drops

drops

A Drizzle-inspired, driver-agnostic SQL toolkit for Go.

drops does not wrap an existing driver — it defines its own minimal Driver interface so it stays out of the way of however you connect to your database. Plug in database/sql, pgx, or your own pool by implementing four methods.

Status

Early. Two dialects ship today:

• drops/pg — PostgreSQL. Full surface: SELECT (joins, grouping, CTEs, set ops, window functions, subqueries), INSERT (RETURNING, ON CONFLICT), UPDATE, DELETE, transactions, DDL (schemas, extensions, sequences, enums, views, functions, triggers, indexes), file-based migrations (Go-native + drizzle-kit compatible), and eager-loaded relations (HasMany, HasOne, BelongsTo).
• drops/cache — driver-agnostic cache interface (Get / Set / Delete / Exists / TTL / Ping / Close plus a MultiCache batch extension). Sentinel errors (ErrNotFound, ErrClosed, ErrInvalidKey).
• drops/cache/memory — in-process LRU-ish backend with TTL, optional janitor goroutine, FIFO eviction on MaxEntries. Zero deps; ideal for tests and the local tier of a two-level cache.
• drops/cache/redis — Redis backend with a minimal RESP2 client
  • bounded connection pool. Zero deps. Supports AUTH (legacy and ACL forms), SELECT db, key prefix, and the same drops.Hook contract used elsewhere.
• drops/qdrant — Qdrant vector database. Focused HTTP client (stdlib only): collections, upsert/delete/retrieve, search / recommend / scroll, and a Must/Should/MustNot filter DSL with Eq/In/Range/HasID/Geo conditions.
• drops/pg with pgvector — Vector(name, dim), HalfVec(name, dim), SparseVec, BitVec column types plus the distance operators (<-> L2, <#> inner product, <=> cosine, <+> L1) for similarity search in Postgres. HNSW/IVFFlat indexes with the right operator class via Index.OpClass(...).
• drops/clickhouse — ClickHouse. Engine-bound tables (MergeTree family + replicated/distributed via Raw), CH-specific types (Array, Nullable, LowCardinality, Decimal, DateTime64, Tuple, Map, Enum8/16), full SELECT (PREWHERE, FINAL, SAMPLE, ASOF JOIN, SETTINGS), batch INSERT, and the analytics-aggregate library (uniq, uniqExact, quantile, argMax, groupArray, quantileTiming, …).

Both dialects share the root drops package (driver interface, Expression, Builder, Hook, transactions).

Install

go get github.com/bernardoforcillo/drops

To use the bundled database/sql adapter (drops/stdlib) you also need a driver — for PostgreSQL, github.com/jackc/pgx/v5/stdlib; for ClickHouse, github.com/ClickHouse/clickhouse-go/v2.

Quick start

import (
    "github.com/bernardoforcillo/drops/pg"
    "github.com/bernardoforcillo/drops/stdlib"
)

// Schema. Each pg.Add returns a typed *pg.Col[T] so subsequent
// comparisons and value bindings are checked at compile time.
var (
    Users    = pg.NewTable("users")
    UserID   = pg.Add(Users, pg.BigSerial("id").PrimaryKey())  // *Col[int64]
    UserName = pg.Add(Users, pg.Text("name").NotNull())         // *Col[string]
    UserAge  = pg.Add(Users, pg.Integer("age"))                 // *Col[int32]
)

type User struct {
    ID   int64
    Name string
    Age  *int32
}

// Connection.
sqlDB, _ := sql.Open("pgx", dsn)
db := pg.New(stdlib.New(sqlDB))

// Insert + RETURNING — Val(v) is type-checked against the column.
var u User
db.Insert(Users).
    Row(UserName.Val("Alice"), UserAge.Val(30)).
    Returning(UserID, UserName, UserAge).
    One(ctx, &u)

// Select with typed predicates.
var users []User
db.Select().
    From(Users).
    Where(UserAge.Gte(18)).
    OrderBy(UserName.Asc()).
    All(ctx, &users)

A complete demonstration without a database is in examples/sqlgen/main.go — it prints generated SQL. A real DB demo (via pgx) is under _examples/postgres/.

ClickHouse

The ClickHouse dialect is the same shape, with ? placeholders and an engine-bound table:

import (
    _ "github.com/ClickHouse/clickhouse-go/v2"
    "github.com/bernardoforcillo/drops/clickhouse"
    "github.com/bernardoforcillo/drops/stdlib"
)

var (
    Events    = clickhouse.NewTable("events")
    EventID   = clickhouse.Add(Events, clickhouse.UUID("id"))
    EventTS   = clickhouse.Add(Events, clickhouse.DateTime("ts", "UTC"))
    EventUser = clickhouse.Add(Events, clickhouse.UInt64("user_id"))
    EventKind = clickhouse.Add(Events, clickhouse.String("kind").LowCardinality())
    EventDur  = clickhouse.Add(Events, clickhouse.Float64("duration_ms"))
)

func init() {
    Events.
        Engine(clickhouse.MergeTree()).
        OrderBy(EventTS, EventUser).
        PartitionBy(clickhouse.ToYYYYMM(EventTS)).
        Setting("index_granularity", "8192")
}

sqlDB, _ := sql.Open("clickhouse", "clickhouse://localhost:9000/default")
db := clickhouse.New(stdlib.New(sqlDB)).WithHook(
    clickhouse.LoggerHookOrSimilar(...), // any drops.Hook
)
defer db.Close()

// DDL.
db.ExecExpr(ctx, clickhouse.CreateTableIfNotExists(Events))

// Batch insert (small batches; for native columnar bulk loads, drop
// to the driver directly).
db.Insert(Events).
    Row(EventID.Val(uuid1), EventTS.Val(t1), EventUser.Val(42),
        EventKind.Val("click"), EventDur.Val(0.25)).
    Row(EventID.Val(uuid2), EventTS.Val(t2), EventUser.Val(43),
        EventKind.Val("view"), EventDur.Val(1.10)).
    Exec(ctx)

// Analytical query — PREWHERE + CH aggregates.
type bucket struct {
    Day   time.Time
    P95   float64
    Hits  int64
}
var rows []bucket
db.Select(
    clickhouse.As(clickhouse.ToStartOfDay(EventTS), "day"),
    clickhouse.As(clickhouse.QuantileTiming(0.95, EventDur), "p95"),
    clickhouse.As(clickhouse.CountAll(), "hits"),
).
    From(Events).
    Prewhere(EventKind.Eq("click")).
    Where(EventTS.Gte(weekAgo)).
    GroupBy(clickhouse.ToStartOfDay(EventTS)).
    OrderBy(clickhouse.ToStartOfDay(EventTS).Asc()).
    All(ctx, &rows)

The clickhouse package mirrors pg's Hook/Ping/Close/InTx contract, identifier validation, and *Col[T] type safety. The differences are intentional: ClickHouse-flavoured SQL (PREWHERE, FINAL, SAMPLE, SETTINGS, ASOF JOIN), engine-bound tables, no RETURNING / ON CONFLICT / foreign keys.

Vectors: pgvector

The pg package speaks pgvector once the extension is installed. Declare vector columns alongside ordinary ones; the distance operators are first-class predicates and ORDER BY expressions.

import "github.com/bernardoforcillo/drops/pg"

var (
    Items         = pg.NewTable("items")
    ItemID        = pg.Add(Items, pg.BigSerial("id").PrimaryKey())
    ItemEmbedding = pg.Add(Items, pg.Vector("embedding", 384)) // []float32
)

// One-time: install the extension and the HNSW index.
db.ExecExpr(ctx, pg.CreateExtensionIfNotExists("vector"))
db.ExecExpr(ctx, pg.CreateTable(Items))
db.ExecExpr(ctx, pg.CreateIndex(
    pg.NewIndex("items_embedding_idx", Items, ItemEmbedding).
        Using("hnsw").
        OpClass(pg.VectorCosineOps).
        With("m = 16, ef_construction = 64"),
))

// k-nearest-neighbours search.
type hit struct {
    ID       int64
    Distance float64
}
var top []hit
db.Select(
    ItemID,
    pg.As(ItemEmbedding.Cosine(query), "distance"),
).
    From(Items).
    OrderBy(ItemEmbedding.Cosine(query)).
    Limit(10).
    All(ctx, &top)

Available types: Vector (float32), HalfVec (float16-on-the-wire, float32 in Go), SparseVec, BitVec. Distance operators: L2Distance (<->), InnerProduct (<#>), CosineDistance (<=>), L1Distance (<+>), HammingDistance, JaccardDistance — plus shorthand methods Embedding.L2 / .IP / .Cosine / .L1 on the column.

Vector database: Qdrant

When pgvector isn't enough — billions of vectors, heavy filtering, or you already run Qdrant — drops/qdrant is a focused HTTP client. Zero external deps (net/http + encoding/json):

import "github.com/bernardoforcillo/drops/qdrant"

cli, _ := qdrant.NewClient("http://localhost:6333",
    qdrant.WithAPIKey(os.Getenv("QDRANT_API_KEY")))

_ = cli.CreateCollection(ctx, "embeddings", qdrant.CollectionConfig{
    Vectors: qdrant.VectorParams{Size: 384, Distance: qdrant.DistanceCosine},
})

_ = cli.Upsert(ctx, "embeddings", []qdrant.Point{
    {ID: "doc-1", Vector: vec1, Payload: map[string]any{"topic": "go",  "draft": false}},
    {ID: "doc-2", Vector: vec2, Payload: map[string]any{"topic": "rust","draft": false}},
})

hits, _ := cli.Search(ctx, "embeddings", qdrant.SearchRequest{
    Vector:      query,
    Limit:       10,
    WithPayload: true,
    Filter: qdrant.Must(
        qdrant.Eq("topic", "go"),
        qdrant.Eq("draft", false),
        qdrant.Range("created_at", qdrant.RangeOpts{Gte: qdrant.F(1700000000)}),
    ),
})

Surface: CreateCollection / DeleteCollection / CollectionInfo / ListCollections, Upsert / DeleteByIDs / DeleteByFilter / Retrieve / Count, Search / Recommend / Scroll, plus a Must / Should / MustNot filter DSL with Eq / In / NotIn / MatchText / Range / HasID / IsEmpty / IsNull / GeoIn / Nest conditions. HTTPError (with Status/Body) and ErrCollectionMissing are exported for errors.As / errors.Is.

Design

Typed columns

Type constructors return *pg.Col[T], where T is the Go value type (int32 for integer, string for text, time.Time for timestamp, []byte for bytea, etc). Builder methods preserve T, so a chained declaration stays typed end-to-end:

UserAge.Eq(30)           // OK
UserAge.Eq("thirty")     // compile error: cannot use string as int32
UserAge.Val(30)          // OK; binds as $N

For places that don't care about the value type — JOIN ON, ON CONFLICT (...), EXCLUDED.col — both *Column and *Col[T] satisfy the pg.ColRef interface, so call sites accept either.

Driver interface

The root package defines:

type Driver interface {
    Exec(ctx context.Context, sql string, args ...any) (Result, error)
    Query(ctx context.Context, sql string, args ...any) (Rows, error)
    Begin(ctx context.Context) (Tx, error)
}

drops itself imports no concrete driver. The stdlib subpackage adapts *sql.DB; you can write your own adapter for pgx.Pool or anything else in a few dozen lines.

Building queries

Every fragment of SQL — a column, an operator, a subquery — is a drops.Expression:

type Expression interface {
    WriteSQL(b *Builder)
}

The Builder handles parameter binding ($N) and identifier quoting. Operators come in two flavours:

• Typed methods on *Col[T] — UserAge.Gte(18), UserName.Like("A%"), UserAge.In(18, 21, 25), UserAge.Between(18, 65). Type-checked.
• Untyped free functions — pg.Eq(a, b), pg.And(...), pg.Or(...), pg.Not(p), pg.In(col, slice). Useful for column-to-column comparisons, AND/OR composition, and slice expansion.

Scanning

All(ctx, &dest) and One(ctx, &dest) scan rows into struct values. Field-to-column mapping rules:

1. drop:"name" struct tag, if present (drop:"-" to skip)
2. exact field name match
3. snake_case of the field name (UserID → user_id)

Unmatched columns go to a discard sink, so projecting fewer columns than the struct has is fine.

Observability

Attach a drops.Hook to log every operation, time queries, raise slow-query alerts, or feed a tracer. Hooks fire for exec, query, begin, commit, rollback, and ping — including those issued through query builders — and are propagated into transaction-bound DBs returned by Begin / InTx.

db := pg.New(stdlib.New(sqlDB)).WithHook(
    pg.LoggerHook(log.Printf, pg.LoggerOptions{
        SlowQuery: 100 * time.Millisecond,
    }),
)

// Compose multiple hooks (metrics + logging):
db = db.WithHook(drops.ChainHooks(metricsHook, loggerHook))

Each event is a drops.QueryEvent{Kind, SQL, Args, Duration, Err}. LoggerHook is one convenience built on top — write your own for OTel, Prometheus, Datadog, etc. in a few lines. db.Ping(ctx) issues SELECT 1 and is the natural shape for a Kubernetes readiness probe.

Sentinel errors

Common failure modes are exported so callers can branch with errors.Is:

Sentinel	When
pg.ErrReturningRequired	INSERT/UPDATE/DELETE.All or .One called without Returning(...)
pg.ErrNoRowsToInsert	Insert(t).Exec with no Row(...)
pg.ErrNoUpdateAssignments	Update(t).Exec with no Set(...)
pg.ErrSchemaRequired	Push called with a nil *Schema
pg.ErrInvalidIdentifier	bad table / schema / column name (empty, NUL, non-UTF8)
pg.ErrNoRows	Select.One / Find.One matched zero rows
pg.ErrNoMigrationsApplied	Migrator.Down with empty history

Transactions

db.InTx(ctx, func(tx *pg.DB) error {
    // tx is a DB bound to the transaction; nil commits, error rolls back.
    return nil
})

Or take an explicit handle with db.Begin(ctx) and call Commit / Rollback yourself.

Relations

Declare relations once, eager-load with Find().With(...):

pg.NewRelations(Users).
    HasMany("posts", Posts, UserID, PostUserID).
    ManyToMany("groups", Groups, UserGroups,
        UserGroupsUserID, UserGroupsGroupID, // junction FKs
        UserID, GroupID,                      // local + target keys
    )
pg.NewRelations(Posts).
    BelongsTo("author", Users, PostUserID, UserID)

type Post struct {
    ID     int64
    UserID int64 `drop:"user_id"`
    Title  string
}
type User struct {
    ID     int64
    Name   string
    Posts  []Post  `dropRel:"posts"`     // matched by tag
    Groups []Group `dropRel:"groups"`    // many-to-many through UserGroups
}

var users []User
db.Find(Users).
    With("posts", "groups").
    Where(UserAge.Gte(18)).
    All(ctx, &users)

Each kind takes a different shape:

Kind	Field type	Queries fired
HasMany	[]Child or []*Child	parent + 1 child query
HasOne	Child or *Child	parent + 1 child query (takes the first match)
BelongsTo	Parent or *Parent	row + 1 parent query
ManyToMany	[]Target or []*Target	parent + 1 junction + 1 target query

Relation fields are matched by dropRel:"<name>" tag first, then by case-insensitive name match.

Nested (deep) relations

Eager-load relations of relations with dot paths. Each edge runs exactly one batched query — With("posts.comments") fetches every parent's posts, then every comment of those posts, regardless of how many rows are involved. Paths that share a prefix are merged, so the shared edge is fetched only once:

type Comment struct {
    ID     int64
    PostID int64 `drop:"post_id"`
    Body   string
}
type Post struct {
    ID       int64
    UserID   int64     `drop:"user_id"`
    Title    string
    Comments []Comment `dropRel:"comments"`
}

pg.NewRelations(Posts).HasMany("comments", Comments, PostID, CommentPostID)

var users []User
db.Find(Users).
    With("posts.comments", "posts.tags"). // posts fetched once, fans out
    All(ctx, &users)
// users[i].Posts[j].Comments is populated in place.

Unknown relations — at any depth — are reported before a single query runs, so a typo in With("posts.commnets") fails fast.

Filtering and ordering an eager load

WithRel configures one relation with a Where filter, an OrderBy, and any deeper relations — all applied to that edge's single batched query, so filtering/sorting costs nothing extra:

var users []User
db.Find(Users).
    WithRel("posts", func(p *pg.RelConfig) {
        p.Where(Published.Eq(true)).      // only published posts
            OrderBy(PostCreatedAt.Desc()). // newest first, per user
            With("comments")               // …and load their comments
    }).
    All(ctx, &users)

The Where is AND-ed onto the IN (parent keys) predicate; the OrderBy sorts the batched result, and because rows are grouped in arrival order each parent's slice comes out correctly sorted. For ManyToMany, OrderBy re-sorts each parent's slice into the target query's order (the default, without OrderBy, follows junction-row order). WithRel and With merge when they name the same edge, so it is still fetched once.

Per-parent LIMIT/OFFSET (drizzle's with: { posts: { limit } }) is not yet supported — a single LIMIT would cap the whole batched result, not each parent's slice, which needs a window-function rewrite.

Migrations

Four pieces ship in the box:

1. pg.GenerateMigration — produces drizzle-kit-format migrations from a Go schema (diff against the previous snapshot).
2. pg.Push — introspects the live database, diffs vs the Go schema, applies the changes directly (drizzle-kit push equivalent; no file history).
3. pg.DrizzleMigrator — applies migrations written in drizzle-kit's format (either by GenerateMigration or by drizzle-kit itself).
4. pg.Migrator — a simpler standalone runner that uses its own file convention. Use this if you don't want any drizzle compatibility.

All four understand the Safe / IF [NOT] EXISTS mode, see Idempotent DDL below.

Generating migrations (pg.GenerateMigration)

Given a *pg.Schema describing your tables, GenerateMigration:

• reads drizzle/meta/_journal.json and the latest meta/<idx>_snapshot.json (if any)
• builds a fresh snapshot from your current Go schema declarations
• diffs the two and emits the SQL to evolve the database
• writes <dir>/<NNNN>_<name>.sql, <dir>/meta/<NNNN>_snapshot.json, and an updated <dir>/meta/_journal.json

The output is byte-for-byte identical between drops and drizzle-kit for the features we both support (tables, columns with PG types, NOT NULL, DEFAULT, single-column UNIQUE, single-column foreign keys with ON DELETE / ON UPDATE). Snapshots round-trip through both tools.

schema := pg.NewSchema(Users, Posts, UserGroups, Groups)

res, err := pg.GenerateMigration(pg.GenerateOptions{
    Schema: schema,
    Dir:    "drizzle",
    Name:   "init", // omit for a random "ancient_forest"-style name
})
if err != nil {
    log.Fatal(err)
}
if res.NoOp {
    log.Println("schema unchanged")
} else {
    log.Printf("wrote %s", res.Tag)
}

Typical workflow: stash this in a cmd/migrate/main.go (or similar) and run go run ./cmd/migrate whenever the schema changes. The output is what drizzle-kit's generate command would produce, so the existing drizzle-orm runtime — or pg.DrizzleMigrator below — can apply it.

A runnable in-memory walkthrough is in examples/generate/main.go.

What GenerateMigration covers (today): CREATE TABLE, DROP TABLE, ADD/DROP COLUMN, ALTER COLUMN type/NOT NULL/DEFAULT, ADD/DROP single-column UNIQUE, ADD/DROP single-column FOREIGN KEY (with ON DELETE/ON UPDATE).

What it does not cover yet: indexes, composite primary keys, composite uniques/FKs, check constraints, enums, sequences, views, RLS policies. The generated snapshot leaves those collections empty, which matches drizzle-kit's "no such constructs declared" state — but if you mix drops's generator with hand-edited or drizzle-kit-authored snapshots that use these features, drops won't be aware of them.

Pushing directly (pg.Push)

For development loops where you'd rather skip the migration file and just sync the database to your current Go schema:

res, err := pg.Push(ctx, db, pg.NewSchema(Users, Posts),
    pg.PushOptions{Safe: true})
if err != nil {
    log.Fatal(err)
}
log.Printf("applied %d statements", len(res.Statements))

Push introspects the live database via information_schema, builds a snapshot from your Go schema, diffs the two, and applies the SQL inside a single transaction. A DryRun: true option returns the statements without executing — useful for previewing in CI.

There is no migration history written: Push is convenient for prototyping and tests, not for production where you want reviewable, reproducible migrations. For those, use GenerateMigration + DrizzleMigrator.

Underneath, Push is just three reusable pieces you can also call separately:

current, _ := pg.Introspect(ctx, db)                  // *Snapshot from the live DB
desired := pg.BuildSnapshot(pg.NewSchema(Users, Posts)) // *Snapshot from the Go schema
stmts := pg.Diff(current, desired, pg.DiffOptions{Safe: true})
// stmts is the SQL diff — execute, review, or pipe wherever

Idempotent DDL

DiffOptions{Safe: true} (and the matching GenerateOptions.Safe / PushOptions.Safe) wraps every destructive or creative DDL in IF [NOT] EXISTS:

Operation	Default	Safe: true
CREATE TABLE	CREATE TABLE "users" (...)	CREATE TABLE IF NOT EXISTS "users" (...)
DROP TABLE	DROP TABLE "users" CASCADE;	DROP TABLE IF EXISTS "users" CASCADE;
ADD COLUMN	... ADD COLUMN "age" integer;	... ADD COLUMN IF NOT EXISTS "age" integer;
DROP COLUMN	... DROP COLUMN "age";	... DROP COLUMN IF EXISTS "age";
DROP CONSTRAINT (FK / UNIQUE)	... DROP CONSTRAINT "...";	... DROP CONSTRAINT IF EXISTS "...";
ALTER COLUMN (type/NULL/default)	unchanged — PostgreSQL has no IF EXISTS form	unchanged

Existence checks

If you need to branch on the live state of the database, four helpers query information_schema:

ok, _ := pg.SchemaExists(ctx, db, "drizzle")
ok, _  = pg.TableExists(ctx, db, "", "users")             // "" → public
ok, _  = pg.ColumnExists(ctx, db, "", "users", "email")
ok, _  = pg.ConstraintExists(ctx, db, "", "users", "users_email_unique")

Drizzle-kit compatible (pg.DrizzleMigrator)

drops reads migrations written by drizzle-kit verbatim. The on-disk layout, hashing, history table, and statement-splitting protocol all match drizzle-orm at apply time, so the same migration set can be applied by either runtime against the same database without conflict.

What it expects (drizzle-kit's default output):

drizzle/
├── 0000_warm_iron_man.sql
├── 0001_serious_jack_flag.sql
└── meta/
    ├── _journal.json
    ├── 0000_snapshot.json
    └── 0001_snapshot.json

What it does:

• Reads meta/_journal.json for ordering and per-entry breakpoints flag.
• Computes sha256(<file bytes>) for each <tag>.sql (same hash drizzle-orm computes).
• Tracks history in drizzle.__drizzle_migrations(id serial pk, hash text, created_at bigint).
• Skips entries whose hash is already in the history table.
• Splits each file on --> statement-breakpoint when the entry has breakpoints: true; runs the file as one statement when false.
• Wraps each migration (statements + history insert) in a single transaction.

//go:embed drizzle/*
var migrations embed.FS

m := pg.NewDrizzleMigrator(db, migrations, "drizzle")
if err := m.Up(ctx); err != nil {
    log.Fatal(err)
}

If your drizzle.config.ts overrides migrationsSchema / migrationsTable, mirror that:

pg.NewDrizzleMigrator(db, migrations, "drizzle").
    WithSchema("public").
    WithTable("schema_migrations")

Go-native (pg.Migrator)

For projects that don't use drizzle-kit, a simpler file or code-driven runner. Migration files use <version>_<name>.{up,down}.sql; history is tracked in _drops_migrations. Supports rollbacks (drizzle's runtime does not).

//go:embed migrations/*.sql
var migrations embed.FS

m := pg.NewMigrator(db)
if err := m.AddFS(migrations, "migrations"); err != nil {
    log.Fatal(err)
}
if err := m.Up(ctx); err != nil {
    log.Fatal(err)
}

Go-defined migrations work with the same Migrator:

m.Add(pg.Migration{
    Version: "0003",
    Name:    "backfill_users",
    Up: func(ctx context.Context, db *pg.DB) error {
        _, err := db.Exec(ctx, `UPDATE users SET status = 'active' WHERE status IS NULL`)
        return err
    },
})

Data hooks (migrating data between schema migrations)

When a schema change needs a data migration alongside it — backfill a new column, copy rows into a split-out table, rewrite a value before an old column is dropped — register a BeforeEach / AfterEach hook. Hooks run inside the same transaction as the migration, so the data change commits atomically with the schema change (or rolls back with it on error).

On the Go-native Migrator, the hook receives the tx-scoped *pg.DB, the Migration, and the direction, so a step can be scoped to one version and one direction:

m.AfterEach(func(ctx context.Context, tx *pg.DB, mig pg.Migration, dir pg.MigrationDirection) error {
    if dir == pg.DirectionUp && mig.Version == "0004" {
        // 0004 added full_name; backfill it from the split columns.
        _, err := tx.Exec(ctx, `UPDATE users SET full_name = first_name || ' ' || last_name`)
        return err
    }
    return nil
})

The DrizzleMigrator gets the same seam. Since drizzle files are pure SQL with no place for Go logic, a DrizzleHook lets a backfill run atomically with a file's statements, keyed by tag:

m := pg.NewDrizzleMigrator(db, migrations, "drizzle")
m.AfterEach(func(ctx context.Context, tx *pg.DB, e pg.DrizzleEntry) error {
    if e.Tag == "0001_add_full_name" {
        _, err := tx.Exec(ctx, `UPDATE users SET full_name = first_name || ' ' || last_name`)
        return err
    }
    return nil
})

A hook that returns an error aborts that migration; the whole transaction rolls back.

PostgreSQL feature surface

In addition to the schema/query/migration story above, the pg package exposes the rest of PostgreSQL's catalog of object types and built-in operators/functions as plain Go helpers. Each returns a drops.Expression that composes anywhere a SQL fragment is expected.

DDL objects beyond tables

pg.CreateSchema("analytics")
pg.CreateExtensionIfNotExists("pgcrypto")
pg.CreateSequenceIfNotExists("user_id_seq", pg.SequenceOptions{Start: ptr(int64(100))})
pg.CreateView("active_users", db.Select(UserID, UserName).From(Users))
pg.CreateMaterializedView("mv_users", q, /*withData*/ true)
pg.RefreshMaterializedView("mv_users", /*concurrently*/ true)
pg.CreateFunction("touch_updated_at", pg.FunctionOptions{
    Returns: "trigger",
    Body:    "BEGIN NEW.updated_at = now(); RETURN NEW; END;",
})
pg.CreateTrigger("users_touch", pg.TriggerOptions{
    Timing: "BEFORE", Events: "UPDATE", Table: Users,
    Execute: "touch_updated_at()",
})
pg.CommentOnColumn(UserName, "display name")

Every constructor has an IfNotExists / IfExists variant.

Indexes

idx := pg.NewIndex("users_email_lower_idx", Users, pg.Lower(UserName)).
    Unique().
    Using("btree").
    Include(UserID.Column).
    Where(UserAge.Gte(18))
db.ExecExpr(ctx, pg.CreateIndex(idx))

Enums

status := pg.NewEnum("user_status", "active", "pending", "banned")
db.ExecExpr(ctx, pg.CreateEnum(status))
var UserStatus = pg.Add(Users, status.Col("status").NotNull().Default("'pending'"))
db.ExecExpr(ctx, pg.AlterEnumAddValue("user_status", "archived", "", "banned"))

Built-in functions

Category	Highlights
Aggregates	Count, CountDistinct, CountAll, Sum, SumDistinct, Avg, AvgDistinct, Min, Max, StringAgg, BoolAnd, BoolOr, Filter(agg, pred)
String	Concat, ConcatWS, ConcatOp (
Math	Abs, Ceil, Floor, Round, Mod, Power, Sqrt, Sign, Exp, Ln, Log, Greatest, Least, Random, Sin/Cos/Tan/Asin/Acos/Atan, Plus/Minus/Mul/Div
Date/time	CurrentDate, CurrentTime, CurrentTimestamp, LocalTime, LocalTimestamp, Now, DateTrunc, Extract, DatePart, Age, IntervalLit, Day/Hour/Minute/Second/Week/Month/Year, MakeDate/MakeTime/MakeTimestamp[TZ], ToDate/ToTimestamp/ToNumber, AtTimeZone
JSON/JSONB	JSONGet (->), JSONGetText (->>), JSONPath (#>), JSONPathText (#>>), JSONBContains (@>), JSONBContainedIn (<@), JSONBHasKey (?), JSONBHasAnyKey (?|), JSONBHasAllKeys (?&), JSONBConcat, JSONBDelete, ToJSON/ToJSONB, JSON[B]ArrayLength, JSON[B]Typeof, JSON[B]BuildObject/Array, JSONBSet, JSONBInsert, JSONBStripNulls, JSONBPretty, JSON[B]Agg, JSON[B]ObjectAgg
Array	ArrayContains (@>), ArrayContainedIn (<@), ArrayOverlaps (&&), ArrayConcat, Any, All, ArrayAgg, Unnest, Cardinality, ArrayLength/Upper/Lower, ArrayAppend/Prepend/Remove/Replace, ArrayPosition/Positions, ArrayToString, StringToArray, ArrayLit
Sequences	NextVal, CurrVal, SetVal
Coercion	Cast(e, "text") (e::text), CastAs(e, "text")
Control flow	Case().When(...).When(...).Else(...).End(), CaseOn(value).When(...).End(), Coalesce

If something isn't covered, fall back to pg.Func("any_pg_function", args...) or drops.Raw{SQL: "...", Args: ...}.

Query constructs

// CTEs (WITH / WITH RECURSIVE).
adults := pg.CTEDef("adults", db.Select(UserID).From(Users).Where(UserAge.Gte(18)))
db.Select(UserID).
    FromExpr(adults.Ref()).
    With(adults).
    All(ctx, &dest)

// Set operations.
a.UnionAll(b).Intersect(c).Except(d)

// DISTINCT ON.
db.Select(UserID, UserName).From(Users).DistinctOn(UserName).OrderBy(UserName.Asc())

// Window functions.
db.Select(
    UserName,
    pg.As(pg.Over(pg.RowNumber(),
        pg.WindowSpec().PartitionBy(UserDept).OrderBy(UserAge.Desc())), "rn"),
).From(Users)

// EXISTS / NOT EXISTS subqueries.
db.Select(UserID).From(Users).Where(pg.Exists(
    db.Select(PostID).From(Posts).Where(PostUserID.EqCol(UserID)),
))

// CASE.
status := pg.Case().
    When(UserAge.Lt(18), "minor").
    When(UserAge.Lt(65), "adult").
    Else("senior").
    End()

// Cast.
pg.Cast(UserAge, "text")  // ("users"."age")::text

Cache

A driver-agnostic cache interface with two ready backends.

type Cache interface {
    Get(ctx context.Context, key string) ([]byte, error)
    Set(ctx context.Context, key string, value []byte, ttl time.Duration) error
    Delete(ctx context.Context, keys ...string) (int, error)
    Exists(ctx context.Context, key string) (bool, error)
    TTL(ctx context.Context, key string) (time.Duration, error)
    Ping(ctx context.Context) error
    Close() error
}

cache.MultiCache extends it with GetMulti / SetMulti for backends that can serve batches in one round-trip. Sentinels: cache.ErrNotFound, cache.ErrClosed, cache.ErrInvalidKey.

In-memory (drops/cache/memory)

Zero deps, safe for concurrent use, optional janitor goroutine and FIFO eviction once MaxEntries is reached. Ideal for tests and the local tier of a two-level cache.

import "github.com/bernardoforcillo/drops/cache/memory"

mc := memory.New(memory.Options{
    MaxEntries: 10_000,
    SweepEvery: time.Minute,
})
defer mc.Close()

_ = mc.Set(ctx, "user:42", payload, 5*time.Minute)
got, err := mc.Get(ctx, "user:42")

Redis (drops/cache/redis)

Production Redis backend with its own minimal RESP2 client and a bounded connection pool. Zero deps. Supports AUTH (legacy + ACL), SELECT db, key prefixes, and the same drops.Hook contract used elsewhere.

import "github.com/bernardoforcillo/drops/cache/redis"

rc := redis.New(redis.Options{
    Addr:        "127.0.0.1:6379",
    Password:    os.Getenv("REDIS_PASSWORD"),
    DB:          0,
    MaxConns:    25,
    IdleTimeout: 5 * time.Minute,
    KeyPrefix:   "app:",
    Hook:        drops.LoggerHook(log.Printf),
})
defer rc.Close()

if err := rc.Ping(ctx); err != nil { /* health-check failed */ }

_ = rc.Set(ctx, "user:42", payload, 5*time.Minute)
got, err := rc.Get(ctx, "user:42")
if errors.Is(err, cache.ErrNotFound) {
    // miss
}

Authentication

Three shapes, pick whichever fits:

// 1. Static (back-compat shorthand). Set Username + Password (or
//    Password alone for legacy single-arg AUTH).
redis.Options{Password: os.Getenv("REDIS_PASSWORD")}

// 2. Explicit static credentials via the provider helper.
redis.Options{Credentials: redis.StaticCredentials("user", "pw")}

// 3. Dynamic credentials — short-lived tokens (AWS ElastiCache IAM,
//    Azure AAD, OIDC, HashiCorp Vault). The provider is called once
//    per new connection, receiving the caller's context so it can
//    honour deadlines and cancellation.
redis.Options{
    Credentials: func(ctx context.Context) (redis.Credentials, error) {
        tok, err := iam.MintAuthToken(ctx, "my-redis-cluster")
        if err != nil { return redis.Credentials{}, err }
        return redis.Credentials{Username: "iam-user", Password: tok}, nil
    },
}

If Credentials is set it overrides Username / Password. If both are empty, the connection skips AUTH entirely (Redis without requirepass).

TLS

// Self-managed: pass any *tls.Config you like (custom RootCAs,
// client certs for mTLS, pinned cipher suites).
rc := redis.New(redis.Options{
    Addr: "redis.example.com:6380",
    TLS:  &tls.Config{ServerName: "redis.example.com", MinVersion: tls.VersionTLS12},
})

// Or pull a sensible default out of a rediss:// URL:
opts, _ := redis.ParseURL("rediss://user:pw@redis.example.com:6380/0")
rc := redis.New(opts) // opts.TLS already populated

Connection URL

opts, err := redis.ParseURL("rediss://iam-user:" + token + "@cluster.example.com:6380/0")
if err != nil { /* malformed */ }
rc := redis.New(opts)

Accepted shapes: redis://[user[:pass]@]host[:port][/db] and rediss://... (same but with TLS).

Production tuning

Every numeric Options field has a sensible default; override when your workload says otherwise:

Field	Default	What it does
MaxConns	10	Hard cap on simultaneous connections
MinIdleConns	0	Pre-dial this many connections at startup
IdleTimeout	5 min	Close conns idle longer than this
MaxLifetime	0 (off)	Close conns past this age regardless of idle status — important when AUTH tokens rotate or a load balancer drains
DialTimeout	5 s	Cap on the TCP+TLS+AUTH+SELECT+SETNAME dance
ReadTimeout / WriteTimeout	3 s each	Per-op deadlines applied when the caller's ctx has none. Set negative to disable
MaxRetries	1	Retry-once on transient I/O errors (EOF, network timeout, protocol corruption). App-level -ERR replies are never retried
ShutdownTimeout	5 s	How long Close waits for in-flight ops to drain before forcing socket closure
ClientName	"drops"	Sent via CLIENT SETNAME on connect so the conn shows up in CLIENT LIST / SLOWLOG / MONITOR

Pool metrics

s := rc.Stats()
fmt.Printf("conns=%d hits=%d misses=%d timeouts=%d stale=%d retries=%d wait=%s/%d\n",
    s.TotalConns, s.Hits, s.Misses, s.Timeouts, s.StaleClosed,
    s.Retries, s.WaitDuration, s.WaitCount)

PoolStats is a snapshot; safe to read concurrently from a metrics emitter. Counters are monotonic across the cache's lifetime.

For richer Redis usage (pub/sub, streams, scripts, cluster, sentinel) reach for a full-featured client like github.com/redis/go-redis/v9 — this package's scope is the cache.Cache contract plus a few utility commands.

Layout

drops/                       driver interface + SQL primitives + Hook
drops/pg/                    Postgres schema, query builders, relations,
                             migrations, snapshot/diff/generate
drops/clickhouse/            ClickHouse schema, engines, query builder,
                             analytical aggregates
drops/qdrant/                Qdrant vector-database HTTP client
drops/cache/                 Cache interface + sentinels
drops/cache/memory/          in-process cache backend
drops/cache/redis/           Redis cache backend (own RESP2 client)
drops/stdlib/                database/sql adapter
drops/examples/sqlgen/       no-deps SQL-generation demo (pg)
drops/examples/generate/     drizzle-kit-style migration generation demo
drops/_examples/postgres/    full DB demo via pgx (excluded from build)

What's not here

• Other dialects (MySQL, SQLite, MSSQL)
• Indexes, composite primary keys, composite uniques, check constraints, enums, sequences, views, RLS in the snapshot/diff generator
• Per-parent LIMIT/OFFSET on eager loads (drizzle's with: { posts: { limit } }) — WithRel supports per-relation Where/OrderBy and dot-path nesting, but a per-parent row cap needs a window-function rewrite
• Down-migration generation from the diff (only Go-native pg.Migrator supports Down, and only when the user writes the down SQL themselves)

The structure leaves room to add these later without churning the existing surface.