cel2sql

Convert CEL (Common Expression Language) expressions to SQL for PostgreSQL, MySQL, SQLite, DuckDB, BigQuery, and Apache Spark SQL

cel2sql makes it easy to build dynamic SQL queries using CEL expressions. Write type-safe, expressive filters in CEL and automatically convert them to SQL for your database of choice.

Quick Start

Installation

go get github.com/spandigital/cel2sql/v3

Basic Example

package main
import (
"fmt""github.com/google/cel-go/cel""github.com/spandigital/cel2sql/v3""github.com/spandigital/cel2sql/v3/pg"
)
funcmain() {
// 1. Define your database table schemauserSchema:=pg.NewSchema([]pg.FieldSchema{
{Name: "name", Type: "text"},
{Name: "age", Type: "integer"},
{Name: "active", Type: "boolean"},
})
// 2. Create CEL environmentenv, _:=cel.NewEnv(
cel.CustomTypeProvider(pg.NewTypeProvider(map[string]pg.Schema{
"User": userSchema,
})),
cel.Variable("user", cel.ObjectType("User")),
)
// 3. Write your filter expression in CELast, _:=env.Compile(`user.age >= 18 && user.active`)
// 4. Convert to SQLsqlWhere, _:=cel2sql.Convert(ast)
fmt.Println(sqlWhere)
// Output: user.age >= 18 AND user.active IS TRUE// 5. Use in your queryquery:="SELECT * FROM users WHERE "+sqlWhere
}

Why cel2sql?

✅ Multi-Dialect: PostgreSQL, MySQL, SQLite, DuckDB, BigQuery, and Apache Spark SQL from a single API ✅ Type-Safe: Catch errors at compile time, not runtime ✅ Rich Features: JSON/JSONB, arrays, regex, timestamps, and more ✅ Well-Tested: 100+ tests including integration tests with real databases ✅ Easy to Use: Simple API, comprehensive documentation ✅ Secure by Default: Built-in protections against SQL injection and ReDoS attacks ✅ Performance Tracked: Continuous benchmark monitoring to prevent regressions

Security Features

cel2sql includes comprehensive security protections:

• 🛡️ Field Name Validation - Prevents SQL injection via field names
• 🔒 JSON Field Escaping - Automatic quote escaping in JSON paths
• 🚫 ReDoS Protection - Validates regex patterns to prevent catastrophic backtracking
• 🔄 Recursion Depth Limits - Prevents stack overflow from deeply nested expressions (default: 100)
• 📏 SQL Output Length Limits - Prevents memory exhaustion from extremely large SQL queries (default: 50,000 chars)
• 🔢 Byte Array Length Limits - Prevents memory exhaustion from large hex-encoded byte arrays (max: 10,000 bytes)
• ⏱️ Context Timeouts - Optional timeout protection for complex expressions

All security features are enabled by default with zero configuration required.

Advanced Options

cel2sql supports optional advanced features via functional options:

import (
"context""log/slog""github.com/spandigital/cel2sql/v3"
)
// Basic conversionsql, err:=cel2sql.Convert(ast)
// With schemas for JSON/JSONB supportsql, err:=cel2sql.Convert(ast,
cel2sql.WithSchemas(schemas))
// With context for timeoutsctx, cancel:=context.WithTimeout(context.Background(), 5*time.Second)
defercancel()
sql, err:=cel2sql.Convert(ast,
cel2sql.WithContext(ctx),
cel2sql.WithSchemas(schemas))
// With logging for observabilitylogger:=slog.New(slog.NewJSONHandler(os.Stdout, nil))
sql, err:=cel2sql.Convert(ast,
cel2sql.WithContext(ctx),
cel2sql.WithSchemas(schemas),
cel2sql.WithLogger(logger))

Available Options:

• WithDialect(dialect.Dialect) - Select target SQL dialect (default: PostgreSQL)
• WithSchemas(map[string]pg.Schema) - Provide table schemas for JSON detection
• WithJSONVariables(vars ...string) - Declare CEL variables that map to flat JSONB columns
• WithColumnAliases(map[string]string) - Map CEL variable names to different SQL column names
• WithContext(context.Context) - Enable cancellation and timeouts
• WithLogger(*slog.Logger) - Enable structured logging
• WithMaxDepth(int) - Set custom recursion depth limit (default: 100)
• WithMaxOutputLength(int) - Set custom SQL output length limit (default: 50000)
• WithParamStartIndex(int) - First placeholder index for ConvertParameterized (default: 1)

Multi-Dialect Support

cel2sql supports 6 SQL dialects. PostgreSQL is the default; select other dialects with WithDialect():

import (
"github.com/spandigital/cel2sql/v3""github.com/spandigital/cel2sql/v3/dialect/mysql""github.com/spandigital/cel2sql/v3/dialect/sqlite""github.com/spandigital/cel2sql/v3/dialect/duckdb""github.com/spandigital/cel2sql/v3/dialect/bigquery""github.com/spandigital/cel2sql/v3/dialect/spark"
)
// PostgreSQL (default - no option needed)sql, err:=cel2sql.Convert(ast)
// MySQLsql, err:=cel2sql.Convert(ast, cel2sql.WithDialect(mysql.New()))
// SQLitesql, err:=cel2sql.Convert(ast, cel2sql.WithDialect(sqlite.New()))
// DuckDBsql, err:=cel2sql.Convert(ast, cel2sql.WithDialect(duckdb.New()))
// BigQuerysql, err:=cel2sql.Convert(ast, cel2sql.WithDialect(bigquery.New()))
// Apache Spark SQLsql, err:=cel2sql.Convert(ast, cel2sql.WithDialect(spark.New()))

Dialect Comparison

Feature	PostgreSQL	MySQL	SQLite	DuckDB	BigQuery	Spark
String concat	||	CONCAT()	||	||	||	concat()
Regex	~ / ~*	REGEXP	unsupported	~ / ~*	REGEXP_CONTAINS()	RLIKE
JSON access	->>'f'	->>'$.f'	json_extract()	->>'f'	JSON_VALUE()	get_json_object()
Arrays	ARRAY[...]	JSON arrays	JSON arrays	[...]	[...]	array(...)
Array index	1-indexed	n/a	n/a	1-indexed	0-indexed (OFFSET)	0-indexed
UNNEST	UNNEST(x)	JSON_TABLE(...)	json_each(x)	UNNEST(x)	UNNEST(x)	EXPLODE(x)
Param placeholder	$1, $2	?, ?	?, ?	$1, $2	@p1, @p2	?, ?
Timestamp cast	TIMESTAMP WITH TIME ZONE	DATETIME	datetime()	TIMESTAMPTZ	TIMESTAMP	TIMESTAMP
Contains	POSITION()	LOCATE()	INSTR()	CONTAINS()	STRPOS()	LOCATE()
Index analysis	BTREE, GIN, GIN+trgm	BTREE, FULLTEXT	BTREE	ART	CLUSTERING, SEARCH_INDEX	not supported in v1

Per-Dialect Type Providers

Each dialect has its own type provider for mapping database types to CEL types. All providers support both pre-defined schemas (NewTypeProvider) and dynamic schema loading (LoadTableSchema):

import"github.com/spandigital/cel2sql/v3/pg"// PostgreSQL (pgxpool connection string)import"github.com/spandigital/cel2sql/v3/mysql"// MySQL (*sql.DB)import"github.com/spandigital/cel2sql/v3/sqlite"// SQLite (*sql.DB)import"github.com/spandigital/cel2sql/v3/duckdb"// DuckDB (*sql.DB)import"github.com/spandigital/cel2sql/v3/bigquery"// BigQuery (*bigquery.Client)import"github.com/spandigital/cel2sql/v3/spark"// Spark SQL (*sql.DB; uses DESCRIBE TABLE)

Query Analysis and Index Recommendations

cel2sql can analyze your CEL queries and recommend database indexes to optimize performance. The AnalyzeQuery() function returns both the converted SQL and dialect-specific index recommendations.

How It Works

AnalyzeQuery() examines your CEL expression and detects patterns that would benefit from indexing, then generates dialect-appropriate DDL:

• Comparison operations (==, >, <, >=, <=) → B-tree (PG/MySQL/SQLite), ART (DuckDB), Clustering (BigQuery)
• JSON/JSONB path operations (->>, ?) → GIN (PG), functional index (MySQL), Search Index (BigQuery), ART (DuckDB)
• Regex matching (matches()) → GIN with pg_trgm (PG), FULLTEXT (MySQL)
• Array operations (comprehensions, IN clauses) → GIN (PG), ART (DuckDB)

Usage

// PostgreSQL (default dialect)sql, recommendations, err:=cel2sql.AnalyzeQuery(ast,
cel2sql.WithSchemas(schemas))
// Or specify a dialectsql, recommendations, err:=cel2sql.AnalyzeQuery(ast,
cel2sql.WithSchemas(schemas),
cel2sql.WithDialect(mysql.New()))
iferr!=nil {
log.Fatal(err)
}
// Use the generated SQLrows, err:=db.Query("SELECT * FROM users WHERE "+sql)
// Review and apply index recommendationsfor_, rec:=rangerecommendations {
fmt.Printf("Column: %s\n", rec.Column)
fmt.Printf("Type: %s\n", rec.IndexType)
fmt.Printf("Reason: %s\n", rec.Reason)
fmt.Printf("Execute: %s\n\n", rec.Expression)
}

Per-Dialect Index Types

Pattern	PostgreSQL	MySQL	SQLite	DuckDB	BigQuery	Spark
Comparison	BTREE	BTREE	BTREE	ART	CLUSTERING	(skip)
JSON access	GIN	BTREE (functional)	(skip)	ART	SEARCH_INDEX	(skip)
Regex	GIN + pg_trgm	FULLTEXT	(skip)	(skip)	(skip)	(skip)
Array membership	GIN	(skip)	(skip)	ART	(skip)	(skip)
Comprehension	GIN	(skip)	(skip)	ART	(skip)	(skip)

Spark indexing depends on the storage layer (Delta Z-order, Iceberg sort, plain Parquet) and is out of scope for v1; index analysis is disabled for the Spark dialect. Unsupported patterns in other dialects are silently skipped.

Example

celExpr:=`person.age > 18 && person.metadata.verified == true`ast, _:=env.Compile(celExpr)
// PostgreSQL recommendationssql, recs, _:=cel2sql.AnalyzeQuery(ast, cel2sql.WithSchemas(schemas))
// Recommendations:// 1. CREATE INDEX idx_person_age_btree ON table_name (person.age);// 2. CREATE INDEX idx_person_metadata_gin ON table_name USING GIN (person.metadata);// MySQL recommendationssql, recs, _=cel2sql.AnalyzeQuery(ast,
cel2sql.WithSchemas(schemas),
cel2sql.WithDialect(mysql.New()))
// Recommendations:// 1. CREATE INDEX idx_person_age_btree ON table_name (person.age);// 2. CREATE INDEX idx_person_metadata_json ON table_name ((CAST(person.metadata->>'$.path' AS CHAR(255))));// BigQuery recommendationssql, recs, _=cel2sql.AnalyzeQuery(ast,
cel2sql.WithSchemas(schemas),
cel2sql.WithDialect(bigquery.New()))
// Recommendations:// 1. ALTER TABLE table_name SET OPTIONS (clustering_columns=['person.age']);// 2. CREATE SEARCH INDEX idx_person_metadata ON table_name (person.metadata);

When to Use

• Development: Discover which indexes your queries need
• Performance tuning: Identify missing indexes causing slow queries
• Production monitoring: Analyze user-generated filter expressions

See examples/index_analysis/ for a complete working example with all 5 dialects.

Parameterized Queries

cel2sql supports parameterized queries (prepared statements) for improved performance, security, and monitoring.

Benefits

🚀 Performance - PostgreSQL caches query plans for parameterized queries, enabling plan reuse across executions 🔒 Security - Parameters are passed separately from SQL, providing defense-in-depth SQL injection protection 📊 Monitoring - Same query pattern appears in logs/metrics, making analysis easier

Usage

// Convert to parameterized SQLresult, err:=cel2sql.ConvertParameterized(ast)
iferr!=nil {
log.Fatal(err)
}
fmt.Println(result.SQL) // "user.age > $1 AND user.name = $2"fmt.Println(result.Parameters) // [18 "John"]// Execute with database/sqlrows, err:=db.Query(
"SELECT * FROM users WHERE "+result.SQL,
result.Parameters...,
)

What Gets Parameterized?

Parameterized (values become placeholders):

• ✅ String literals: 'John' → $1
• ✅ Numeric literals: 42, 3.14 → $1, $2
• ✅ Byte literals: b"data" → $1

Kept Inline (for query plan optimization):

• ❌ TRUE, FALSE - Boolean constants
• ❌ NULL - Null values

PostgreSQL's query planner optimizes better when it knows boolean and null values at plan time.

Example Comparison

celExpr:=`user.age > 18 && user.active == true && user.name == "John"`ast, _:=env.Compile(celExpr)
// Non-parameterized (inline values)sql, _:=cel2sql.Convert(ast)
// SQL: user.age > 18 AND user.active IS TRUE AND user.name = 'John'// Parameterized (placeholders + parameters)result, _:=cel2sql.ConvertParameterized(ast)
// SQL: user.age > $1 AND user.active IS TRUE AND user.name = $2// Parameters: [18 "John"]// Note: TRUE is kept inline for query plan efficiency

Prepared Statements

For maximum performance with repeated queries, use prepared statements:

result, _:=cel2sql.ConvertParameterized(ast)
// Prepare oncestmt, err:=db.Prepare("SELECT * FROM users WHERE "+result.SQL)
deferstmt.Close()
// Execute multiple times with different parametersrows1, _:=stmt.Query(25) // age > 25rows2, _:=stmt.Query(30) // age > 30rows3, _:=stmt.Query(35) // age > 35 (reuses cached plan!)

See the parameterized example for a complete working demo with PostgreSQL integration.

Common Use Cases

1. User Filters

// CEL: Simple comparisonuser.age>21&&user.country=="USA"// SQL: user.age > 21 AND user.country = 'USA'

2. Text Search

// CEL: String operationsuser.email.startsWith("admin") ||user.name.contains("John")
// SQL: user.email LIKE 'admin%' OR POSITION('John' IN user.name) > 0

3. Date Filters

// CEL: Date comparisonsuser.created_at>timestamp("2024-01-01T00:00:00Z")
// SQL: user.created_at > CAST('2024-01-01T00:00:00Z' AS TIMESTAMP WITH TIME ZONE)

4. JSON/JSONB Fields

// CEL: JSON field accessuser.preferences.theme=="dark"// SQL: user.preferences->>'theme' = 'dark'

5. Array Operations

// CEL: Check if all items matchuser.scores.all(s, s>=60)
// SQL: NOT EXISTS (SELECT 1 FROM UNNEST(user.scores) AS s WHERE NOT (s >= 60))

6. Multi-Dimensional Arrays

cel2sql supports PostgreSQL multi-dimensional arrays (1D, 2D, 3D, 4D+) with automatic dimension detection:

// Define schema with multi-dimensional arraysschema:=pg.NewSchema([]pg.FieldSchema{
{Name: "tags", Type: "text", Repeated: true, Dimensions: 1}, // 1D: text[]
{Name: "matrix", Type: "integer", Repeated: true, Dimensions: 2}, // 2D: integer[][]
{Name: "cube", Type: "float", Repeated: true, Dimensions: 3}, // 3D: float[][][]
})
// CEL: size() automatically uses correct dimensionast, _:=env.Compile("size(data.matrix) > 0")
// SQL: COALESCE(ARRAY_LENGTH(data.matrix, 2), 0) > 0// Or load dimensions automatically from databaseprovider, _:=pg.NewTypeProviderWithConnection(ctx, connString)
provider.LoadTableSchema(ctx, "products") // Dimensions detected from schema

Dimension Detection:

• Detects dimensions from PostgreSQL type strings (integer[][], _int4[])
• Works with both bracket notation and underscore notation
• Defaults to 1D for backward compatibility when no schema is provided

Documentation

• 📖 Getting Started Guide - Step-by-step tutorial
• 🔧 JSON/JSONB Support - Working with JSON data
• 🎯 Array Comprehensions - Advanced array operations
• 🔍 Regex Matching - Pattern matching with regex
• 🛡️ Security Guide - Security features and best practices
• 📚 Operators Reference - Complete operator list
• 💡 Examples - More code examples

Supported Features

Feature	CEL Example	PostgreSQL SQL
Comparisons	age > 18	age > 18
Logic	active && verified	active IS TRUE AND verified IS TRUE
Strings	name.startsWith("A")	name LIKE 'A%'
Lists	"admin" in roles	'admin' IN UNNEST(roles)
Multi-Dim Arrays	size(matrix) > 0	COALESCE(ARRAY_LENGTH(matrix, 2), 0) > 0
JSON	data.key == "value"	data->>'key' = 'value'
Regex	email.matches(r".*@test\.com")	email ~ '.*@test\.com'
Dates	created_at.getFullYear() == 2024	EXTRACT(YEAR FROM created_at) = 2024
Conditionals	age > 30 ? "senior" : "junior"	CASE WHEN age > 30 THEN 'senior' ELSE 'junior' END

Regex Matching Limitations

cel2sql automatically converts CEL's RE2 regex patterns to PostgreSQL POSIX regex. While most common patterns work, some RE2 features are not supported and will return errors:

Supported:

• ✅ Basic patterns: .*, [a-z]+, \d{3}
• ✅ Case-insensitive flag: (?i)pattern → Uses ~* operator
• ✅ Character classes: \d, \w, \s (converted to POSIX)
• ✅ Non-capturing groups: (?:...) (converted to regular groups)

Unsupported:

• ❌ Lookahead assertions: (?=...), (?!...)
• ❌ Lookbehind assertions: (?<=...), (?<!...)
• ❌ Named capture groups: (?P<name>...)
• ❌ Inline flags (except (?i)): (?m), (?s), (?-i), etc.

ReDoS Protection: cel2sql includes automatic validation to prevent Regular Expression Denial of Service attacks:

• Pattern length limited to 500 characters
• Nested quantifiers blocked: (a+)+ ❌
• Quantified alternation blocked: (a|a)* ❌
• Capture group limit: 20 maximum
• Nesting depth limit: 10 levels

See Regex Matching documentation for complete details, safe pattern examples, and performance tips.

Type Mapping

CEL Type	PostgreSQL	MySQL	SQLite	DuckDB	BigQuery
int	bigint	SIGNED	INTEGER	BIGINT	INT64
double	double precision	DECIMAL	REAL	DOUBLE	FLOAT64
bool	boolean	UNSIGNED	INTEGER	BOOLEAN	BOOL
string	text	CHAR	TEXT	VARCHAR	STRING
bytes	bytea	BINARY	BLOB	BLOB	BYTES
list	ARRAY	JSON array	JSON array	LIST	ARRAY
timestamp	timestamptz	DATETIME	datetime()	TIMESTAMPTZ	TIMESTAMP
duration	INTERVAL	INTERVAL	string modifier	INTERVAL	INTERVAL

Dynamic Schema Loading

Load table schemas directly from your database at runtime instead of defining them manually. Each dialect provider supports introspecting table schemas from a live database connection.

PostgreSQL

import"github.com/spandigital/cel2sql/v3/pg"// PostgreSQL accepts a connection string and manages its own connection poolprovider, _:=pg.NewTypeProviderWithConnection(ctx, "postgres://user:pass@localhost/db")
deferprovider.Close()
provider.LoadTableSchema(ctx, "users")
env, _:=cel.NewEnv(
cel.CustomTypeProvider(provider),
cel.Variable("user", cel.ObjectType("users")),
)

MySQL

import (
"database/sql"
_ "github.com/go-sql-driver/mysql""github.com/spandigital/cel2sql/v3/mysql"
)
// MySQL accepts a *sql.DB — you own the connectiondb, _:=sql.Open("mysql", "user:pass@tcp(localhost:3306)/mydb?parseTime=true")
deferdb.Close()
provider, _:=mysql.NewTypeProviderWithConnection(ctx, db)
provider.LoadTableSchema(ctx, "users")
env, _:=cel.NewEnv(
cel.CustomTypeProvider(provider),
cel.Variable("user", cel.ObjectType("users")),
)
sql, _:=cel2sql.Convert(ast, cel2sql.WithDialect(mysqlDialect.New()),
cel2sql.WithSchemas(provider.GetSchemas()))

SQLite

import (
"database/sql"
_ "modernc.org/sqlite""github.com/spandigital/cel2sql/v3/sqlite"
)
db, _:=sql.Open("sqlite", "mydb.sqlite")
deferdb.Close()
provider, _:=sqlite.NewTypeProviderWithConnection(ctx, db)
provider.LoadTableSchema(ctx, "users")
env, _:=cel.NewEnv(
cel.CustomTypeProvider(provider),
cel.Variable("user", cel.ObjectType("users")),
)
sql, _:=cel2sql.Convert(ast, cel2sql.WithDialect(sqliteDialect.New()),
cel2sql.WithSchemas(provider.GetSchemas()))

DuckDB

import (
"database/sql""github.com/spandigital/cel2sql/v3/duckdb"
)
// DuckDB accepts *sql.DB — works with any DuckDB driver (requires CGO)db, _:=sql.Open("duckdb", "mydb.duckdb")
deferdb.Close()
provider, _:=duckdb.NewTypeProviderWithConnection(ctx, db)
provider.LoadTableSchema(ctx, "users")
env, _:=cel.NewEnv(
cel.CustomTypeProvider(provider),
cel.Variable("user", cel.ObjectType("users")),
)
sql, _:=cel2sql.Convert(ast, cel2sql.WithDialect(duckdbDialect.New()),
cel2sql.WithSchemas(provider.GetSchemas()))

BigQuery

import (
"cloud.google.com/go/bigquery"
bqprovider "github.com/spandigital/cel2sql/v3/bigquery"
)
// BigQuery uses the BigQuery client API (not database/sql)client, _:=bigquery.NewClient(ctx, "my-project")
deferclient.Close()
provider, _:=bqprovider.NewTypeProviderWithClient(ctx, client, "my_dataset")
provider.LoadTableSchema(ctx, "users")
env, _:=cel.NewEnv(
cel.CustomTypeProvider(provider),
cel.Variable("user", cel.ObjectType("users")),
)
sql, _:=cel2sql.Convert(ast, cel2sql.WithDialect(bigqueryDialect.New()),
cel2sql.WithSchemas(provider.GetSchemas()))

Notes

• PostgreSQL manages its own connection pool via pgxpool — call provider.Close() when done.
• MySQL, SQLite, DuckDB accept a *sql.DB you provide — you own the connection lifecycle. Close() is a no-op.
• BigQuery accepts a *bigquery.Client + dataset ID — you own the client lifecycle. Close() is a no-op.
• All providers also support pre-defined schemas via NewTypeProvider(schemas) if you don't need runtime introspection.

See Getting Started Guide for more details.

Requirements

• Go 1.24 or higher

CGO Requirement (DuckDB only)

The DuckDB dialect's LoadTableSchema requires a DuckDB Go driver (e.g., github.com/marcboeker/go-duckdb) which depends on CGO and a C/C++ compiler. This means:

• You must have CGO_ENABLED=1 (the Go default on most platforms)
• A C/C++ compiler must be installed (GCC, Clang, or MSVC)
• Cross-compilation requires a C cross-compiler for the target platform

All other dialects (PostgreSQL, MySQL, SQLite, BigQuery) use pure Go drivers and do not require CGO.

If you only use DuckDB with pre-defined schemas via duckdb.NewTypeProvider() (no live database connection), CGO is not required.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

Apache 2.0 - See LICENSE for details.

Related Projects

• CEL-Go - Common Expression Language implementation in Go
• CEL Spec - Common Expression Language specification

Need Help?

• 📖 Check the documentation
• 💬 Open an issue
• 🌟 Star the repo if you find it useful!