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https://issues.apache.org/jira/browse/HDFS-15987?focusedWorklogId=584637&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-584637
 ]

ASF GitHub Bot logged work on HDFS-15987:
-----------------------------------------

                Author: ASF GitHub Bot
            Created on: 17/Apr/21 15:03
            Start Date: 17/Apr/21 15:03
    Worklog Time Spent: 10m 
      Work Description: whbing commented on a change in pull request #2918:
URL: https://github.com/apache/hadoop/pull/2918#discussion_r615265357



##########
File path: 
hadoop-hdfs-project/hadoop-hdfs/src/main/java/org/apache/hadoop/hdfs/tools/offlineImageViewer/PBImageTextWriter.java
##########
@@ -649,14 +679,123 @@ private void output(Configuration conf, FileSummary 
summary,
         is = FSImageUtil.wrapInputStreamForCompression(conf,
             summary.getCodec(), new BufferedInputStream(new LimitInputStream(
                 fin, section.getLength())));
-        outputINodes(is);
+        INodeSection s = INodeSection.parseDelimitedFrom(is);
+        LOG.info("Found {} INodes in the INode section", s.getNumInodes());
+        int count = outputINodes(is, out);
+        LOG.info("Outputted {} INodes.", count);
       }
     }
     afterOutput();
     long timeTaken = Time.monotonicNow() - startTime;
     LOG.debug("Time to output inodes: {}ms", timeTaken);
   }
 
+  /**
+   * STEP1: Multi-threaded process sub-sections
+   * Given n (1<n<=k) threads to process k sections,
+   * E.g. 10 sections and 4 threads, grouped as follows:
+   * |---------------------------------------------------------------|
+   * | (0    1    2)    (3    4    5)    (6    7)     (8    9)       |
+   * | thread[0]        thread[1]        thread[2]    thread[3]      |
+   * |---------------------------------------------------------------|
+   *
+   * STEP2: Merge files.
+   */
+  private void outputInParallel(Configuration conf, FileSummary summary,

Review comment:
       Thanks @Hexiaoqiao for guidance. Other possible sub-sections can also be 
optimized, but may not be the focus of optimization, i think. Analyse as below.
   
   There are several steps to parse fsimage in the case of DELIMITED format:
   - 1) Load string table                        
   - 2) Load inode references              
   - 3) Handle INODE to memory or levelDB  
   - 4) Handle INODE_DIR to memory or levelDB         
   - 5) Output INODE                                                    
   For example In our practice, it takes 7 hours for to parse a large fsimage 
file, and just the  5th step which only uses INODE takes more than 6 hours. So 
I did parallelization in 5th step.
   
   The 3rd and 4th steps are basically memory operations, which are not very 
time-consuming. It may be possible to use INODE_SUB or INODE_DIR_SUB feature 
for parallel processing, but I am not sure if it is necessary to do so.
   
   Hope to discuss further to clarify whether other sub-sections need to be 
processed, Thanks!




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Issue Time Tracking
-------------------

    Worklog Id:     (was: 584637)
    Time Spent: 1h  (was: 50m)

> Improve oiv tool to parse fsimage file in parallel with delimited format
> ------------------------------------------------------------------------
>
>                 Key: HDFS-15987
>                 URL: https://issues.apache.org/jira/browse/HDFS-15987
>             Project: Hadoop HDFS
>          Issue Type: Sub-task
>            Reporter: Hongbing Wang
>            Assignee: Hongbing Wang
>            Priority: Major
>              Labels: pull-request-available
>          Time Spent: 1h
>  Remaining Estimate: 0h
>
> The purpose of this Jira is to improve oiv tool to parse fsimage file with 
> sub-sections (see -HDFS-14617-) in parallel with delmited format. 
> 1.Serial parsing is time-consuming
> The time to serially parse a large fsimage with delimited format (e.g. `hdfs 
> oiv -p Delimited -t <tmp> ...`) is as follows: 
> {code:java}
> 1) Loading string table:                 -> Not time consuming.
> 2) Loading inode references:             -> Not time consuming
> 3) Loading directories in INode section: -> Slightly time consuming (3%)
> 4) Loading INode directory section:      -> A bit time consuming (11%)
> 5) Output:                               -> Very time consuming (86%){code}
> Therefore, output is the most parallelized stage.
> 2.How to output in parallel
> The sub-sections are grouped in order, and each thread processes a group and 
> outputs it to the file corresponding to each thread, and finally merges the 
> output files.
> 3. The result of a test
> {code:java}
>  input fsimage file info:
>  3.4G, 12 sub-sections, 55976500 INodes
>  -----------------------------------------
>  Threads TotalTime OutputTime MergeTime
>  1       18m37s     16m18s      –
>  4        8m7s      4m49s       41s{code}
>  
>  
>  



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