Golang Database
Samuel BertheCommunityCreated by samberDistributed by Samuel Berthe
- Skill
- Tool integration
- Software development
- MIT
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a set of best practices for interacting with databases in Go using standard libraries like database/sql, sqlx, or pgx. It emphasizes explicit SQL usage over ORMs, focusing on security, performance, and maintainability. The guide covers essential topics including parameterized queries to prevent SQL injection, proper context propagation, transaction management, connection pool configuration, and robust error handling. It is designed for backend engineers who prioritize observable and reliable database code.
What it does
- Provides patterns for secure, parameterized SQL queries to prevent injection.
- Outlines best practices for struct scanning and handling NULLable database columns.
- Defines standards for transaction management and isolation levels.
- Explains how to configure and monitor database connection pools for performance.
- Details proper error handling and context propagation for database operations.
- Offers guidance on choosing between database/sql, sqlx, and pgx.
When to use it
- When writing new repository functions or query helpers in Go.
- When auditing existing Go code for database-related bugs or security vulnerabilities.
- When debugging performance issues related to connection pooling or query execution.
- When determining the best approach for mapping database rows to Go structs.
Advantages
- Promotes explicit, readable SQL that is easier to debug and test.
- Reduces the risk of SQL injection through mandatory parameterization.
- Improves application reliability by enforcing proper resource management like closing rows.
- Helps optimize database performance through correct connection pool settings.
Usage examples
- “How do I safely query a user by email in Go without using an ORM?”
- The agent will provide a code snippet using db.GetContext with a parameterized placeholder ($1 or ?) to ensure the query is safe from SQL injection.
- “Review this code for database connection leaks.”
- The agent will scan the provided code for missing defer rows.Close() calls or improper use of db.Query instead of db.Exec.
- “How should I handle a NULLable column in my database struct?”
- The agent will recommend using pointer fields (e.g., *string) for struct members and explain how they interact with database scanning and JSON marshaling.
- “What are the recommended settings for my database connection pool?”
- The agent will provide a configuration block for SetMaxOpenConns, SetMaxIdleConns, and related methods, explaining the trade-offs for each.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/samber/golang-database/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip golang-database.zip -d .agents/skills
Documentation by samber
Persona: You are a Go backend engineer who writes safe, explicit, and observable database code. You treat SQL as a first-class language — no ORMs, no magic — and you catch data integrity issues at the boundary, not deep in the application.
Modes:
Write mode — generating new repository functions, query helpers, or transaction wrappers: follow the skill's sequential instructions; launch a background agent to grep for existing query patterns and naming conventions in the codebase before generating new code.
Review/debug mode — auditing or debugging existing database code: use a sub-agent to scan for missing
rows.Close(), un-parameterized queries, missing context propagation, and absent error checks in parallel with reading the business logic.
Community default. A company skill that explicitly supersedes
samber/cc-skills-golang@golang-databaseskill takes precedence.
Go Database Best Practices
Go's database/sql provides a solid foundation for database access. Use sqlx or pgx on top of it for ergonomics — never an ORM.
When using sqlx or pgx, refer to the library's official documentation and code examples for current API signatures.
Best Practices Summary
Use sqlx or pgx, not ORMs — ORMs hide SQL, generate unpredictable queries, and make debugging harder
Queries MUST use parameterized placeholders — NEVER concatenate user input into SQL strings
Context MUST be passed to all database operations — use
*Contextmethod variants (QueryContext,ExecContext,GetContext)sql.ErrNoRowsMUST be handled explicitly — distinguish "not found" from real errors usingerrors.IsRows MUST be closed after iteration —
defer rows.Close()immediately afterQueryContextcallsNEVER use
db.Queryfor statements that don't return rows —Queryreturns*Rowswhich must be closed; if you forget, the connection leaks back to the pool. Usedb.ExecinsteadUse transactions for multi-statement operations — wrap related writes in
BeginTxx/CommitUse
SELECT ... FOR UPDATEwhen reading data you intend to modify — prevents race conditionsSet custom isolation levels when default READ COMMITTED is insufficient (e.g., serializable for financial operations)
Handle NULLable columns with pointer fields (
*string,*int) orsql.NullXxxtypesConnection pool MUST be configured —
SetMaxOpenConns,SetMaxIdleConns,SetConnMaxLifetime,SetConnMaxIdleTimeUse external tools for migrations — golang-migrate or Flyway, never hand-rolled or AI-generated migration SQL
Batch operations in reasonable sizes — not row-by-row (too many round trips), not millions at once (locks and memory)
Never create or modify database schemas — a schema that looks correct on toy data can create hotspots, lock contention, or missing indexes under real production load. Schema design requires understanding of data volumes, access patterns, and production constraints that AI does not have
Avoid hidden SQL features — do not rely on triggers, views, materialized views, stored procedures, or row-level security in application code
Library Choice
| Library | Best for | Struct scanning | PostgreSQL-specific |
|---|---|---|---|
database/sql | Portability, minimal deps | Manual Scan | No |
sqlx | Multi-database projects | StructScan | No |
pgx | PostgreSQL (30-50% faster) | pgx.RowToStructByName | Yes (COPY, LISTEN, arrays) |
| GORM/ent | Avoid | Magic | Abstracted away |
Why NOT ORMs:
Unpredictable query generation — N+1 problems you cannot see in code
Magic hooks and callbacks (BeforeCreate, AfterUpdate) make debugging harder
Schema migrations coupled to application code
Learning the ORM API is harder than learning SQL, and the abstraction leaks
Parameterized Queries
// ✗ VERY BAD — SQL injection vulnerability
query := fmt.Sprintf("SELECT * FROM users WHERE email = '%s'", email)
// ✓ Good — parameterized (PostgreSQL)
var user User
err := db.GetContext(ctx, &user, "SELECT id, name, email FROM users WHERE email = $1", email)
// ✓ Good — parameterized (MySQL)
err := db.GetContext(ctx, &user, "SELECT id, name, email FROM users WHERE email = ?", email)Dynamic IN clauses
query, args, err := sqlx.In("SELECT * FROM users WHERE id IN (?)", ids)
if err != nil {
return fmt.Errorf("building IN clause: %w", err)
}
query = db.Rebind(query) // adjust placeholders for your driver
err = db.SelectContext(ctx, &users, query, args...)Dynamic column names
Never interpolate column names from user input. Use an allowlist:
allowed := map[string]bool{"name": true, "email": true, "created_at": true}
if !allowed[sortCol] {
return fmt.Errorf("invalid sort column: %s", sortCol)
}
query := fmt.Sprintf("SELECT id, name, email FROM users ORDER BY %s", sortCol)For more injection prevention patterns, see the samber/cc-skills-golang@golang-security skill.
Struct Scanning and NULLable Columns
Use db:"column_name" tags for sqlx, pgx.CollectRows with pgx.RowToStructByName for pgx. Handle NULLable columns with pointer fields (*string, *time.Time) — they work cleanly with both scanning and JSON marshaling. See Scanning Reference for examples of all approaches.
Error Handling
func GetUser(id string) (*User, error) {
var user User
err := db.GetContext(ctx, &user, "SELECT id, name FROM users WHERE id = $1", id)
if err != nil {
if errors.Is(err, sql.ErrNoRows) {
return nil, ErrUserNotFound // translate to domain error
}
return nil, fmt.Errorf("querying user %s: %w", id, err)
}
return &user, nil
}or:
func GetUser(id string) (u *User, exists bool, err error) {
var user User
err := db.GetContext(ctx, &user, "SELECT id, name FROM users WHERE id = $1", id)
if err != nil {
if errors.Is(err, sql.ErrNoRows) {
return nil, false, nil // "no user" is not a technical error, but a domain error
}
return nil, false, fmt.Errorf("querying user %s: %w", id, err)
}
return &user, true, nil
}Always close rows
rows, err := db.QueryContext(ctx, "SELECT id, name FROM users")
if err != nil {
return fmt.Errorf("querying users: %w", err)
}
defer rows.Close() // prevents connection leaks
for rows.Next() {
// ...
}
if err := rows.Err(); err != nil { // always check after iteration
return fmt.Errorf("iterating users: %w", err)
}Common database error patterns
| Error | How to detect | Action |
|---|---|---|
| Row not found | errors.Is(err, sql.ErrNoRows) | Return domain error |
| Unique constraint | Check driver-specific error code | Return conflict error |
| Connection refused | err != nil on db.PingContext | Fail fast, log, retry with backoff |
| Serialization failure | PostgreSQL error code 40001 | Retry the entire transaction |
| Context canceled | errors.Is(err, context.Canceled) | Stop processing, propagate |
Context Propagation
Always use the *Context method variants to propagate deadlines and cancellation:
// ✗ Bad — no context, query runs until completion even if client disconnects
db.Query("SELECT ...")
// ✓ Good — respects context cancellation and timeouts
db.QueryContext(ctx, "SELECT ...")For context patterns in depth, see the samber/cc-skills-golang@golang-context skill.
Transactions, Isolation Levels, and Locking
For transaction patterns, isolation levels, SELECT FOR UPDATE, and locking variants, see Transactions.
Connection Pool
db.SetMaxOpenConns(25) // limit total connections
db.SetMaxIdleConns(10) // keep warm connections ready
db.SetConnMaxLifetime(5 * time.Minute) // recycle stale connections
db.SetConnMaxIdleTime(1 * time.Minute) // close idle connections fasterFor sizing guidance and formulas, see Database Performance.
Migrations
Use an external migration tool. Schema changes require human review with understanding of data volumes, existing indexes, foreign keys, and production constraints.
Recommended tools:
golang-migrate — CLI + Go library, supports all major databases
Flyway — JVM-based, widely used in enterprise environments
Atlas — modern, declarative schema management
Migration SQL should be written and reviewed by humans, versioned in source control, and applied through CI/CD pipelines.
Avoid Hidden SQL Features
Do not rely on triggers, views, materialized views, stored procedures, or row-level security in application code — they create invisible side effects and make debugging impossible. Keep SQL explicit and visible in Go where it can be tested and version-controlled.
Schema Creation
This skill does NOT cover schema creation. AI-generated schemas are often subtly wrong — missing indexes, incorrect column types, bad normalization, or missing constraints. Schema design requires understanding data volumes, access patterns, query profiles, and business constraints. Use dedicated database tooling and human review.
Deep Dives
Transactions — Transaction boundaries, isolation levels, deadlock prevention,
SELECT FOR UPDATETesting Database Code — Mock connections, integration tests with containers, fixtures, schema setup/teardown
Database Performance — Connection pool sizing, batch processing, indexing strategy, query optimization
Struct Scanning — Struct tags, NULLable column handling, JSON marshaling patterns
Cross-References
→ See
samber/cc-skills-golang@golang-securityskill for SQL injection prevention patterns→ See
samber/cc-skills-golang@golang-contextskill for context propagation to database operations→ See
samber/cc-skills-golang@golang-error-handlingskill for database error wrapping patterns→ See
samber/cc-skills-golang@golang-testingskill for database integration test patterns
References
Files
- ATTRIBUTION.mdAdded by AutoGineer902 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md11.3 KiB
- evals/evals.json15.0 KiB
- references/performance.md9.0 KiB
- references/scanning.md2.2 KiB
- references/testing.md6.5 KiB
- references/transactions.md1.5 KiB
references/performance.md
Database Performance
Table of Contents
Connection Pool Sizing
Configuration
Monitoring
Prometheus Metrics
Batch Processing
Sweet spot: 100–1,000 rows per batch
Batch INSERT with sqlx
Bulk INSERT with pgx (PostgreSQL COPY protocol)
Cursor-based pagination (avoid OFFSET)
Indexing Strategy
Use SQL MCP to check existing indexes
When to suggest adding indexes
When to suggest removing indexes
Query Performance Tips
Connection Pool Sizing
Configuration
db, err := sqlx.Connect("postgres", dsn)
if err != nil {
return fmt.Errorf("connecting to database: %w", err)
}
db.SetMaxOpenConns(25) // total connections (match your DB capacity)
db.SetMaxIdleConns(10) // keep connections warm, reduce handshake overhead
db.SetConnMaxLifetime(5 * time.Minute) // recycle connections (DNS changes, server restarts)
db.SetConnMaxIdleTime(1 * time.Minute) // release idle connections back to the pool| Setting | Too low | Too high |
|---|---|---|
MaxOpenConns | Requests queue waiting for conn | DB overwhelmed, context switches |
MaxIdleConns | Cold connections, slow queries | Wasted memory holding idle conns |
ConnMaxLifetime | Frequent reconnection overhead | Stale connections after failover |
ConnMaxIdleTime | Same as MaxIdleConns too low | Idle conns consume server memory |
Monitoring
Check pool stats in production to detect exhaustion:
stats := db.Stats()
slog.Info("db pool",
"open", stats.OpenConnections,
"in_use", stats.InUse,
"idle", stats.Idle,
"wait_count", stats.WaitCount, // total waits for a connection
"wait_duration", stats.WaitDuration, // total wait time
)If WaitCount keeps climbing, increase MaxOpenConns or optimize slow queries.
Prometheus Metrics
Use a custom Prometheus collector to export pool metrics on-demand (scales to multiple pools automatically):
type DBCollector struct {
pools map[string]*sqlx.DB
}
func NewDBCollector(pools map[string]*sqlx.DB) *DBCollector {
return &DBCollector{pools: pools}
}
func (c *DBCollector) Describe(ch chan<- *prometheus.Desc) {
ch <- prometheus.NewDesc("db_open_connections", "Number of open connections", []string{"pool"}, nil)
ch <- prometheus.NewDesc("db_in_use_connections", "Connections currently in use", []string{"pool"}, nil)
ch <- prometheus.NewDesc("db_idle_connections", "Idle connections in pool", []string{"pool"}, nil)
ch <- prometheus.NewDesc("db_total_latency_seconds", "Total latency for a connection", []string{"pool"}, nil)
}
func (c *DBCollector) Collect(ch chan<- prometheus.Metric) {
for poolName, db := range c.pools {
stats := db.Stats()
ch <- prometheus.MustNewConstMetric(
prometheus.NewDesc("db_open_connections", "Number of open connections", []string{"pool"}, nil),
prometheus.GaugeValue, float64(stats.OpenConnections), poolName)
ch <- prometheus.MustNewConstMetric(
prometheus.NewDesc("db_in_use_connections", "Connections currently in use", []string{"pool"}, nil),
prometheus.GaugeValue, float64(stats.InUse), poolName)
ch <- prometheus.MustNewConstMetric(
prometheus.NewDesc("db_idle_connections", "Idle connections in pool", []string{"pool"}, nil),
prometheus.GaugeValue, float64(stats.Idle), poolName)
ch <- prometheus.MustNewConstMetric(
prometheus.NewDesc("db_wait_duration_seconds_total", "Total connection wait duration", []string{"pool"}, nil),
prometheus.CounterValue, stats.WaitDuration.Seconds(), poolName)
}
}
func init() {
pools := map[string]*sqlx.DB{
"primary": mainDB,
"replica": replicaDB,
}
prometheus.MustRegister(NewDBCollector(pools))
}Collector advantages:
Metrics are collected on-demand during scrapes (no background goroutine)
Always returns current state (no stale data between scrapes)
Scales to multiple pools automatically
Lower memory footprint (no metric state in memory)
Alert thresholds:
Open connections approaching
MaxOpenConns→ risk of request queuingWait count climbing steadily → pool is exhausted, increase
MaxOpenConnsIdle connections too high → reduce
MaxIdleConnsor lowerConnMaxIdleTime
Batch Processing
Avoid two extremes:
Row-by-row — N round trips for N rows, extremely slow
One giant batch — locks tables, consumes memory, can timeout and block other queries
Sweet spot: 100–1,000 rows per batch
Adjust based on row size and database load. Larger rows → smaller batches.
Batch INSERT with sqlx
func insertUsersBatch(ctx context.Context, db *sqlx.DB, users []User) error {
const batchSize = 500
for i := 0; i < len(users); i += batchSize {
end := min(i+batchSize, len(users))
batch := users[i:end]
_, err := db.NamedExecContext(ctx, `INSERT INTO users (name, email) VALUES (:name, :email)`, batch)
if err != nil {
return fmt.Errorf("inserting users batch %d-%d: %w", i, end, err)
}
}
return nil
}Bulk INSERT with pgx (PostgreSQL COPY protocol)
For maximum throughput on PostgreSQL, use pgx.CopyFrom which uses the binary COPY protocol — significantly faster than multi-row INSERT:
rows := make([][]any, len(users))
for i, u := range users {
rows[i] = []any{u.Name, u.Email}
}
_, err := pool.CopyFrom(ctx,
pgx.Identifier{"users"},
[]string{"name", "email"},
pgx.CopyFromRows(rows),
)Cursor-based pagination (avoid OFFSET)
For reading large datasets, use cursor-based pagination instead of OFFSET. OFFSET re-scans skipped rows, getting slower as you paginate deeper:
// ✗ Bad — OFFSET re-scans rows, O(offset + limit)
SELECT * FROM events ORDER BY created_at LIMIT 100 OFFSET 10000
// ✓ Good — cursor-based, O(limit) regardless of depth
SELECT * FROM events WHERE created_at > $1 ORDER BY created_at LIMIT 100Indexing Strategy
Never create or drop indexes yourself. Index changes affect production query performance and write throughput. Always suggest to the developer and let them decide.
Use SQL MCP to check existing indexes
When a SQL MCP tool is available, query the database to check existing indexes before suggesting new ones:
-- PostgreSQL: list indexes on a table
SELECT indexname, indexdef
FROM pg_indexes
WHERE tablename = 'users';
-- Check for unused indexes (low scan count relative to writes)
SELECT schemaname, relname, indexrelname, idx_scan, idx_tup_read
FROM pg_stat_user_indexes
WHERE idx_scan < 10
ORDER BY idx_scan;When to suggest adding indexes
Foreign key columns (PostgreSQL does NOT auto-index foreign keys)
Columns frequently used in
WHERE,JOIN, orORDER BYComposite indexes for multi-column queries (leftmost column is most selective)
Partial indexes for filtered queries (
WHERE active = true)
When to suggest removing indexes
Indexes with near-zero
idx_scancount (nobody reads them)Duplicate indexes (same columns in same order)
Indexes on write-heavy tables that slow down INSERT/UPDATE/DELETE
Wide composite indexes where a narrower one would suffice
Always present findings as suggestions with data (scan counts, table size), never execute DDL yourself.
Query Performance Tips
EXPLAIN ANALYZEbefore optimizing — measure, don't guessList columns explicitly — avoid
SELECT *, it fetches unnecessary data and breaks struct scanning when schema changesUse
LIMITfor pagination, always with anORDER BYPrefer
EXISTSoverCOUNTfor existence checks —EXISTSstops at the first matchAvoid N+1 queries — use
JOINor batchWHERE id IN (...)instead of querying in a loopSuggest improvements, never execute them — performance changes (indexes, query rewrites, configuration) need human review in context of production data and workload patterns
Rules:
Batch operations SHOULD use 100–1,000 rows per batch — adjust based on row size and database load.
Cursor-based pagination MUST replace
OFFSETfor large datasets — the cursor column MUST be chosen based on actual indexes (e.g.,created_at,user_id).NEVER create indexes blindly — check existing indexes, measure with
EXPLAIN ANALYZE, and present findings as suggestions.N+1 queries MUST be eliminated — use
JOINor batchWHERE id IN (...).
→ See samber/cc-skills-golang@golang-observability skill for database metrics and query monitoring. → See samber/cc-skills@promql-cli skill for querying pool metrics (db_open_connections, db_in_use_connections, db_idle_connections) via CLI.
Requirements and compatibility
Written for: Claude Code, OpenAI Codex
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Claude Code frontmatter
The author says: Designed for Claude Code, Codex or similar harness, and for projects using Golang.
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- samber
- Publisher
- Samuel Berthe (Community)
- Distributed by
- Samuel Berthe
- Source
- samber/cc-skills-golang · skills/golang-database · commit bb1d43d
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by samber or Samuel Berthe.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download golang-database.zip