> ## Documentation Index
> Fetch the complete documentation index at: https://docs.maia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Optimize

export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => {
  const allWarehouses = [...warehouses.map(w => ({
    name: w,
    supported: true
  })), ...unsupportedWarehouses.map(w => ({
    name: w,
    supported: false
  }))];
  return <div style={{
    background: 'var(--colors-background-light, #f9fafb)',
    border: '1px solid var(--colors-border-default, #e5e7eb)',
    borderRadius: '12px',
    padding: '20px 28px',
    marginBottom: '28px',
    boxShadow: '0 1px 4px rgba(0,0,0,0.10)'
  }}>
      <table style={{
    width: '100%',
    borderCollapse: 'collapse'
  }}>
        <tbody>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle',
    width: '180px'
  }}>Project Availability</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>
              <div style={{
    display: 'flex',
    flexWrap: 'wrap',
    gap: '8px'
  }}>
                {allWarehouses.map((w, i) => <span key={i} style={{
    background: w.supported ? '#dcfce7' : '#fee2e2',
    color: w.supported ? '#15803d' : '#b91c1c',
    border: `1px solid ${w.supported ? '#bbf7d0' : '#fca5a5'}`,
    borderRadius: '9999px',
    padding: '3px 12px',
    fontSize: '0.85rem',
    fontWeight: '500',
    whiteSpace: 'nowrap'
  }}>
                    {w.name} {w.supported ? '✅' : '❌'}
                  </span>)}
              </div>
            </td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Component Type</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{componentType}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Inputs</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{connectionInputs}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Outputs</td>
            <td style={{
    verticalAlign: 'middle'
  }}>{connectionOutputs}</td>
          </tr>
        </tbody>
      </table>
    </div>;
};

<ComponentMetadata warehouses={["Databricks"]} unsupportedWarehouses={["Snowflake", "Amazon Redshift", "BigQuery"]} componentType="Orchestration" connectionInputs="One" connectionOutputs="Unlimited" />

Optimize the layout of Databricks data with the Optimize orchestration component. You can optionally optimize a subset of data or colocate data by column. If you don't specify colocation, bin-packing optimization is performed.

Bin-packing optimization is idempotent. This means that if the operation is run twice on the same dataset, the second run has no effect. Bin-packing aims to produce evenly balanced data files with respect to their size on disk, but not necessarily the number of tuples per file. Typically, however, the two measures are often correlated.

Z-Ordering is *not* idempotent. However, Z-Ordering does aim to be an incremental operation. The time taken for Z-Ordering isn't guaranteed to reduce over multiple runs. Z-Ordering aims to produce evenly balanced data files with respect to the number of tuples, but not necessarily data size on disk. While the two measures are often correlated, situations can occur where this is not the case, leading to skews in optimisation times for tasks.

***

## Properties

<ResponseField name="Name" type="string" required>
  A human-readable name for the component.
</ResponseField>

{/* <!-- param-start:[catalog] | warehouses: [databricks] --> */}

<ResponseField name="Catalog" type="drop-down" required>
  Select a [Databricks Unity Catalog](https://docs.databricks.com/en/data-governance/unity-catalog/index.html). The special value `[Environment Default]` uses the catalog defined in the environment. Selecting a catalog will determine which databases are available in the next parameter.
</ResponseField>

{/* <!-- param-start:[schema] | warehouses: [databricks] --> */}

<ResponseField name="Schema (Database)" type="drop-down" required>
  The Databricks schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Create and manage schemas](https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html) to learn more.
</ResponseField>

{/* <!-- param-start:[table] | warehouses: [databricks] --> */}

<ResponseField name="Table" type="drop-down" required>
  The table to be optimized. Only one table can be selected per instance of the component.
</ResponseField>

{/* <!-- param-start:[partition] | warehouses: [databricks] --> */}

<ResponseField name="Partition" type="expression editor">
  The partition columns to include in the optimization process with the related condition. The default is none.
</ResponseField>

{/* <!-- param-start:[zOrder] | warehouses: [databricks] --> */}

<ResponseField name="Z Order" type="column editor">
  The columns to include in the optimization process. This list should exclude any partition columns. The default is none.
</ResponseField>