ipythonblocks.org Move: Part 2 – Data Migration

This is Part 2 in a series of blog posts describing my move of ipythonblogs.org from Rackspace to Heroku. In Part 1 of this series I described my motivation for the move and the broad changes I expect to make as part of the migration. In this post I’ll describe the grid data model and how I migrated the existing grid data from SQLite to Postgres. Other posts are:

  • Part 1: Introduction and Architecture
  • Part 2: Data Migration
  • Part 3: Database Interface Updates
  • Part 4: Application Updates

SQLite Data Model

Grids are stored in a table with this schema:

sqlite> .schema --indent public_grids
CREATE TABLE public_grids(
  id INTEGER NOT NULL,
  ipb_version TEXT,
  python_version TEXT,
  grid_data TEXT,
  secret BOOLEAN,
  code_cells TEXT,
  ipb_class TEXT,
  PRIMARY KEY(id)
);

Simple enough, but the python_version, grid_data, and code_cells columns are actually encoded JSON data. In the application code all reads and writes are wrapped by code that calls json.loads or json.reads. Still, this a relatively simple data model compared with most web applications where there are many tables linked by foreign keys and other relationships. Here there’s one table that holds everything. This would have been a good fit for a document database like MongoDB, but it also works fine in SQL.

(The DB actually contains two tables with identical schemas, one for public grids and one for secret grids. The main practical difference between public and secret grids is that secret grids can’t be discovered via the “random grid” feature on ipythonblocks.org.)

Postgres Data Model

Postgres supports JSON data types so I’ll be able to dispense with the manual de/serialization code. I’m also using SQLAlchemy now so the tables are defined in Python:

"""Definition of SQL tables used to store ipythonblocks grid data"""
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql as pg
from sqlalchemy.ext.declarative import declarative_base

Base = declarative_base()


class CommonColumnsMixin:
    """Columns common to both public and secret grids"""
    __table_args__ = {'schema': 'public'}  # "public" is postgres' default schema

    id = sa.Column(sa.Integer, primary_key=True)
    ipb_version = sa.Column(sa.Text, nullable=False)
    python_version = sa.Column(pg.JSONB, nullable=False)
    grid_data = sa.Column(pg.JSONB, nullable=False)
    code_cells = sa.Column(pg.JSONB)
    ipb_class = sa.Column(sa.Text, nullable=False)
    created_at = sa.Column(
        sa.DateTime(timezone=True), nullable=False,
        server_default=sa.text('NOW()'))


class PublicGrid(CommonColumnsMixin, Base):
    """Table to hold public grids (discoverable via "random")"""
    __tablename__ = 'public_grids'

    # no-op column, but put it here anyway
    secret = sa.Column(sa.Boolean, nullable=False, default=False)


class SecretGrid(CommonColumnsMixin, Base):
    """Table to hold secret grids"""
    __tablename__ = 'secret_grids'

    # no-op column, but put it here anyway
    secret = sa.Column(sa.Boolean, nullable=False, default=True)

This is using SQLAlchemy’s ORM and a mixin class to store the common columns.

With SQLAlchemy’s support for Postgres’ JSON types the JSON data stored in the tables will automatically be converted to Python lists and dictionaries when I load query results (and it also works the other way when writing data). (You can even write queries based on things inside of JSON columns, but that’s not a feature I’m using in the app.)

The one thing I’ve added to the data model is a created_at column that will record when a grid was sent to ipythonblocks.org. (server_default=sa.text('NOW()') means that the Postgres NOW() function will be called to provide a value if one is not provided by the client during an INSERT query.)

Migration

With the tables defined I’m ready to copy data from SQLite to Postgres. I used scp to copy the SQLite database file from the ipythonblocks.org server to my laptop so I could run the migration locally. (Everything in a SQLite database is stored in a single file and it’s beautiful how the database file is platform-independent.) Then I wrote a Python script to copy the data from SQLite to Postgres:

"""Script for migrating ipythonblocks grid data from SQLite to Postgres"""
import contextlib
import json
import os
from pathlib import Path

import sqlalchemy as sa
from sqlalchemy.orm import sessionmaker

# The module in the ipythonblocks.org application code that contains
# table definitions
from app import models

# SQLite DB related variables
SQLITEDB = 'sqlite:///' + str(Path.home() / 'ipborg.db')
SQLITE_ENGINE = sa.create_engine(str(SQLITEDB))
SQLITE_META = sa.MetaData(bind=SQLITE_ENGINE)

# Postgres DB related variables
DBURL = os.environ['DATABASE_URL']  # could be local or remote server
PSQL_ENGINE = sa.create_engine(DBURL)
SESSION = sessionmaker(bind=PSQL_ENGINE)

# columns that are serialized JSON in the SQLite DB
JSONIZE_KEYS = {'python_version', 'code_cells', 'grid_data'}

# drop and recreate tables in the destination DB so we're always
# starting fresh
models.Base.metadata.drop_all(bind=PSQL_ENGINE)
models.Base.metadata.create_all(bind=PSQL_ENGINE)


def sqlite_row_to_sa_row(row, sa_cls):
    """
    Convert a row from the SQLite DB (a SQLAlchemy RowProxy instance)
    into an ORM instance such as PublicGrid or SecretGrid
    (exact class provided by the sa_cls argument). This takes care of
    de-serializing the JSON data stored in the SQLite DB.

    """
    d = dict(row)
    for key in JSONIZE_KEYS:
        d[key] = json.loads(d[key]) if d[key] else None

    return sa_cls(**d)


def sqlite_table_to_sa_rows(table_name, sa_cls):
    """
    Yields SQLAlchemy ORM instances of sa_cls from a SQLite table
    specified by table_name.

    """
    table = sa.Table(table_name, SQLITE_META, autoload=True)
    results = SQLITE_ENGINE.execute(table.select())
    for row in results:
        yield sqlite_row_to_sa_row(row, sa_cls)


@contextlib.contextmanager
def session_context():
    session = SESSION()
    try:
        yield session
        session.commit()
    except:
        session.rollback()
        raise
    finally:
        session.close()

def migrate():
    """
    Trigger the reading from SQLite, transformation of JSON data,
    and writing to Postgres.

    """
    with session_context() as session:
        session.add_all(sqlite_table_to_sa_rows('public_grids', models.PublicGrid))
        session.add_all(sqlite_table_to_sa_rows('secret_grids', models.SecretGrid))

        # Because all the grids added so far already had IDs, the sequences
        # backing the id columns in the grid tables haven't been advanced
        # at all. When trying to add a new table the sequence would provide
        # a key of 1, which would then collide with the existing grids.
        # We need to manually set the sequences behind the table primary keys
        # so that when new grids are added with no IDs the automatically
        # generated IDs are actually available.
        max_public_id = session.query(sa.func.max(models.PublicGrid.id)).scalar()
        max_secret_id = session.query(sa.func.max(models.SecretGrid.id)).scalar()

        session.execute(sa.text(
            f'select setval(\'public_grids_id_seq\', {max_public_id})'))
        session.execute(sa.text(
            f'select setval(\'secret_grids_id_seq\', {max_secret_id})'))


if __name__ == '__main__':
    migrate()

The total number of rows involved was less than 100 so I could easily load all the data into memory at once and didn’t worry about streaming. I ran this to copy the data both to a local testing database and to the production database on Heroku.

What’s Next

In the next post I’ll describe updating the application database access code to use SQLAlchemy and remove the use of memcached.

ipythonblocks.org Move: Part 2 – Data Migration

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