import { TraceData } from '../../core/entities/trace_data';
import { TraceInfo } from '../../core/entities/trace_info';
import { ArtifactsClient } from './base';
import { AuthProvider } from '../../auth';
/**
 * Options for creating a DatabricksArtifactsClient.
 */
export interface DatabricksArtifactsClientOptions {
    /**
     * The Databricks workspace host URL
     */
    host: string;
    /**
     * Authentication provider
     */
    authProvider: AuthProvider;
}
export declare class DatabricksArtifactsClient implements ArtifactsClient {
    private host;
    private headersProvider;
    constructor(options: DatabricksArtifactsClientOptions);
    /**
     * Upload trace data (spans) to the backend using artifact repository pattern.
     *
     * 1. Get credentials for upload
     * 2. Serialize trace data to JSON
     * 3. Upload to cloud storage using the credentials
     */
    uploadTraceData(traceInfo: TraceInfo, traceData: TraceData): Promise<void>;
    /**
     * Download trace data (spans) from cloud storage
     * Uses artifact repository pattern with signed URLs
     */
    downloadTraceData(traceInfo: TraceInfo): Promise<TraceData>;
    /**
     * Get credentials for uploading trace data
     * Endpoint: GET /api/2.0/mlflow/traces/{request_id}/credentials-for-data-upload
     */
    private getCredentialsForTraceDataUpload;
    /**
     * Get credentials for downloading trace data
     * Endpoint: GET /mlflow/traces/{trace_id}/credentials-for-data-download
     */
    private getCredentialsForTraceDataDownload;
    /**
     * Upload data to cloud storage using the provided credentials
     */
    private uploadToCloudStorage;
    /**
     * Upload data to cloud storage using signed URL (AWS S3 or GCP Storage)
     */
    private uploadToSignedUrl;
    /**
     * Upload data to Azure Blob Storage using SAS URI
     * Uses simple PUT for all uploads since traces rarely exceed 100MB
     * https://learn.microsoft.com/en-us/azure/storage/common/storage-sas-overview
     */
    private uploadToAzureBlob;
    /**
     * Upload data to Azure Data Lake Storage Gen2 using SAS URI
     * https://learn.microsoft.com/en-us/rest/api/storageservices/data-lake-storage-gen2
     */
    private uploadToAzureAdlsGen2;
    /**
     * Download data from cloud storage using signed URL
     */
    private downloadFromSignedUrl;
}
/**
 * HTTP header for artifact upload/download
 */
export interface HttpHeader {
    name: string;
    value: string;
}
/**
 * Artifact credential information for upload/download
 */
export interface ArtifactCredentialInfo {
    /** ID of the MLflow Run containing the artifact */
    run_id?: string;
    /** Relative path to the artifact */
    path?: string;
    /** Signed URI credential for artifact access */
    signed_uri: string;
    /** HTTP headers for upload/download (optional, may not be present) */
    headers?: HttpHeader[];
    /** Type of signed credential URI */
    type: ArtifactCredentialType;
}
/**
 * Enum for artifact credential types.
 * This ensures type safety when handling different cloud storage providers.
 */
export type ArtifactCredentialType = 'AWS_PRESIGNED_URL' | 'GCP_SIGNED_URL' | 'AZURE_SAS_URI' | 'AZURE_ADLS_GEN2_SAS_URI';