in the file system. There are multiple worker threads running concurrently, given that the file overwriting takes longer than the batch interval, and each worker thread writes to an exclusive file not touched by other threads, to guarantee consistency and correctness. Both the batch interval and the number of worker threads can be configured through the write options.
Review comment:
I don't think we need to go into worker threads as such(sets responses, etc). We can say that timeline server marker handler queues marker requests every 20 ms and writes all markers to next marker file in a round robin fashion. don't think we need to get into impl details.
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
Review comment:
".... these duplicate data files are cleaned up when the commit is finalized."
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
+
+Next, we dive into the existing marker mechanism, explain its performance problem, and demonstrate the new timeline-server-based marker mechanism to address the problem.
+
+## Existing direct marker file mechanism and its limitations
+
+The **existing marker mechanism** simply creates a new marker file corresponding to each data file, with the marker filename as described above. Each marker file is written to the file system in the same directory hierarchy, i.e., commit instant and partition path, under a temporary folder `.hoodie/.temp` under the base path of the Hudi table. For example, the figure below shows one example of the marker files created and the corresponding data files when writing data to the Hudi table. When getting or deleting all the marker file paths, the mechanism first lists all the paths under the temporary folder, `.hoodie/.temp/<commit_instant>`, and then does the operation.
Review comment:
Do call out that these files does not have any content as such.
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
+
+Next, we dive into the existing marker mechanism, explain its performance problem, and demonstrate the new timeline-server-based marker mechanism to address the problem.
+
+## Existing direct marker file mechanism and its limitations
+
+The **existing marker mechanism** simply creates a new marker file corresponding to each data file, with the marker filename as described above. Each marker file is written to the file system in the same directory hierarchy, i.e., commit instant and partition path, under a temporary folder `.hoodie/.temp` under the base path of the Hudi table. For example, the figure below shows one example of the marker files created and the corresponding data files when writing data to the Hudi table. When getting or deleting all the marker file paths, the mechanism first lists all the paths under the temporary folder, `.hoodie/.temp/<commit_instant>`, and then does the operation.
+
+
+
+As the number of data files to write increases, so does the number of marker files to create. This can create performance bottlenecks for cloud storage such as AWS S3. In AWS S3, each file create and delete call triggers an HTTP request and there is [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. When the number of data files to write concurrently and the number of marker files is huge, the marker file operations become the performance bottleneck. In one case, the marker file deletion takes an hour to finish due to S3 rate-limiting for a bulk insert operation running for a few hours. Such behavior degrades the performance of the write.
Review comment:
"This can create performance bottlenecks for cloud storage such as AWS S3 when we are looking at very large writes say 10K data files or more"
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
Review comment:
".... In the next write operation, the write client rolls back the failed commit before proceeding with the new write. This rollback is done with the help of marker files to identify the data files written as part of the failed commit".
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
+
+Next, we dive into the existing marker mechanism, explain its performance problem, and demonstrate the new timeline-server-based marker mechanism to address the problem.
+
+## Existing direct marker file mechanism and its limitations
+
+The **existing marker mechanism** simply creates a new marker file corresponding to each data file, with the marker filename as described above. Each marker file is written to the file system in the same directory hierarchy, i.e., commit instant and partition path, under a temporary folder `.hoodie/.temp` under the base path of the Hudi table. For example, the figure below shows one example of the marker files created and the corresponding data files when writing data to the Hudi table. When getting or deleting all the marker file paths, the mechanism first lists all the paths under the temporary folder, `.hoodie/.temp/<commit_instant>`, and then does the operation.
+
+
+
+As the number of data files to write increases, so does the number of marker files to create. This can create performance bottlenecks for cloud storage such as AWS S3. In AWS S3, each file create and delete call triggers an HTTP request and there is [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. When the number of data files to write concurrently and the number of marker files is huge, the marker file operations become the performance bottleneck. In one case, the marker file deletion takes an hour to finish due to S3 rate-limiting for a bulk insert operation running for a few hours. Such behavior degrades the performance of the write.
+
+## Timeline-server-based marker mechanism improving write performance
+
+To address the performance bottleneck due to rate-limiting of AWS S3 explained above, we introduce a **new marker mechanism leveraging the timeline server**, which optimizes the marker-related latency for file systems with non-trivial file I/O latency. The **timeline server** in Hudi serves as a centralized place for providing the file system and timeline views. As shown below, the new timeline-server-based marker mechanism delegates the marker creation and other marker-related operations from individual executors to the timeline server for centralized processing. The timeline server maintains the created markers in memory for corresponding marker requests. The timeline server achieves consistency by periodically flushing the in-memory markers to a limited number of underlying files in the file system. In such a way, the number of actual file operations and latency related to markers can be significantly reduced even with a huge number of data files, thus improving the performan
ce of the writes.
+
+
+
+To improve the efficiency of processing marker creation requests, we design the batch processing in the handler of marker requests at the timeline server. Each marker creation request is handled asynchronously in the Javalin timeline server and queued before processing. For every batch interval, e.g., 20ms, a dispatching thread pulls the pending requests from the queue and sends them to the worker thread for processing. Each worker thread processes the marker creation requests, sets the responses, and flushes the new markers by overwriting the underlying file storing the markers in the file system. There are multiple worker threads running concurrently, given that the file overwriting takes longer than the batch interval, and each worker thread writes to an exclusive file not touched by other threads, to guarantee consistency and correctness. Both the batch interval and the number of worker threads can be configured through the write options.
+
+
+
+
+Note that the worker thread always checks whether the marker has already been created by comparing the marker name from the request with the memory copy of all markers maintained at the timeline server. The underlying files storing the markers are only read upon the first marker request (lazy loading). The responses of requests are only sent back once the new markers are flushed to the files, so that in the case of the timeline server failure, the timeline server can recover the already created markers. These ensure consistency between the file system and the in-memory copy, and improve the performance of processing marker requests.
+
+## Marker-related write options
+
+We introduce the following new marker-related write options in 0.9.0 release, to configure the marker mechanism.
+
+| Property Name | Default | Meaning |
+| ------------- | ----------- | :-------------:|
+| `hoodie.write.markers.type` | direct | Marker type to use. Two modes are supported: (1) `direct`: individual marker file corresponding to each data file is directly created by the writer; (2) `timeline_server_based`: marker operations are all handled at the timeline service which serves as a proxy. New marker entries are batch processed and stored in a limited number of underlying files for efficiency. |
+| `hoodie.markers.timeline_server_based.batch.num_threads` | 20 | Number of threads to use for batch processing marker creation requests at the timeline server. |
+| `hoodie.markers.timeline_server_based.batch.interval_ms` | 50 | The batch interval in milliseconds for marker creation batch processing. |
+
+## Performance
+
+We evaluate the write performance over both direct and timeline-server-based marker mechanisms by bulk-inserting a large dataset using Amazon EMR with Spark and S3. The input data is around 100GB. We configure the write operation to generate a large number of data files concurrently by setting the max parquet file size to be 1MB and parallelism to be 240.
+
+As shown below, the timeline-server-based marker mechanism generates much fewer files storing markers because of the batch processing, leading to much less time on marker-related I/O operations, thus achieving 31% lower write completion time compared to the direct marker file mechanism.
+
+| Marker Type | Input | Num data file generated | Files created for markers | Marker deletion time | Bulk Insert Time (including marker deletion) |
+| ----------- | --------- | :---------: | :---------: | :---------: | :---------: |
+| Direct | ~100GB | 165k | 165k | 15min | 55min |
+| Timeline-server-based | ~100GB | 165k | 20 | ~3s | 38min |
+
+## Conclusion
+
+We identify that the existing direct marker file mechanism incurs performance bottlenecks due to the rate-limiting of file create and delete calls on cloud storage like AWS S3. To address this issue, we introduce a new marker mechanism leveraging the timeline server, which delegates the marker creation and other marker-related operations from individual executors to the timeline server and uses batch processing to improve performance. Performance evaluations on Amazon EMR with Spark and S3 show that the marker-related I/O latency and overall write time are reduced.
Review comment:
Let's be cautious when we say that direct marker file is a perf bottleneck. Do always suffix that its an issue only for large writes.
Also, do call out some where that for hdfs this new marker type is not yet supported.
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
Review comment:
we should probably expand the 2nd statement here to be more explicit.
"Markers serves as a way to track data files of interest rather than listing all files in your table. Lets discuss where exactly these markers come in handy"...
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
Review comment:
minor rewording.
"Before writing each data file, the Hudi write client creates a marker first in the file system. " -> "Hudi creates the marker file before creating the corresponding data file in the file system."
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
+
+Next, we dive into the existing marker mechanism, explain its performance problem, and demonstrate the new timeline-server-based marker mechanism to address the problem.
+
+## Existing direct marker file mechanism and its limitations
+
+The **existing marker mechanism** simply creates a new marker file corresponding to each data file, with the marker filename as described above. Each marker file is written to the file system in the same directory hierarchy, i.e., commit instant and partition path, under a temporary folder `.hoodie/.temp` under the base path of the Hudi table. For example, the figure below shows one example of the marker files created and the corresponding data files when writing data to the Hudi table. When getting or deleting all the marker file paths, the mechanism first lists all the paths under the temporary folder, `.hoodie/.temp/<commit_instant>`, and then does the operation.
+
+
+
+As the number of data files to write increases, so does the number of marker files to create. This can create performance bottlenecks for cloud storage such as AWS S3. In AWS S3, each file create and delete call triggers an HTTP request and there is [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. When the number of data files to write concurrently and the number of marker files is huge, the marker file operations become the performance bottleneck. In one case, the marker file deletion takes an hour to finish due to S3 rate-limiting for a bulk insert operation running for a few hours. Such behavior degrades the performance of the write.
+
+## Timeline-server-based marker mechanism improving write performance
+
+To address the performance bottleneck due to rate-limiting of AWS S3 explained above, we introduce a **new marker mechanism leveraging the timeline server**, which optimizes the marker-related latency for file systems with non-trivial file I/O latency. The **timeline server** in Hudi serves as a centralized place for providing the file system and timeline views. As shown below, the new timeline-server-based marker mechanism delegates the marker creation and other marker-related operations from individual executors to the timeline server for centralized processing. The timeline server maintains the created markers in memory for corresponding marker requests. The timeline server achieves consistency by periodically flushing the in-memory markers to a limited number of underlying files in the file system. In such a way, the number of actual file operations and latency related to markers can be significantly reduced even with a huge number of data files, thus improving the performan
ce of the writes.
+
+
+
+To improve the efficiency of processing marker creation requests, we design the batch processing in the handler of marker requests at the timeline server. Each marker creation request is handled asynchronously in the Javalin timeline server and queued before processing. For every batch interval, e.g., 20ms, a dispatching thread pulls the pending requests from the queue and sends them to the worker thread for processing. Each worker thread processes the marker creation requests, sets the responses, and flushes the new markers by overwriting the underlying file storing the markers in the file system. There are multiple worker threads running concurrently, given that the file overwriting takes longer than the batch interval, and each worker thread writes to an exclusive file not touched by other threads, to guarantee consistency and correctness. Both the batch interval and the number of worker threads can be configured through the write options.
+
+
+
+
+Note that the worker thread always checks whether the marker has already been created by comparing the marker name from the request with the memory copy of all markers maintained at the timeline server. The underlying files storing the markers are only read upon the first marker request (lazy loading). The responses of requests are only sent back once the new markers are flushed to the files, so that in the case of the timeline server failure, the timeline server can recover the already created markers. These ensure consistency between the file system and the in-memory copy, and improve the performance of processing marker requests.
+
+## Marker-related write options
+
+We introduce the following new marker-related write options in 0.9.0 release, to configure the marker mechanism.
+
+| Property Name | Default | Meaning |
+| ------------- | ----------- | :-------------:|
+| `hoodie.write.markers.type` | direct | Marker type to use. Two modes are supported: (1) `direct`: individual marker file corresponding to each data file is directly created by the writer; (2) `timeline_server_based`: marker operations are all handled at the timeline service which serves as a proxy. New marker entries are batch processed and stored in a limited number of underlying files for efficiency. |
Review comment:
"...directly created by the writer" -> " ... directly created by the executor"
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
+
+Next, we dive into the existing marker mechanism, explain its performance problem, and demonstrate the new timeline-server-based marker mechanism to address the problem.
+
+## Existing direct marker file mechanism and its limitations
+
+The **existing marker mechanism** simply creates a new marker file corresponding to each data file, with the marker filename as described above. Each marker file is written to the file system in the same directory hierarchy, i.e., commit instant and partition path, under a temporary folder `.hoodie/.temp` under the base path of the Hudi table. For example, the figure below shows one example of the marker files created and the corresponding data files when writing data to the Hudi table. When getting or deleting all the marker file paths, the mechanism first lists all the paths under the temporary folder, `.hoodie/.temp/<commit_instant>`, and then does the operation.
+
+
+
+As the number of data files to write increases, so does the number of marker files to create. This can create performance bottlenecks for cloud storage such as AWS S3. In AWS S3, each file create and delete call triggers an HTTP request and there is [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. When the number of data files to write concurrently and the number of marker files is huge, the marker file operations become the performance bottleneck. In one case, the marker file deletion takes an hour to finish due to S3 rate-limiting for a bulk insert operation running for a few hours. Such behavior degrades the performance of the write.
Review comment:
"...marker files is huge, the marker file operations could take up non-trivial time during write operation, sometimes to the order of mins or more."
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
Review comment:
"deletes all markers pertaining to a commit when it succeeds"
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
Review comment:
We can also add a line what happens if not for markers.
"If not for such markers to track the per commit data files, we have to list all files in the file system and correlate with files seen in timeline and then delete the ones that belong to partial write failures. As you could imagine, this will be very costly in a very large installation of datalake"
##########
File path: website/blog/2021-08-18-improving-marker-mechanism.md
##########
@@ -0,0 +1,65 @@
+---
+title: "Improving Marker Mechanism in Apache Hudi"
+excerpt: "We introduce a new marker mechanism leveraging the timeline server to address performance bottlenecks due to rate-limiting on cloud storage like AWS S3."
+author: yihua
+category: blog
+---
+Write operations in an Apache Hudi table use markers to efficiently identify the data files written to the file system. In this blog, we dive into the design of the existing direct marker file mechanism and explain its performance problem on cloud storage like AWS S3. We demonstrate how we improve the write performance with timeline-server-based markers.
+
+<!--truncate-->
+
+## What is a marker and why it’s needed in write operations on Hudi Table
+
+A **marker** in Hudi, such as a marker file with a unique filename, is a label to indicate that a corresponding data file exists in the file system. Each marker entry is composed of three parts, the data file name, the marker extension (`.marker`), and the I/O type (`CREATE`, `MERGE`, or `APPEND`). For example, the marker `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet.marker.CREATE` indicates that the corresponding data file is `91245ce3-bb82-4f9f-969e-343364159174-0_140-579-0_20210820173605.parquet` and the I/O type is `CREATE`. Before writing each data file, the Hudi write client creates a marker first in the file system. Markers are persistent in the file system unless they are explicitly deleted by the write client. The write client deletes all markers when the commit is successful.
+
+The markers are useful for efficiently carrying out different operations by the write client. Two important operations use markers to find the data files of interest, instead of scanning the whole Hudi table:
+ - **Identifying duplicate data files**: in Spark, the Hudi write client delegates the data file writing to multiple executors. One executor can fail the task, leaving partial data files written, and Spark retries the task in this case until it succeeds. The markers help efficiently identify the partial data files written, which contain duplicate data compared to the data files written by the successful trial later, and these duplicate data files are cleaned up before the write and commit are finalized.
+ - **Rolling back failed commits**: the write operation can fail in the middle, leaving some data files written in the file system. In this case, the marker entries stay in the file system as the commit is failed. In the next write operation, the write client first rolls back the failed commits, by identifying the data files written in these commits through the markers and deleting them.
+
+Next, we dive into the existing marker mechanism, explain its performance problem, and demonstrate the new timeline-server-based marker mechanism to address the problem.
+
+## Existing direct marker file mechanism and its limitations
+
+The **existing marker mechanism** simply creates a new marker file corresponding to each data file, with the marker filename as described above. Each marker file is written to the file system in the same directory hierarchy, i.e., commit instant and partition path, under a temporary folder `.hoodie/.temp` under the base path of the Hudi table. For example, the figure below shows one example of the marker files created and the corresponding data files when writing data to the Hudi table. When getting or deleting all the marker file paths, the mechanism first lists all the paths under the temporary folder, `.hoodie/.temp/<commit_instant>`, and then does the operation.
+
+
+
+As the number of data files to write increases, so does the number of marker files to create. This can create performance bottlenecks for cloud storage such as AWS S3. In AWS S3, each file create and delete call triggers an HTTP request and there is [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. When the number of data files to write concurrently and the number of marker files is huge, the marker file operations become the performance bottleneck. In one case, the marker file deletion takes an hour to finish due to S3 rate-limiting for a bulk insert operation running for a few hours. Such behavior degrades the performance of the write.
+
+## Timeline-server-based marker mechanism improving write performance
+
+To address the performance bottleneck due to rate-limiting of AWS S3 explained above, we introduce a **new marker mechanism leveraging the timeline server**, which optimizes the marker-related latency for file systems with non-trivial file I/O latency. The **timeline server** in Hudi serves as a centralized place for providing the file system and timeline views. As shown below, the new timeline-server-based marker mechanism delegates the marker creation and other marker-related operations from individual executors to the timeline server for centralized processing. The timeline server maintains the created markers in memory for corresponding marker requests. The timeline server achieves consistency by periodically flushing the in-memory markers to a limited number of underlying files in the file system. In such a way, the number of actual file operations and latency related to markers can be significantly reduced even with a huge number of data files, thus improving the performan
ce of the writes.
+
+
+
+To improve the efficiency of processing marker creation requests, we design the batch processing in the handler of marker requests at the timeline server. Each marker creation request is handled asynchronously in the Javalin timeline server and queued before processing. For every batch interval, e.g., 20ms, a dispatching thread pulls the pending requests from the queue and sends them to the worker thread for processing. Each worker thread processes the marker creation requests, sets the responses, and flushes the new markers by overwriting the underlying file storing the markers in the file system. There are multiple worker threads running concurrently, given that the file overwriting takes longer than the batch interval, and each worker thread writes to an exclusive file not touched by other threads, to guarantee consistency and correctness. Both the batch interval and the number of worker threads can be configured through the write options.
+
+
+
+
+Note that the worker thread always checks whether the marker has already been created by comparing the marker name from the request with the memory copy of all markers maintained at the timeline server. The underlying files storing the markers are only read upon the first marker request (lazy loading). The responses of requests are only sent back once the new markers are flushed to the files, so that in the case of the timeline server failure, the timeline server can recover the already created markers. These ensure consistency between the file system and the in-memory copy, and improve the performance of processing marker requests.
+
+## Marker-related write options
+
+We introduce the following new marker-related write options in 0.9.0 release, to configure the marker mechanism.
+
+| Property Name | Default | Meaning |
+| ------------- | ----------- | :-------------:|
+| `hoodie.write.markers.type` | direct | Marker type to use. Two modes are supported: (1) `direct`: individual marker file corresponding to each data file is directly created by the writer; (2) `timeline_server_based`: marker operations are all handled at the timeline service which serves as a proxy. New marker entries are batch processed and stored in a limited number of underlying files for efficiency. |
+| `hoodie.markers.timeline_server_based.batch.num_threads` | 20 | Number of threads to use for batch processing marker creation requests at the timeline server. |
+| `hoodie.markers.timeline_server_based.batch.interval_ms` | 50 | The batch interval in milliseconds for marker creation batch processing. |
+
+## Performance
+
+We evaluate the write performance over both direct and timeline-server-based marker mechanisms by bulk-inserting a large dataset using Amazon EMR with Spark and S3. The input data is around 100GB. We configure the write operation to generate a large number of data files concurrently by setting the max parquet file size to be 1MB and parallelism to be 240.
Review comment:
please do call out that this is a degenerate case so that this creates huge number of data files to gauge the perf difference, but its unlikely users will set max parquet file size to 1Mb.
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