Transparency

This package is a port of PHP Sentience Database

It was ported using AI agents mostly powered by:

• Orchestrator: Deepseek V4 Flash (occational GLM 5.2 or Qwen 3.6 35B A3B)
• Sub agents: Qwen 3.6 35B A3B (occational Gemma 4 E2B)

The original PHP is made almost entirely by hand (except for the ExpressionF parsing). The workflow went as follows:

1. Copy sentience/database package to this directory
2. Let Deepseek V4 Flash explore the codebase and write a simple SQLite compatible port, with only CRUD queries, plan in PLAN.md
3. Let a new session with Deepseek V4 Flash as the orchestrator, and Qwen 3.6 35B A3B as subagent implement this first plan
4. Add DDL queries
5. Write Postgres implementation using the same setup
6. Write MySQL implementation using the same setup
7. Refine codebase

From a moral and environmental perspective, i've tried to use as much local AI as possible. The total token cost of this port is about $14 in Openrouter credits, most of which was used on GLM 5.2, even though Deepseek was the primary model used.

Coding agents work best if you give them a clear structure. In this case, having a human crafted package as an example, in a language with similar features, provied to be a great task for these models.

flowmaticdb — Python Database Abstraction

A multi-dialect database abstraction layer for Python, supporting PostgreSQL, SQLite, MySQL and MariaDB. Ported from the PHP library sentience/database.

flowmaticdb gives you a fluent query builder API, driver-level adapters, dialect-aware SQL generation, a unified result abstraction and a schema migration runner — all with strict type hints and zero magic strings.

Quick Start

pip install flowmaticdb                 # SQLite works out of the box
pip install "flowmaticdb[postgres]"     # PostgreSQL via psycopg
pip install "flowmaticdb[asyncpg]"      # PostgreSQL via asyncpg (the default driver)
pip install "flowmaticdb[mysql]"        # MySQL and MariaDB
pip install "flowmaticdb[all]"          # every driver
pip install "flowmaticdb[dev]"          # pytest, mypy, ruff

The package itself has no dependencies — a driver extra is only needed for a server database, since sqlite3 ships with Python.

from flowmaticdb.database import DB

# Connect to any supported database
db = DB.connect_sqlite(":memory:")
# db = DB.connect_postgresql("mydb", host="localhost", user="postgres")
# db = DB.connect_mysql("mydb", host="localhost", user="root")

# Fluent query building
result = (
    db.select("users")
    .columns(["id", "name", "email"])
    .where_equals("active", True)
    .where_greater_than("age", 18)
    .order_by_asc("name")
    .limit(10)
    .execute()
)

# Fetch results
for row in result.fetch_dicts():
    print(row["name"], row["email"])

first = result.fetch_dict()  # Single row or None
count = result.scalar()      # First column of first row

Supported Databases

connect_mariadb() is connect_mysql() with the dialect told it is talking to MariaDB, which is what enables native RETURNING (≥ 10.5) and shifts the ON CONFLICT and JSON version gates. Use it rather than connect_mysql() against a MariaDB server.

Connecting to a Database

SQLite

from flowmaticdb.database import DB

# In-memory
db = DB.connect_sqlite(":memory:")

# File-based
db = DB.connect_sqlite("/path/to/database.sqlite")

# With options
db = DB.connect_sqlite("mydb.db", options={
    "read_only": False,
    "journal_mode": "WAL",
    "foreign_keys": 1,
    "busy_timeout": 5000,
    "encoding": "UTF-8",
})

Every SQLite option and its default:

The first three are already what a threaded server wants, so passing them is usually redundant — a bare DB.connect_sqlite("mydb.db") is opened with a WAL journal, foreign keys ON and a 500 ms busy timeout. Raise busy_timeout above the default when writers contend heavily.

Connections are opened with sqlite3's same-thread check enabled — the stock behaviour. Each thread gets its own connection, so nothing needs to cross threads; handing a handle from get_connection() (or a Result reading from one) to another thread raises sqlite3.ProgrammingError instead of corrupting state silently. Pass options={"check_same_thread": False} to opt out.

A file-backed database gives every thread its own connection (see Threads and Concurrency), so concurrent writers are separate SQLite writers competing for the same file. Use "journal_mode": "WAL" plus a "busy_timeout" so they wait on each other instead of failing with database is locked.

An in-memory database is the exception: it lives inside the connection that opened it, so a second connection would be a second, empty database. Threads therefore share the single handle (with the same-thread check off, since sharing is the point), statements are serialized through a lock, and transaction state is process-wide rather than per thread. Use a file (WAL is enough to keep it fast) when threads need real isolation.

PostgreSQL

db = DB.connect_postgresql(
    "mydb",
    host="localhost",
    port=5432,
    user="postgres",
    password="secret",
    options={
        "sslmode": "require",
        "search_path": "public",
    },
)

Two drivers are supported and asyncpg_adapter picks between them. It defaults to True, so the stock connection uses AsyncpgAdapter and needs the asyncpg extra:

db = DB.connect_postgresql("mydb", user="postgres")                        # asyncpg
db = DB.connect_postgresql("mydb", user="postgres", asyncpg_adapter=False)  # psycopg

Both share PostgresqlDialect and behave identically through the public API. asyncpg is coroutine-only, so AsyncpgAdapter owns a private event loop on a daemon thread and blocks on it — the DB surface stays synchronous either way. It binds temporal values natively and rejects the client_encoding option, since asyncpg is UTF-8 only.

MySQL

db = DB.connect_mysql(
    "mydb",
    host="localhost",
    port=3306,
    user="root",
    password="secret",
    options={
        "charset": "utf8mb4",
        "connect_timeout": 10,
    },
)

# Same signature; tells the dialect it is MariaDB
db = DB.connect_mariadb("mydb", host="localhost", user="root", password="secret")

Arguments every connect_* accepts

ensure_always_connected trades a liveness check per statement for never handing a dead connection to a query. On PostgreSQL and MySQL that check pings the server, so it is not free — prefer calling reconnect_if_disconnected() once at the top of a request when the cost matters.

Reconnecting

A long-lived DB outlives its connection: servers close idle sessions, restart, or drop the socket. Three methods cover that:

db.is_connected()               # is the connection still usable?
db.reconnect_if_disconnected()  # reconnect only if it is not; returns whether it did
db.reconnect()                  # unconditionally drop and reopen

A reconnect opens a completely fresh connection: the startup_queries and options given at connect time are reapplied, and any open transaction is gone (the savepoint bookkeeping is reset to match). All three act on the calling thread's connection only — other threads keep theirs and reconnect on their own when they find their own connection broken.

def handle_request(db):
    db.reconnect_if_disconnected()
    return db.select("users").execute().fetch_dicts()

is_connected() pings the server on PostgreSQL (psycopg) and MySQL, so an idle connection whose backend died is detected before the next query rather than after it fails. Two caveats:

• Reconnecting a SQLite :memory: database opens an empty one — the old database only ever existed inside the dropped handle.
• Inside an open transaction the PostgreSQL check falls back to the local connection status (no ping), so a transaction is never disturbed by it.

Threads and Concurrency

A DB is safe to share between threads. Build one at startup and use it from every worker — a threaded server (FastAPI's def endpoints on its worker thread pool, Gunicorn/Uvicorn threads, a ThreadPoolExecutor) needs nothing else:

db = DB.connect_postgresql("mydb", user="postgres")   # module level

@app.get("/users")
def list_users():                     # runs on a worker thread
    return db.select("users").execute().fetch_dicts()

@app.on_event("shutdown")
def shutdown():
    db.close()

Each thread gets its own driver connection, opened the first time that thread runs a query and reused for the rest of its life. That is what makes sharing safe: threads never interleave statements, cursors, or transaction state on one handle, so a begin_transaction() on one worker cannot swallow another worker's write.

db.adapter.connection_count()   # live connections, across all threads

What follows from the model:

• Transactions, savepoints and last_insert_id() are per thread. A transaction belongs to the thread that opened it; other threads are unaffected and see nothing of it until it commits.
• Connection count tracks thread count. N worker threads means up to N server connections, so keep the server's own max_connections above your thread-pool size, or cap this side with max_concurrent_connections= (see Limiting concurrent connections). Connections belonging to finished threads are closed automatically when a new thread opens one.
• close() is global, everything else is local. close() is the shutdown hook and closes every thread's connection; reconnect(), is_connected() and _disconnect() act on the caller's connection alone. A query after close() raises AdapterError rather than quietly opening a new connection — reconnect() revives the adapter if you really want it back.
• Results belong to their thread. A ResultABC reads from a live cursor on the connection that ran the query. Consume it on the thread that created it and pass rows (or snapshot_result(result)) to other threads, not the result itself. On SQLite that rule is enforced: sqlite3's same-thread check is on by default again, so a stray cross-thread fetch raises instead of misbehaving.
• The query builders are per call and the dialect is immutable, so neither needs any care.

Two engine-specific notes: SQLite :memory: cannot give threads separate connections and shares one instead (see SQLite above), and the AsyncpgAdapter's private event loop is shared by all threads while its connections are not — asyncpg cannot run two queries on one connection at once.

Limiting Concurrent Connections

By default a thread that needs a connection opens one, however many are already live. Every connect_* method takes max_concurrent_connections to cap that, and acquire_connection_timeout to bound how long a thread waits for its turn:

db = DB.connect_postgresql(
    "mydb",
    user="postgres",
    max_concurrent_connections=10,   # at most 10 live connections, across all threads
    acquire_connection_timeout=5.0,  # give up after 5s of waiting
)

A thread that needs a new connection while the cap is reached blocks until a slot frees, and slots are handed out in arrival order — first to wait is first served. A thread that already has a connection never waits; it just reuses its own, so a capped adapter cannot deadlock against itself.

db.adapter.max_concurrent_connections  # 10
db.adapter.connection_count()          # live connections right now, never above the cap

With no acquire_connection_timeout a thread waits indefinitely. Set one and a thread that waits too long raises ConnectionLimitError (a subclass of AdapterError, so existing handlers keep working) instead of hanging:

from flowmaticdb import ConnectionLimitError

try:
    rows = db.select("users").execute().fetch_dicts()
except ConnectionLimitError:
    ...   # shed the request rather than pile up behind the cap

A slot is held for the lifetime of the thread, not the query. Connections are per thread (above), so a slot only comes free when its thread exits, is swept as finished, or close() runs — not when a query returns. Under a long-lived worker pool an idle worker still holds its slot, so max_concurrent_connections must be at least the number of workers that run queries concurrently or requests will queue behind threads that are doing nothing. Size it as a safety ceiling against runaway thread growth, not as a way to serve N workers from fewer than N connections:

# FastAPI def endpoints run on AnyIO's worker pool (41 threads by default)
db = DB.connect_postgresql("mydb", user="postgres", max_concurrent_connections=45)

Debug Callback

All connection methods accept a debug_callback for query logging:

def debug(sql: str, duration: float, error: str | None):
    print(f"[{duration:.4f}s] {sql}")
    if error:
        print(f"  ERROR: {error}")

db = DB.connect_sqlite(":memory:", debug_callback=debug)

Query Building

All query builders return Self for seamless method chaining.

SELECT

# Basic select
db.select("users").execute()

# With columns
db.select("users").columns(["id", "name"]).execute()

# Alias the table
db.select_table("users", "u").columns(["u.id", "u.name"]).execute()

# Sub-query as source
sub = db.select("active_users").columns(["id"])
db.select_sub_query(sub, "a").execute()

# Change table (fluent)
q = db.select("users")
q.table("admins").execute()

# Count
count: int = db.select("users").where_equals("active", True).count()

INSERT

# Single row
db.insert("users").values({"name": "Alice", "age": 30}).execute()

# Multiple rows
db.insert("users").values(
    {"name": "Bob", "age": 25},
    {"name": "Charlie", "age": 35},
).execute()

# With RETURNING — native on PostgreSQL and SQLite >= 3.35, emulated elsewhere
result = db.insert("users").values({"name": "Dave"}).returning(["id"]).last_insert_id("id").execute()
new_id = result.scalar()

# ON CONFLICT — native on PostgreSQL, SQLite >= 3.24 and MySQL, emulated elsewhere
db.insert("users").values({"name": "Alice"}).on_conflict_do_nothing(["name"]).execute()
db.insert("users").values({"name": "Alice", "age": 31}).on_conflict_do_update(
    ["name"], {"age": 31}
).execute()

# Get last insert ID
db.insert("users").values({"name": "Eve"}).last_insert_id("id").execute()
last_id = db.last_insert_id()

Emulated RETURNING and ON CONFLICT

Both clauses work on every dialect. When the dialect cannot express one, the insert falls back to the equivalent sequence of statements by itself — writing a per-driver fallback is never necessary:

Emulated RETURNING reads the inserted row back by primary key, so it needs to know that column. Supply it with last_insert_id("id") (or emulate_returning("id")); without it the insert raises QueryError rather than handing back an empty result:

db.insert("users").values({"name": "Dave"}).returning(["id"]).last_insert_id("id").execute()

emulate_returning("id") and emulate_on_conflict("id") are only needed to opt into the emulation on a dialect that has the clause natively — for instance to get identical statement sequences across environments. emulate_on_conflict() also takes in_transaction=True to wrap its select-then-write in a transaction.

returning() on UPDATE and DELETE is not emulated: on a dialect without native RETURNING the clause is dropped and the result holds no rows.

Conflict targets

The first argument to on_conflict_do_nothing() / on_conflict_do_update() is read two different ways, and the difference is not portable:

Per dialect:

• PostgreSQL renders both.
• SQLite renders the column list, and raises on the string form rather than guessing which constraint was meant.
• MySQL and MariaDB ignore the target entirely — the insert becomes INSERT IGNORE or ON DUPLICATE KEY UPDATE, neither of which names one — so either form is accepted and neither has any effect on which conflict matches.

db.insert("users").values({"name": "Alice"}).on_conflict_do_nothing("name").execute()
# QueryError: Named ON CONFLICT constraints are not supported by SQLite

Pass a list unless the code is deliberately PostgreSQL-only.

UPDATE

db.update("users").updates({"age": 26}).where_equals("name", "Bob").execute()

# With RETURNING
result = (
    db.update("users")
    .updates({"age": 27})
    .where_equals("name", "Bob")
    .returning(["id", "age"])
    .execute()
)
updated = result.fetch_dict()

DELETE

db.delete("users").where_equals("name", "Alice").execute()

# Change table
q = db.delete("users")
q.table("old_users").execute()

# With RETURNING
result = db.delete("users").where_less_than("age", 18).returning(["id"]).execute()

CREATE TABLE

# Using convenience methods
db.create_table("users").if_not_exists() \
    .identity("id") \
    .string("name", not_null=True) \
    .integer("age") \
    .boolean("active", default=True) \
    .datetime("created_at") \
    .json("preferences") \
    .execute()

# Using raw column definitions
db.create_table("posts").if_not_exists() \
    .column("id", TypeEnum.INT, not_null=True) \
    .column("title", TypeEnum.STRING, not_null=True) \
    .column("body", "TEXT") \
    .primary_keys("id") \
    .execute()

# With constraints
db.create_table("orders").if_not_exists() \
    .identity("id") \
    .integer("user_id") \
    .string("status") \
    .unique_constraint(["status", "user_id"], name="uq_orders_status_user") \
    .foreign_key_constraint(
        "user_id", "users", "id",
        referential_actions=["ON DELETE CASCADE"],
    ) \
    .execute()

ALTER TABLE

# Add columns
db.alter_table("users") \
    .add_string("email", size=255) \
    .add_int("score", not_null=True, default=0) \
    .execute()

# Rename / drop columns
db.alter_table("users") \
    .rename_column("name", "full_name") \
    .drop_column("temp_field") \
    .execute()

# Add constraints
db.alter_table("users") \
    .add_unique_constraint(["email"], name="uq_users_email") \
    .add_foreign_key_constraint("role_id", "roles", "id") \
    .execute()

# Drop constraints
db.alter_table("users") \
    .drop_constraint("uq_users_email") \
    .execute()

# Raw alter
db.alter_table("users").alter("ALTER COLUMN age SET NOT NULL").execute()

An AlterTableQuery emits one statement per alteration, not one combined statement, so to_query_with_params() returns a list[QueryWithParams] where every other builder returns a single one:

query = db.alter_table("users").add_string("nickname", size=64).add_int("score", default=0)
for qwp in query.to_query_with_params():
    print(qwp.query)
# ALTER TABLE "users" ADD COLUMN "nickname" VARCHAR(64)
# ALTER TABLE "users" ADD COLUMN "score" BIGINT DEFAULT 0

On MySQL each of those statements also commits implicitly, so a multi-step alter_table() cannot be rolled back halfway.

Indexes

There is no index builder — indexes go through exec():

db.exec('CREATE INDEX IF NOT EXISTS "idx_posts_user_id" ON "posts" ("user_id")')

IF NOT EXISTS on an index is supported by PostgreSQL, SQLite and MariaDB but not by MySQL, which needs a catalog check instead:

exists = db.prepared(
    "SELECT count(*) FROM information_schema.statistics "
    "WHERE table_schema = database() AND table_name = ? AND index_name = ?",
    ["posts", "idx_posts_user_id"],
).scalar()

Use dialect.escape_identifier() to quote names rather than hardcoding quotes, since PostgreSQL and SQLite use " while MySQL uses `.

DROP TABLE

db.drop_table("posts").execute()
db.drop_table("posts").if_exists().execute()

WHERE Conditions

Every condition method has four variants:

Available Conditions

# Comparison
.where_equals("name", "Alice")
.where_not_equals("status", "banned")
.where_less_than("age", 18)
.where_less_than_or_equals("age", 65)
.where_greater_than("score", 100)
.where_greater_than_or_equals("score", 0)

# Null checks
.where_is_null("deleted_at")
.where_is_not_null("email")

# Pattern matching
.where_like("name", "Alice%")        # SQL LIKE
.where_not_like("email", "%@spam.com")
.where_starts_with("username", "admin")  # LIKE 'admin%'
.where_ends_with("filename", ".pdf")     # LIKE '%.pdf'
.where_contains("bio", "engineer")       # LIKE '%engineer%'
.where_not_contains("bio", "spam")       # NOT LIKE '%spam%'

# File globbing (SQLite)
.where_glob("path", "*.txt")
.where_not_glob("path", "*.tmp")

# Set membership
.where_in("id", [1, 2, 3])
.where_not_in("role", ["guest", "anon"])

# Range
.where_between("age", 18, 65)
.where_not_between("age", 0, 17)

# Empty string
.where_empty("middle_name")
.where_not_empty("full_name")

# Regex
.where_regex("email", r"^[a-z]+@")
.where_not_regex("email", r"^test@")

# Subquery existence
sub = db.select("orders").columns(["user_id"])
.where_exists(sub)
.where_not_exists(sub)

# Grouped conditions
.where_group(lambda g: (
    g.where_equals("plan", "premium")
     .or_where_group(lambda g2: (
         g2.where_equals("plan", "free")
            .where_less_than("trial_days", 30)
     ))
))
.where_not_group(lambda g: g.where_equals("role", "internal"))

# Raw SQL conditions
.where_raw("EXTRACT(YEAR FROM created_at) = ?", [2026])
.or_where_raw("last_login IS NOT NULL")

# Custom operator
.where_operator("json_data", "@>", '{"vip": true}')

HAVING Conditions

Exactly the same methods as WHERE, prefixed with having_* / or_having_*:

db.select("users") \
    .columns(["plan", "count(*)"]) \
    .group_by(["plan"]) \
    .having_greater_than("count(*)", 5) \
    .having_between("avg(age)", 18, 65) \
    .having_group(lambda g: g.where_equals("plan", "enterprise")) \
    .execute()

JOINs

from flowmaticdb import raw, identifier

query = db.select("users").columns(["users.id", "posts.title"])

# INNER JOIN with ON conditions — the callback receives the Join,
# every join method returns the query so you can keep chaining
query.inner_join_table(
    "posts",
    lambda join: join
        .on(["users", "id"], ["p", "user_id"])       # ON users.id = p.user_id
        .or_on(["p", "status"], ["'published'"]),    # OR p.status = 'published'
    "p",
)

# LEFT JOIN
query.left_join_table("comments", lambda join: join.on(["p", "id"], ["c", "post_id"]), "c")

# CROSS JOIN (never takes ON conditions)
query.cross_join("sessions")

# LATERAL joins
query.left_join_lateral_sub_query(sub_query, "sq")
query.inner_join_lateral_sub_query(sub_query, "sq")
query.cross_join_lateral_sub_query(sub_query, "sq")

# Raw join SQL (e.g. for aggregates)
query.join(raw("LEFT JOIN (SELECT user_id, count(*) AS cnt FROM orders GROUP BY user_id) AS o ON o.user_id = users.id"))

Join ON Conditions

Join objects support all the same condition methods as WHERE:

query.inner_join(
    "orders",
    lambda join: join
        .where_equals(["orders", "user_id"], ["users", "id"])
        .where_greater_than("orders.total", 100),
)

DISTINCT, GROUP BY, ORDER BY, LIMIT, OFFSET

db.select("users") \
    .distinct()                   # DISTINCT
    .distinct(["category"])       # DISTINCT ON (PostgreSQL only)
    .group_by(["plan", "status"]) \
    .order_by_asc("name") \
    .order_by_desc("created_at")  # Multiple orderings
    .limit(50) \
    .offset(10) \
    .execute()

UNION / UNION ALL

active  = db.select("users").where_equals("active", True)
archived = db.select("archived_users")

db.select("users") \
    .columns(["id", "name"]) \
    .union(active) \
    .union_all(archived) \
    .execute()

Transactions

# Explicit transaction
db.begin_transaction()
try:
    db.insert("users").values({"name": "Alice"}).execute()
    db.insert("users").values({"name": "Bob"}).execute()
    db.commit_transaction()
except Exception:
    db.rollback_transaction()

# With context-manager-style callback
def work(database):
    database.insert("users").values({"name": "Charlie"}).execute()
    database.insert("users").values({"name": "Dave"}).execute()

db.transaction(work)  # Auto commit/rollback

# Savepoints for nested transactions
db.begin_transaction()
db.begin_transaction("savepoint_1")
db.commit_transaction("savepoint_1")
db.rollback_transaction()  # Rolls back main transaction

Working with Results

All execute() calls return a ResultABC object.

Fetching Data

result = db.select("users").execute()

# Single row
row: dict | None = result.fetch_dict()

# All rows
rows: list[dict] = result.fetch_dicts()

# First column of first row
val: Any = result.scalar()
val = result.scalar("name")  # Named column

# Column metadata
cols: dict[str, str] = result.columns()  # {"id": "integer", "name": "text", ...}

# Hydrate into objects
class User:
    def __init__(self):
        self.id = 0
        self.name = ""

user = result.fetch_object(User)       # Single
users = result.fetch_objects(User)     # List

Snapshotting a Result

Freeze a live cursor result into an in-memory Result:

from flowmaticdb.result import snapshot_result

live_result = db.select("users").execute()
snapshot = snapshot_result(live_result)  # Can be iterated repeatedly

Result Methods Summary

Table API

High-level table wrapper for common patterns:

from flowmaticdb.database import Table

# Create a table reference
table = Table(db, db.dialect, "users")

# Shortcuts
table.select()                              # SELECT *
table.select(["id", "name"])                # SELECT id, name
table.insert({"name": "Alice"})            # INSERT
table.update({"age": 30})                  # UPDATE ... (add WHERE separately)
table.delete()                              # DELETE ... (add WHERE separately)

# Smart operations
table.select_or_insert(["name"], ["Alice"])  # SELECT first, INSERT if not found
table.insert_or_ignore(["name"], ["Bob"])   # INSERT ... ON CONFLICT DO NOTHING
table.insert_or_update(
    ["name"], ["Charlie"],
    conflict="name",
    updates={"age": 40},
)                                         # INSERT ... ON CONFLICT DO UPDATE

# DDL
table.create(lambda q: q.identity("id").string("name"))
table.create_if_not_exists(...)
table.drop()
table.drop_if_exists()
table.truncate()

# Introspection
table.columns()     # list[str] — column names
table.is_empty()    # bool

db.table("users") is the same thing without repeating the dialect:

table = db.table("users")

Migrations

Schema changes as ordered, reversible Python files. One migration is one file holding exactly one MigrationABC subclass:

from __future__ import annotations

from typing import TYPE_CHECKING

from flowmaticdb.migrations import MigrationABC

if TYPE_CHECKING:
    from flowmaticdb.database import DB


class CreateUsersTable(MigrationABC):
    def up(self, db: DB) -> None:
        db.create_table("users").if_not_exists() \
            .identity("id") \
            .string("email", size=255, not_null=True) \
            .execute()

    def down(self, db: DB) -> None:
        db.drop_table("users").if_exists().execute()

up() and down() are abstract and receive the DB as an argument rather than holding one. in_transaction() is concrete and returns True; override it to return False for a migration that must not run inside a transaction.

Migrator

from flowmaticdb.migrations import Migrator

migrator = Migrator(db, "app/migrations")          # migrations_table="migrations"

migrator.init()                                     # create the bookkeeping table
migrator.up()                                       # apply everything pending, as one batch
migrator.down()                                     # roll back the most recent batch
path = migrator.create("add_email_to_users")        # write a new migration file

The bookkeeping table records filename, batch and applied_at per applied migration.

Rules

• init() must run before up() or down(). Both read the bookkeeping table directly and fail if it does not exist yet.
• create(name) uses name verbatim in the filename, spaces included. Pass snake_case.
• Ordering is a plain sort of filenames, which is what the timestamp prefix is for. Do not rename a file once it has been applied — the table keys on it.
• Exactly one MigrationABC subclass per file. Zero raises DatabaseError, and so does more than one. Classes imported from elsewhere do not count, so shared helpers are fine.
• Files beginning with _ are skipped, which is where helper modules go. They are not importable as a package, though — the loader reads each migration by path, so add the directory to sys.path before importing a sibling helper.
• Each migration runs inside a transaction unless in_transaction() returns False. On MySQL that guarantee is limited: DDL commits implicitly, so a file with several DDL statements cannot be rolled back halfway. Keep one logical change per file.
• down() reverses a whole batch, not one file — everything a single up() applied comes back off together.

Adopting migrations for a database that already exists

Write the current schema as an initial migration with if_not_exists() on every create and if_exists() on every drop. Applied to a database that already has the schema it creates nothing, records one row, and leaves the data untouched; applied to an empty one it builds everything. The same file therefore works for both existing deployments and fresh checkouts.

Drop tables in reverse dependency order in down(), or foreign keys will block the drop.

A CLI

There is no console entry point; wire one up in the project:

import sys

from flowmaticdb.migrations import Migrator

from app.db import connect

db = connect()
migrator = Migrator(db, "app/migrations")

command = sys.argv[1]

if command == "create":
    print(migrator.create(sys.argv[2]))
else:
    migrator.init()
    if command == "up":
        migrator.up()
    elif command == "down":
        migrator.down()

db.close()

Run up on deploy, before the application starts serving — not from a request handler, and not from every worker at once.

Expressions

Import module-level factory functions:

from flowmaticdb import raw, identifier, alias, expression, sub_query, current_timestamp, now

Available Expressions

db.select(raw("COUNT(*) AS cnt")).table("users").execute()

# Schema-qualified table reference
db.select(identifier(["public", "users"])).execute()

# Alias in joins
join = query.inner_join(alias("users", "u"))
join.on(identifier(["u", "id"]), identifier(["posts", "user_id"]))

EXPLAIN Queries

plan = db.select("users").where_equals("name", "Alice").explain()
for row in plan:
    print(row)

Raw Query Execution

For one-off SQL that doesn't need the query builder:

# DDL (no parameters) — one statement per call, never a script
db.exec("CREATE TABLE temp (id INTEGER PRIMARY KEY)")

# DML with parameters
from flowmaticdb import QueryWithParams
qwp = QueryWithParams(query="SELECT * FROM users WHERE name = ?", params=["Alice"])
result = db.query_with_params(qwp)
rows = result.fetch_dicts()

# Prepared statement shortcut
result = db.prepared("SELECT * FROM users WHERE age > ? AND active = ?", [18, True])

exec() hands the string straight to the driver, which accepts one statement per call. A whole .sql file cannot be passed through it — split it, or rebuild it with the query builder:

db.exec("CREATE TABLE a (id INTEGER); CREATE TABLE b (id INTEGER);")
# sqlite3.ProgrammingError: You can only execute one statement at a time.

QueryWithParams

The core data structure that travels from query builders through dialects to adapters:

from flowmaticdb import QueryWithParams

qwp = QueryWithParams(query="SELECT * FROM users WHERE age > ?", params=[18])

# Convert %s placeholders to ? positional
qwp2 = qwp.percent_s_to_question_marks()

# Interpolate values into SQL string (for debugging / emulation)
sql = qwp.to_sql(dialect)
# Returns: SELECT * FROM users WHERE age > 18

Exception Hierarchy

DatabaseError
├── AdapterError        — Adapter-level issues (connection, configuration)
├── DriverError         — Driver/connection errors
├── QueryError          — Query building errors (e.g., unsupported SQL feature)
└── QueryWithParamsError — Parameterized query errors

from flowmaticdb import DatabaseError, QueryError

try:
    db.select("users").execute()
except QueryError as e:
    print(f"Query error: {e}")
except DatabaseError as e:
    print(f"Database error: {e}")

Datetime, JSON and Boolean Values

datetime objects, JSON documents and booleans are serialized on the way into the database and deserialized on the way back out, on every adapter.

from datetime import datetime, timezone

db.create_table("events").if_not_exists() \
    .identity("id") \
    .datetime("happened_at") \
    .json("payload") \
    .execute()

db.insert("events").values({
    "happened_at": datetime.now(timezone.utc),
    "payload": {"kind": "signup", "tags": ["a", "b"]},
}).execute()

row = db.select("events").execute().fetch_dict()
row["happened_at"]   # datetime
row["payload"]       # dict

TypeEnum.JSON (the .json() column builder) maps to the best type the server has: JSONB on PostgreSQL ≥ 9.4, JSON on PostgreSQL ≥ 9.2, MySQL ≥ 5.7.8, MariaDB ≥ 10.2.7 and SQLite, and TEXT on anything older. TypeEnum.DATETIME maps to TIMESTAMPTZ, DATETIME(6) and DATETIME respectively.

Nested values json does not know are rendered rather than raising: datetime, date and time become ISO-8601 strings, Decimal becomes a string (a float would lose precision). A JSON column holding text that is not valid JSON is handed back as that text instead of failing the fetch.

How each driver is wired up:

SQLite stores only primitives, so SQLiteAdapter registers custom datatypes with the sqlite3 module and opens its connections with detect_types=sqlite3.PARSE_DECLTYPES. Conversion is keyed off the column's declared type, so a DATETIME, JSON or BOOLEAN table column is converted while an expression (count(*), a computed alias) has no declared type and is returned as-is. Datetimes are written as full ISO-8601, so microseconds and UTC offsets survive the round trip.

Booleans

Only PostgreSQL has a boolean type. SQLite stores 0/1 under a declared BOOLEAN column and MySQL stores a TINYINT, and both are read back as a real bool:

db.create_table("flags").if_not_exists().identity("id").boolean("active").execute()
db.insert("flags").values({"active": True}).execute()

db.select("flags").execute().scalar("active")   # True, not 1

The two emulating dialects reach that differently, which is worth knowing when reading a table this library did not create:

• SQLite is exact — the converter fires on the column's declared BOOLEAN/BOOL type, and nothing else is touched.
• MySQL reports BOOL and TINYINT as the same wire type and drops the display width, so every TINYINT column reads back as a bool. TypeEnum.INT never maps to TINYINT (it is INTEGER/BIGINT), so a schema this library created is unaffected; a foreign table storing small numbers in a TINYINT is. Select such a column as CAST(col AS SIGNED) — or let a pydantic model coerce at the edge — if you need the number.

PostgreSQL arrays — PostgresArray

A bare list is a JSON document on every dialect, PostgreSQL included. PostgreSQL also has a real array type, but nothing in the value itself says which of the two is meant, so the array reading is opt-in — wrap the value in PostgresArray:

from flowmaticdb import PostgresArray

db.insert("rows").values({
    "id": 1,
    "actual_json_column": [1, 2, 3, 4],
    "postgres_array_column": PostgresArray([5, 6, 7, 8]),
}).execute()

# Also works anywhere else a value is bound, e.g. the array containment operators
db.select("rows").where_operator("tags", "@>", PostgresArray(["a", "b"])).execute()

Both PostgreSQL drivers behave identically here: a bare list aimed at an array column is sent as JSON and rejected, rather than quietly being taken as an array. Element types are left to the driver, so PostgresArray([datetime(...)]) binds as timestamptz[], not text[].

Dialects with no array type unwrap PostgresArray back to JSON, so a query written for PostgreSQL still runs against SQLite and MySQL.

Reading is unaffected — an array column always comes back as a plain list.

Dialect-Specific Behavior

PostgreSQL

SQLite

MySQL

General ANSI (SQLDialect base)

• LIMIT / OFFSET — Standard ANSI syntax
• LIMIT ? OFFSET ? — Parameterized
• No native ON CONFLICT, RETURNING, DISTINCT ON, or LATERAL — INSERT emulates the first two
• No GLOB support
• Regex raises QueryError

Architecture

┌────────────────────────────────────────────────────┐
│                   User Code                        │
│   DB.connect_*() → Database → Query Builders      │
└──────────────────┬─────────────────────────────────┘
                   │
          ┌────────┴────────┐
          ▼                 ▼
    ┌──────────┐    ┌──────────────┐
    │ Dialects │    │  Adapters    │
    │ ──────── │    │ ──────────   │
    │ SQL gen  │    │ Connection   │
    │ + types  │    │ + execution  │
    └────┬─────┘    └──────┬───────┘
         │                 │
         ▼                 ▼
    ┌──────────┐    ┌──────────────┐
    │ Query    │    │   Result     │
    │ Builders │    │ ──────────   │
    │ ──────── │    │ fetch_dict() │
    │ Fluent   │    │ fetch_dicts()│
    │ chaining │    │ scalar()     │
    └──────────┘    └──────────────┘

Five Pillars

1. Dialects — Database-specific SQL generation

   • DialectABC — Abstract base
   • SQLDialect — ANSI SQL (~713 lines; overridable in subclasses)
   • PostgresqlDialect — PostgreSQL overrides
   • SQLiteDialect — SQLite overrides
   • MySQLDialect — MySQL overrides

2. Adapters — Connection wrappers

   • AdapterABC — Abstract base
   • SQLiteAdapter — Wraps sqlite3.Connection
   • PsycopgAdapter — Wraps psycopg.Connection
   • AsyncpgAdapter — Wraps asyncpg.Connection on a private event loop
   • MySQLAdapter — Wraps mysql.connector.Connection

3. Query Builders — Fluent SQL construction

   • SelectQuery — SELECT with WHERE/HAVING/JOINs/GROUP BY/ORDER BY/LIMIT/OFFSET/UNION
   • InsertQuery — INSERT with ON CONFLICT/RETURNING
   • UpdateQuery — UPDATE with WHERE/RETURNING
   • DeleteQuery — DELETE with WHERE/RETURNING
   • CreateTableQuery — CREATE TABLE with columns, keys, constraints
   • AlterTableQuery — ALTER TABLE (add/rename/drop columns, constraints)
   • DropTableQuery — DROP TABLE

4. Results — Unified result set

   • ResultABC — Abstract base
   • Result — In-memory result (snapshot)
   • SQLite3Result — Wraps sqlite3.Cursor
   • PsycopgResult — Wraps psycopg cursor
   • AsyncpgResult — Wraps an asyncpg record set
   • MySQLResult — Wraps mysql.connector.cursor

5. Migrations — Ordered, reversible schema changes

   • MigrationABC — Abstract base (up(), down(), in_transaction())
   • Migrator — Discovery, bookkeeping and execution

Mixin Architecture

Query builders use Python multiple inheritance for composable behavior:

Enums Reference

from flowmaticdb.query.enums import ConditionEnum
# =, <>, <, <=, >, >=, BETWEEN, NOT BETWEEN, LIKE, NOT LIKE,
# GLOB, NOT GLOB, IN, NOT IN, REGEX, NOT REGEX, EXISTS, NOT EXISTS, RAW

from flowmaticdb.query.enums import ChainEnum
# AND, OR

from flowmaticdb.query.enums import JoinEnum
# LEFT JOIN, LEFT JOIN LATERAL, INNER JOIN, INNER JOIN LATERAL,
# CROSS JOIN, CROSS JOIN LATERAL

from flowmaticdb.query.enums import OrderByDirectionEnum
# ASC, DESC

from flowmaticdb.query.enums import UnionEnum
# UNION, UNION ALL

from flowmaticdb.query.enums import TypeEnum
# BOOL, INT, FLOAT, STRING, DATETIME, JSON

from flowmaticdb.query.enums import ReferentialActionEnum
# ON_UPDATE_NO_ACTION, ON_UPDATE_SET_NULL, ON_UPDATE_CASCADE,
# ON_DELETE_NO_ACTION, ON_DELETE_SET_NULL, ON_DELETE_CASCADE

Import Notes

A leading underscore on a module name marks it as a private implementation detail — never import from it directly. Each package's public API is exactly its __init__.py __all__; import from the package instead. This holds without exception, including the exception classes and helper functions, which live in _exceptions.py and _helpers.py and are re-exported from flowmaticdb.

• PsycopgAdapter, MySQLAdapter — Import from flowmaticdb.adapters, NOT a submodule

• PostgresArray — Re-exported from the top-level package: from flowmaticdb import PostgresArray (it also lives in flowmaticdb.query.expressions)

• PsycopgResult, MySQLResult — Import from flowmaticdb.result, NOT a submodule

• raw(), identifier(), alias(), expression(), sub_query(), current_timestamp(), now() — Module-level functions, imported from flowmaticdb

• snapshot_result() — Import from flowmaticdb.result

• MigrationABC, Migrator — Import from flowmaticdb.migrations

from flowmaticdb.adapters import PsycopgAdapter, AsyncpgAdapter, MySQLAdapter
from flowmaticdb.result import PsycopgResult, MySQLResult, snapshot_result
from flowmaticdb.migrations import MigrationABC, Migrator
from flowmaticdb import raw, identifier, alias, expression, sub_query, current_timestamp, now

Qualified Column References

Use two-element lists for schema-qualified or table-qualified column names:

# Correct: table-qualified
.where_equals(["users", "name"], "Alice")

# Correct: schema-qualified
.where_equals(["public", "users", "name"], "Alice")

# Correct: using identifier()
.where_equals(identifier(["users", "name"]), "Alice")

# WRONG: "users.name" is treated as a single identifier
# and escaped as "users.name" (non-existent column)

The same lists work in columns(), group_by() and returning() — every identifier is escaped segment by segment, however deeply the list nests:

db.select("users").columns([["users", "email"], "name"]).execute()
# SELECT "users"."email", "name" FROM "users"

# With an alias, pass the qualified column as the dict value
db.select("users").columns({"mail": ["users", "email"]}).execute()
# SELECT "users"."email" AS "mail" FROM "users"

For raw JOIN clauses and aggregate expressions, use raw():

query.join(raw("LEFT JOIN orders o ON o.user_id = users.id"))

Schema-qualified table references work with plain lists:

db.insert(["public", "users"]).values({"name": "Alice"}).execute()
db.delete(["schema", "table"]).where_equals("id", 1).execute()
db.update(["schema", "table"]).updates({"name": "Bob"}).execute()
db.create_table(["schema", "table"]).identity("id").string("name").execute()

Database-Specific Notes

Placeholder Conversion

All dialects emit ? as the placeholder. Each adapter converts to its driver's native format:

• PostgreSQL: ? → %s via question_marks_to_percent_s() (psycopg expects %s)
• MySQL: ? → %s via question_marks_to_percent_s() (mysql-connector expects %s)
• SQLite: %s → ? via percent_s_to_question_marks() (SQLite uses ? natively; handles user-provided %s)

Both conversion methods use REGEX_PATTERN to skip placeholders inside quoted strings and comments.

DDL vs DML

• DDL (CREATE, ALTER, DROP, BEGIN, COMMIT): Use adapter.exec(sql) — no parameter binding
• DML (SELECT, INSERT, UPDATE, DELETE): Use adapter.query_with_params(dialect, qwp) — uses parameterized queries

Development

Setup

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Running Tests

# All 191 tests
python3 -m pytest

# Unit tests only (no database needed)
python3 -m pytest tests/test_dialect_sql.py
python3 -m pytest tests/test_select_query.py

# SQLite integration (in-memory, no setup)
python3 -m pytest tests/test_integration_sqlite.py

# PostgreSQL integration (requires Docker)
docker compose up -d postgres
python3 -m pytest tests/test_integration_postgres.py

# MySQL integration (requires Docker)
docker compose up -d mysql
python3 -m pytest tests/test_integration_mysql.py

# Single test
python3 -m pytest tests/test_dialect_sql.py -k "test_select"

# Type checking
python3 -m mypy src/flowmaticdb

# Linting
python3 -m ruff check src/flowmaticdb/ tests/

Run Demo

python3 main.py

Connects to MySQL by default. Edit main.py to switch to SQLite or PostgreSQL.

Requirements

• Python ≥ 3.11
• asyncpg>=0.29 (PostgreSQL, the default adapter — optional)
• psycopg[binary]>=3.1 (PostgreSQL, with asyncpg_adapter=False — optional)
• mysql-connector-python>=9.0 (MySQL and MariaDB adapter — optional)
• SQLite uses the standard library (sqlite3)

License

MIT