automated-orchestration

name	automated-orchestration
description	Use when building multi-stage analysis pipelines that need checkpoint/resume, when long-running jobs fail mid-execution and lose progress, when processing large data archives that exceed memory or rate limits, or when analysis depth should adapt to data volume

Automated Orchestration with Checkpoints

Overview

Coordinate end-to-end analytical pipelines with checkpoint/resume capabilities and activity-based depth adjustment. The core principle: every stage that can fail must be resumable, and every pipeline must adapt its depth to the data it encounters.

When to Use

• Pipeline processes multiple stages sequentially and failures lose hours of work
• Large data corpus requires chunked processing with rate-limit awareness
• Analysis depth should scale with data volume (shallow scan vs. deep dive)
• Long-running jobs need graceful interruption and restart
• Multiple data sources must be combined across stages with partial-failure tolerance

When NOT to use:

• Single-shot scripts under 5 minutes with no intermediate state
• Pipelines where full re-execution is cheap and acceptable
• Real-time streaming (use event-driven patterns instead)

Core Pattern

WITHOUT orchestration:          WITH orchestration:

fetch_all_data()       -->      Stage 1: fetch  [checkpoint] -->
process_everything()   -->      Stage 2: parse  [checkpoint] -->
analyze_all()          -->      Stage 3: enrich [checkpoint] -->
generate_report()               Stage 4: report [checkpoint]

Fails at analyze?               Fails at enrich?
Start over from scratch.        Resume from Stage 3 checkpoint.

Quick Reference

Concept	Pattern	Anti-pattern
Checkpointing	JSON/pickle per stage, atomic writes	No checkpoints on jobs > 5 min
Idempotency	Delete-then-write, upsert by key	Append-only without dedup
Depth adjustment	Scale analysis tiers to item count	Hardcoded depth for all inputs
Rate limits	Exponential backoff with jitter	Retry loops without delay
Progress	Structured log per stage with counts	Silent processing, no visibility
Failure handling	Isolate stages, continue on partial	Abort entire pipeline on one error

Pipeline Architecture

1. Define stages as idempotent units

Each stage reads an input artifact, produces an output artifact, and writes a checkpoint on success. Re-running a completed stage is a no-op.

import json, os, time, hashlib
from pathlib import Path

class PipelineStage:
    """Base for an idempotent, checkpointed pipeline stage."""

    def __init__(self, name, checkpoint_dir="checkpoints"):
        self.name = name
        self.checkpoint_dir = Path(checkpoint_dir)
        self.checkpoint_dir.mkdir(parents=True, exist_ok=True)

    @property
    def checkpoint_path(self):
        return self.checkpoint_dir / f"{self.name}.json"

    def is_complete(self):
        """Check if this stage already finished successfully."""
        if not self.checkpoint_path.exists():
            return False
        cp = json.loads(self.checkpoint_path.read_text())
        return cp.get("status") == "complete"

    def load_checkpoint(self):
        """Load partial progress for resume."""
        if self.checkpoint_path.exists():
            return json.loads(self.checkpoint_path.read_text())
         {: , : [], : []}

     ():
        
        tmp = .checkpoint_path.with_suffix()
        tmp.write_text(json.dumps(state, indent=, default=))
        tmp.rename(.checkpoint_path)  

     ():
        
         .is_complete():
            ()
             ._load_output()

        state = .load_checkpoint()
        already_done = (state.get(, []))
        items = [i  i  input_data  ._item_key(i)   already_done]

        (
              )

         i, item  (items):
            :
                result = .process_item(item)
                state[].append(._item_key(item))
                
                 (i + ) % .checkpoint_interval == :
                    .save_checkpoint(state)
             Exception  e:
                state[].append({
                    : ._item_key(item),
                    : (e), : time.time()
                })
                .save_checkpoint(state)
                
                

        state[] = 
        state[] = time.time()
        .save_checkpoint(state)
         ._load_output()

    
     ():
         NotImplementedError

     ():
        
         hashlib.md5(json.dumps(item, sort_keys=).encode()).hexdigest()

     ():
        
         NotImplementedError

    checkpoint_interval =   

2. Orchestrate stages with a pipeline runner

class Pipeline:
    """Run stages in sequence with checkpoint awareness."""

    def __init__(self, stages, checkpoint_dir="checkpoints"):
        self.stages = stages
        self.checkpoint_dir = Path(checkpoint_dir)
        self.log = []

    def run(self, initial_input):
        data = initial_input
        for stage in self.stages:
            t0 = time.time()
            try:
                data = stage.run(data)
                elapsed = time.time() - t0
                self.log.append({
                    "stage": stage.name, "status": "complete",
                    "elapsed_s": round(elapsed, 1),
                    "items": len(stage.load_checkpoint().get("processed", [])),
                    "errors": len(stage.load_checkpoint().get("errors", []))
                })
            except KeyboardInterrupt:
                self.log.append({"stage": stage.name, "status": "interrupted"})
                print(f"\n[pipeline] Interrupted at {stage.name}. "
                      f"Re-run to resume from this stage.")
                break
            except Exception  e:
                .log.append({
                    : stage.name, : , : (e)
                })
                ()
                
         data

     ():
        
        report = ._build_report()
        Path(path).parent.mkdir(parents=, exist_ok=)
        Path(path).write_text(report)

     ():
        lines = []
        lines.append()
        lines.append()
        lines.append()
        lines.append()
         entry  .log:
            lines.append(
                
                
                
                
                
            )
        
        failures = [e  e  .log  e[] != ]
         failures:
            lines.append()
             f  failures:
                lines.append(
                             + (    f  ))
        :
            lines.append()
            lines.append()
         .join(lines) + 

3. Activity-based depth adjustment

Scale analysis intensity to data volume. Avoid spending deep-analysis resources on tiny datasets, or shallow-scanning massive ones without prioritization.

def choose_depth(item_count, thresholds=None):
    """Select analysis depth tier based on item count.

    Returns a dict of parameters that downstream stages consume.
    Callers should NOT hardcode depth -- always derive from data.
    """
    thresholds = thresholds or {
        "shallow":  (0, 100),      # quick summary only
        "standard": (101, 5000),   # full analysis per item
        "deep":     (5001, 50000), # sampled deep + full summary
        "archival": (50001, None), # statistical sampling + key items only
    }
    for tier, (lo, hi) in thresholds.items():
        if hi is None and item_count >= lo:
            return {"tier": tier, "sample_rate": 0.05, "full_analysis": False}
        if lo <= item_count <= hi:
            return {"tier": tier,
                    "sample_rate": 1.0 if tier in ("shallow","standard") else 0.2,
                    "full_analysis": tier in (, )}
     {: , : , : }

4. Rate-limit-aware fetching

import random

def fetch_with_backoff(fetch_fn, max_retries=5, base_delay=1.0):
    """Retry with exponential backoff + jitter. Respects rate limits."""
    for attempt in range(max_retries):
        try:
            return fetch_fn()
        except RateLimitError:
            delay = base_delay * (2 ** attempt) + random.uniform(0, 1)
            print(f"  Rate limited. Waiting {delay:.1f}s (attempt {attempt+1})")
            time.sleep(delay)
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(base_delay)
    raise RuntimeError(f"Failed after {max_retries} retries")

Handling Insufficient Data

Situation	Action
Pipeline has nothing to process	Log "no input data", write report noting empty input, exit cleanly with status "no_data"
A stage produces empty output	Log warning, pass empty collection to next stage, do NOT skip or error. Let downstream stages handle empty input gracefully
External API unavailable	Retry with backoff. After max retries, mark stage as "degraded", continue with cached/partial data if available. Log what was skipped
Checkpoint file corrupted	Delete checkpoint, re-run stage from scratch. Log the corruption event

Common Mistakes

Mistake	Fix
No checkpointing on jobs > 5 min	Add checkpoint writes every N items and on stage completion
Monolithic single-function pipeline	Split into discrete stages with clear input/output contracts
Silently skipping failed items	Log every failure with item ID, error, timestamp
Hardcoded analysis depth	Use choose_depth() or equivalent based on actual item count
Retry without backoff	Always exponential backoff with jitter for external calls
Non-atomic checkpoint writes	Write to .tmp then rename (atomic on POSIX)
Ignoring partial failures	Collect errors, continue processing, summarize in report
No progress visibility	Log stage entry/exit, item counts, elapsed time

Boundaries

SHOULD do:

• Manage pipeline stage sequencing and dependencies
• Handle interruptions gracefully (Ctrl+C saves checkpoint)
• Scale depth to data size automatically
• Log progress with structured, parseable output
• Produce a status report at docs/analysis/03-automated-orchestration.md

Should NOT do:

• Make analytical conclusions (that is for domain-specific downstream skills)
• Silently skip failed stages without logging
• Retry indefinitely without backoff
• Decide what data means -- only move it through stages reliably

Report Output

The pipeline MUST end by writing its status to docs/analysis/03-automated-orchestration.md containing:

• Pipeline run timestamp
• Table of stages: name, status (complete/failed/interrupted/skipped), item count, error count, elapsed time
• Section listing any interruptions or skipped steps with reasons
• Summary of depth tier selected and why