Data engineering
Part 6 of 6 · Workflow OrchestrationChoosing & Operating Workflow Orchestrators - Airflow vs Dagster vs Prefect vs Temporal vs Step Functions vs Argo, Testing & Migration
Airflow vs Dagster vs Prefect vs Argo vs Temporal vs Step Functions vs cron+queue on run model, graph, state model, latency, duration, dynamic-ness, backfills, human waits, ops burden and cost drivers; what goes wrong if you pick otherwise; observability signals; testing (runnable DAG integrity test and workflow replay-test gate); migrations (cron to Airflow, Airflow 2 to 3, Airflow to Dagster, to durable engines); interview Q&A; decision chart.
- 1Gist
- 2Maps
- 3Q&A
- 4Sandbox
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Question ladder
L1
What decides the orchestrator first?
Answer
What a run is: a data interval or partition, a business entity, a container step, or a single message.
L2
Step Functions Standard vs Express?
Answer
Standard runs up to a year with exactly-once workflow execution; Express runs up to five minutes and is at-least-once when async.
L3
When is cron plus a queue enough?
Answer
Single idempotent steps with no cross-step dependencies, long waits or per-item visibility needs.
L4
What does a DAG integrity test check?
Answer
Imports, cycles, risky top-level code and conventions such as retries and explicit catchup.
L5
What does a replay test check?
Answer
That a candidate build replays real production histories without non-determinism.
L6
Which metric says add Temporal workers?
Answer
Rising Schedule-To-Start latency with high worker slot utilization.
L7
How do you migrate between durable engines?
Answer
Drain old executions, or start new ones with the current state at a checkpoint; histories do not port.
Failure modes
Unsafe workflow change reaches production
Without replay tests in CI, an unpatched change strands running executions.
A bad Dag file breaks parsing
No integrity test caught an import error or top-level API call before deploy.
Silent backlog
Monitoring only failures misses queued work until it is very late.
Misconceptions
Express is the cheap version of Standard for everything.
Express is at-least-once and capped at five minutes; payments needing exactly-once steps want Standard.
Migrating means moving in-flight executions.
Drain or checkpoint instead; move new work first.
Self-hosting is free.
Airflow's metadata DB and Temporal's persistence layer are serious stateful systems.
Interviewer traps
Choosing Airflow for one run per order.
Per-entity, low-latency processes fit Temporal or Step Functions Standard.
Removing a patch because tests on new histories pass.
Replay old histories too; patch removal is safe only after old executions are gone.
Design scenario
Same prompt for every reader.
Requirements
Backfillable nightly jobs, reliable refunds across three services, visibility into stuck items, and no big-bang migration.
Failure assumptions
- A cron script has top-level network calls.
- A refund workflow change adds a step.
- The refund backlog grows during an incident.
Constraints
- Small platform team.
- AWS-heavy stack with some on-prem data.
Prompt
A team runs 200 cron scripts and a hand-rolled refund state machine. Plan the orchestrators, the tests that gate deploys and the migration.
API
How do services start, signal and query refunds, and how do data jobs get their intervals?
Data
Which metrics, histories and test fixtures does each orchestrator need?
Architecture
Which tools run which jobs, and how are cron jobs and the old state machine drained?
Overview
Choosing an orchestrator is choosing what a run is and who pays the operational bill. Airflow, Dagster and Prefect run batch work over data intervals and partitions; Argo Workflows runs container-per-step pipelines on Kubernetes; Temporal and Step Functions run durable business processes; and a plain cron plus a queue with idempotent consumers is still the right answer more often than vendors admit. Once chosen, operating it well comes down to four habits: observe the queue and scheduler (not just task failures), test structure before deploy (DAG integrity tests, workflow replay tests), migrate gradually behind a stable interface, and design every unit of work to be retried.
The comparison
| Dimension | Airflow 3.x | Dagster | Prefect 3 | Argo Workflows | Temporal | Step Functions | Cron + queue |
|---|---|---|---|---|---|---|---|
| What a run is | A Dag run per data interval or asset event | Materialization of asset partitions | A flow run (Python call) | A Workflow custom resource | A workflow execution per business entity or event | A state machine execution | A message |
| Graph | Declared, parsed ahead; dynamic mapping | Declared asset graph | Discovered at run time | Declared YAML DAG or steps | Discovered as code runs | Declared JSON/YAML (ASL) | None (you write it) |
| State model | Task instances in a metadata DB | Asset materializations, run and event log | Flow and task run states | Node status on the CR | Event history plus mutable state, per step | Execution history, per state transition | Whatever you store |
| Start latency | Seconds+ (scheduler loop, executor) | Seconds+ | Seconds | Pod scheduling per step | Low; built for request paths | Low (Express), moderate (Standard) | As fast as your queue |
| Longest run | Hours to days in practice | Same | Same | Same | Unbounded with continue-as-new | 1 year (Standard), 5 minutes (Express) | Per message |
| Dynamic-ness | Mapping over runtime lists | Dynamic partitions and outputs | Arbitrary Python | withParam, loops | Arbitrary code | Map and Distributed Map states | Arbitrary |
| Backfills over time | First-class | First-class (partitions) | Manual by parameters | Manual | Manual (start executions) | Manual | Manual |
| Waiting on humans or events | HITL operators (3.1+), deferrable triggers, assets | Sensors | Pauses, events | Suspend templates | Signals, updates, durable timers | Callback with task token (Standard) | You build it |
| Ops burden self-hosted | Scheduler, Dag processor, triggerer, API server, DB, executor | Webserver, daemon, DB | Server, DB, workers | Controller on your cluster | Cluster (Frontend, History, Matching, Worker) plus Cassandra or SQL plus visibility | None (managed only) | Low |
| Managed options | MWAA, Cloud Composer, Astronomer | Dagster+ | Prefect Cloud | Vendor platforms | Temporal Cloud | It is managed | Cloud schedulers and queues |
| Cost driver | Infra for components and workers | Infra or credits per materialization | Infra or Cloud plan | Cluster compute | Infra or Cloud actions and storage | State transitions (Standard); requests, duration and memory (Express) | Infra |
| Sweet spot | Batch data platforms with many integrations | Asset-centric analytics and ML | Python-heavy teams wanting low ceremony | Kubernetes-native batch and ML | Long-running, reliable business processes | AWS-native service orchestration | Simple periodic or event jobs |
Numbers above come from the vendors' docs (Step Functions durations; Temporal history and payload limits are on the previous pages). Costs depend on your volume, so compare with your own workload rather than list prices.
Gate deploys on structural tests or watch production?
Prefer
Gate deploys on integrity and replay tests
Catch structural breaks in CI before any run sees them.
- broken.py's import error and crm_sync.py's top-level calls blocked the deploy.
- v3-unsafe failed on order-101 and order-102 and was blocked.
- v3-patched passed on all 5 histories and shipped.
Alternative
Deploy and watch for failures
Rely on production alerts to find problems.
- Running executions stall with non-determinism errors.
- A bad Dag file breaks parsing for every Dag in the bundle.
- Failure-only alerts miss growing backlogs.
Choosing, step by step
Diagram 1 condensed: the questions that pick a family and a tool.
- 1
Describe a run
An interval, an entity, a container step, or a message. - 2
Check waits and duration
Human waits, timers and long durations point to durable execution. - 3
Check backfills and lineage
Date-range backfills and partition grids point to a DAG tool. - 4
Weigh the ops bill
Managed options vs running a scheduler, database or cluster yourself. - 5
Simple jobs stay simple
A single idempotent step is fine on cron plus a queue.
Diagram 1: choosing, step by step
Decisions
- 1
Step 1: write the one-sentence definition of a run
- nextStep 2: driven by time windows or data partitions?
- ?
Step 2: driven by time windows or data partitions?
- nextStep 3: assets and lineage are the main concern?
- nextStep 5: multi-step, must survive crashes, waits or compensations?
- ?
Step 3: assets and lineage are the main concern?
- nextDagster or Airflow with assets
- nextStep 4: every step a container on Kubernetes?
- 4
Dagster or Airflow with assets
- ?
Step 4: every step a container on Kubernetes?
- nextArgo Workflows
- nextAirflow or Prefect
- 6
Argo Workflows
- 7
Airflow or Prefect
- ?
Step 5: multi-step, must survive crashes, waits or compensations?
- nextCron plus queue with idempotent consumers
- nextStep 6: all on AWS and logic fits a state machine?
- 9
Cron plus queue with idempotent consumers
- nextStep 7: later need retries, visibility, timers per item?
- ?
Step 6: all on AWS and logic fits a state machine?
- nextStep Functions, Standard or Express by duration and semantics
- nextTemporal or another code-first durable engine
- 11
Step Functions, Standard or Express by duration and semantics
- 12
Temporal or another code-first durable engine
- ?
Step 7: later need retries, visibility, timers per item?
- nextFailure path: you are rebuilding an orchestrator, migrate to H
- nextKeep it simple
- 14
Failure path: you are rebuilding an orchestrator, migrate to H
- 15
Keep it simple
Lesson map
Choosing & Operating Workflow Orchestrators - Airflow vs Dagster vs Prefect vs Temporal vs Step Functions vs Argo, Testing & Migration
Airflow vs Dagster vs Prefect vs Argo vs Temporal vs Step Functions vs cron+queue on run model, graph, state model, latency, duration, dynamic-ness, backfills, human waits, ops burden and cost drivers; what goes wrong if you pick otherwise; observability signals; testing (runnable DAG integrity test and workflow replay-test gate); migrations (cron to Airflow, Airflow 2 to 3, Airflow to Dagster, to durable engines); interview Q&A; decision chart.
Architecture. Architecture
Select a node to see why it exists, or an edge to see the protocol, direction, effect, and consequence.
Mermaid export
flowchart TB a["Step 1: write the one-sentence definition of a run"] b["Step 2: driven by time windows or data partitions?"] c["Step 3: assets and lineage are the main concern?"] d["Dagster or Airflow with assets"] e["Step 4: every step a container on Kubernetes?"] f["Argo Workflows"] g["Airflow or Prefect"] h["Step 5: multi-step, must survive crashes, waits or compensations?"] i["Cron plus queue with idempotent consumers"] j["Step 6: all on AWS and logic fits a state machine?"] k["Step Functions, Standard or Express by duration and semantics"] l["Temporal or another code-first durable engine"] m["Step 7: later need retries, visibility, timers per item?"] x["Failure path: you are rebuilding an orchestrator, migrate to H"] n["Keep it simple"] a -->|continues| b b -->|continues| c c -->|continues| d c -->|continues| e e -->|continues| f e -->|continues| g b -->|continues| h h -->|continues| i h -->|continues| j j -->|continues| k j -->|continues| l i -->|continues| m m -->|continues| x m -->|continues| n
What happens if you choose otherwise
| You picked | For | What goes wrong |
|---|---|---|
| Airflow | Per-request workflows (one run per order) | Scheduler and DB overhead per run, no signals or step journal, latency in seconds |
| Temporal | Warehouse ELT over date partitions | No partition grid or catchup; you rebuild backfill tooling and must keep data out of history |
| Step Functions Express | Payments needing exactly-once steps | At-least-once execution (async) and a five-minute cap; Standard is the right type |
| Step Functions Standard | Very high-volume event transforms | Cost per state transition grows with volume; Express exists for this |
| Argo Workflows | Thousands of tiny steps | Pod start-up per step dominates; batch the steps |
| Cron + queue | A five-step, multi-day onboarding flow | You hand-build timers, retries, compensation, visibility and stuck-item tooling |
| Prefect or Dagster | Cross-service business transactions | Built for data runs; durable waits and compensation are not the core model |
Observability: what to watch
| Signal | Batch DAG systems | Durable execution |
|---|---|---|
| "Is work waiting for capacity?" | Tasks in scheduled or queued, executor open and queued slots, pool usage | Schedule-To-Start latency per task queue, worker slot utilization |
| "Is the brain healthy?" | Scheduler heartbeat and loop duration, Dag processing time and import errors, metadata DB latency | Frontend and History latency, persistence latency, shard health |
| "Are we late?" | Deadline Alerts, partition freshness checks | Workflow duration percentiles, timers firing late, open executions by age |
| "Is something stuck?" | Long running tasks, zombie or orphaned task detection | Workflow task failures (non-determinism), activities retrying for hours |
| Tracing | OpenTelemetry metrics and traces (3.3 records timers as histograms) | SDK interceptors for OpenTelemetry, metrics per activity type |
Alert on symptoms consumers feel (freshness, end-to-end latency, stuck executions) and use component metrics for diagnosis.
Testing: structure before behaviour
DAG integrity tests load every Dag file in CI and fail on import errors, cycles, risky top-level code and missing conventions. Real suites use DagBag in pytest; this one uses ast so it runs anywhere:
"""DAG integrity tests: cheap CI checks that catch most Airflow-style outages
before a file ever reaches the scheduler. Real setups load every file with
DagBag (or `airflow dags list-import-errors`) inside pytest; here we check
three toy Dag files statically with Python's ast module so it runs anywhere.
Checks:
1. the file parses (no import errors)
2. no expensive top-level code (network, DB, Variable reads at parse time)
3. no dynamic start_date such as datetime.now()
4. every task sets retries, and the Dag sets catchup explicitly
5. the dependency graph is acyclic
"""
import ast
FILES = {
"orders_daily.py": '''
from airflow.sdk import dag, task
import pendulum
@dag(schedule="@daily", start_date=pendulum.datetime(2026, 1, 1, tz="UTC"), catchup=False)
def orders_daily():
@task(retries=3)
def extract(): ...
@task(retries=3)
def load(rows): ...
load(extract())
''',
"crm_sync.py": '''
from airflow.sdk import dag, task, Variable
import datetime, requests
TOKEN = Variable.get("crm_token") # runs on EVERY parse
ACCOUNTS = requests.get("https://crm.example/accounts").json()
@dag(schedule="@hourly", start_date=datetime.datetime.now())
def crm_sync():
@task
def push(): ...
push()
''',
"broken.py": '''
from airflow.sdk import dag, task
@dag(schedule=None
def broken(): ...
''',
}
EXPENSIVE = {("requests", "get"), ("Variable", "get"), ("psycopg2", "connect"), ("boto3", "client")}
DEPS = {"orders_daily.py": {"extract": [], "load": ["extract"]},
"crm_sync.py": {"push": []}}
def dotted(call):
f = call.func
return (f.value.id, f.attr) if isinstance(f, ast.Attribute) and isinstance(f.value, ast.Name) else None
def acyclic(deps):
seen, stack = set(), set()
def dfs(n):
if n in stack: return False
if n in seen: return True
stack.add(n); ok = all(dfs(u) for u in deps[n]); stack.discard(n); seen.add(n); return ok
return all(dfs(n) for n in deps)
def check(name, src):
problems = []
try:
tree = ast.parse(src)
except SyntaxError as e:
return [f"import error: {e.msg} (line {e.lineno})"]
for node in tree.body: # module-level statements only
if isinstance(node, ast.FunctionDef): continue # function bodies run at task time
for call in [n for n in ast.walk(node) if isinstance(n, ast.Call)]:
if dotted(call) in EXPENSIVE:
problems.append(f"top-level call {'.'.join(dotted(call))}() runs on every parse")
for fn in [n for n in ast.walk(tree) if isinstance(n, ast.FunctionDef)]:
for dec in fn.decorator_list:
if not isinstance(dec, ast.Call):
if isinstance(dec, ast.Name) and dec.id == "task": problems.append(f"task {fn.name}: retries not set")
continue
kw = {k.arg: k.value for k in dec.keywords}
if getattr(dec.func, "id", "") == "dag":
sd = kw.get("start_date")
if sd is not None and "now" in ast.unparse(sd): problems.append("dynamic start_date (datetime.now())")
if "catchup" not in kw: problems.append("catchup not set explicitly")
if getattr(dec.func, "id", "") == "task" and "retries" not in kw:
problems.append(f"task {fn.name}: retries not set")
if name in DEPS and not acyclic(DEPS[name]): problems.append("cycle in task graph")
return problems
failed = 0
for name, src in FILES.items():
probs = check(name, src)
failed += bool(probs)
print(f"{'PASS' if not probs else 'FAIL'} {name}")
for p in probs: print(f" - {p}")
print(f"\n{len(FILES) - failed} passed, {failed} failed -> {'block the deploy' if failed else 'ship it'}")Output:
PASS orders_daily.py
FAIL crm_sync.py
- top-level call Variable.get() runs on every parse
- top-level call requests.get() runs on every parse
- dynamic start_date (datetime.now())
- catchup not set explicitly
- task push: retries not set
FAIL broken.py
- import error: '(' was never closed (line 3)
1 passed, 2 failed -> block the deployReplay tests are the durable-execution equivalent: export real event histories from production and replay them against the candidate build; any failure means that change would strand running executions. Temporal SDKs ship a Replayer for this (for example, replay_workflows in Python), and the recommended practice is to fail CI if any replay fails.
// Replay tests: before deploying new workflow code, replay a sample of real
// event histories (exported from production) against it. Any history that no
// longer replays would get stuck in production with a non-determinism error.
// Temporal SDKs ship a Replayer for exactly this; this is a simplified model.
type RecordedHistory = { id: string; startedOn: string; cmds: string[]; markers: string[] };
type Candidate = { name: string; run: (h: RecordedHistory) => string[] };
// Recorded histories: three in-flight orders from v1 code, two from v2.
const histories: RecordedHistory[] = [
{ id: "order-101", startedOn: "v1", cmds: ["charge"], markers: [] },
{ id: "order-102", startedOn: "v1", cmds: ["charge", "ship"], markers: [] },
{ id: "order-103", startedOn: "v1", cmds: [], markers: [] },
{ id: "order-201", startedOn: "v2", cmds: ["fraud_check", "charge"], markers: ["add-fraud-check"] },
{ id: "order-202", startedOn: "v2", cmds: ["fraud_check"], markers: ["add-fraud-check"] },
];
// Each candidate returns the commands its code would issue while replaying a history.
const patchedPath = (h: RecordedHistory) => (h.markers.includes("add-fraud-check") || h.cmds.length === 0 ? ["fraud_check", "charge", "ship"] : ["charge", "ship"]);
const candidates: Candidate[] = [
{ name: "v3-unsafe (adds fraud_check, no patch)", run: () => ["fraud_check", "charge", "ship"] },
{ name: "v3-patched (keeps add-fraud-check patch)", run: patchedPath },
{ name: "v3-patch-removed-too-early", run: () => ["fraud_check", "charge", "ship"] },
{ name: "v3-reorders ship before charge", run: (h) => (h.markers.length || !h.cmds.length ? ["fraud_check", "ship", "charge"] : ["ship", "charge"]) },
];
function replays(c: Candidate, h: RecordedHistory): boolean {
const issued = c.run(h);
return h.cmds.every((cmd, i) => issued[i] === cmd); // recorded prefix must match exactly
}
for (const c of candidates) {
const failures = histories.filter((h) => !replays(c, h)).map((h) => h.id);
const verdict = failures.length ? `FAIL on ${failures.join(", ")}` : "PASS on all 5 histories";
console.log(`${failures.length ? "BLOCK" : "SHIP "} ${c.name.padEnd(42)} ${verdict}`);
}
console.log("\nNote: v3-patch-removed-too-early fails only on v1 histories, so it becomes safe");
console.log("once no v1 execution is still open or needs to be replayed (for example, for queries or resets).");Output:
BLOCK v3-unsafe (adds fraud_check, no patch) FAIL on order-101, order-102
SHIP v3-patched (keeps add-fraud-check patch) PASS on all 5 histories
BLOCK v3-patch-removed-too-early FAIL on order-101, order-102
BLOCK v3-reorders ship before charge FAIL on order-101, order-102, order-201
Note: v3-patch-removed-too-early fails only on v1 histories, so it becomes safe
once no v1 execution is still open or needs to be replayed (for example, for queries or resets).ExpectedBLOCK v3-unsafe (adds fraud_check, no patch) FAIL on order-101, order-102 SHIP v3-patched (keeps add-fraud-check patch) PASS on all 5 histories BLOCK v3-patch-removed-too-early FAIL on order-101, order-102 BLOCK v3-reorders ship before charge FAIL on order-101, order-102, order-201 Note: v3-patch-removed-too-early fails only on v1 histories, so it becomes safe once no v1 execution is still open or needs to be replayed (for example, for queries or resets).
Press Run. Snippets must be self-contained — no network, files, or native modules.
| Test layer | Batch DAGs | Durable execution |
|---|---|---|
| Unit | Task callables as plain functions with fixed interval inputs | Activities as plain functions; workflows in the SDK test environment with mocked activities |
| Structure | DAG integrity tests (imports, cycles, conventions) | Replay tests against recorded histories |
| Time | Run tasks for specific intervals, including DST days | Time-skipping test environments fast-forward timers (Temporal provides one) |
| Integration | dag.test() or a local stack against staging data | Local dev server, end-to-end against staging services |
| Data | Quality checks per partition (Airflow checks, Dagster asset checks) | Invariants on activity outputs |
Migrations between orchestrators
- Cron to Airflow or Dagster: wrap existing scripts as tasks first (same code, now with retries, logs and history), then make them interval-aware and idempotent one by one.
- Airflow 2 to 3: run the upgrade checks (
ruffrules for Airflow 3 andairflow config update), move imports toairflow.sdk, replaceexecution_datewithlogical_date, SLAs with Deadline Alerts, SubDAGs with task groups, direct metadata-DB access in tasks with the API, and handlelogical_date=Nonefor asset-triggered runs. Upgrading requires Airflow 2.7 or later. - Airflow to Dagster (or the reverse): migrate per domain, connect the two through assets or file markers, keep the same storage layout so both can coexist.
- Hand-rolled state machine to Temporal or Step Functions: put the new engine behind the same API; start only new entities on it, let old ones drain on the old system, and never move a half-finished entity mid-flight without a clear handover state.
- Between durable engines: there is no history format portability. Drain old executions, or migrate at a checkpoint by starting a new execution with the current state as input (the continue-as-new idea across systems).
Pitfalls
- Choosing by brand instead of by the shape of a run. Write the sentence first.
- No structural tests: a single bad Dag file or an unpatched workflow change reaches production.
- Monitoring only failures: a queue backlog fails nothing until it is very late.
- Migrating in-flight executions: drain or checkpoint instead.
- Underestimating self-hosting: Airflow's metadata DB and Temporal's persistence layer are serious stateful systems; budget for them or buy managed.
Interview Q&A
Design a nightly pipeline that loads 500 tables, then a system that processes refunds across three services. Same tool?
Answer
Usually not. The nightly load is interval-driven batch with backfills and lineage, a fit for Airflow or Dagster with idempotent partition overwrites. Refunds are per-entity, low-latency, multi-step with compensation, a fit for Temporal or Step Functions Standard. A DAG task can start workflows if needed.
Step Functions Standard vs Express?
Answer
Standard runs up to a year with exactly-once workflow execution, supports callbacks and .sync integrations, is priced per state transition and keeps history for 90 days. Express runs up to five minutes, is at-least-once (async) or at-most-once (sync), priced by requests, duration and memory, and suits high-volume event processing.
How do you test that a workflow code change is safe?
Answer
Replay a sample of recent production histories against the new code in CI; if any replay fails, the change needs a patch or versioned deployment. Unit-test activities separately and use a time-skipping environment for timers.
What is a DAG integrity test?
Answer
A CI test that loads every Dag file and asserts no import errors, no cycles, no expensive top-level code, and team conventions such as retries, owners and explicit catchup.
When is cron plus a queue the right answer?
Answer
When each job is a single idempotent step, failures can simply be retried by the queue, there are no cross-step dependencies or long waits, and nobody needs per-item visibility. The moment you add timers, compensations and "where is item 42?", you are building an orchestrator.
What metric tells you to add Temporal workers?
Answer
Rising Schedule-To-Start latency on a task queue, together with high worker slot utilization, means tasks wait for workers.
What should Airflow 2 to 3 migrations replace?
Answer
execution_date with logical_date, SLAs with Deadline Alerts, SubDAGs with task groups, direct metadata DB access with the API, and imports moved to airflow.sdk.
What should you alert on for orchestrators?
Answer
Symptoms consumers feel, such as freshness, end-to-end latency and stuck executions, using component metrics for diagnosis.
Check yourself
Write a CI step for your orchestrator: an integrity test that imports every Dag file, or a replay test that runs 20 recent production histories against the candidate build. Decide what result blocks the deploy.
Elsewhere in the library
These pages stay as they are. This lesson only points at them: Observability Triad — Metrics, Logs & Distributed Tracing, SLIs, SLOs & Error Budgets, Testing Strategies — Pyramid, Contracts, Property-Based & Flakes, Resilience Patterns — Circuit Breakers, Bulkheads & Load Shedding, Durable Objects in Production - Resets, Deploys & Class Migrations, Observability, Testing & Interview Q&A, HPA, VPA & Autoscaling Gotchas — Metrics, Stabilization & Thrash.