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The following commit(s) were added to refs/heads/master by this push:
new 2b036970 [#1143] docs: Correct sequence number text by reducing
paragraph indentation by 1 space (#1144)
2b036970 is described below
commit 2b036970453bd795710c66b92fd3a1a6ea0ef43c
Author: Bowen Liang <[email protected]>
AuthorDate: Mon Aug 14 09:48:32 2023 +0800
[#1143] docs: Correct sequence number text by reducing paragraph
indentation by 1 space (#1144)
---
README.md | 18 +++++++++---------
1 file changed, 9 insertions(+), 9 deletions(-)
diff --git a/README.md b/README.md
index 17eb872b..5e39813b 100644
--- a/README.md
+++ b/README.md
@@ -49,15 +49,15 @@ Depending on different situations, Uniffle supports Memory
& Local, Memory & Rem
* Spark driver ask coordinator to get shuffle server for shuffle process
* Spark task write shuffle data to shuffle server with following step:
- 1. Send KV data to buffer
- 2. Flush buffer to queue when buffer is full or buffer manager is full
- 3. Thread pool get data from queue
- 4. Request memory from shuffle server first and send the shuffle data
- 5. Shuffle server cache data in memory first and flush to queue when buffer
manager is full
- 6. Thread pool get data from queue
- 7. Write data to storage with index file and data file
- 8. After write data, task report all blockId to shuffle server, this step
is used for data validation later
- 9. Store taskAttemptId in MapStatus to support Spark speculation
+ 1. Send KV data to buffer
+ 2. Flush buffer to queue when buffer is full or buffer manager is full
+ 3. Thread pool get data from queue
+ 4. Request memory from shuffle server first and send the shuffle data
+ 5. Shuffle server cache data in memory first and flush to queue when buffer
manager is full
+ 6. Thread pool get data from queue
+ 7. Write data to storage with index file and data file
+ 8. After write data, task report all blockId to shuffle server, this step is
used for data validation later
+ 9. Store taskAttemptId in MapStatus to support Spark speculation
* Depending on different storage types, the spark task will read shuffle data
from shuffle server or remote storage or both of them.
