关于Hadoop FIFO调度器对多输入单作业Map任务分配顺序的技术问询

Great question—let’s break this down clearly, since single multi-input jobs can feel like a hidden layer even when you’ve mastered multi-job scheduling logic for FIFO, Fair, and Capacity schedulers.

First, a quick clarification: The FIFO scheduler is job-level first-in-first-out—it prioritizes entire jobs based on submission order, allocating all available cluster resources to the earliest submitted job until it completes (or releases resources). But when it comes to within a single multi-input job, how Map tasks are ordered and assigned is handled by the job's application master (YARN) or job tracker (Hadoop 1.x), with rules that tie directly to how input splits are generated.

1. How Multi-Input Jobs Generate Map Tasks #

When you create a multi-input job (using MultipleInputs or equivalent APIs to specify multiple input paths/formats), Hadoop processes each input source independently:

• For each input path/format pair, Hadoop splits the data into InputSplits (each split is roughly a block size, configurable via mapreduce.input.fileinputformat.split.maxsize etc.).
• All splits from all input sources are combined into a single task queue for the job’s Map phase.

2. FIFO Scheduler’s Role in Task Execution Order #

Once the FIFO scheduler has allocated resources to your job (since it’s the earliest pending job), the job’s task manager will pull tasks from the queue in a strict sequence. Here’s what determines that sequence:

• Input source addition order: The splits from the first input source you add (e.g., first MultipleInputs.addInputPath() call) will appear before splits from subsequent input sources.
• File/directory traversal order within each source: For a single input path, HDFS traverses files in lexicographical (dictionary) order by default. Splits from earlier files in this order will be queued before splits from later files.
• No dependency-based reordering: Since Map tasks are stateless and independent, the FIFO scheduler (and Hadoop’s task execution logic) doesn’t reorder tasks based on data dependencies. They’re executed in the exact order the splits were generated.

3. Example to Illustrate #

Suppose you set up your job like this:

MultipleInputs.addInputPath(job, new Path("/data/logs/2024-01"), TextInputFormat.class, LogMapper.class);
MultipleInputs.addInputPath(job, new Path("/data/logs/2024-02"), TextInputFormat.class, LogMapper.class);

• All splits from /data/logs/2024-01 (ordered by HDFS file lex order) will be queued first.
• Only after all those Map tasks are completed (or as slots free up) will splits from /data/logs/2024-02 start executing.

4. Resource Allocation Impact #

The FIFO scheduler will allocate as many Map slots as possible to your job (up to the cluster’s total available Map slots, or the job’s configured maximum parallelism). If your job has 80 Map tasks and the cluster has 16 Map slots, the first 16 tasks will run in parallel. As each task finishes, the next task in the queue is immediately assigned to the free slot—no reordering happens here.

Key Takeaway #

For a single multi-input job under the FIFO scheduler:

• Job-level priority is determined by submission order (FIFO’s core rule).
• Within the job, Map task execution order is driven by the sequence of input split generation, which depends on your input source addition order and HDFS’s file traversal order.
• There’s no inherent "smart" ordering—tasks run in the queue order as soon as resources are available.

内容的提问来源于stack exchange，提问作者Salah Atef