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https://issues.apache.org/jira/browse/HUDI-1138?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17387761#comment-17387761
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ASF GitHub Bot commented on HUDI-1138:
--------------------------------------
yihua commented on a change in pull request #3233:
URL: https://github.com/apache/hudi/pull/3233#discussion_r677110192
##########
File path:
hudi-timeline-service/src/main/java/org/apache/hudi/timeline/service/TimelineService.java
##########
@@ -52,36 +53,50 @@
private int serverPort;
private Configuration conf;
+ private transient HoodieEngineContext context;
private transient FileSystem fs;
private transient Javalin app = null;
private transient FileSystemViewManager fsViewsManager;
private final int numThreads;
private final boolean shouldCompressOutput;
private final boolean useAsync;
+ private final int markerBatchNumThreads;
Review comment:
These have been included in the TimelineService.Config and we use the
config instance to pass all config values.
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> Re-implement marker files via timeline server
> ---------------------------------------------
>
> Key: HUDI-1138
> URL: https://issues.apache.org/jira/browse/HUDI-1138
> Project: Apache Hudi
> Issue Type: Improvement
> Components: Writer Core
> Affects Versions: 0.9.0
> Reporter: Vinoth Chandar
> Assignee: Ethan Guo
> Priority: Blocker
> Labels: pull-request-available
> Fix For: 0.9.0
>
>
> Even as you can argue that RFC-15/consolidated metadata, removes the need for
> deleting partial files written due to spark task failures/stage retries. It
> will still leave extra files inside the table (and users will pay for it
> every month) and we need the marker mechanism to be able to delete these
> partial files.
> Here we explore if we can improve the current marker file mechanism, that
> creates one marker file per data file written, by
> Delegating the createMarker() call to the driver/timeline server, and have it
> create marker metadata into a single file handle, that is flushed for
> durability guarantees
>
> P.S: I was tempted to think Spark listener mechanism can help us deal with
> failed tasks, but it has no guarantees. the writer job could die without
> deleting a partial file. i.e it can improve things, but cant provide
> guarantees
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