hotdata-ibis 0.1.4

Ibis backend for Hotdata federated SQL API (depends on the hotdata SDK only; not hotdata-runtime)

hotdata-ibis

Use Ibis to create on-demand databases, upload data, and query with Python expressions — get pandas or Arrow results back without writing SQL.

Requirements: Python 3.10+, ibis-framework ≥10,<11, hotdata ≥0.2.3.

Install

pip install hotdata-ibis
# or: uv pip install hotdata-ibis

Quickstart: create a database and query it

import time
import pandas as pd
import ibis

con = ibis.hotdata.connect(
    api_url="https://api.hotdata.dev",
    token="YOUR_API_KEY",
    workspace_id="ws_...",
)

# 1. Create a database and declare the tables you'll load
con.create_database("sales", tables=["orders"])

# 2. Upload a pandas DataFrame (or PyArrow table)
df = pd.DataFrame({
    "order_id": [1, 2, 3],
    "amount": [9.99, 49.99, 5.00],
    "region": ["west", "east", "west"],
})
con.create_table("orders", df, database=("sales", "main"), overwrite=True)

# 3. Uploads are async — wait briefly before querying
time.sleep(2)

# 4. Query with Ibis expressions
#    Managed tables are always accessed with catalog "default"
t = con.table("orders", database=("default", "main"))
result = (
    t.group_by("region")
    .agg(total=t.amount.sum())
    .order_by(ibis.desc("total"))
    .execute()  # returns a pandas DataFrame
)

# 5. Clean up
con.drop_table("orders", database=("sales", "main"))
con.drop_database("sales")

Connect

con = ibis.hotdata.connect(
    api_url="https://api.hotdata.dev",
    token="YOUR_API_KEY",
    workspace_id="ws_...",
    # optional
    session_id=None,           # sandbox id (X-Session-Id header)
    timeout=120.0,             # per-request HTTP timeout in seconds
    verify_ssl=True,           # False to skip TLS verification, or path to CA bundle
    default_connection=None,   # default catalog (connection id); auto-detected if only one exists
    default_schema=None,       # default schema; auto-detected if only one exists
    database_id=None,          # bind an existing managed database id at connect time
    poll_interval_s=0.25,      # polling interval for async queries
    poll_timeout_s=600.0,      # max time to wait for a query result
)

URL-style also works, with the same parameters as query string keys:

con = ibis.connect(
    "hotdata://api.hotdata.dev/"
    "?token=...&workspace_id=ws_..."
    "&default_connection=my_conn&default_schema=public"
)

Managed databases

Managed databases are the primary way to bring data into Hotdata with Ibis. Declare a database and its tables, upload data, and query immediately.

Create and load

# Declare the database and all table names up front
con.create_database("analytics", tables=["events", "users"])

# Upload from a pandas DataFrame
con.create_table("events", events_df, database=("analytics", "main"), overwrite=True)

# PyArrow tables also work
import pyarrow as pa
table = pa.table({"id": [1, 2], "name": ["alice", "bob"]})
con.create_table("users", table, database=("analytics", "main"), overwrite=True)

# Schema-only (no data): creates an empty table with the declared schema
import ibis.expr.schema as sch
con.create_table(
    "staging",
    schema=sch.Schema({"id": "int64", "ts": "timestamp"}),
    database=("analytics", "main"),
)

Table names must be declared when the database is created — you cannot upload to a table name that was not listed in tables=.

Query

When querying, use "default" as the catalog:

t = con.table("events", database=("default", "main"))

result = (
    t.filter(t.event_type == "click")
    .group_by("user_id")
    .agg(n=t.count())
    .execute()
)

Or with raw SQL:

result = con.sql(
    'SELECT user_id, COUNT(*) AS n '
    'FROM "default"."main"."events" '
    'WHERE event_type = \'click\' '
    'GROUP BY user_id'
).execute()

Delete

Pass force=True to silently skip errors when the database or table does not exist:

con.drop_table("events", database=("analytics", "main"))
con.drop_table("events", database=("analytics", "main"), force=True)  # no-op if missing

con.drop_database("analytics")
con.drop_database("analytics", force=True)  # no-op if missing

Addressing summary

Operation	database= argument
create_table / drop_table	("your-database-name", schema)
con.table(...) when querying	("default", schema)

Querying

Ibis expressions

t = con.table("orders", database=("default", "main"))

summary = (
    t.filter(t.amount > 10)
    .group_by("region")
    .agg(total=t.amount.sum(), n=t.count())
    .order_by(ibis.desc("total"))
    .execute()
)

.execute() returns a pandas DataFrame. .to_pyarrow() returns an Arrow table. .to_pyarrow_batches() returns a RecordBatchReader — note that Hotdata returns a single Arrow IPC payload per query, so this method downloads the full result first and then splits it into local batches.

Raw SQL

base = con.sql(
    'SELECT * FROM "default"."main"."orders"',
    dialect="postgres",
)
result = base.filter(base.amount > 10).execute()

You can chain Ibis expressions on the result of con.sql(...).

Connecting to existing sources

If you have existing databases or warehouses connected to your Hotdata workspace (Postgres, Snowflake, BigQuery, etc.), you can query them through the same Ibis connection:

con = ibis.hotdata.connect(
    api_url="https://api.hotdata.dev",
    token="YOUR_API_KEY",
    workspace_id="ws_...",
    default_connection="my_postgres",
    default_schema="public",
)

t = con.table("orders")  # resolves to my_postgres.public.orders

Discover what's available:

con.list_catalogs()                                    # connection IDs
con.list_databases(catalog="my_postgres")              # schemas
con.list_tables(database=("my_postgres", "public"))    # tables

What's supported

Feature	Status
create_database / drop_database (managed)	✅
create_table from pandas / PyArrow / schema-only	✅
drop_table	✅
con.table(...) with full schema metadata	✅
Ibis expressions: filter, select, join, group_by, agg, order_by, limit	✅
con.sql(...) raw SQL	✅
.execute() → pandas, .to_pyarrow(), .to_pyarrow_batches()	✅
list_catalogs, list_databases, list_tables	✅
Arrow / Parquet column types (timestamp, decimal, list, duration, …)	✅
Temporary tables	❌
In-memory tables (ibis.memtable(...))	❌
Python UDFs	❌
INSERT / UPDATE / DELETE on external connections	❌

SQL compilation uses Ibis's Postgres dialect. Column types returned by Hotdata's information schema are resolved via PyArrow's type system, so Parquet-loaded tables with Arrow-native types (timestamps with time zones, decimals, lists, durations) are mapped correctly to Ibis types.

Development

uv sync   # installs dev group (pytest, ruff, httpx)
uv run pytest
uv run ruff check src tests

CI: uv sync --locked && uv run pytest.

Examples

Set your credentials, then run any example script:

export HOTDATA_API_KEY=...
export HOTDATA_WORKSPACE=...
uv run python examples/01_catalog_introspection.py
uv run python examples/02_execute_sql.py 'SELECT COUNT(*) AS n FROM tpch.tpch_sf1.customer'
uv run python examples/03_connect_via_url.py
uv run python examples/04_ibis_table_workflows.py

References

• Hotdata documentation
• Hotdata Python SDK
• Ibis documentation