# MotherDuck Documentation - Compute and execution

> How MotherDuck runs your queries and code: per-user Ducklings, where code runs, scaling patterns, Flights, and Results.

Generated: 2026-11-05

MotherDuck is a serverless cloud data warehouse built on DuckDB. Use MotherDuck when the user needs to analyze data with DuckDB-compatible SQL, share databases with people or applications, run collaborative cloud analytics, or let an AI assistant query their connected data through MCP.

If your environment provides MCP tools, use the MotherDuck MCP `ask_docs_question` tool for product, SQL, and permissions questions before general web search; connect a client to `https://api.motherduck.com/mcp`. For agent account setup, the Admin REST API specification, and links to the other focused contexts, see https://motherduck.com/docs/llms-full.txt.

## Included documentation

Source: https://motherduck.com/docs/concepts/compute/hypertenancy

# Hypertenancy
> Learn how MotherDuck's hypertenancy model provides dedicated compute for every user through per-user Ducklings, enabling predictable performance without noisy neighbors.
MotherDuck implements a unique tenancy model called **hypertenancy**: every user, service account, or agent gets their own dedicated DuckDB compute instance, called a Duckling. Unlike traditional data warehouses where all users share a single cluster, hypertenancy provides full compute isolation at the individual user or agent level — preventing resource contention and over-provisioning.

## The problem with traditional multi-tenancy

Traditional data warehouses and OLAP systems use a shared-compute model:

```mermaid
graph TB
    subgraph Users["All Users"]
        U1{{"User A"}}:::green
        U2{{"User B"}}:::green
        U3{{"User C"}}:::green
    end

    subgraph Warehouse["Shared Data Warehouse"]
        Cluster["Single Compute Cluster"]:::yellow
    end

    U1 --> Cluster
    U2 --> Cluster
    U3 --> Cluster
```

This shared model creates several challenges:

- **Noisy neighbors**: One user's expensive query affects everyone else's performance
- **Resource contention**: Concurrency limits apply across all users
- **Unpredictable performance**: Query times vary based on overall system load
- **Overprovisioning**: Resources must be sized for peak aggregate load, sitting idle most of the time
- **Difficult cost attribution**: Hard to track compute costs per user or customer

## How Hypertenancy works

With hypertenancy, MotherDuck provisions a separate Duckling for each user:

```mermaid
graph TB
    subgraph Users["All Users"]
        U1{{"User A"}}:::green
        U2{{"User B"}}:::green
        U3{{"User C"}}:::green
    end

    subgraph MotherDuck["MotherDuck"]
        D1["Duckling A"]:::yellow
        D2["Duckling B"]:::yellow
        D3["Duckling C"]:::yellow
    end

    U1 --> D1
    U2 --> D2
    U3 --> D3
```

Each Duckling is a complete DuckDB instance with dedicated CPU, memory, and fast SSD spill space. This architecture delivers:

- **Perfect isolation**: No noisy neighbors—one user's workload never impacts another
- **Predictable performance**: Dedicated resources mean consistent query times
- **Independent scaling**: Each user's compute can be sized to their specific needs
- **Per-user billing**: Compute costs directly attributable to individual users
- **Fast cold starts**: Ducklings start in under ~100ms

## Scaling with Hypertenancy

Hypertenancy supports both vertical and horizontal scaling, letting you match compute resources to actual demand.

### Vertical scaling: Duckling sizes

Each user's Duckling can be configured to different sizes based on their workload requirements:

| Duckling Size | Best For |
|---------------|----------|
| **Pulse** | Ad-hoc queries, read-heavy workloads, high-concurrency analytics |
| **Standard** | Core analytical workflows, ETL/ELT pipelines |
| **Jumbo** | Large-scale batch processing, complex joins |
| **Mega** | Demanding jobs with high data volumes |
| **Giga** | Largest and toughest batch workloads |

You can adjust Duckling size per user through the [MotherDuck UI](/about-motherduck/billing/duckling-sizes/#changing-duckling-sizes) or [REST API](/sql-reference/rest-api/ducklings-set-duckling-config-for-user/).

For example, in a customer-facing analytics scenario, you might provision:
- **Pulse** Ducklings for most customers running standard dashboards
- **Standard** or **Jumbo** Ducklings for enterprise customers with heavier workloads
- **Mega** or **Giga** Ducklings for batch data loading jobs

### Horizontal scaling: read scaling

When a single user needs to handle many concurrent queries—such as a service account powering a customer-facing application—you can enable [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/). Read scaling provisions additional read-only Ducklings that share the same data but distribute query load:

```mermaid
graph TB
    subgraph App["Application Users"]
        E1{{"End User 1"}}:::green
        E2{{"End User 2"}}:::green
        E3{{"End User 3"}}:::green
        E4{{"End User 4"}}:::green
    end
    S1[Service Account]:::watermelon

    subgraph MotherDuck["MotherDuck (Customer X)"]
        RW["Read-Write Duckling<br/>(Data Loading)"]
        R1["Read Scaling Duckling 1"]
        R2["Read Scaling Duckling 2"]
    end
    S1 --> RW
    E1 --> R1
    E2 --> R1
    E3 --> R2
    E4 --> R2

```

Read scaling lets you serve hundreds or thousands of concurrent end users through a single service account while maintaining predictable performance.

## Hypertenancy use cases

Hypertenancy is a strong advantage when you need per-user or per-customer isolation, predictable concurrency, or clean cost attribution: customer-facing analytics, multi-tenant SaaS, high-concurrency agentic workloads, and separated development and production pipelines. For a single analyst running ad-hoc queries, it works the same way but stays out of your way: you get consistent performance without tuning a shared cluster and without extra configuration.

### Customer-facing analytics

Hypertenancy is particularly powerful for [customer-facing analytics](/getting-started/customer-facing-analytics/). Each of your customers can have their own service account with isolated Ducklings:

- **Data isolation**: Each customer's data stays in their own database
- **Compute isolation**: One customer's workload never impacts another
- **Cache isolation**: Each customer's Duckling maintains its own cache, so cached query results and data remain private and predictable
- **Independent sizing**: Scale resources per customer based on their tier or needs
- **Predictable costs**: Bill customers accurately based on their actual compute usage

For a hands-on guide to building customer-facing analytics with per-customer service accounts, see the [Builder's Guide](/key-tasks/customer-facing-analytics/3-tier-cfa-guide/).

### Development and production pipelines

Service accounts enable clean separation between deployment environments. Each environment gets its own isolated compute:

| Environment | Service Account | Duckling Size | Purpose |
|-------------|-----------------|---------------|---------|
| Local/Dev | `dev-pipeline` | Pulse | Interactive development and testing |
| Staging | `staging-pipeline` | Standard | Pre-production validation |
| Production | `prod-pipeline` | Standard/Jumbo/... | Production workloads |

This separation ensures:
- Development experiments never impact production performance
- Each environment has appropriately sized compute
- Clear cost attribution per environment
- Easy rollback by switching service account credentials

### Data warehouse and data pipeline workloads

For data pipelines, you can assign dedicated service accounts to different stages of your data workflow. If you're using dbt you can run dbt models with different duckling sizes.

| Pipeline Stage | Service Account | Duckling Size | Workload Pattern |
|----------------|-----------------|---------------|------------------|
| Ingestion | `ingest-service` | Jumbo/Mega | Bulk data loading, high I/O |
| Transformation | `transform-service-standard` / `transform-service-jumbo` /  | Standard/Jumbo | dbt models, ETL jobs |
| Reporting | `reporting-service` | Pulse (read scaling) | Dashboard queries, read-heavy |

This pattern provides:
- **Workload isolation**: Heavy batch ingestion jobs won't slow down interactive reporting queries
- **Right-sized compute**: Each stage gets the Duckling size optimized for its workload
- **Cost visibility**: Track compute costs per pipeline stage
- **Independent scheduling**: Run ingestion during off-peak hours without affecting daytime analysts

### Analytics and data science

For internal analytics teams, hypertenancy means analysts and data scientists each get their own compute. A data scientist running a complex ML feature extraction job won't slow down an analyst building a quick dashboard.

## Why single-node beats distributed for per-user compute

Traditional distributed data warehouses use clusters with multiple nodes that coordinate to execute queries. This architecture introduces:

- Network latency between nodes
- Coordination overhead
- Data shuffling costs

For queries that operate on one user's data at a time (the common pattern in hypertenancy), single-node execution on a Duckling eliminates this overhead entirely. The result is often faster query performance and lower costs compared to distributed systems, especially for interactive analytics workloads.

DuckDB's efficient columnar execution, combined with MotherDuck's fast storage architecture, means queries can handle datasets larger than memory with minimal performance impact.

## Related content

- **Learn about Duckling sizes**: [Duckling Sizes](/about-motherduck/billing/duckling-sizes/)
- **Configure read scaling**: [Read Scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/)
- **Build customer-facing analytics**: [Customer-Facing Analytics Overview](/getting-started/customer-facing-analytics/)
- **Set up per-customer service accounts**: [Create and configure service accounts](/key-tasks/service-accounts-guide/create-and-configure-service-accounts/)

---

Source: https://motherduck.com/docs/concepts/compute/flights

# Flights
> How Flights run scheduled Python next to your MotherDuck data, and when to reach for one instead of SQL, a Dive, or an external orchestrator.
A **Flight** is a Python program that MotherDuck schedules and runs, with direct access to your databases. It can do whatever Python can: call external APIs, use any PyPI package, process files or run custom logic.

## Flights overview

MotherDuck runs two compute planes against the same storage:

```mermaid
flowchart LR
    User{{"You"}}:::green

    subgraph MotherDuck["MotherDuck"]
        Runtime["Python runtime<br/>(per Flight run)"]:::yellow
        Duckling["Duckling<br/>(SQL compute)"]:::yellow
    end

    DB[("Your databases")]:::yellow

    User -->|"interactive SQL"| Duckling
    User -->|"create / schedule"| Runtime
    Runtime -->|"md:"| Duckling
    Duckling --> DB
```

**Ducklings** run your SQL. They're per-user (see [Hypertenancy](/concepts/compute/hypertenancy)) and start in under ~100ms.

The **Python runtime** runs your scheduled Python. You provide the Flight's source code, and each run gets its own isolated runtime that executes the source as a plain script and exits, so end the script with `if __name__ == "__main__": main()`. A run starts from the Flight's schedule or an `MD_RUN_FLIGHT` call.

A Flight reaches your data the same way any DuckDB client does: the Python runtime opens an `md:` connection, which routes through a Duckling that executes the SQL against your databases. MotherDuck injects an access token into the Flight's environment so the connection works without you embedding credentials.

## The relationship to SQL

Flights and SQL talk to each other in both directions.

**From Python to SQL.** A Flight uses the DuckDB Python client to run any query you can express in SQL:

```python
import duckdb

def main():
    con = duckdb.connect("md:")
    con.execute("INSERT INTO sales.daily_totals SELECT * FROM read_parquet('s3://incoming/today.parquet')")

if __name__ == "__main__":
    main()
```

Anything in the SQL reference is available here: aggregations, MotherDuck table functions, attach commands, and so on.

**From SQL to Flights.** The [Flights SQL functions](/sql-reference/motherduck-sql-reference/flights/), such as `MD_CREATE_FLIGHT` and `MD_LIST_FLIGHT_RUNS`, let you create, schedule, and monitor Flights from anywhere you run SQL: a DuckDB CLI, your BI tool, or another Flight.

**When to pick which.** A reasonable rule of thumb:

- **Stay in SQL** for a one-off query, or one you already schedule elsewhere.
- **Use a Flight** to run a query on a schedule with retries and a run history, or when the work goes beyond SQL: calling an external service, installing a Python package, reading a format SQL can't, or chaining steps.

## What you can build with Flights

- **Ingest.** Pull data from external sources (Postgres, BigQuery, Snowflake, S3, APIs) and write it to MotherDuck tables. [dltHub](/integrations/ingestion/dlt)'s `dlt` library is the recommended ingest library: it gives you a declarative pipeline with schema evolution, incremental loading, and a MotherDuck destination.
- **Transform.** Read MotherDuck data, run Python-heavy transformations (machine-learning features, geospatial work), and write the result back. When the transformation is graph-shaped, [dbt](https://docs.getdbt.com/) with the `dbt-duckdb` adapter is the recommended approach.
- **AI enrichment.** Run LLM and embedding work over your data on a schedule. Call MotherDuck's [`prompt`](/sql-reference/motherduck-sql-reference/ai-functions/prompt/) and [`embedding`](/sql-reference/motherduck-sql-reference/ai-functions/embedding/) functions from SQL to classify, summarize, or extract fields row by row, or call a model API from Python and write the results back.
- **Export and delivery.** Write query results to object storage as Parquet or CSV — a scheduled drop in S3, Google Cloud Storage, or Azure Blob Storage for a partner or a downstream pipeline to pick up.
- **Reverse ETL and notifications.** Read from MotherDuck and push to a downstream operational system: post a daily summary to Slack, sync an aggregate to a SaaS tool, update a record in a CRM.

## Availability and plan limits

Flights run on every MotherDuck plan. On the **Lite** plan, your Flight limits depend on whether a credit card is on file. **Business** and **Enterprise** are pay-as-you-go.

|  | Lite (with limits) | Lite (unlimited) | Business & Enterprise |
|---|---|---|---|
| Flight compute | 30 minutes per day | Drawn from your monthly compute allocation, then pay-as-you-go | Pay-as-you-go |
| Usage window | Daily (resets each day) | Monthly | Monthly |
| Scheduling | Manual (on-demand) only | Manual and scheduled (cron) | Manual and scheduled (cron) |
| Maximum runtime per run | 1 hour | 1 hour | 8 hours (default) |
| Concurrent runs | 1 | Unlimited | Unlimited |
| Flight instance sizes | F4 | F4, F16 | F4, F16, F32 |
| Flights you can create | Unlimited | Unlimited | Unlimited |

On Lite (with limits), runs pause at the daily limit until the next day. Usage beyond the [compute allocation](/about-motherduck/billing/pricing/#plan-comparison) is billed at [Flight rates](/about-motherduck/billing/pricing/#flights-pricing).

### Flight instance sizes

Each run gets an instance size (F4, F16, or F32) that sets the CPU and memory of the Flight's Python runtime. SQL sent over `md:` runs on a [Duckling](/about-motherduck/billing/duckling-sizes), not on the Flight. See [Flight sizes](/about-motherduck/billing/flight-sizes/) for specs, defaults, and pricing.

## Creating a Flight

You can create a Flight with the [MotherDuck MCP server](/key-tasks/ai-and-motherduck/mcp-workflows/mcp-setup/): describe the job in natural language, and the agent writes the Python, sets the schedule, and runs it for you with the MCP's Flight tools (`create_flight`, `edit_flight_source`, `run_flight`, and the rest).

You can also use [`MD_CREATE_FLIGHT`](/sql-reference/motherduck-sql-reference/flights/) and the other Flight SQL functions.

## Integrating with the tools you already use

- **dltHub** for ingest. Generate or hand-write a `dlt` pipeline that uses MotherDuck as the destination, then deploy it as a Flight.
- **DuckDB Community Extensions** that MotherDuck doesn't load, such as BigQuery, Snowflake, or [YAML](https://duckdb.org/community_extensions/extensions/yaml). See [Run a local DuckDB with community extensions](#run-a-local-duckdb-with-community-extensions).
- **Anything on PyPI.** Pull in any pip-installable package through `requirements.txt`.
- **MCP and AI agents.** Agents create, edit, and run Flights through the [MotherDuck MCP Server](/sql-reference/mcp/).

You are responsible for the code you run and the packages it installs. Flights doesn't scan your code or dependencies for vulnerabilities or malicious intent, so avoid untrusted packages and pin dependency versions.

## Beyond Python

A Flight is a Linux process that runs your Python program, with shell access through `subprocess`. That opens up two patterns worth knowing.

### Install and call system binaries

`git` ships preinstalled in the runtime, so cloning a repository needs no setup. For other command-line tools (ffmpeg, Playwright, anything available as a Debian package), use `subprocess` to install them with `apt-get` first.

```python
import subprocess

def main():
    subprocess.run(["git", "clone", "https://github.com/example/repo"], check=True)
    subprocess.run(["apt-get", "install", "-y", "ffmpeg"], check=True)

if __name__ == "__main__":
    main()
```

### Run a local DuckDB with community extensions

A Flight can also open an in-process DuckDB connection to load [community extensions](https://duckdb.org/community_extensions/) that MotherDuck's server-side runtime doesn't support. For example, the [`bigquery`](https://duckdb.org/community_extensions/extensions/bigquery) and [`snowflake`](https://duckdb.org/community_extensions/extensions/snowflake) extensions let a Flight read directly from another warehouse and write the result to MotherDuck.

```python
import duckdb

def main():
    local = duckdb.connect()  # local in-process DuckDB
    local.execute("INSTALL bigquery FROM community; LOAD bigquery;")
    local.execute("ATTACH 'project=my-project' AS bq (TYPE bigquery, READ_ONLY)")

    # read from BigQuery into the Flight, then write the result to MotherDuck
    events = local.sql("SELECT * FROM bq.analytics.events").df()

    md = duckdb.connect("md:")
    md.execute("INSERT INTO raw.events SELECT * FROM events")

if __name__ == "__main__":
    main()
```

Keep heavy compute outside the Flight runtime: let the source warehouse and MotherDuck do the heavy lifting, and let the Flight move data between them.

## How Flights are scoped

- **A Flight runs as a triggered job.** A run starts from its schedule or a `MD_RUN_FLIGHT` call, executes the script to completion, and exits; a single run can be long or short.
- **One Flight is one process.** A run isn't a worker pool with a queue or distributed state. Fan out by calling `MD_RUN_FLIGHT` from a Flight, or use a Python thread pool within a run; you write the coordination. Flights fit MotherDuck-centric work where data, schedule, and compute live together. For workflows across many external systems, an orchestrator like Airflow or Prefect fits better.
- **Runs are unattended.** You read a run's output afterward from its [logs](/sql-reference/motherduck-sql-reference/flights/md-get-flight-logs) rather than stepping through it live. To explore data interactively, run queries in the SQL editor instead.
- **A Flight belongs to the user who created it.** Only the owner can edit or delete it; others need a [`run` grant](/sql-reference/motherduck-sql-reference/flights/grant-run-on-flight) to run it. Admins can see every Flight and cancel its runs.

## Connecting to external systems

A Flight often reaches out to a system you control: a Postgres database, an API, or object storage behind a firewall. When that system restricts inbound traffic by IP, allowlist the addresses below so the Flight can connect.

Outbound connections from a Flight come from a fixed set of IP addresses, determined by your MotherDuck region. To check your organization's region, run [`SELECT region FROM md_user_info();`](/sql-reference/motherduck-sql-reference/md-user-info).

| Region | IP addresses |
|---|---|
| US East (N. Virginia) `us-east-1` | `54.145.91.19`, `32.195.40.107` |
| US West (Oregon) `us-west-2` | `54.214.239.87`, `54.148.103.191` |
| Europe (Frankfurt) `eu-central-1` | `3.124.118.210`, `63.177.162.15` |
| Europe (Dublin) `eu-west-1` | `54.77.125.86`, `18.200.12.243` |
| Asia Pacific (Tokyo) `ap-northeast-1` | `35.73.139.124`, `57.182.73.46` |
| Asia Pacific (Sydney) `ap-southeast-2` | `54.66.229.129`, `32.236.248.88` |

These addresses are for outbound connections only. A Flight does not accept inbound connections, so there is nothing to open in the other direction.

## Related resources

- [Where code runs](/concepts/compute/execution-environments/) — choose between a local DuckDB client, MotherDuck cloud SQL, and a Flight.
- [Incrementally ingest BigQuery into MotherDuck](/cookbook/flight-bigquery-ingest/) — a scheduled BigQuery replication template.
- [Flights SQL reference](/sql-reference/motherduck-sql-reference/flights/) — `MD_CREATE_FLIGHT`, `MD_RUN_FLIGHT`, and the rest of the Flight table functions.
- [Hypertenancy](/concepts/compute/hypertenancy) — why Ducklings give every user dedicated SQL compute.
- [Creating visualizations with Dives](/key-tasks/dives) — interactive React apps over MotherDuck data.
- [MotherDuck MCP Server](/sql-reference/mcp/) — the MCP surface for AI agents that create and manage Flights.

---

Source: https://motherduck.com/docs/concepts/compute/scaling-patterns

# Workload scaling patterns
> Choose the right compute size, scaling approach, and connection model for your MotherDuck workload using a decision flowchart and workload-to-pattern matrix.
Choose a scaling pattern based on query complexity, concurrency, and whether your workload mostly reads or writes data.

## How MotherDuck scales per workload

MotherDuck scales workloads through compute units called [Ducklings](/concepts/compute/hypertenancy/). Each user or service account gets a dedicated Duckling and read scaling pool, and you can adjust three levers to match your workload:

| Scaling lever | What it does | When to use it |
|---|---|---|
| **Vertical scaling** | Resize your Duckling ([Pulse through Giga](/about-motherduck/billing/duckling-sizes)) | Queries need more CPU or memory|
| **Horizontal scaling** | Add read-only Ducklings through [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/) | Many concurrent users running read queries |
| **Workload isolation** | Create separate [service accounts](/key-tasks/service-accounts-guide/create-and-configure-service-accounts/) | Teams or pipelines that should not share compute, for example to prevent a large data ingestion impacting the queries of analysts. |

These levers are complementary. For example, you might use a Jumbo Duckling (vertical) for data loading and use a pool of Pulse Ducklings with read scaling (horizontal) for your dashboard users. You can connect through any [supported interface](/key-tasks/authenticating-and-connecting-to-motherduck/postgres-endpoint/), including the native DuckDB SDK, the Postgres endpoint, or DuckDB WASM.

:::tip[Not sure what you need?]
Follow the [decision flowchart](#decision-flowchart) at the bottom of this page to find the right scaling pattern for your workload.
:::

## Understanding the scaling levers

### Vertical scaling: Duckling sizes

Diagram summary: Vertical scaling moves a workload from a smaller Duckling to larger Duckling sizes as query load grows.

When a single query needs more resources, move to a larger Duckling. Larger Ducklings have more CPU, memory, and extra SSD space to be used whenever queries use up too much memory. This helps with:

- Complex joins and aggregations
- Large data loading jobs
- Queries that process more data than fits in memory

Duckling sizes range from **Pulse** (lightweight, on-demand billing) to **Giga** (maximum resources for the heaviest batch jobs). See [Duckling sizes](/about-motherduck/billing/duckling-sizes/) for the full comparison.

**When to size up**: If queries are slow and you see high values for `BYTES_SPILLED_TO_DISK` or `WAIT_TIME` in your [query history](/sql-reference/motherduck-sql-reference/md_information_schema/query_history/), your Duckling may need more memory.

### Horizontal scaling: Read scaling

Diagram summary: Horizontal scaling adds read-only Ducklings so concurrent users can run read queries across a pool.

When you need to serve many concurrent read queries, [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/) adds read-only Duckling replicas behind your account. Key concepts:

- **Supported sizes**: Read scaling supports Pulse, Standard, and Jumbo Ducklings by default. [Contact support](https://motherduck.com/docs/troubleshooting/support/) for larger sized read scaling Ducklings.
- **Pool size**: The default pool is 4 Ducklings, configurable up to 16 (soft limit). [Contact support](https://motherduck.com/contact-us/support/) for higher limits.
- **Eventual consistency**: Read replicas lag a few minutes behind the primary. Use [`CREATE SNAPSHOT`](/sql-reference/motherduck-sql-reference/create-snapshot/) and [`REFRESH DATABASES`](/sql-reference/motherduck-sql-reference/refresh-database/) if you need tighter synchronization.
- **One Duckling per user**: For the best performance, aim for one Duckling per concurrent user. This takes full advantage of DuckDB's single-node architecture.

### Session affinity and routing

Diagram summary: Session affinity routes repeat connections with the same `session_name` to the same Duckling when possible, improving cache locality.

By default, read scaling distributes connections across the pool in round-robin fashion. When the number of connections exceeds your pool size, new connections share existing Ducklings.

For workloads where users run unique queries, use [`session_name`](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/#session-affinity-with-session-name) to route a user's connections to the same Duckling. This improves performance because:

- **Cache locality**: DuckDB caches data per instance. Routing the same user to the same Duckling means their subsequent queries benefit from a warm cache.
- **Consistent view**: Queries within a session see a consistent snapshot of the data.
- **Better isolation**: Concurrent users do not compete for the same Duckling's resources.

Set `session_name` to a user ID, session ID, or any stable identifier to benefit from caching where possible. You can [set the Duckling cooldown period](/docs/about-motherduck/billing/duckling-sizes/#configuring-the-cooldown-period) to match your use case and keep the Duckling alive with its cache.

**When to use `session_name`**: Use it when users run unique, personalized queries, for example in customer-facing analytics or multi-tenant dashboards. Skip it when all users run the same queries (such as a shared reporting dashboard), since a shared connection pool already routes efficiently.

### Workload isolation: Service accounts

[Service accounts](/key-tasks/service-accounts-guide/create-and-configure-service-accounts/) give you full compute and data isolation between workloads. Each service account gets its own Duckling and read scaling pool, which makes it easier to track usage and billing.

Use separate service accounts when:

- Different teams should never share compute (for example, production vs. development)
- A data loading pipeline should not compete with queries from analysts
- You need your customers to be able to write back to a database in a [customer-facing analytics](/key-tasks/customer-facing-analytics/3-tier-cfa-guide/) setup, or have a separate read scaling pool for each customer.

If you need a visual interface to manage these service accounts, the UI lets you impersonate a service account and adjust settings and run queries as the service account.

## Quick reference: workload patterns

Use this matrix to match your workload to a configuration.

| Use case | Users | Concurrency | R/W | Overlap | Weight | Duckling size | Scaling approach |
|---|---|---|---|---|---|---|---|
| *Ad-hoc analyst* | 👤 | Sequential | R/W | — | ⚡/🏋️ | Pulse / Standard+ | Default (single Duckling) |
| *dbt or ELT pipeline* | 👤 | Concurrent | W | — | 🏋️ | Jumbo / Mega | Default (single Duckling) |
| *Scheduled ingestion job* | 👤 | Sequential | W | — | 🏋️ | Jumbo+ | Default + dedicated service account |
| *BI dashboard (Omni, Hex, Metabase)* | 👥👥 | Concurrent | R | High | ⚡ | Pulse / Standard | Read scaling (shared pool) |
| *Embedded analytics* | 👥👥 | Concurrent | R | Low | ⚡/🏋️ | Pulse / Standard / Jumbo | Read scaling + `session_name` |
| *Customer-facing app (3-tier)* | 👥👥 | Concurrent | R | Low | ⚡ | Standard | Read scaling + `session_name` |
| *Serverless function (Lambda, Workers)* | 👥👥 | Concurrent | R | Varies | ⚡ | Standard | Read scaling |
| *Multi-team production* | 👥👥 | Concurrent | R/W | Low | ⚡/🏋️ | Per team | Separate service accounts |

### Reading the matrix

**Input columns** describe your workload:

- **Users**: How many people or clients connect: 👤 single, 👥👥 many
- **Concurrency**: Whether queries run one at a time (sequential) or in parallel (concurrent)
- **R/W**: Whether the workload reads (R), writes (W), or both (R/W)
- **Overlap**: Whether different users tend to run the same queries (high) or unique queries (low)
- **Weight**: Whether queries are light (⚡ sub-second) or heavy (🏋️ seconds to minutes)

**Output columns** recommend a configuration:

- **Duckling size**: Which [Duckling size](/about-motherduck/billing/duckling-sizes/) to use
- **Scaling approach**: Which horizontal scaling method to apply

## Choosing an interface

Your choice of interface does not change the scaling levers available to you, but it does affect session management and connection behavior.

| Interface | Best for | Session management |
|---|---|---|
| Native SDK (Python, Node.js, Java) | Client applications, scripts, dbt | Instance cache, `session_name` |
| [Postgres endpoint](/key-tasks/authenticating-and-connecting-to-motherduck/postgres-endpoint/) | Serverless functions, BI tools, environments without DuckDB | Per-connection |
| DuckDB WASM | Browser-based applications | Client-side compute |

## Cost considerations

Scaling decisions affect your compute costs:

- **Vertical scaling** increases the per-second cost of your Duckling. Larger Ducklings cost more but finish heavy queries faster.
- **Horizontal scaling** adds Ducklings proportional to active sessions, not total users. Idle Ducklings shut down after the configured [cooldown period](/about-motherduck/billing/duckling-sizes/).
- **Pulse Ducklings** use per-query billing (minimum 1 compute-unit second), making them cost-effective for sporadic, lightweight workloads.
- **Cooldown tuning** balances cost against cache warmth. A longer cooldown keeps the cache warm for returning users but costs more during idle periods.

See [Duckling sizes](/about-motherduck/billing/duckling-sizes/) and [pricing](/about-motherduck/billing/pricing/) for the full cost breakdown.

## Decision flowchart

If you are not sure where to start, follow this flowchart:

```mermaid
flowchart TD
    WriteQ{"Write-heavy?<br/>(data loading, ETL)"}:::yellow

    WriteQ -->|Yes| WeightQ{"Heavy queries?<br/>(complex joins, large loads)"}:::yellow
    WriteQ -->|No| ConcQ{"How many concurrent<br/>read users?"}:::yellow

    WeightQ -->|Yes| SizeUp["Size up your Duckling<br/>(Jumbo / Mega / Giga)"]:::green
    WeightQ -->|No| StdDuckling["Standard Duckling"]:::green

    SizeUp --> Isolation{"Need compute isolation<br/>between workloads?"}:::yellow
    StdDuckling --> Isolation
    Isolation -->|Yes| SvcAcct["Separate service accounts"]:::green
    Isolation -->|No| SingleAcct["Single service account"]:::green

    ConcQ -->|"1-5"| Default["Default Duckling,<br/>size for your heaviest query"]:::green
    ConcQ -->|"5-50"| OverlapQ{"Do users read the<br/>same data?"}:::yellow
    ConcQ -->|"50+"| OverlapQ2{"Do users read the<br/>same data?"}:::yellow

    OverlapQ -->|"Yes, mostly shared"| SharedPool["Read scaling<br/>(shared connection pool)"]:::green
    OverlapQ -->|"No, differs per user"| SessionHint["Read scaling<br/>+ session_name"]:::green

    OverlapQ2 -->|"Yes, mostly shared"| HighConcShared["Read scaling at max<br/>connection pool size"]:::green
    OverlapQ2 -->|"No, differs per user"| HighConcUnique["Read scaling at max<br/>connection pool size<br/>+ session_name"]:::green
```

## Related content

- [Hypertenancy](/concepts/compute/hypertenancy/): how MotherDuck's per-user compute model works
- [Duckling sizes](/about-motherduck/billing/duckling-sizes/): compare sizes and configure your Ducklings
- [Read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/): set up read-only Duckling pools
- [Create and configure service accounts](/key-tasks/service-accounts-guide/create-and-configure-service-accounts/): create isolated compute for teams and pipelines
- [Postgres endpoint](/key-tasks/authenticating-and-connecting-to-motherduck/postgres-endpoint/): connect through the PostgreSQL wire protocol
- [Customer-facing analytics](/key-tasks/customer-facing-analytics/3-tier-cfa-guide/): build multi-tenant analytics with per-customer isolation

---

Source: https://motherduck.com/docs/concepts/compute/execution-environments

# Where code runs
> Choose a local DuckDB client, MotherDuck cloud SQL, or a Flight based on the extensions, credentials, and schedule your workload needs.
MotherDuck workloads run in one of three environments. Choose the environment
before choosing an authentication method or an extension: the same SQL text can
have different capabilities depending on where it runs.

| Environment | Use it for | Community extensions | Credentials available to the code |
| --- | --- | --- | --- |
| **Local\* DuckDB client** | Interactive development, one-off loads and Dual Execution | Install and load community extensions like `bigquery` | Local environment variables, Application Default Credentials (ADC), and local secrets |
| **MotherDuck Duckling** | SQL editor, remote MCP queries, and direct cloud-storage reads | Cannot install or load community extensions | MotherDuck storage secrets, such as `TYPE GCS` or `TYPE S3`|
| **Flight Python runtime** | Scheduled ingestion and Python workflows | Install and load extensions in the Flight's in-process DuckDB | `TYPE flights` secrets attached to that Flight, plus the injected MotherDuck token |

::::note
Local means a non-MotherDuck environment in this case. That could be your own machine,
a third-party ETL service or even a browser (WASM).
::::

## Interfaces, runtimes, and secrets

The interface where you write code and the runtime where that code executes
are usually different places. Each interface hands work to one of the three
runtimes, and each runtime reaches its own set of credentials. When a query
cannot find a credential look at its runtime to understand where it can fail.

```mermaid
flowchart LR
    subgraph Interfaces["Interfaces"]
        LocalClient{{"Local* DuckDB<br/>CLI, Python, dbt"}}:::green
        Chat{{"Chat client through<br/>remote MCP server"}}:::green
        UI{{"MotherDuck UI<br/>notebook"}}:::green
        Trigger{{"Scheduled Flight<br/>or manual run"}}:::green
    end

    subgraph Runtimes["Runtimes"]
        Local["Local DuckDB<br/>your machine"]
        Cloud["MotherDuck<br/>Duckling"]:::yellow
        FlightRT["Flight Python runtime<br/>in-process DuckDB"]:::yellow
    end

    subgraph Credentials["Credentials the runtime reaches"]
        LocalCreds[("Env vars, ADC,<br/>local secrets")]:::db
        Stored[("Stored secrets<br/>TYPE GCS, S3, ...")]:::db
        FlightSecrets[("TYPE flights secrets<br/>as env vars")]:::db
    end

    LocalClient --> Local
    LocalClient -->|"md:"| Cloud
    Chat --> Cloud
    UI --> Cloud
    Trigger --> FlightRT
    FlightRT -->|"md:"| Cloud

    Local --> LocalCreds
    Cloud --> Stored
    FlightRT --> FlightSecrets
```

Flights and Ducklings run on MotherDuck infrastructure. The local DuckDB client is the
one interface that is also a runtime: your SQL executes in the same process,
and only the work that touches an attached `md:` database moves to a Duckling (including related secrets).
Every other interface is remote from its runtime. The UI notebook and a chat
client (through the MCP server) send SQL to a Duckling, and a Flight has no interactive
interface at all: it starts from its schedule or a manual run, and its in-process DuckDB connects
back to cloud SQL like any other client unless it's explicitly told to run local to the Flight runtime only.

Credentials do not cross runtimes on their own. A Duckling cannot read your
laptop's environment variables, a local extension cannot authenticate with a
`TYPE flights` secret, and a Flight's in-process DuckDB sees only its own
environment variables, though the cloud side of its `md:` connection uses
stored secrets like any other Duckling session.

Four caveats worth knowing.

- **Write access can be prevented in multiple ways**
A Duckling can only write when accessed with a read-write token.
Even with a read-write token the MCP server's `query` tool rejects write statements
before they reach MotherDuck, unless the `query_rw` is enabled and used instead.

- **A read-only connection can still use a stored secret.** A secret you saved
with `CREATE SECRET ... IN MOTHERDUCK` is available to the `query` tool and to a
read scaling connection, so a read-only path can select from `s3://` or `gs://`.
What it cannot do is create or drop one: `CREATE SECRET` is a write statement.
Storing a secret also needs the create-secrets permission from your
[role](/concepts/security-and-access/roles-and-access-control/), which Admin and Builder have and
Explorer does not.

- **A Flight runs read-write by default.**
Flights run with a default read-write token injected into each run that inherits your identity and can write.
To narrow that, create the Flight with `access_token_name` pointing at a token with fewer privileges, such as a service
account scoped to one database. See [Authentication, config, and secrets](/key-tasks/flights/flights-authentication-config-and-secrets/).

- **A share does not carry secrets.** Stored secrets are scoped to the user who
created them. A consumer of your [share](/key-tasks/sharing-data/sharing-overview/)
queries it on their own Duckling with their own stored secrets, so a view that
reads from a private S3 bucket with `s3://` works for you and fails for them until
they store their own secret for that bucket.

## Unsupported extensions and connectors

Some connectors and community extension are not supported on Ducklings or even by DuckDB.
These come in two flavors.

- **Community DuckDB extensions.** The `bigquery` extension is the well-known
  case. A Duckling does not install or load community extensions, so a read-only
  MCP query cannot call `INSTALL bigquery`, `ATTACH ... TYPE bigquery`,
  `bigquery_scan`, or `bigquery_query`. Use a local DuckDB client for
  interactive work, or run DuckDB in a Flight for scheduled ingestion.
- **Vendor Python clients.** For sources without a first-class DuckDB extension,
  such as Snowflake or Databricks, ingestion runs through the vendor's Python
  SDK, typically fetching results as Arrow and registering them with DuckDB. The
  [Snowflake ingestion Flight](/cookbook/flight-snowflake-ingest/) is the
  reference for this pattern.

Core DuckDB extensions such as `postgres` and `mysql` load in Ducklings, so they
do not need this workaround. Cloud storage (`s3://`, `gs://`) also
works from Ducklings directly with a stored secret, which covers Delta and
Iceberg tables on S3, GCS, or Azure and by extension most Databricks or lakehouse
data reachable through its object store.

## Third-party ETL tools

Managed pipelines like dlt, Fivetran, or Airbyte are often not an execution
environment of their own: they are clients that write to MotherDuck. Where they
run decides which credentials they see.

- **Run the tool inside a Flight** to reuse Flight scheduling, logs, and
  `TYPE flights` secrets for source credentials. See the
  [dlt ingestion Flight](/cookbook/flight-dlt-ingest/) and the
  [Postgres ingestion Flight](/cookbook/flight-postgres-ingest/) for the
  in-Flight pattern.
- **Run the tool on its own infrastructure** and point it at MotherDuck through
  a DuckDB SDK or the
  [Postgres endpoint](/key-tasks/authenticating-and-connecting-to-motherduck/postgres-endpoint/).
  MotherDuck is the destination and the tool's own runtime holds source
  credentials.

## Secret types match the environment

A `TYPE GCS`, `TYPE S3`, or `TYPE AZURE` secret is a cloud-storage provider
secret. MotherDuck cloud SQL can select from a `gs://`, `s3://`, or `abfss://`
path and connects to the storage service remotely. See
[Google Cloud Storage](/integrations/cloud-storage/google-cloud-storage/) for
the GCS example.

A `TYPE flights` secret is different: it stores key-value parameters for a
Flight. Attach its name with `flight_secret_names` when creating the Flight.
The Flight runtime exposes each parameter as an environment variable named
`<secret_name>_<parameter_name>`. A SQL editor or MCP query session cannot read
those parameters or use the secret to authenticate a community extension or a
vendor SDK.

Use `CREATE OR REPLACE SECRET` with a complete replacement definition when a
secret needs a different type. Changing a cloud-storage secret to a Flights
secret also changes its parameter model, so replace the provider fields with a
`PARAMS MAP` and attach the resulting secret to the relevant Flight.

## Choose a path by source shape

- **Object storage (Parquet, CSV, JSON, Delta, Iceberg on S3, GCS, or Azure).**
  Store a `TYPE S3`, `TYPE GCS`, or `TYPE AZURE` secret and query from cloud
  SQL. No extra runtime needed.
- **A source with an in-process DuckDB extension, such as BigQuery.** Use the
  extension in a local DuckDB client for interactive exploration, or in a
  Flight for scheduled ingestion. See the
  [BigQuery integration](/integrations/databases/bigquery/) and the
  [BigQuery ingestion Flight](/cookbook/flight-bigquery-ingest/); the GA4
  recipe specializes the latter for daily export shards.
- **A source reachable only through a vendor SDK, such as Snowflake or
  Databricks.** Run ingestion in a Flight and land data into MotherDuck through
  its `md:` connection. See the
  [Snowflake ingestion Flight](/cookbook/flight-snowflake-ingest/).
- **A managed ETL tool.** Point it at MotherDuck as a destination, either from
  inside a Flight or from the tool's own runtime, per
  [Third-party ETL tools](#third-party-etl-tools) above.

## Related content

- [Dual Execution](/concepts/architecture-and-capabilities/#dual-execution)
- [CREATE SECRET](/sql-reference/motherduck-sql-reference/create-secret/)
- [Flights](/concepts/compute/flights/)

---

Source: https://motherduck.com/docs/concepts/compute/results

# Results
> Results
**RESULT** provides asynchronous query execution with a transparent cache. Create a RESULT to run a SELECT in the
background, then query it like a table while controlling its lifecycle (pause, resume, cancel, drop). You can think of
a result as a view with an attached cache that is used whenever possible to speed up queries.
Results are stored in memory and will only remain visible until your client-side DuckDB session is restarted.

For the SQL syntax reference, see [`RESULT`](/sql-reference/motherduck-sql-reference/result).

## Core concepts

### What is a RESULT?

```sql
CREATE RESULT <result_name> AS <select_query>;
FROM <result_name> SELECT ...;
```

A RESULT is a named relation in your DuckDB database that:

- Runs the provided `SELECT` in the background (creation is non-blocking)
- Caches rows produced by that statement as it runs
- Provides lifecycle management (pause, resume, cancel, drop)
- Can be queried like a regular table
- Maintains execution state and progress information

### Result states

Results can be in one of three states:

- **BUILDING**: Query is actively running and appending rows to the cache
- **PAUSED**: Query execution is temporarily paused
- **DONE**: Query execution has completed, which can occur for three reasons:
    1. Query finished successfully
    2. Query was preemptively stopped (e.g., aborted by the user)
    3. Query encountered an error

## Interacting with results

### Creating results

When you create a RESULT, the provided `SELECT` starts running in the background. You can query the result like a normal
table at any time.
results, you can query the result just like you would query a normal table.

```sql
-- Basic syntax
CREATE RESULT <result_name> AS <select_query>;

-- With conflict resolution
CREATE RESULT IF NOT EXISTS <result_name> AS <select_query>;
CREATE OR REPLACE RESULT <result_name> AS <select_query>;

-- Accessing the result
FROM <result_name> LIMIT <n>;
```

### Accessing results

You can query a result like a table. The relation appears quickly after creation, although the background `SELECT` may still
be running.
query creating the result has completed successfully. This occurs very quickly and does not mean that the `SELECT`
statement associated with the result has completed running.

```sql
FROM <result_name> LIMIT <n>;
```

There is **no guarantee** the cache is complete when you query a result. Depending on the state of the `RESULT` and your query, the system
may read from the cache, wait for additional rows, or bypass the cache and re-run the original `SELECT`.

The decision tree below shows how the `FROM my_result LIMIT 100` accessing the RESULT `my_result` behaves.

```mermaid
flowchart TD
    start(("FROM my_result LIMIT 100")):::circle -->|Completed successfully| cache(((Read from cache))):::circle
    start -->|"RESULT is not running (PAUSED/DONE with error)"| enough
    start -->|RESULT is BUILDING| enough_building
    enough_building{"Has enough data?<br> (cache > 100)"}:::green -->|Yes| cache
    enough_building -->|No| access_limit
    access_limit{"access limit < 500,000<br>(100 < 500,000)"}:::green -->|Yes| delay
    access_limit -->|No| rerun
    delay(Wait for 100 rows in cache<br> or result complete) --> cache
    enough{"Has enough data?<br> (cache > 100) OR DONE without error?"}:::green -->|Yes| cache
    enough -->|No| rerun(((Re-run query))):::circle
```

### Lifecycle management

On creation, new results start in the **BUILDING** state. While building, you can **PAUSE**, **RESUME**, **CANCEL**, or **DROP** the result.
Pause suspends execution, resume continues from where it stopped. Cancel stops the job permanently and it cannot
be resumed. Canceled results can still be queried, but they will not append any new rows to the cache.
When a result is dropped, it is permanently deleted and can no longer be queried. Dropping a result also removes its associated cache.

```mermaid
stateDiagram-v2
    [*] --> BUILDING: Result Created
    BUILDING --> PAUSED: PAUSE RESULT
    PAUSED --> BUILDING: RESUME RESULT
    BUILDING --> DONE: SELECT statement completes
    BUILDING --> DONE: CANCEL RESULT
    PAUSED --> DONE: CANCEL RESULT
    note right of BUILDING
        Query is actively running
    end note
    note right of PAUSED
        Query execution paused. Can be resumed.
    end note
    note right of DONE
        Execution finished: completed, error, or canceled.
    end note
    note left of DONE
        PAUSE/RESUME will error when in DONE state
    end note
```

#### Pause result

```sql
PAUSE RESULT <result_name>;
PAUSE RESULT IF EXISTS <result_name>;
```

#### Resume result

```sql
RESUME RESULT <result_name>;
RESUME RESULT IF EXISTS <result_name>;
```

#### Cancel result

```sql
CANCEL RESULT <result_name>;
CANCEL RESULT IF EXISTS <result_name>;
```

#### Drop result

```sql
DROP RESULT <result_name>;
DROP RESULT IF EXISTS <result_name>;
```

### Introspecting results

Use `SHOW ALL RESULTS` to list all your results alongside their status and progress. The returned table also includes:

1. `name`: The name of the result
2. `error`: Any error message associated with the result (is empty if no error occurred)
3. `status`: The current status of the result (BUILDING, PAUSED, DONE)
4. `row_count`: The number of rows in the result cache. This grows as the result builds and is not
   stable within the same transaction (it will increase as the result is being built).

```sql
SHOW ALL RESULTS;

--| name  | error                                                               | status   | row_count |
--|-------|---------------------------------------------------------------------|----------|-----------|
--| foo   | (empty)                                                             | DONE     | 100,000   |
--| bar   | INTERRUPT Error: The RESULT "bar" has been manually canceled.       | DONE     | 10,000    |
--| hello | (empty)                                                             | PAUSED   | 1,000     |
--| world | (empty)                                                             | BUILDING | 100       |
```

If you want to order the results, filter them or limit the output you can use the `MD_SHOW_RESULTS` table function:

```sql
FROM MD_SHOW_RESULTS() WHERE name = 'foo';

--| name | error   | status | row_count |
--|------|---------|--------|-----------|
--| foo  | (empty) | DONE   | 100,000   |
```

## Best practices

- Use `LIMIT` when you need only a small sample so that `RESULT` can serve them quickly from the cache.
- Prefer deterministic `SELECT` statements for predictable caching and reuse.
- Pause or cancel long-running results you do not need immediately and remember to drop them when no longer in use.

## Notes and limitations

- `RESULT` accepts `SELECT` statements only.
- The cache may be partial while the result is building. Queries may wait briefly, use the cache, or re-run the `SELECT`.
- A canceled result cannot be resumed.
- Results are stored in memory and will not persist across client restarts.

## See also
- [Building data applications with MotherDuck](https://motherduck.com/blog/building-data-applications-with-motherduck/)
- [MotherDuck wasm npm package](https://www.npmjs.com/package/@motherduck/wasm-client?activeTab=readme)
- [MotherDuck wasm example repository](https://github.com/motherduckdb/wasm-client)

---

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```text
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