Matlab Connect Databricks Jdbc

Connect MATLAB to Databricks via JDBC

Use when establishing a JDBC connection from MATLAB to Databricks using Database Toolbox and the MATLAB Interface for Databricks package. This skill covers connection class selection, authentication configuration, driver setup, and connection optimization. Once connected, use standard Database Toolbox functions (sqlread, fetch, sqlwrite, execute) on the j.Connection object for data operations.

Prerequisites

• MATLAB Interface for Databricks package installed and on the MATLAB path
• Database Toolbox installed

When to Use

• Connecting MATLAB to a Databricks cluster via JDBC
• Connecting MATLAB to a Databricks SQL Warehouse via JDBC
• Configuring Databricks authentication (PAT, OauthU2M, OauthM2M)
• Setting up the Databricks JDBC driver (Simba or OSS)
• Creating a standalone JDBC connection without the full Databricks package
• Running MATLAB on a Databricks cluster and connecting back via JDBC
• Optimizing write performance for large data transfers to Databricks
• User mentions keywords: Databricks, JDBCConnection, SQL Warehouse, databricks.JDBCConnection, StandaloneJDBCConnection, Databricks JDBC, Databricks connect, Databricks cluster, Databricks authentication

When NOT to Use

• Connecting via ODBC (use databricks.ODBCConnection directly)
• Using Databricks Connect (Python-based Spark API, not Database Toolbox)
• Using the Databricks REST APIs (Clusters, Jobs, DBFS, etc.)
• Using MLflow from MATLAB (separate module in the package)
• Executing SQL via the Statement Execution REST API (not JDBC)

Critical Rules

Connection

• ALWAYS prefer databricks.JDBCConnection or StandaloneJDBCConnection over manually constructing a JDBC URL with database(). Manual URL construction is not wrong, but the URL format is complex and error-prone, and may expose connection details in source code. The connection classes handle URL construction, driver classpath, and authentication automatically.
• ALWAYS use StandaloneJDBCConnection when the user does not have the MATLAB Interface for Databricks package installed. Never fall back to manual database() with JDBC URL construction.
• ALWAYS call close(j) or close(j.Connection) when the connection is no longer needed.
• ALWAYS verify a connection succeeded by checking that j.Connection.Message is empty. A non-empty message indicates a driver error.

Authentication

• ALWAYS let the connection class handle authentication via the unified provider chain. The default method is OauthU2M. Do not hardcode tokens in source code.
• NEVER use the JDBC driver's built-in OauthU2M when running MATLAB on Databricks in a browser. The driver attempts to open a browser window, which fails. Use useDriverAuth=false instead.

Drivers

• The Simba driver (non-OSS, v2.7.3 to <3.0.0) is the default and ships with the package. It works with MATLAB's default Java 8 environment.
• The OSS driver (v3.0.3+) requires Java 11 or greater. Set MATLAB's Java environment with jenv before using it. The OSS driver uses Arrow for faster large data transfers.
• StandaloneJDBCConnection supports the Simba driver only. Do not use useDriverType="oss" with standalone connections.

Function Reference

| Function / Class | Purpose | When to Use |

|-----------------|---------|-------------|

| databricks.JDBCConnection | Creates a JDBC connection with full package support | Default choice when the MATLAB Interface for Databricks package is installed |

| StandaloneJDBCConnection | Creates a JDBC connection with zero package dependencies | When embedding Databricks connectivity in a standalone codebase |

| databricks.SQLWarehouse.connect() | Connects to a SQL Warehouse by ID; returns a database.jdbc.connection directly | When targeting a SQL Warehouse instead of a cluster |

| j.Connection | The underlying database.jdbc.connection object from JDBCConnection or StandaloneJDBCConnection | Pass this to sqlread, fetch, sqlwrite, execute, etc. Equivalent to conn created using database() in Database Toolbox |

| databricks.internal.isOnDatabricks() | Returns true if MATLAB is running on a Databricks cluster | Use to branch connection logic for on-cluster vs off-cluster scenarios |

| j.testConnection() | Verifies the connection is working | After creating a connection to confirm success |

| j.saveSource() | Saves connection as a Database Toolbox data source | When using Database Explorer app for interactive exploration |

| j.copyToken() | Copies the auth token to clipboard | When Database Explorer prompts for credentials |

| close(j) | Closes the connection and releases resources | When done with the connection |

Decision Framework

Which class should I use?

| Scenario | Class | Why |

|----------|-------|-----|

| Full package installed, targeting a cluster | databricks.JDBCConnection | Handles auth, URL, driver classpath automatically |

| Targeting a specific compute endpoint by HTTP path | databricks.JDBCConnection(httpPath="/sql/1.0/warehouses/abc") | Overrides the default cluster routing |

| Full package installed, targeting a SQL Warehouse | databricks.SQLWarehouse.connect() | Builds connection from warehouse metadata via REST API |

| No package installed or standalone integration | StandaloneJDBCConnection | Zero dependencies on the Databricks package |

| Already have a databricks.Cluster object | databricks.JDBCConnection(cluster=myCluster) | Routes connection to a specific cluster object |

| MATLAB running on a Databricks cluster | databricks.JDBCConnection(authMethod="OauthU2M", useDriverAuth=false) | Driver browser auth does not work in-browser MATLAB |

Which JDBC driver should I use?

| Scenario | Driver | Notes |

|----------|--------|-------|

| Java 8 (MATLAB default) | Simba (default) | Ships with the package, no setup needed |

| Java 11+ available, both drivers present | OSS (auto-selected) | Uses Arrow for better large-transfer performance |

| Need explicit control | useDriverType='oss' or 'simba' | Overrides auto-selection |

For driver installation and Java configuration details, see references/driver-selection.md.

Which authentication method should I use?

| Scenario | Auth Method | Required Config |

|----------|-------------|-----------------|

| Individual interactive use (default) | OauthU2M | host in .databrickscfg |

| Automated services or CI/CD pipelines | OauthM2M | host, client_id, client_secret in .databrickscfg |

| Simple token-based access | PAT | host, token in .databrickscfg |

| Multiple workspaces in .databrickscfg | Any + profileName="myprofile" | Named profile in .databrickscfg via profileName argument |

| Running on Databricks cluster | OauthU2M + useDriverAuth=false | Package-managed auth (not driver-managed) |

| Opaque token from external source | Token passthrough | passthroughAccessToken argument |

| Azure Entra ID managed workspace | OauthU2M + OauthService="EntraID" | Sets scope resolution to Azure AD instead of Databricks-native |

For authentication configuration details including .databrickscfg format and environment variables, see references/authentication.md.

Common Patterns

Pattern 1: Cluster Connection (Default)

% Connect to a Databricks cluster using default authentication
j = databricks.JDBCConnection();

% Use Database Toolbox functions on the connection
data = sqlread(j.Connection, "mycatalog.myschema.mytable");

% Close when done
close(j);

Pattern 2: Cluster Connection with Catalog and Schema

% Set default catalog and schema to simplify table references
j = databricks.JDBCConnection(catalog="mycatalog", schema="myschema");

% Now table names do not need full qualification
data = sqlread(j.Connection, "mytable");

close(j);

Pattern 3: SQL Warehouse Connection

% Create a warehouse object and set its ID
warehouse = databricks.SQLWarehouse;
warehouse.id = "abc123def456";

% Connect (defaults to JDBC mode)
conn = warehouse.connect();

% Query data
data = fetch(conn, "SELECT * FROM mycatalog.myschema.mytable LIMIT 10");

% Close when done
close(conn);

Pattern 4: Write-Optimized Connection (Simba Driver)

Simba driver write performance improves significantly with native query mode. This is enabled by default when connections are created, but can be controlled explicitly.

% Default behavior: UseNativeQuery=1, EnableNativeParameterizedQuery=0
j = databricks.JDBCConnection();
sqlwrite(j.Connection, "mycatalog.myschema.mytable", data);
close(j);

% To disable the optimization (not recommended for writes)
j = databricks.JDBCConnection(useNativeQuery=false, enableNativeParameterizedQuery=true);

SQL Warehouse variant:

warehouse = databricks.SQLWarehouse;
warehouse.id = "abc123def456";
conn = warehouse.connect(useNativeQuery=true, enableNativeParameterizedQuery=false);
sqlwrite(conn, "mycatalog.myschema.mytable", data);
close(conn);

Pattern 5: On-Databricks Connection

When running MATLAB directly on a Databricks cluster (browser-based), the JDBC driver's OAuth flow cannot open a browser. Use package-managed auth instead. Detect the environment with databricks.internal.isOnDatabricks().

% Detect if running on Databricks
if databricks.internal.isOnDatabricks()
    j = databricks.JDBCConnection(authMethod="OauthU2M", useDriverAuth=false);
else
    j = databricks.JDBCConnection();
end

data = fetch(j.Connection, "SELECT * FROM mycatalog.myschema.mytable LIMIT 10");
close(j);

Pattern 6: Standalone Connection (No Package)

When the user does NOT have the MATLAB Interface for Databricks package installed, use StandaloneJDBCConnection. This class requires only Database Toolbox and the Simba JDBC driver jar -- no Databricks package dependencies.

Setup:

• Place StandaloneJDBCConnection.m and databricks_standalone_jdbc_settings.json on the MATLAB path
• Edit the JSON file with host, orgId, clusterId, and auth settings
• Construct the connection:

% Add standalone class folder to path (if not already)
addpath("path/to/standalone/folder");

% StandaloneJDBCConnection reads config from databricks_standalone_jdbc_settings.json
j = StandaloneJDBCConnection(schema="myschema", catalog="mycatalog");
data = fetch(j.Connection, "SELECT * FROM mytable LIMIT 10");
close(j);

The JSON settings file (databricks_standalone_jdbc_settings.json) must contain:

• host: Databricks workspace URL (e.g., "https://adb-123.1.azuredatabricks.net")
• orgId: Workspace org ID
• clusterId: Cluster or SQL Warehouse ID
• jarFilePath: Path to the Simba JDBC driver jar

For the full JSON template and all fields, see references/standalone-jdbc.md.

Pattern 7: Database Explorer Integration

% Create and save a connection as a data source
j = databricks.JDBCConnection();
j.saveSource();

% Copy the token to clipboard for pasting into Database Explorer
j.copyToken();

% Open Database Explorer, select the saved data source,
% enter "token" as username, paste the token as password
databaseExplorer

Pattern 8: Error-Safe Connection Cleanup

Use onCleanup or try/catch to guarantee the connection closes even when queries fail.

j = databricks.JDBCConnection(catalog="main", schema="analytics");
cleanup = onCleanup(@() close(j));

% If this errors, cleanup still runs
data = fetch(j.Connection, "SELECT * FROM large_table WHERE id > 1000");

Common Mistakes

% NOT RECOMMENDED: manually constructing a JDBC URL with database()
% This works but is error-prone and may expose connection details in code
conn = database("default", "token", myToken, ...
    "com.databricks.client.jdbc.Driver", ...
    "jdbc:databricks://myhost:443/default;transportMode=http;ssl=1;...");
% RECOMMENDED: let JDBCConnection handle URL construction
j = databricks.JDBCConnection();
conn = j.Connection;

% WRONG: using driver auth when running MATLAB on Databricks
j = databricks.JDBCConnection();  % Driver tries to open a browser, fails
% CORRECT: disable driver auth in browser environment
j = databricks.JDBCConnection(authMethod="OauthU2M", useDriverAuth=false);

% WRONG: hardcoding a PAT token in source code
j = databricks.JDBCConnection(token="dapi1234567890abcdef");
% CORRECT: store token in .databrickscfg and let the auth chain find it
j = databricks.JDBCConnection(authMethod="PAT");

% WRONG: using the OSS driver with Java 8
j = databricks.JDBCConnection(useDriverType="oss");  % Error: Java 11+ required
% CORRECT: set Java version first, then use OSS driver
jenv("/path/to/java11");  % Requires MATLAB restart
j = databricks.JDBCConnection(useDriverType="oss");

% WRONG: forgetting to close the connection
j = databricks.JDBCConnection();
data = sqlread(j.Connection, "mytable");
% Connection left open, resources leaked
% CORRECT: always close
close(j);

Unity Catalog Naming

When accessing resources governed by Unity Catalog, names containing hyphens must be enclosed in backticks within SQL queries:

% Schema name contains a hyphen
data = fetch(j.Connection, "SELECT * FROM mycatalog.`my-schema`.mytable");

For sqlread and sqlwrite, set the catalog and schema on the connection instead:

j = databricks.JDBCConnection(catalog="mycatalog", schema="my-schema");
data = sqlread(j.Connection, "mytable");

Checklist

Before finalizing Databricks JDBC connection code, verify:

• [ ] Using databricks.JDBCConnection, StandaloneJDBCConnection, or SQLWarehouse.connect() (preferred over manual database() URL construction)
• [ ] Authentication method appropriate for the scenario (OauthU2M for interactive, OauthM2M for services, PAT for simple access)
• [ ] No tokens or secrets hardcoded in source code
• [ ] close(j) or close(conn) called when done
• [ ] If on Databricks: useDriverAuth=false is set
• [ ] If using OSS driver: Java 11+ configured via jenv
• [ ] If writing large data with Simba: UseNativeQuery optimization is active (default)
• [ ] Connection verified via empty j.Connection.Message or j.testConnection()

Troubleshooting

Issue: Connection returns empty database.jdbc.connection with a message

• Solution: Check the j.Connection.Message property. Common causes: incorrect host, expired token, cluster not running, wrong driver on classpath.

Issue: "Driver class not found on Java class path"

• Solution: The JDBC driver jar is not on MATLAB's dynamic Java class path. databricks.JDBCConnection adds it automatically. For StandaloneJDBCConnection, call javaaddpath("path/to/Shaded-Databricks-JDBC-Driver-0.0.2.jar") first.

Issue: OSS driver fails with Java version error

• Solution: The OSS driver requires Java 11+. Check with jenv and set a compatible JDK: jenv("/path/to/java11"). Restart MATLAB after changing.

Issue: "An ODBC/JDBC datasource exists with the same name as the database"

• Solution: A saved data source has the same name as the schema. Rename the data source or use a different dataSourceName argument.

Issue: Connection takes a long time to establish

• Solution: A Databricks cluster or SQL Warehouse may be starting from a stopped state (cold start). This can take several minutes. Check the cluster/warehouse status in the Databricks UI or via warehouse.refresh(). MATLAB blocks until the compute resource is ready.

Issue: Connection hangs or times out

• Solution: Verify the cluster or SQL Warehouse is running. For SQL Warehouses, warehouse.refresh() shows the current state. MATLAB blocks while a stopped warehouse starts.

Issue: Token cache errors on Linux/macOS with driver v2.7.x

• Solution: Known driver bug. Upgrade to driver v2.7.3+ or disable caching: enableTokenCache=false. See the JDBCWorkflow.md documentation in the package for version-specific details.

Issue: OauthU2M fails when running MATLAB on Databricks

• Solution: The JDBC driver's OAuth flow tries to open a browser, which fails in browser-based MATLAB. Use databricks.JDBCConnection(authMethod="OauthU2M", useDriverAuth=false).

Still stuck? Consult the shipping documentation for detailed guidance:

doc databricks.JDBCConnection                          % Class reference
edit(databricksRoot(-2, "Documentation", "JDBCWorkflow.md"))  % JDBC workflow guide
edit(databricksRoot("Standalone", "README.md"))         % Standalone setup guide

If none of the above resolves your issue, email [email protected] for direct support from the MATLAB-Databricks team.

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