owlbear 0.5.0

Feathers and claws for your data lake

owlbear

Feathers and claws for your data lake.

Owlbear is a Python client that bridges Athena and Trino to Polars DataFrames via PyArrow. A wise chimera — part Owl (Athena, goddess of wisdom), part Bear (Polars, the bear constellation). Query your data lake with SQL, get back fast, typed DataFrames — no serialization or ODBC overhead.

Features

• Two backends: AthenaClient (AWS Athena via boto3) and TrinoClient (direct Trino connection)
• Shared Presto-family type conversion — both backends produce identically typed Polars DataFrames
• Parameterized queries for safe value binding
• Athena result reuse for repeated queries
• Pagination support for large result sets (Athena) and row limits (both)
• Query cancellation and execution monitoring (Athena)
• Built-in retry logic with exponential backoff (Athena)
• MCP server for AI assistant integration (schema discovery + query execution)

Installation

# With Athena backend
pip install "owlbear[athena]"

# With Trino backend
pip install "owlbear[trino]"

# Both backends
pip install "owlbear[all]"

# MCP server (includes both backends)
pip install "owlbear[mcp]"

For Development

git clone https://github.com/jdonaldson/owlbear.git
cd owlbear
pip install -e ".[dev]"

Prerequisites

• Python 3.10+
• Athena: AWS credentials configured (via AWS CLI, environment variables, or IAM roles) and an S3 bucket for query results
• Trino: A running Trino cluster with network access

Quick Start

Athena

from owlbear import AthenaClient

client = AthenaClient(
    database="my_database",
    output_location="s3://my-bucket/athena-results/",
    region="us-east-1"
)

execution_id = client.query("SELECT * FROM orders LIMIT 5")
df = client.results(execution_id)
print(df)

shape: (5, 4)
┌─────────────┬────────────┬──────────────┬────────────┐
│ customer_id ┆ order_date ┆ order_amount ┆ status     │
│ ---         ┆ ---        ┆ ---          ┆ ---        │
│ i64         ┆ date       ┆ f64          ┆ str        │
╞═════════════╪════════════╪══════════════╪════════════╡
│ 1001        ┆ 2024-03-15 ┆ 249.99       ┆ shipped    │
│ 1002        ┆ 2024-03-15 ┆ 89.50        ┆ delivered  │
│ 1003        ┆ 2024-03-16 ┆ 1024.00      ┆ processing │
│ 1001        ┆ 2024-03-17 ┆ 54.25        ┆ shipped    │
│ 1004        ┆ 2024-03-17 ┆ 399.99       ┆ delivered  │
└─────────────┴────────────┴──────────────┴────────────┘

Trino

from owlbear import TrinoClient

client = TrinoClient(
    host="trino.example.com",
    port=443,
    user="analyst",
    catalog="hive",
    schema="default",
)

df = client.query("SELECT * FROM orders LIMIT 5")
print(df)

shape: (5, 4)
┌─────────────┬────────────┬──────────────┬────────────┐
│ customer_id ┆ order_date ┆ order_amount ┆ status     │
│ ---         ┆ ---        ┆ ---          ┆ ---        │
│ i64         ┆ date       ┆ f64          ┆ str        │
╞═════════════╪════════════╪══════════════╪════════════╡
│ 1001        ┆ 2024-03-15 ┆ 249.99       ┆ shipped    │
│ 1002        ┆ 2024-03-15 ┆ 89.50        ┆ delivered  │
│ 1003        ┆ 2024-03-16 ┆ 1024.00      ┆ processing │
│ 1001        ┆ 2024-03-17 ┆ 54.25        ┆ shipped    │
│ 1004        ┆ 2024-03-17 ┆ 399.99       ┆ delivered  │
└─────────────┴────────────┴──────────────┴────────────┘

Usage Examples

Parameterized Queries

# Athena — parameters are passed as strings
execution_id = client.query(
    "SELECT * FROM orders WHERE customer_id = ?",
    parameters=["1001"],
)
df = client.results(execution_id)

# Trino — parameters are passed as native Python values
df = trino_client.query(
    "SELECT * FROM orders WHERE customer_id = ?",
    parameters=[1001],
)

Result Reuse (Athena)

# Re-use cached results for up to 60 minutes
execution_id = client.query(
    "SELECT COUNT(*) FROM orders",
    result_reuse_max_age=60,
)

Asynchronous Query Execution

# Start query without waiting
execution_id = client.query(
    "SELECT * FROM large_table",
    wait_for_completion=False
)

# Check query status
query_info = client.get_query_info(execution_id)
print(f"Query status: {query_info['Status']['State']}")

# Wait for completion and get results when ready
client._wait_for_completion(execution_id)
df = client.results(execution_id)

Using Work Groups

execution_id = client.query(
    query="SELECT COUNT(*) FROM my_table",
    work_group="my-workgroup"
)
df = client.results(execution_id)

Using with Existing boto3 Session

import boto3
from owlbear import AthenaClient

session = boto3.Session(profile_name='my-profile')
client = AthenaClient.from_session(
    session=session,
    database="my_db",
    output_location="s3://my-bucket/results/"
)

Query Management

# List available work groups
work_groups = client.list_work_groups()

# Cancel a running query
client.cancel_query(execution_id)

# Get detailed query information
query_info = client.get_query_info(execution_id)
print(f"Execution time: {query_info['Statistics']['TotalExecutionTimeInMillis']}ms")
print(f"Data processed: {query_info['Statistics']['DataProcessedInBytes']} bytes")

MCP Server

Owlbear includes an MCP server so AI assistants can query your data lake directly.

Install

pip install "owlbear[mcp]"

Tools

Tool	Description
execute_query(sql, max_rows=500)	Run arbitrary SQL, return JSON rows (max_rows capped at 10,000)
list_databases(limit=0, offset=0)	List all available databases (paginated)
list_tables(database?, limit=0, offset=0)	List tables in a database (paginated, defaults to configured database)
describe_table(table)	Show columns and types for a table
get_schema_context(tables)	Batch describe multiple comma-separated tables at once
profile_table(table, sample_size=100, stats_sample_pct=0)	One-call profiling: schema, row count, column stats, sample rows. Use stats_sample_pct (1-100) to sample stats on large tables
generate_snippet(table, operation)	Generate backend-aware owlbear + Polars Python code (load, filter, aggregate, join)

Prompts

Prompt	Description
explore_table(table)	Guided exploration: describe schema, sample rows, suggest queries, summarize
build_pipeline(table, goal)	Generate a data pipeline with owlbear boilerplate for a stated goal

Resource

URI	Description
owlbear://config	Current backend configuration (type, database, region/host)

Example: Profile a Table

Ask your AI assistant:

Profile the orders table

The assistant calls profile_table("orders") and gets back:

{
  "table": "orders",
  "row_count": 156398,
  "columns": [
    {"column": "order_id", "type": "bigint", "null_count": 0, "distinct_count": 156398, "min": "1", "max": "156398"},
    {"column": "customer_id", "type": "bigint", "null_count": 0, "distinct_count": 8423, "min": "1001", "max": "9999"},
    {"column": "order_date", "type": "date", "null_count": 0, "distinct_count": 731, "min": "2023-01-01", "max": "2024-12-31"},
    {"column": "amount", "type": "double", "null_count": 12, "distinct_count": 45210, "min": "0.99", "max": "9999.99"},
    {"column": "status", "type": "varchar", "null_count": 0, "distinct_count": 4, "min": "cancelled", "max": "shipped"},
    {"column": "items", "type": "array<varchar>", "null_count": 85}
  ],
  "sample_rows": [{"order_id": 1, "customer_id": 1001, "order_date": "2023-01-01", "amount": 249.99, "status": "shipped", "items": ["widget-a", "widget-b"]}],
  "stats_sampled": false,
  "stats_sample_pct": 0
}

Scalar columns (numbers, strings, dates) get distinct_count, min, and max. Complex columns (arrays, maps, structs) get only null_count.

For large tables, use stats_sample_pct to avoid full table scans on the stats query:

Profile the orders table with 10% sampling

The assistant calls profile_table("orders", stats_sample_pct=10) — the stats query uses TABLESAMPLE BERNOULLI(10) while row count remains exact via COUNT(*).

Example: Batch Schema Lookup

What columns do the orders and customers tables have?

The assistant calls get_schema_context("mydb.orders, mydb.customers") and gets schemas for both tables in one response — with per-table error isolation if one fails.

Example: Generate Starter Code

Generate an aggregation snippet for the orders table

The assistant calls generate_snippet("orders", "aggregate") and returns backend-aware Python code using real column names. With OWLBEAR_BACKEND=athena:

from owlbear import AthenaClient
import polars as pl

client = AthenaClient(
    database="your_database",
    output_location="s3://your-bucket/results/",
)

eid = client.query("SELECT * FROM orders LIMIT 10000")
df = client.results(eid)
result = df.group_by("status").agg(pl.col("amount").mean())
print(result.head())

With OWLBEAR_BACKEND=trino, the snippet uses TrinoClient and the simpler client.query() → DataFrame API (no execution_id / client.results()).

Environment Variables

Variable	Backend	Description	Default
OWLBEAR_BACKEND	both	athena or trino	athena
OWLBEAR_DATABASE	both	Default database/schema	default
OWLBEAR_S3_OUTPUT_LOCATION	athena	S3 path for query results	(required)
AWS_REGION	athena	AWS region	us-east-1
AWS_PROFILE	athena	AWS profile (via boto3)	—
OWLBEAR_TRINO_HOST	trino	Trino hostname	(required)
OWLBEAR_TRINO_PORT	trino	Trino port	443
OWLBEAR_TRINO_USER	trino	Trino user	—
OWLBEAR_TRINO_CATALOG	trino	Trino catalog	—

Example .mcp.json (Athena)

{
  "mcpServers": {
    "owlbear": {
      "command": "owlbear-mcp",
      "env": {
        "OWLBEAR_DATABASE": "my_database",
        "OWLBEAR_S3_OUTPUT_LOCATION": "s3://my-bucket/athena-results/",
        "AWS_REGION": "us-east-1",
        "AWS_PROFILE": "my-profile"
      }
    }
  }
}

Example .mcp.json (Trino)

{
  "mcpServers": {
    "owlbear": {
      "command": "owlbear-mcp",
      "env": {
        "OWLBEAR_BACKEND": "trino",
        "OWLBEAR_TRINO_HOST": "trino.example.com",
        "OWLBEAR_TRINO_PORT": "443",
        "OWLBEAR_TRINO_USER": "analyst",
        "OWLBEAR_TRINO_CATALOG": "hive",
        "OWLBEAR_DATABASE": "default"
      }
    }
  }
}

Type Mapping

Owlbear automatically converts Presto/Trino/Athena SQL types to PyArrow (and then to Polars):

SQL Type	PyArrow Type
boolean	bool_()
tinyint	int8()
smallint	int16()
integer	int32()
bigint	int64()
real / float	float32()
double	float64()
decimal(p,s)	decimal128(p, s)
varchar / char / string	string()
varbinary / binary	binary()
date	date32()
timestamp	timestamp("us")
timestamp with time zone	timestamp("us", tz="UTC")
time	time64("us")
interval day to second	duration("us")
interval year to month	month_day_nano_interval()
array<T>	list_(T)
map<K,V>	map_(K, V)

Nested types like array<array<integer>> and map<varchar,array<bigint>> are fully supported.

Configuration

Environment Variables

export AWS_ACCESS_KEY_ID=your_access_key
export AWS_SECRET_ACCESS_KEY=your_secret_key
export AWS_DEFAULT_REGION=us-east-1

IAM Permissions

Your AWS credentials need the following permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "athena:StartQueryExecution",
                "athena:GetQueryExecution",
                "athena:GetQueryResults",
                "athena:StopQueryExecution",
                "athena:ListWorkGroups"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": "arn:aws:s3:::your-athena-results-bucket/*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "glue:GetDatabase",
                "glue:GetTable",
                "glue:GetPartitions"
            ],
            "Resource": "*"
        }
    ]
}

Testing

pytest tests/ -v

Development

git clone https://github.com/jdonaldson/owlbear.git
cd owlbear
pip install -e ".[dev]"

black .        # format
ruff check .   # lint
mypy src/      # type check

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository on GitHub
2. Create a feature branch
3. Make your changes with tests
4. Ensure all tests pass and code is formatted
5. Submit a pull request