Data Pipeline Orchestration and Workflow Management Questions

import random
import math

def compute_sleep(attempt, base_delay, max_delay):
    """
    Compute sleep interval for attempt n using exponential backoff + full jitter.
    attempt: 1-based attempt number (int)
    base_delay: base delay in seconds (float)
    max_delay: maximum delay cap in seconds (float)
    Returns: sleep_seconds (float)
    """
    # sanitize inputs
    if attempt < 1:
        attempt = 1
    base = max(0.0, float(base_delay))
    maxd = max(0.0, float(max_delay))
    # exponential growth: base * 2^(attempt-1)
    exp = base * (2 ** (attempt - 1))
    # cap by max_delay
    cap = min(exp, maxd) if maxd > 0 else exp
    # full jitter: uniform between 0 and cap
    sleep = random.uniform(0, cap)
    return sleep

# PSEUDOCODE (Python-like)
def safe_reprocess_controller(start_date, end_date, dag_list):
    validate_dates(start_date, end_date)
    dag_list = normalize_and_expand_dependencies(dag_list)   # includes upstream deps
    lock_key = make_lock_key(start_date, end_date, dag_list)
    with distributed_lock(lock_key, timeout=2h) as lock:
        pause_downstream_consumers(dag_list)                 # e.g., feature-store consumers, BI jobs
        dependency_order = topological_sort(dag_list)       # upstream -> downstream
        staging_ids = {}                                    # map dag -> staging location
        checksums = {}
        for dag in dependency_order:
            staging = create_staging_partition(dag, start_date, end_date)  # isolated path
            staging_ids[dag] = staging
            # run recompute job with input reads pinned to either existing canonical data
            # or previously computed staging outputs for upstreams (ensures deterministic input)
            job_id = submit_recompute_job(dag, start_date, end_date, output=staging, idempotency_token=lock_key)
            wait_for_job(job_id)
            checksums[dag] = compute_checksums_and_counts(staging)
            if not validate_against_expectations(dag, checksums[dag]):
                abort_and_cleanup(staging_ids, reason="checksum mismatch")
                resume_downstream_consumers(dag_list)
                raise ReprocessError("Validation failed for " + dag)
        # All staging outputs validated -> atomic make-visible
        # Use transactional metadata update / atomic pointer swap per dataset
        with metadata_transaction():
            for dag in dependency_order:
                publish_staging_as_active(dag, staging_ids[dag], start_date, end_date, metadata={'checksum': checksums[dag], 'reprocess_id': lock_key})
        resume_downstream_consumers(dag_list)
    # lock released here

# Helpers
def distributed_lock(key, timeout):
    # Use redis/cosmos/etcd/DB row with TTL to prevent concurrent reprocess for same range
    pass

def create_staging_partition(dag, start_date, end_date):
    # create isolated path like s3://bucket/dag/reprocess/{reprocess_id}/...
    pass

def submit_recompute_job(dag, start_date, end_date, output, idempotency_token):
    # Job runner must be idempotent: detect repeated submissions with same token and skip duplicate runs
    pass

def compute_checksums_and_counts(path):
    # e.g., per-partition checksum, global checksum (hash of sorted row hashes), row count
    pass

def metadata_transaction():
    # Use database transaction or cloud metastore atomic update to swap pointers
    pass