j-perm-sql 0.3.0

j-perm plugin for SQL

j-perm-sql

A j-perm plugin that builds and executes SQL queries from j-perm constructs.

• SQL is described with a tree of $-constructs ($select, $col, $val, predicates, joins, …).
• A top-level operation renders that tree to a parameterized (sql, params) pair and hands it to a configurable executor (any ORM's raw-execute function): op: sql for read-only SELECT, and (opt-in) op: sql_write for INSERT/UPDATE/DELETE.
• The SQL constructs live in isolated named pipelines: they mean nothing outside those operations. {"$select": …} used as an ordinary value is just a dict.

Scope: the full standard SELECT surface (read-only) via install_sql, plus standard INSERT/UPDATE/DELETE (row content only) via install_sql_write. Schema DDL (CREATE/ALTER/DROP of tables/columns) and non-universal DML (RETURNING, ON CONFLICT/upsert, UPDATE … FROM, DELETE … USING) are intentionally out of scope.

Install

pip install j-perm-sql

Requires j-perm >= 1.10.0: 1.9.0 made run_pipeline a passthrough invoker (which this plugin relies on), and 1.10.0 added the nested_spec_pipeline compile hook and per-pipeline CompiledSpec execution that let op: sql be compiled end-to-end (see Compilation).

Quick start

from j_perm import build_default_engine
from j_perm_sql import install_sql

def run_sql(sql, params):
    # any ORM's raw execute: cursor.execute(sql, params); return rows
    ...

engine = build_default_engine()
install_sql(engine, run_sql, paramstyle="qmark")

engine.apply(
    {"op": "sql", "to": "/rows", "query": {"$select": {
        "columns": [{"$col": {"name": "id"}}, {"$col": {"name": "name"}}],
        "from": {"table": "users"},
        "where": {"$gte": [{"$col": {"name": "age"}}, {"$val": 18}]},
        "order_by": [{"expr": {"$col": {"name": "name"}}}],
        "limit": 50,
    }}},
    source={}, dest={},
)
# run_sql receives:  ('SELECT "id", "name" FROM "users" WHERE "age" >= ? ORDER BY "name" LIMIT 50', [18])
# result is written to dest at /rows

install_sql patches an existing engine — it registers the isolated SQL pipeline and the op: sql operation. It composes with any engine and any other plugins.

The op: sql operation

{"op": "sql", "query": <SQL construct tree>, "to": "/dest/path"}

• query — the SQL construct tree.
• to — optional destination pointer (template-expanded); the executor's result is written there. If omitted, the result is discarded.

Writing data (INSERT/UPDATE/DELETE)

Writing is a separate, opt-in install — you don't get it unless you ask for it, and op: sql stays guaranteed read-only even when both are installed:

from j_perm_sql import install_sql, install_sql_write

install_sql(engine, run_sql)          # read-only  op: sql      (optional)
install_sql_write(engine, run_sql)    # write      op: sql_write

install_sql_write(engine, executor, *, paramstyle="qmark", dialect=None, op="sql_write") registers an isolated write pipeline (the full SELECT surface plus the DML statements, so WHERE predicates, SET expressions and INSERT … SELECT subqueries all work) and the op: sql_write operation. It is independent of install_sql — either may be installed alone, in any order. It selects the sync/async handler the same way (by asyncio.iscoroutinefunction).

{"op": "sql_write", "query": <DML construct tree>, "to": "/dest/path"}

$insert — exactly one of values / query:

{"$insert": {
  "into": "users",                    # or {"table": "users", "schema": "app"}
  "columns": ["name", "age"],         # optional
  "values": [[{"$val": "Ann"}, {"$val": 30}], ...],   # cells are operands ($val to bind)
  # ── OR ──
  "query": {"$select": {...}},        # INSERT … SELECT
}}
# → INSERT INTO "users" ("name", "age") VALUES (?, ?)   params=["Ann", 30]

$update — single table:

{"$update": {
  "table": "users",                   # or {"table": "users", "schema": "app"}
  "set": {"name": {"$val": "Bob"},
          "visits": {"$add": [{"$col": "visits"}, {"$val": 1}]}},
  "where": {"$eq": [{"$col": "id"}, {"$val": 5}]},      # or "all": true
}}
# → UPDATE "users" SET "name" = ?, "visits" = ("visits" + ?) WHERE "id" = ?

$delete — single table:

{"$delete": {
  "from": "sessions",                 # or {"table": "sessions", "schema": "app"}
  "where": {"$lt": [{"$col": "last_seen"}, {"$val": "2020-01-01"}]},  # or "all": true
}}
# → DELETE FROM "sessions" WHERE "last_seen" < ?

set values, $insert cells, and where predicates are ordinary read constructs, so the full expression/predicate/subquery surface (including correlated subqueries) is available, and data is always bound as parameters.

WHERE guard. A $update / $delete without a where raises unless you pass an explicit "all": true. This prevents an accidental full-table update/delete.

Parameterization & injection safety

Data values are always bound as parameters, never interpolated:

• $val (and the data sides of $in, $between, $values, $update's set values, and $insert's row cells) emit a placeholder and add the value to params.
• Identifiers (table/column/alias names) are validated against a conservative charset and quoted.
• Function names, CAST types, join types, sort directions, etc. are validated against whitelists.

{"$eq": [{"$col": {"name": "name"}}, {"$val": {"$ref": "/user_input"}}]}
# → '"name" = ?'   with the (possibly malicious) value safely in params

Parameters come from inside j-perm — no external param source. Inside $val the value expression is resolved with j-perm's normal value pipeline, so $ref, ${…} templates, and @: dest-pointers all work. The params list handed to the executor is simply the values j-perm itself computed from the document; the parameter binding exists only for injection safety and the driver's paramstyle. The whole flow (source → SQL → bound values) is self-contained in one j-perm run; the executor is just the pipe to the driver. This applies equally to read (op: sql) and write (op: sql_write).

Dialect / RenderOptions

Everything that genuinely differs between databases is configurable:

from j_perm_sql import RenderOptions

install_sql(engine, run_sql, dialect=RenderOptions(
    paramstyle="numeric",      # qmark (?) | format (%s) | numeric ($1) | named (:p1)
    identifier_quote='"',      # e.g. "`" for MySQL
    pagination="fetch",        # "limit" (LIMIT n OFFSET m) | "fetch" (OFFSET m ROWS FETCH FIRST n ROWS ONLY)
    concat_operator="||",      # "||" or "+"
))

Sync & async

Both install_sql and install_sql_write inspect the executor: a coroutine function registers the async handler (use with engine.apply_async); a regular function registers the sync handler (use with engine.apply).

async def run_sql(sql, params): ...
install_sql(engine, run_sql)                  # async
await engine.apply_async(spec, source=…, dest=…)

Compilation

op: sql / op: sql_write are compilable. engine.compile(spec) compiles the query subtree against the isolated SQL pipeline (the engine never needs to understand the SQL constructs — it routes the nested spec through the registered pipeline by name), and the rendered tree is dispatched through the compiled path with per-node memoisation. Re-applying the same CompiledSpec keeps every node compiled; only $val data is re-bound from the live context on each run.

compiled = engine.compile([{"op": "sql", "to": "/rows", "query": query}])
compiled.apply(source={"wanted": 1}, dest={})    # renders + executes, fully compiled
compiled.apply(source={"wanted": 2}, dest={})    # reuses compiled nodes, re-binds values

This requires the nested_spec_pipeline compile hook and per-pipeline CompiledSpec execution added in the core engine (see the "What gets compiled" section of the main j-perm README).

Construct reference

Query

Construct	Form
$select	{with?, distinct?, columns?, from?, joins?, where?, group_by?, having?, order_by?, limit?/offset? | fetch?}
$union / $union_all / $intersect / $except	{"$union": [q1, q2, …], order_by?, limit?…}
$values	{"$values": [[…row…], …]} (table source or IN)

Write (DML) — only via install_sql_write / op: sql_write

Construct	Form
$insert	{into, columns?, values | query} (exactly one of values/query)
$update	{table, set: {col: operand}, where? | "all": true}
$delete	{from, where? | "all": true}

Projection / expressions

Construct	Renders
$col	"t"."name" [AS "alias"]; "id"; *; "t".*
$val	a bound parameter
$func / $call	NAME([DISTINCT ]args)[ OVER (…)][ AS "alias"] (use "*" for COUNT(*))
$cast	CAST(expr AS TYPE)
$case	searched CASE WHEN … THEN … [ELSE …] END
$concat	`(a
$add $sub $mul $div $mod	(a op b …)

Projection items may also be {"expr": <operand>, "as": "alias"}.

Predicates (WHERE / HAVING / ON)

$and $or $not · $eq $ne $gt $gte $lt $lte · $in/$not_in (list or subquery) · $between/$not_between · $like/$not_like (+ escape) · $is_null/$is_not_null · $exists/$not_exists · $any/$all/$some (quantified).

FROM / JOIN — table source = a table name (string), a {table, as?, schema?} dict, a nested $select/$values (derived table, needs as), or lateral: true. $join: {type, table, as?, on? | using?, natural?} with type inner/left/right/full/cross.

Windows — over on $func: {partition_by?, order_by?, frame?} where frame is {type: "rows"|"range", start, end?} and a bound is "unbounded preceding" | "unbounded following" | "current row" | {preceding: n} | {following: n}.

GROUP BY — a list of expressions, or {"$rollup": […]} / {"$cube": […]} / {"$grouping_sets": [[…], …]}.

CTE — with: [{name, columns?, recursive?, query: $select}].

See tests/ for end-to-end examples.

Portability caveats

The DSL renders standard SQL and does not validate that a target database supports every feature — portability is the query author's responsibility:

• LIMIT/OFFSET vs OFFSET/FETCH, RIGHT/FULL JOIN, INTERSECT/EXCEPT, NATURAL JOIN, NULLS FIRST/LAST, LATERAL, GROUPING SETS/ROLLUP/CUBE, and the concatenation operator (|| vs +) are not universal.
• CTEs and window functions are standard but require recent versions (e.g. MySQL ≥ 8, SQLite ≥ 3.25 for windows / ≥ 3.8.3 for CTEs).

Use RenderOptions to match the target dialect's placeholder style, identifier quoting, pagination form, and concatenation operator.

License

MIT