Apply the fail-early (fail-fast) pattern to detect and report errors at the earliest possible point. Covers input validation with guard clauses, meaningful error messages, assertion functions, and anti-patterns that silently swallow failures. Primary examples in R with general/polyglot guidance. Use when writing functions that accept external input, adding input validation before CRAN submission, refactoring code that silently produces wrong results, reviewing PRs for error-handling quality, ...
Scanned 9/3/2026
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---
name: fail-early-pattern
locale: wenyan
source_locale: en
source_commit: 82c77053
fence_basis_commit: 82c77053
translator: "Julius Brussee homage — caveman"
translation_date: "2026-04-24"
description: >
Apply the fail-early (fail-fast) pattern to detect and report errors at
the earliest possible point. Covers input validation with guard clauses,
meaningful error messages, assertion functions, and anti-patterns that
silently swallow failures. Primary examples in R with general/polyglot
guidance. Use when writing functions that accept external input, adding
input validation before CRAN submission, refactoring code that silently
produces wrong results, reviewing PRs for error-handling quality, or
hardening internal APIs against invalid arguments.
license: MIT
allowed-tools: Read Write Edit Bash Grep Glob
metadata:
author: Philipp Thoss
version: "1.0"
domain: general
complexity: intermediate
language: multi
tags: error-handling, validation, defensive-programming, guard-clauses, fail-fast
---
# 早敗
將敗者宜早敗、明敗、附最多脈。此技定早敗之模:於系界驗入、以守句拒壞態於傳前、書答*何*敗、*何處*、*何以*、*如何修*之誤訊。
## 用時
- 書或審受外入(用數、API 應、文容)之函
- CRAN 提前加包函之入驗
- 重構默生誤果之碼
- 審 PR 之誤處之質
- 強內 API 之抗壞參
## 入
- **必要**:欲施此模之函或模
- **必要**:信界之識(外數何入)
- **可選**:欲重構之既誤處碼
- **可選**:目語(默 R;亦適 Python、TypeScript、Rust)
## 法
### 第一步:識信界
映外數入系之處。此乃需驗之點:
- 公 API 函(R 包中出函)
- 用面參
- 文 I/O(讀配、數文、用上傳)
- 網應(API 呼、庫詢)
- 環變與系配
唯汝已驗碼呼之內輔函通常無需重驗。
**得:** 不信數入汝碼之入點之列。
**敗則:** 若界不明,自日或缺報之誤反溯以尋壞數首入處。
### 第二步:於入點加守句
於各公函頂驗入,於工始前。
**R(基):**
```r
calculate_summary <- function(data, method = c("mean", "median", "trim"), trim_pct = 0.1) {
# Guard: type check
if (!is.data.frame(data)) {
stop("'data' must be a data frame, not ", class(data)[[1]], call. = FALSE)
}
# Guard: non-empty
if (nrow(data) == 0L) {
stop("'data' must have at least one row", call. = FALSE)
}
# Guard: argument matching
method <- match.arg(method)
# Guard: range check
if (!is.numeric(trim_pct) || trim_pct < 0 || trim_pct > 0.5) {
stop("'trim_pct' must be a number between 0 and 0.5, got: ", trim_pct, call. = FALSE)
}
# --- All guards passed, begin real work ---
# ...
}
```
**R(rlang/cli——包宜用):**
```r
calculate_summary <- function(data, method = c("mean", "median", "trim"), trim_pct = 0.1) {
rlang::check_required(data)
if (!is.data.frame(data)) {
cli::cli_abort("{.arg data} must be a data frame, not {.cls {class(data)}}.")
}
if (nrow(data) == 0L) {
cli::cli_abort("{.arg data} must have at least one row.")
}
method <- rlang::arg_match(method)
if (!is.numeric(trim_pct) || trim_pct < 0 || trim_pct > 0.5) {
cli::cli_abort("{.arg trim_pct} must be between 0 and 0.5, not {.val {trim_pct}}.")
}
# ...
}
```
**通(TypeScript):**
```typescript
function calculateSummary(data: DataFrame, method: Method, trimPct: number): Summary {
if (data.rows.length === 0) {
throw new Error(`data must have at least one row`);
}
if (trimPct < 0 || trimPct > 0.5) {
throw new RangeError(`trimPct must be between 0 and 0.5, got: ${trimPct}`);
}
// ...
}
```
**得:** 諸公函首守句,拒壞入於任何副效或算前。
**敗則:** 若驗邏長(守逾十五行),析 `validate_*` 輔或用 `stopifnot()` 為簡型斷。
### 第三步:書義誤訊
每誤訊當答四問:
1. **何**敗——何參或操
2. **何處**——函名或脈(`cli::cli_abort` 自動)
3. **何以**——預何對受何
4. **如何修**——若修非顯
**善訊:**
```r
# What + Why (expected vs. actual)
stop("'n' must be a positive integer, got: ", n, call. = FALSE)
# What + Why + How to fix
cli::cli_abort(c(
"{.arg config_path} does not exist: {.file {config_path}}",
"i" = "Create it with {.run create_config({.file {config_path}})}."
))
# What + context
cli::cli_abort(c(
"Column {.val {col_name}} not found in {.arg data}.",
"i" = "Available columns: {.val {names(data)}}"
))
```
**惡訊:**
```r
stop("Error") # What failed? No idea
stop("Invalid input") # Which input? What's wrong with it?
stop(paste("Error in step", i)) # No actionable information
```
**得:** 誤訊自證——初見誤者可診而修,不讀源。
**敗則:** 察近三缺報。若任一需讀源方解,其誤訊須改。
### 第四步:偏 stop() 於 warning()
用 `stop()`(或 `cli::cli_abort()`)於函不能生正果時。用 `warning()` 唯函仍能生有義果而呼者當知之憂。
**要則:** 若用或默得誤答,乃 `stop()`,非 `warning()`。
```r
# CORRECT: stop when result would be wrong
read_config <- function(path) {
if (!file.exists(path)) {
stop("Config file not found: ", path, call. = FALSE)
}
yaml::read_yaml(path)
}
# CORRECT: warn when result is still usable
summarize_data <- function(data) {
if (any(is.na(data$value))) {
warning(sum(is.na(data$value)), " NA values dropped from 'value' column", call. = FALSE)
data <- data[!is.na(data$value), ]
}
# proceed with valid data
}
```
**得:** `stop()` 用於將生誤果之況;`warning()` 留於降質但有效之果。
**敗則:** 察既 `warning()` 呼。若警後函返無義,改為 `stop()`。
### 第五步:以斷之內不變
於正碼中「不當生」之況用斷。於開發時捕程誤:
```r
# R: stopifnot for internal invariants
process_chunk <- function(chunk, total_size) {
stopifnot(
is.list(chunk),
length(chunk) > 0,
total_size > 0
)
# ...
}
# R: explicit assertion with context
merge_results <- function(left, right) {
if (ncol(left) != ncol(right)) {
stop("Internal error: column count mismatch (", ncol(left), " vs ", ncol(right),
"). This is a bug — please report it.", call. = FALSE)
}
# ...
}
```
**得:** 內不變以斷,缺於違處立現,非三呼後以謎誤。
**敗則:** 若 `stopifnot()` 訊過謎,改以明 `if/stop` 附脈。
### 第六步:重構反模
識並修此常反模:
**反模一:空 tryCatch(吞誤)**
```r
# BEFORE: Error silently disappears
result <- tryCatch(
parse_data(input),
error = function(e) NULL
)
# AFTER: Log, re-throw, or return a typed error
result <- tryCatch(
parse_data(input),
error = function(e) {
cli::cli_abort("Failed to parse input: {e$message}", parent = e)
}
)
```
**反模二:默值遮壞入**
```r
# BEFORE: Caller never knows their input was ignored
process <- function(x = 10) {
if (!is.numeric(x)) x <- 10 # silently replaces bad input
x * 2
}
# AFTER: Tell the caller about the problem
process <- function(x = 10) {
if (!is.numeric(x)) {
stop("'x' must be numeric, got ", class(x)[[1]], call. = FALSE)
}
x * 2
}
```
**反模三:suppressWarnings 為修**
```r
# BEFORE: Hiding the symptom instead of fixing the cause
result <- suppressWarnings(as.numeric(user_input))
# AFTER: Validate explicitly, handle the expected case
if (!grepl("^-?\\d+\\.?\\d*$", user_input)) {
stop("Expected a number, got: '", user_input, "'", call. = FALSE)
}
result <- as.numeric(user_input)
```
**反模四:統括之異處**
```r
# BEFORE: Every error treated the same
tryCatch(
complex_operation(),
error = function(e) message("Something went wrong")
)
# AFTER: Handle specific conditions, let unexpected ones propagate
tryCatch(
complex_operation(),
custom_validation_error = function(e) {
cli::cli_warn("Validation issue: {e$message}")
fallback_value
}
# Unexpected errors propagate naturally
)
```
**得:** 反模以明驗或具體誤處代之。
**敗則:** 若去 `tryCatch` 致連敗,上游碼有驗缺。修源,非症。
### 第七步:驗早敗重構
運試集以確誤徑正工:
```r
# Verify error messages are triggered
testthat::expect_error(calculate_summary("not_a_df"), "must be a data frame")
testthat::expect_error(calculate_summary(data.frame()), "at least one row")
testthat::expect_error(calculate_summary(mtcars, trim_pct = 2), "between 0 and 0.5")
# Verify valid inputs still work
testthat::expect_no_error(calculate_summary(mtcars, method = "mean"))
```
```bash
# Run full test suite
Rscript -e "devtools::test()"
```
**得:** 諸試過。誤徑試確壞入觸預誤訊。
**敗則:** 若既試賴默敗(如壞入返 NULL),更之以預新誤。
## 驗
- [ ] 諸公函於工前驗入
- [ ] 誤訊答:何敗、何處、何以、如何修
- [ ] `stop()` 用於生誤果之況
- [ ] `warning()` 唯用於降質有效之果
- [ ] 無默吞誤之空 `tryCatch`
- [ ] 無 `suppressWarnings()` 代正驗
- [ ] 無默遮壞入之默值
- [ ] 內不變用 `stopifnot()` 或明斷
- [ ] 各驗守有誤徑試
- [ ] 重構後試集過
## 陷
- **驗過深**:於信界(公 API)驗,非諸內輔。過驗加噪損性能
- **無脈之誤訊**:「Invalid input」迫呼者猜。恆含參名、預類/域、實收值
- **當用 stop() 時用 warning()**:若警後函返垃圾,呼者默得誤答。用 `stop()`,令呼者決如何處
- **tryCatch 吞誤**:`tryCatch(..., error = function(e) NULL)` 藏缺。若必捕,記或附脈重擲
- **忘 call. = FALSE**:R 中 `stop("msg")` 默含呼,於用顯噪。用面函用 `call. = FALSE`。`cli::cli_abort()` 自動
- **於試驗而非碼**:試驗行而不護產呼者。驗屬函本
- **雜系誤 R 本**:於 WSL 或 Docker,`Rscript` 或解至跨平台包而非原 R。以 `which Rscript && Rscript --version` 察。偏原 R(如 Linux/WSL 之 `/usr/local/bin/Rscript`)以穩。見 [Setting Up Your Environment](../../guides/setting-up-your-environment.md) 之 R 路配
## 參
- `write-testthat-tests` - 書驗誤徑之試
- `review-pull-request` - 審碼缺驗與默敗
- `review-software-architecture` - 於系級評誤處策
- `create-skill` - 循 agentskills.io 標造新技
- `security-audit-codebase` - 與入驗疊之安全審
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