Moltres vs Ibis Comparison

Executive Summary

This document compares Moltres and Ibis: two Python libraries that provide a portable DataFrame API with SQL (or backend-native) execution. Ibis is “the portable Python dataframe library” with a single API across many backends; Moltres is a PySpark-style DataFrame layer for SQL with full CRUD and async support. Both compile expressions to SQL (or the backend’s native form) and run on the engine rather than in Python memory.

Key Findings

API style: Ibis has its own DataFrame API (e.g. con.table(), group_by(), agg(), order_by()); Moltres provides a PySpark-style API (db.table().select(), group_by() / groupBy()) and optional Pandas/Polars interfaces.
Backends: Ibis supports about 20 backends (BigQuery, DuckDB, PostgreSQL, Snowflake, PySpark, Polars, DataFusion, Trino, etc.); Moltres is SQLAlchemy-based (SQLite, PostgreSQL, MySQL, DuckDB, and any SQLAlchemy-supported database).
CRUD: Moltres provides INSERT (via Records), UPDATE, and DELETE; Ibis focuses on read and transform (no UPDATE/DELETE in its public API).
Async: Moltres has full async/await; Ibis does not emphasize async in its README.
SQL mixing: Ibis allows t.sql("SELECT ...") to embed SQL and continue with the DataFrame API; Moltres compiles Python expressions to SQL and offers show_sql(), plan summary, and validation.

Use Case Recommendations

Choose Moltres when:
- You want a PySpark-style or pandas/polars-style API on SQL databases.
- You need UPDATE/DELETE or bulk INSERT with a DataFrame-like API.
- You want full async/await and framework integrations (FastAPI, Django, Streamlit, SQLModel).
- You use SQLAlchemy-backed databases and prefer a single pip install moltres with optional extras.
Choose Ibis when:
- You need one API across many backends (BigQuery, Snowflake, DuckDB, Polars, PySpark, Trino, etc.).
- You want to mix raw SQL and DataFrame code (t.sql("SELECT ...")) and iterate locally then deploy by changing the backend.
- You prefer Ibis’s own API and interactive mode (ibis.options.interactive = True).
- You use non-SQL backends (e.g. Polars, DataFusion) or cloud warehouses with first-class Ibis support.

1. Initialization and Connection

Ibis

import ibis

# Set default backend (optional)
ibis.set_backend("duckdb")

# Create connection per backend
con = ibis.duckdb.connect()
con = ibis.postgres.connect("postgresql://user:pass@localhost/db")
con = ibis.bigquery.connect(project_id="my-project")
con = ibis.polars.connect()

Moltres

from moltres import connect, async_connect

# Synchronous connection
db = connect("sqlite:///example.db")
db = connect("postgresql://user:pass@localhost/db")

# Async connection
db = async_connect("postgresql+asyncpg://user:pass@localhost/db")
db = async_connect("sqlite+aiosqlite:///example.db")

Comparison

Aspect	Ibis	Moltres
Entry point	Backend-specific connection (e.g. `ibis.duckdb.connect()`)	`Database` from `connect()` or `async_connect()`
Backend switch	`ibis.set_backend(...)` or different `con`	Different DSN or engine passed to `connect()`
Async support	Not highlighted in overview	Full async via `async_connect()` and `AsyncDatabase`

2. DataFrame API Style

Ibis

Ibis uses its own API: snake_case methods, keyword aggregations, and a portable expression model. Interactive mode prints table previews.

import ibis
ibis.options.interactive = True
con = ibis.duckdb.connect()
t = con.table("penguins")  # or con.read_csv(...), ibis.examples.penguins.fetch()
g = t.group_by("species", "island").agg(count=t.count()).order_by("count")
# g is displayed automatically in interactive mode

Moltres

Moltres provides a PySpark-style API (camelCase or snake_case) and optional Pandas/Polars interfaces. Execution is explicit (e.g. .collect()).

from moltres import col, connect
from moltres.expressions import functions as F

db = connect("sqlite:///example.db")
df = (
    db.table("orders")
    .select()
    .join(db.table("customers").select(), on=[col("orders.customer_id") == col("customers.id")])
    .where(col("active") == True)
    .group_by("country")
    .agg(F.sum(col("amount")).alias("total_amount"))
)
results = df.collect()

Comparison

Aspect	Ibis	Moltres
API lineage	Ibis-native (portable across backends)	PySpark-style; optional Pandas/Polars
Naming	snake_case (e.g. `group_by`, `order_by`)	Both camelCase and snake_case
Execution	Lazy; execute when needed (e.g. `execute()`, or interactive display)	Lazy; `.collect()` or similar to run
Interactive display	`ibis.options.interactive = True`	No built-in interactive mode; use `.collect()` or pandas/polars result

3. Supported Backends and Execution

Ibis

Ibis supports about 20 backends, including SQL and DataFrame engines:

SQL: BigQuery, ClickHouse, DuckDB, Exasol, MySQL, Oracle, PostgreSQL, RisingWave, SingleStoreDB, SQL Server, SQLite, Snowflake, Trino, Apache Druid, Flink, Impala.
Other: Apache DataFusion, Apache PySpark, Polars, Theseus.

Install per backend, e.g. pip install 'ibis-framework[duckdb,examples]'. Same API across backends; expressions compile to SQL or the backend’s native form.

Moltres

Moltres is SQLAlchemy-based. Documented first-class: SQLite, PostgreSQL, MySQL, DuckDB. Any SQLAlchemy-supported database works (e.g. BigQuery/Snowflake via community drivers). Single pip install moltres; optional extras for async drivers, Pandas, Polars, integrations.

Comparison

Aspect	Ibis	Moltres
Scope	~20 backends (SQL + DataFusion, Polars, PySpark)	SQLAlchemy-based (any compatible DB)
First-class SQL	BigQuery, Postgres, Snowflake, DuckDB, Trino, etc.	SQLite, PostgreSQL, MySQL, DuckDB
Non-SQL backends	Yes (Polars, DataFusion, PySpark)	No (SQL only)
Portability	“One API, many backends”; switch backend with one line	One stack (SQL); switch DB via DSN or engine

4. SQL and Expression Compilation

Ibis

Ibis compiles expressions to SQL (for SQL backends) or to the backend’s native API. You can inspect SQL and mix raw SQL with the DataFrame API.

# Compile expression to SQL string
ibis.to_sql(g)

# Mix SQL and Python: run SQL, get back an Ibis table expression
a = t.sql("SELECT species, island, count(*) AS count FROM penguins GROUP BY 1, 2")
b = a.order_by("count")

Moltres

Moltres compiles logical plans to SQL via its SQL compiler. No embedded raw SQL strings; you build expressions in Python and can inspect or validate the generated SQL.

df.show_sql()           # Print compiled SQL
sql_string = df.sql      # Property with SQL text
df.plan_summary()       # Query plan summary
df.validate()           # Validate before execution
df.performance_hints()  # Optimization hints

Comparison

Aspect	Ibis	Moltres
View SQL	`ibis.to_sql(expr)`	`df.show_sql()`, `df.sql`
Mix raw SQL	`t.sql("SELECT ...")` returns expression	No; build with expressions only
Plan / validation	Backend-dependent	`plan_summary()`, `visualize_plan()`, `validate()`, `performance_hints()`

5. CRUD and Mutations

Ibis

Ibis is oriented around read and transform: tables, filters, joins, aggregations, and writing results (e.g. to tables or files). Its README and overview do not describe UPDATE or DELETE on existing rows.

Moltres

Moltres provides full SQL CRUD with a DataFrame-like and database API:

from moltres import col, connect
from moltres.io.records import Records

db = connect("sqlite:///example.db")

# Insert rows
Records.from_list([
    {"id": 1, "name": "Alice", "email": "alice@example.com"},
    {"id": 2, "name": "Bob", "email": "bob@example.com"},
], database=db).insert_into("users")

# Update rows
db.update("users", where=col("active") == 0, set={"active": 1})

# Delete rows
db.delete("users", where=col("email").is_null())

Comparison

Aspect	Ibis	Moltres
INSERT	Via write path (e.g. to table or file)	`Records.from_list(...).insert_into(table)`
UPDATE	Not in overview/docs	`db.update(table, where=..., set=...)`
DELETE	Not in overview/docs	`db.delete(table, where=...)`

6. Async Support

Ibis

Ibis’s README and main docs do not emphasize an async API for connections or execution.

Moltres

Moltres supports async end-to-end: async_connect(), AsyncDatabase, and async table/DataFrame operations (e.g. await df.collect(), await db.table(...)).

Comparison

Aspect	Ibis	Moltres
Async API	Not documented in overview	Full async: `async_connect()`, `AsyncDatabase`, async collect/table ops

7. Installation and Optional Dependencies

Ibis

# One backend + examples
pip install 'ibis-framework[duckdb,examples]'

# Other backends (examples from docs)
pip install 'ibis-framework[postgres]'
pip install 'ibis-framework[bigquery]'
pip install 'ibis-framework[snowflake]'
pip install 'ibis-framework[polars]'
# See https://ibis-project.org/install for full list

Moltres

pip install moltres
pip install moltres[async-postgresql]   # Async PostgreSQL
pip install moltres[pandas,polars]     # Pandas/Polars result formats
pip install moltres[sqlmodel]           # SQLModel/Pydantic
pip install moltres[streamlit]          # Streamlit

Comparison

Aspect	Ibis	Moltres
Default install	Backend-specific extras (e.g. `[duckdb,examples]`)	Single package; core supports multiple DBs via SQLAlchemy
Extras	Per-backend (duckdb, postgres, bigquery, polars, etc.)	Optional async, pandas, polars, sqlmodel, streamlit, etc.

8. Summary Table

Feature	Ibis	Moltres
API style	Ibis-native portable API	PySpark-style + optional Pandas/Polars
Entry point	Backend connection (e.g. `ibis.duckdb.connect()`)	`connect()` / `async_connect()` → Database
Backends	~20 (BigQuery, Snowflake, DuckDB, Polars, PySpark, Trino, etc.)	SQLAlchemy (SQLite, Postgres, MySQL, DuckDB, others)
Non-SQL backends	Yes (Polars, DataFusion, PySpark)	No
Mix raw SQL	`t.sql("SELECT ...")`	No; expression-only
INSERT / UPDATE / DELETE	Write path; no UPDATE/DELETE in overview	Full CRUD: Records, `db.update()`, `db.delete()`
Async	Not highlighted	Full async support
SQL display	`ibis.to_sql(expr)`	`show_sql()`, `.sql`, `plan_summary()`, `validate()`
Interactive display	`ibis.options.interactive = True`	No; use `.collect()` or result format
License	Apache-2.0	MIT

References

Ibis: GitHub, Documentation
Moltres: GitHub, Documentation