Hadoop CombineFileInputFormat原理及源码分析

// mapping from a rack name to the list of blocks it has
HashMap<String, List<OneBlockInfo>> rackToBlocks = 
                          new HashMap<String, List<OneBlockInfo>>();
 
// mapping from a block to the nodes on which it has replicas
HashMap<OneBlockInfo, String[]> blockToNodes = 
                          new HashMap<OneBlockInfo, String[]>();
 
// mapping from a node to the list of blocks that it contains
HashMap<String, List<OneBlockInfo>> nodeToBlocks = 
                          new HashMap<String, List<OneBlockInfo>>();

// populate all the blocks for all fileslong totLength = 0;
for (int i = 0; i < paths.length; i++) {
  files[i] = new OneFileInfo(paths[i], job, 
                             rackToBlocks, blockToNodes, nodeToBlocks, rackToNodes);
  totLength += files[i].getLength();
}

                //
                 保存当前切片所包含的数据块
    ArrayList<OneBlockInfo> validBlocks = new ArrayList<OneBlockInfo>();
    // 保存当前切片中的数据块属于哪些节点
    ArrayList<String> nodes = new ArrayList<String>();
    // 保存当前切片的大小long curSplitSize = 0;
 
    // process all nodes and create splits that are local to a node. 
    // 依次处理每个节点上的数据块for (Iterator<Map.Entry<String, List<OneBlockInfo>>> iter = nodeToBlocks.entrySet().iterator(); iter.hasNext();) {
      Map.Entry<String, List<OneBlockInfo>> one = iter.next();
      nodes.add(one.getKey());
      List<OneBlockInfo> blocksInNode = one.getValue();
 
      // for each block, copy it into validBlocks. Delete it from blockToNodes so that the same block does not appear in 
      // two different splits.
      // 依次处理每个数据块，注意blockToNodes变量的作用，它保证了同一数据块不会出现在两个切片中for (OneBlockInfo oneblock : blocksInNode) {
        if (blockToNodes.containsKey(oneblock)) {
          validBlocks.add(oneblock);
          blockToNodes.remove(oneblock);
          curSplitSize += oneblock.length;
 
          // if the accumulated split size exceeds the maximum, then create this split.
          // 如果数据块累积大小大于或等于maxSize，则形成一个切片if (maxSize != 0 && curSplitSize >= maxSize) {
            // create an input split and add it to the splits array            addCreatedSplit(job, splits, nodes, validBlocks);
            curSplitSize = 0;
            validBlocks.clear();
          }
        }
      }
      // if there were any blocks left over and their combined size is
      // larger than minSplitNode, then combine them into one split.
      // Otherwise add them back to the unprocessed pool. It is likely 
      // that they will be combined with other blocks from the same rack later on.
      // 如果剩余数据块大小大于或等于minSizeNode，则将这些数据块构成一个切片；
      // 如果剩余数据块大小小于minSizeNode，则将这些数据块归还给blockToNodes，交由后期“同一机架”过程处理if (minSizeNode != 0 && curSplitSize >= minSizeNode) {
        // create an input split and add it to the splits array        addCreatedSplit(job, splits, nodes, validBlocks);
      } else {
        for (OneBlockInfo oneblock : validBlocks) {
          blockToNodes.put(oneblock, oneblock.hosts);
        }
      }
      validBlocks.clear();
      nodes.clear();
      curSplitSize = 0;
    }

// if blocks in a rack are below the specified minimum size, then keep them
    // in ‘overflow‘. After the processing of all racks is complete, these overflow
    // blocks will be combined into splits.
    // overflowBlocks用于保存“同一机架”过程处理之后剩余的数据块
    ArrayList<OneBlockInfo> overflowBlocks = new ArrayList<OneBlockInfo>();
    ArrayList<String> racks = new ArrayList<String>();
 
    // Process all racks over and over again until there is no more work to do.while (blockToNodes.size() > 0) {
      // Create one split for this rack before moving over to the next rack. 
      // Come back to this rack after creating a single split for each of the 
      // remaining racks.
      // Process one rack location at a time, Combine all possible blocks that
      // reside on this rack as one split. (constrained by minimum and maximum
      // split size).
 
      // iterate over all racks 
      // 依次处理每个机架for (Iterator<Map.Entry<String, List<OneBlockInfo>>> iter = 
           rackToBlocks.entrySet().iterator(); iter.hasNext();) {
        Map.Entry<String, List<OneBlockInfo>> one = iter.next();
        racks.add(one.getKey());
        List<OneBlockInfo> blocks = one.getValue();
 
        // for each block, copy it into validBlocks. Delete it from 
        // blockToNodes so that the same block does not appear in 
        // two different splits.boolean createdSplit = false;
        // 依次处理该机架的每个数据块for (OneBlockInfo oneblock : blocks) {
          if (blockToNodes.containsKey(oneblock)) {
            validBlocks.add(oneblock);
            blockToNodes.remove(oneblock);
            curSplitSize += oneblock.length;
      
            // if the accumulated split size exceeds the maximum, then create this split.
            // 如果数据块累积大小大于或等于maxSize，则形成一个切片if (maxSize != 0 && curSplitSize >= maxSize) {
              // create an input split and add it to the splits array              addCreatedSplit(job, splits, getHosts(racks), validBlocks);
              createdSplit = true;
              break;
            }
          }
        }
 
        // if we created a split, then just go to the next rackif (createdSplit) {
          curSplitSize = 0;
          validBlocks.clear();
          racks.clear();
          continue;
        }
 
        if (!validBlocks.isEmpty()) {
          // 如果剩余数据块大小大于或等于minSizeRack，则将这些数据块构成一个切片if (minSizeRack != 0 && curSplitSize >= minSizeRack) {
            // if there is a mimimum size specified, then create a single split
            // otherwise, store these blocks into overflow data structure            addCreatedSplit(job, splits, getHosts(racks), validBlocks);
          } else {
            // There were a few blocks in this rack that remained to be processed.
            // Keep them in ‘overflow‘ block list. These will be combined later.
            // 如果剩余数据块大小小于minSizeRack，则将这些数据块加入overflowBlocks            overflowBlocks.addAll(validBlocks);
          }
        }
        curSplitSize = 0;
        validBlocks.clear();
        racks.clear();
      }
    }

// Process all overflow blocksfor (OneBlockInfo oneblock : overflowBlocks) {
      validBlocks.add(oneblock);
      curSplitSize += oneblock.length;
 
      // This might cause an exiting rack location to be re-added,
      // but it should be ok.for (int i = 0; i < oneblock.racks.length; i++) {
        racks.add(oneblock.racks[i]);
      }
 
      // if the accumulated split size exceeds the maximum, then 
      // create this split.
      // 如果剩余数据块大小大于或等于maxSize，则将这些数据块构成一个切片if (maxSize != 0 && curSplitSize >= maxSize) {
        // create an input split and add it to the splits array        addCreatedSplit(job, splits, getHosts(racks), validBlocks);
        curSplitSize = 0;
        validBlocks.clear();
        racks.clear();
      }
    }

// Process any remaining blocks, if any.if (!validBlocks.isEmpty()) {
      addCreatedSplit(job, splits, getHosts(racks), validBlocks);
    }