Databricks SQL Driver for Node.js
The Databricks SQL Driver for Node.js is a Node.js library that allows you to use JavaScript code to run SQL commands on Databricks compute resources.
Requirements
A development machine running Node.js, version 14 or higher. To print the installed version of Node.js, run the command
node -v
. To install and use different versions of Node.js, you can use tools such as Node Version Manager (nvm).Node Package Manager (
npm
). Later versions of Node.js already includenpm
. To check whethernpm
is installed, run the commandnpm -v
. To installnpm
if needed, you can follow instructions such as the ones at Download and install npm.The @databricks/sql package from npm. To install the
@databricks/sql
package in your Node.js project as a dependency, usenpm
to run the following command from within the same directory as your project:npm i @databricks/sql
If you want to install and use TypeScript in your Node.js project as
devDependencies
, usenpm
to run the following commands from within the same directory as your project:npm i -D typescript npm i -D @types/node
An existing cluster or SQL warehouse.
The Server Hostname and HTTP Path value for the existing cluster or SQL warehouse.
Authentication
The Databricks SQL Driver for Node.js supports the following Databricks authentication types:
Note
Basic authentication using a Databricks username and password reached end of life on July 10, 2024. See End of life for Databricks-managed passwords.
Note
As a security best practice, you should not hard code connection variable values into your code. Instead, you should retrieve these connection variable values from a secure location. For example, the code snippets and examples in this article use environment variables.
Databricks personal access token authentication
To use the Databricks SQL Driver for Node.js with authentication, you must first create a Databricks personal access token. For details on this step, see Databricks personal access tokens for workspace users.
To authenticate the Databricks SQL Driver for Node.js, use the following code snippet. This snippet assumes that you have set the following environment variables:
DATABRICKS_SERVER_HOSTNAME
set to the Server Hostname value for your cluster or SQL warehouse.DATABRICKS_HTTP_PATH
, set to HTTP Path value for your cluster or SQL warehouse.DATABRICKS_TOKEN
, set to the Databricks personal access token.
To set environment variables, see your operating system’s documentation.
const { DBSQLClient } = require('@databricks/sql');
const serverHostname = process.env.DATABRICKS_SERVER_HOSTNAME;
const httpPath = process.env.DATABRICKS_HTTP_PATH;
const token = process.env.DATABRICKS_TOKEN;
if (!token || !serverHostname || !httpPath) {
throw new Error("Cannot find Server Hostname, HTTP Path, or " +
"personal access token. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME, " +
"DATABRICKS_HTTP_PATH, and DATABRICKS_TOKEN.");
}
const client = new DBSQLClient();
const connectOptions = {
token: token,
host: serverHostname,
path: httpPath
};
client.connect(connectOptions)
// ...
import { DBSQLClient } from "@databricks/sql";
const serverHostname: string = process.env.DATABRICKS_SERVER_HOSTNAME || '';
const httpPath: string = process.env.DATABRICKS_HTTP_PATH || '';
const token: string = process.env.DATABRICKS_TOKEN || '';
if (token == '' || serverHostname == '' || httpPath == '') {
throw new Error("Cannot find Server Hostname, HTTP Path, or personal access token. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME, " +
"DATABRICKS_HTTP_PATH, and DATABRICKS_TOKEN.");
}
const client: DBSQLClient = new DBSQLClient();
const connectOptions = {
token: token,
host: serverHostname,
path: httpPath
};
client.connect(connectOptions)
// ...
OAuth user-to-machine (U2M) authentication
Databricks SQL Driver for Node.js versions 1.3.0 and above support OAuth user-to-machine (U2M) authentication.
To authenticate the Databricks SQL Driver for Node.js with OAuth U2M authentication, use the following code snippet. This snippet assumes that you have set the following environment variables:
DATABRICKS_SERVER_HOSTNAME
set to the Server Hostname value for your cluster or SQL warehouse.DATABRICKS_HTTP_PATH
, set to HTTP Path value for your cluster or SQL warehouse.
To set environment variables, see your operating system’s documentation.
const { DBSQLClient } = require('@databricks/sql');
const serverHostname = process.env.DATABRICKS_SERVER_HOSTNAME;
const httpPath = process.env.DATABRICKS_HTTP_PATH;
if (!serverHostname || !httpPath) {
throw new Error("Cannot find Server Hostname or HTTP Path. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME " +
"and DATABRICKS_HTTP_PATH.");
}
const client = new DBSQLClient();
const connectOptions = {
authType: "databricks-oauth",
host: serverHostname,
path: httpPath
};
client.connect(connectOptions)
// ...
import { DBSQLClient } from "@databricks/sql";
const serverHostname: string = process.env.DATABRICKS_SERVER_HOSTNAME || '';
const httpPath: string = process.env.DATABRICKS_HTTP_PATH || '';
if (serverHostname == '' || httpPath == '') {
throw new Error("Cannot find Server Hostname or HTTP Path. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME " +
"and DATABRICKS_HTTP_PATH.");
}
const client: DBSQLClient = new DBSQLClient();
const connectOptions = {
authType: "databricks-oauth",
host: serverHostname,
path: httpPath
};
client.connect(connectOptions)
// ...
OAuth machine-to-machine (M2M) authentication
Databricks SQL Driver for Node.js versions 1.5.0 and above support OAuth machine-to-machine (M2M) authentication.
To use the Databricks SQL Driver for Node.js with OAuth M2M authentication, you must do the following:
Create a Databricks service principal in your Databricks workspace, and create an OAuth secret for that service principal.
To create the service principal and its OAuth secret, see Authenticate access to Databricks with a service principal using OAuth (OAuth M2M). Make a note of the service principal’s UUID or Application ID value, and the Secret value for the service principal’s OAuth secret.
Give the service principal access to your cluster or warehouse. See Compute permissions or Manage a SQL warehouse.
To authenticate the Databricks SQL Driver for Node.js, use the following code snippet. This snippet assumes that you have set the following environment variables:
DATABRICKS_SERVER_HOSTNAME
set to the Server Hostname value for your cluster or SQL warehouse.DATABRICKS_HTTP_PATH
, set to HTTP Path value for your cluster or SQL warehouse.DATABRICKS_CLIENT_ID
, set to the service principal’s UUID or Application ID value.DATABRICKS_CLIENT_SECRET
, set to the Secret value for the service principal’s OAuth secret.
To set environment variables, see your operating system’s documentation.
const { DBSQLClient } = require('@databricks/sql');
const serverHostname = process.env.DATABRICKS_SERVER_HOSTNAME;
const httpPath = process.env.DATABRICKS_HTTP_PATH;
const clientId = process.env.DATABRICKS_CLIENT_ID;
const clientSecret = process.env.DATABRICKS_CLIENT_SECRET;
if (!serverHostname || !httpPath || !clientId || !clientSecret) {
throw new Error("Cannot find Server Hostname, HTTP Path, or " +
"service principal ID or secret. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME, " +
"DATABRICKS_HTTP_PATH, DATABRICKS_CLIENT_ID, and " +
"DATABRICKS_CLIENT_SECRET.");
}
const client = new DBSQLClient();
const connectOptions = {
authType: "databricks-oauth",
host: serverHostname,
path: httpPath,
oauthClientId: clientId,
oauthClientSecret: clientSecret
};
client.connect(connectOptions)
// ...
import { DBSQLClient } from "@databricks/sql";
const serverHostname: string = process.env.DATABRICKS_SERVER_HOSTNAME || '';
const httpPath: string = process.env.DATABRICKS_HTTP_PATH || '';
const clientId: string = process.env.DATABRICKS_CLIENT_ID || '';
const clientSecret: string = process.env.DATABRICKS_CLIENT_SECRET || '';
if (serverHostname == '' || httpPath == '' || clientId == '' || clientSecret == '') {
throw new Error("Cannot find Server Hostname, HTTP Path, or " +
"service principal ID or secret. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME, " +
"DATABRICKS_HTTP_PATH, DATABRICKS_CLIENT_ID, and " +
"DATABRICKS_CLIENT_SECRET.");
}
const client: DBSQLClient = new DBSQLClient();
const connectOptions = {
authType: "databricks-oauth",
host: serverHostname,
path: httpPath,
oauthClientId: clientId,
oauthClientSecret: clientSecret
};
client.connect(connectOptions)
// ...
Query data
The following code example demonstrates how to call the Databricks SQL Driver for Node.js to run a basic SQL query on a Databricks compute resource. This command returns the first two rows from the trips
table in the samples
catalog’s nyctaxi
schema.
Note
The following code example demonstrates how to use a Databricks personal access token for authentication. To use other available Databricks authentication types instead, see Authentication.
This code example retrieves the token
, server_hostname
and http_path
connection variable values from a set of Databricks environment variables. These environment variables have the following environment variable names:
DATABRICKS_TOKEN
, which represents your Databricks personal access token from the requirements.DATABRICKS_SERVER_HOSTNAME
, which represents the Server Hostname value from the requirements.DATABRICKS_HTTP_PATH
, which represents the HTTP Path value from the requirements.
You can use other approaches to retrieving these connection variable values. Using environment variables is just one approach among many.
The following code example demonstrates how to call the Databricks SQL Connector for Node.js to run a basic SQL command on a cluster or SQL warehouse. This command returns the first two rows from the trips
table.
const { DBSQLClient } = require('@databricks/sql');
const token = process.env.DATABRICKS_TOKEN;
const serverHostname = process.env.DATABRICKS_SERVER_HOSTNAME;
const httpPath = process.env.DATABRICKS_HTTP_PATH;
if (!token || !serverHostname || !httpPath) {
throw new Error("Cannot find Server Hostname, HTTP Path, or personal access token. " +
"Check the environment variables DATABRICKS_TOKEN, " +
"DATABRICKS_SERVER_HOSTNAME, and DATABRICKS_HTTP_PATH.");
}
const client = new DBSQLClient();
const connectOptions = {
token: token,
host: serverHostname,
path: httpPath
};
client.connect(connectOptions)
.then(async client => {
const session = await client.openSession();
const queryOperation = await session.executeStatement(
'SELECT * FROM samples.nyctaxi.trips LIMIT 2',
{
runAsync: true,
maxRows: 10000 // This option enables the direct results feature.
}
);
const result = await queryOperation.fetchAll();
await queryOperation.close();
console.table(result);
await session.close();
await client.close();
})
.catch((error) => {
console.error(error);
});
import { DBSQLClient } from '@databricks/sql';
import IDBSQLSession from '@databricks/sql/dist/contracts/IDBSQLSession';
import IOperation from '@databricks/sql/dist/contracts/IOperation';
const serverHostname: string = process.env.DATABRICKS_SERVER_HOSTNAME || '';
const httpPath: string = process.env.DATABRICKS_HTTP_PATH || '';
const token: string = process.env.DATABRICKS_TOKEN || '';
if (serverHostname == '' || httpPath == '' || token == '') {
throw new Error("Cannot find Server Hostname, HTTP Path, or personal access token. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME, " +
"DATABRICKS_HTTP_PATH, and DATABRICKS_TOKEN.");
}
const client: DBSQLClient = new DBSQLClient();
const connectOptions = {
host: serverHostname,
path: httpPath,
token: token
};
client.connect(connectOptions)
.then(async client => {
const session: IDBSQLSession = await client.openSession();
const queryOperation: IOperation = await session.executeStatement(
'SELECT * FROM samples.nyctaxi.trips LIMIT 2',
{
runAsync: true,
maxRows: 10000 // This option enables the direct results feature.
}
);
const result = await queryOperation.fetchAll();
await queryOperation.close();
console.table(result);
await session.close();
client.close();
})
.catch((error) => {
console.error(error);
});
Output:
┌─────────┬─────┬────────┬───────────┬───────┬─────────┬────────┬───────┬───────┬────────┬────────┬────────┐
│ (index) │ _c0 │ carat │ cut │ color │ clarity │ depth │ table │ price │ x │ y │ z │
├─────────┼─────┼────────┼───────────┼───────┼─────────┼────────┼───────┼───────┼────────┼────────┼────────┤
│ 0 │ '1' │ '0.23' │ 'Ideal' │ 'E' │ 'SI2' │ '61.5' │ '55' │ '326' │ '3.95' │ '3.98' │ '2.43' │
│ 1 │ '2' │ '0.21' │ 'Premium' │ 'E' │ 'SI1' │ '59.8' │ '61' │ '326' │ '3.89' │ '3.84' │ '2.31' │
└─────────┴─────┴────────┴───────────┴───────┴─────────┴────────┴───────┴───────┴────────┴────────┴────────┘
Sessions
All IDBSQLSession
methods that return IOperation
objects in the API Reference have the following common parameters that affect their behavior:
Setting
runAsync
totrue
starts asynchronous mode.IDBSQLSession
methods put operations into the queue and return as quickly as possible. The current state of the returnedIOperation
object might vary, and the client is responsible for checking its status before using the returnedIOperation
. See Operations. SettingrunAsync
tofalse
means thatIDBSQLSession
methods wait for operations to complete. Databricks recommends always settingrunAsync
totrue
.Setting
maxRows
to a non-null value enables direct results. With direct results, the server tries to wait for operations to complete and then fetches a portion of the data. Depending on how much work the server was able to complete within the defined time,IOperation
objects return in some intermediate state instead of in some pending state. Very often all the metadata and query results are returned within a single request to the server. The server usesmaxRows
to determine how many records it can return immediately. However, the actual chunk may be of a different size; seeIDBSQLSession.fetchChunk
. Direct results are enabled by default. Databricks recommends against disabling direct results.
Operations
As described in Sessions, IOperation
objects that are returned by IDBSQLSession
session methods in the API Reference are not fully populated. The related server operation might still be in progress, such as waiting for the Databricks SQL warehouse to start, running the query, or fetching the data. The IOperation
class hides these details from users. For example, methods such fetchAll
, fetchChunk
, and getSchema
wait internally for operations to complete and then return results. You can use the IOperation.finished()
method to explicitly wait for operations to complete. These methods take a callback that is periodically called while waiting for operations to complete. Setting the progress
option to true
attempts to request extra progress data from the server and pass it to that callback.
The close
and cancel
methods can be called at any time. When called, they immediately invalidate the IOperation
object; all pending calls such as fetchAll
, fetchChunk
, and getSchema
are immediately canceled and an error is returned. In some cases, the server operation might have already completed and the cancel
method affects only the client.
The fetchAll
method calls fetchChunk
internally and collects all of the data into an array. While this is convenient, it might cause out of memory errors when used on large datasets. fetchAll
options are typically passed to fetchChunk
.
Fetch chunks of data
Fetching data chunks uses the following code pattern:
do {
const chunk = await operation.fetchChunk();
// Process the data chunk.
} while (await operation.hasMoreRows());
The fetchChunk
method in the API Reference processes data in small portions to reduce memory consumption. fetchChunk
first waits for operations to complete if they have not already completed, then calls a callback during the wait cycle, and then fetches the next data chunk.
You can use the maxRows
option to specify the desired chunk size. However, the returned chunk might have a different size, smaller or even sometimes larger. fetchChunk
does not attempt to prefetch data internally, in order to slice it into the requested portions. It sends the maxRows
option to then server and returns whatever the server returns. Do not confuse this maxRows
option with the one in IDBSQLSession
. maxRows
passed to fetchChunk
defines the size of each chunk and does not do anything else.
Manage files in Unity Catalog volumes
The Databricks SQL Driver enables you to write local files to Unity Catalog volumes, download files from volumes, and delete files from volumes, as shown in the following example:
const { DBSQLClient } = require('@databricks/sql');
const serverHostname = process.env.DATABRICKS_SERVER_HOSTNAME;
const httpPath = process.env.DATABRICKS_HTTP_PATH;
const token = process.env.DATABRICKS_TOKEN;
if (!token || !serverHostname || !httpPath) {
throw new Error("Cannot find Server Hostname, HTTP Path, or " +
"personal access token. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME, " +
"DATABRICKS_HTTP_PATH, and DATABRICKS_TOKEN.");
}
const client = new DBSQLClient();
const connectOptions = {
token: token,
host: serverHostname,
path: httpPath
};
client.connect(connectOptions)
.then(async client => {
const session = await client.openSession();
// Write a local file to a volume in the specified path.
// For writing local files to volumes, you must first specify the path to the
// local folder that contains the file to be written.
// Specify OVERWRITE to overwrite any existing file in that path.
await session.executeStatement(
"PUT 'my-data.csv' INTO '/Volumes/main/default/my-volume/my-data.csv' OVERWRITE", {
stagingAllowedLocalPath: ["/tmp/"]
}
);
// Download a file from a volume in the specified path.
// For downloading files in volumes, you must first specify the path to the
// local folder that will contain the downloaded file.
await session.executeStatement(
"GET '/Volumes/main/default/my-volume/my-data.csv' TO 'my-downloaded-data.csv'", {
stagingAllowedLocalPath: ["/Users/paul.cornell/samples/nodejs-sql-driver/"]
}
)
// Delete a file in a volume from the specified path.
// For deleting files from volumes, you must add stagingAllowedLocalPath,
// but its value will be ignored. As such, in this example, an empty string is
// specified.
await session.executeStatement(
"REMOVE '/Volumes/main/default/my-volume/my-data.csv'", {
stagingAllowedLocalPath: [""]
}
)
await session.close();
await client.close();
})
.catch((error) => {
console.error(error);
});
import { DBSQLClient } from '@databricks/sql';
const serverHostname: string | undefined = process.env.DATABRICKS_SERVER_HOSTNAME;
const httpPath: string | undefined = process.env.DATABRICKS_HTTP_PATH;
const token: string | undefined = process.env.DATABRICKS_TOKEN;
if (!token || !serverHostname || !httpPath) {
throw new Error("Cannot find Server Hostname, HTTP Path, or " +
"personal access token. " +
"Check the environment variables DATABRICKS_SERVER_HOSTNAME, " +
"DATABRICKS_HTTP_PATH, and DATABRICKS_TOKEN.");
}
const client: DBSQLClient = new DBSQLClient();
const connectOptions = {
token: token,
host: serverHostname,
path: httpPath
};
client.connect(connectOptions)
.then(async client => {
const session = await client.openSession();
// Write a local file to a volume in the specified path.
// For writing local files to volumes, you must first specify the path to the
// local folder that contains the file to be written.
// Specify OVERWRITE to overwrite any existing file in that path.
await session.executeStatement(
"PUT 'my-data.csv' INTO '/Volumes/main/default/my-volume/my-data.csv' OVERWRITE", {
stagingAllowedLocalPath: ["/tmp/"]
}
);
// Download a file from a volume in the specified path.
// For downloading files in volumes, you must first specify the path to the
// local folder that will contain the downloaded file.
await session.executeStatement(
"GET '/Volumes/main/default/my-volume/my-data.csv' TO 'my-downloaded-data.csv'", {
stagingAllowedLocalPath: ["/Users/paul.cornell/samples/nodejs-sql-driver/"]
}
)
// Delete a file in a volume from the specified path.
// For deleting files from volumes, you must add stagingAllowedLocalPath,
// but its value will be ignored. As such, in this example, an empty string is
// specified.
await session.executeStatement(
"REMOVE '/Volumes/main/default/my-volume/my-data.csv'", {
stagingAllowedLocalPath: [""]
}
)
await session.close();
await client.close();
})
.catch((error: any) => {
console.error(error);
});
Configure logging
The logger provides information for debugging problems with the connector. All DBSQLClient
objects are instantiated with a logger that prints to the console, but by passing in a custom logger, you can send this information to a file. The following example shows how to configure a logger and change its level.
const { DBSQLLogger, LogLevel } = require('@databricks/sql');
const logger = new DBSQLLogger({
filepath: 'log.txt',
level: LogLevel.info
});
// Set logger to different level.
logger.setLevel(LogLevel.debug);
import { DBSQLLogger, LogLevel } from '@databricks/sql';
const logger = new DBSQLLogger({
filepath: 'log.txt',
level: LogLevel.info,
});
// Set logger to different level.
logger.setLevel(LogLevel.debug);
For additional examples, see the examples folder in the databricks/databricks-sql-nodejs repository on GitHub.
Testing
To test your code, you can use JavaScript test frameworks such as Jest. To test your code under simulated conditions without calling Databricks REST API endpoints or changing the state of your Databricks accounts or workspaces, you can use Jest’s built-in mocking frameworks.
For example, given the following file named helpers.js
containing a getDBSQLClientWithPAT
function that uses a Databricks personal access token to return a connection to a Databricks workspace, a getAllColumnsFromTable
function that uses the connection to get the specified number of data rows from the specified table (for example, the trips
table in the samples
catalog’s nyctaxi
schema), and a printResults
function to print the data rows’ content:
// helpers.js
const { DBSQLClient } = require('@databricks/sql');
async function getDBSQLClientWithPAT(token, serverHostname, httpPath) {
const client = new DBSQLClient();
const connectOptions = {
token: token,
host: serverHostname,
path: httpPath
};
try {
return await client.connect(connectOptions);
} catch (error) {
console.error(error);
throw error;
}
}
async function getAllColumnsFromTable(client, tableSpec, rowCount) {
let session;
let queryOperation;
try {
session = await client.openSession();
queryOperation = await session.executeStatement(
`SELECT * FROM ${tableSpec} LIMIT ${rowCount}`,
{
runAsync: true,
maxRows: 10000 // This option enables the direct results feature.
}
);
} catch (error) {
console.error(error);
throw error;
}
let result;
try {
result = await queryOperation.fetchAll();
} catch (error) {
console.error(error);
throw error;
} finally {
if (queryOperation) {
await queryOperation.close();
}
if (session) {
await session.close();
}
}
return result;
}
function printResult(result) {
console.table(result);
}
module.exports = {
getDBSQLClientWithPAT,
getAllColumnsFromTable,
printResult
};
And given the following file named main.js
that calls the getDBSQLClientWithPAT
, getAllColumnsFromTable
, and printResults
functions:
// main.js
const { getDBSQLClientWithPAT, getAllColumnsFromTable, printResult } = require('./helpers');
const token = process.env.DATABRICKS_TOKEN;
const serverHostname = process.env.DATABRICKS_SERVER_HOSTNAME;
const httpPath = process.env.DATABRICKS_HTTP_PATH;
const tableSpec = process.env.DATABRICKS_TABLE_SPEC;
if (!token || !serverHostname || !httpPath) {
throw new Error("Cannot find Server Hostname, HTTP Path, or personal access token. " +
"Check the environment variables DATABRICKS_TOKEN, " +
"DATABRICKS_SERVER_HOSTNAME, and DATABRICKS_HTTP_PATH.");
}
if (!tableSpec) {
throw new Error("Cannot find table spec in the format catalog.schema.table. " +
"Check the environment variable DATABRICKS_TABLE_SPEC."
)
}
getDBSQLClientWithPAT(token, serverHostname, httpPath)
.then(async client => {
const result = await getAllColumnsFromTable(client, tableSpec, 2);
printResult(result);
await client.close();
})
.catch((error) => {
console.error(error);
});
The following file named helpers.test.js
tests whether the getAllColumnsFromTable
function returns the expected response. Rather than creating a real connection to the target workspace, this test mocks a DBSQLClient
object. The test also mocks some data that conforms to the schema and values that are in the real data. The test returns the mocked data through the mocked connection and then checks whether one of the mocked data rows’ values matches the expected value.
// helpers.test.js
const { getDBSQLClientWithPAT, getAllColumnsFromTable, printResult} = require('./helpers')
jest.mock('@databricks/sql', () => {
return {
DBSQLClient: jest.fn().mockImplementation(() => {
return {
connect: jest.fn().mockResolvedValue({ mock: 'DBSQLClient'})
};
}),
};
});
test('getDBSQLClientWithPAT returns mocked Promise<DBSQLClient> object', async() => {
const result = await getDBSQLClientWithPAT(
token = 'my-token',
serverHostname = 'mock-server-hostname',
httpPath = 'mock-http-path'
);
expect(result).toEqual({ mock: 'DBSQLClient' });
});
const data = [
{
tpep_pickup_datetime: new Date(2016, 1, 13, 15, 51, 12),
tpep_dropoff_datetime: new Date(2016, 1, 13, 16, 15, 3),
trip_distance: 4.94,
fare_amount: 19.0,
pickup_zip: 10282,
dropoff_zip: 10171
},
{
tpep_pickup_datetime: new Date(2016, 1, 3, 17, 43, 18),
tpep_dropoff_datetime: new Date(2016, 1, 3, 17, 45),
trip_distance: 0.28,
fare_amount: 3.5,
pickup_zip: 10110,
dropoff_zip: 10110
}
];
const mockDBSQLClientForSession = {
openSession: jest.fn().mockResolvedValue({
executeStatement: jest.fn().mockResolvedValue({
fetchAll: jest.fn().mockResolvedValue(data),
close: jest.fn().mockResolvedValue(null)
}),
close: jest.fn().mockResolvedValue(null)
})
};
test('getAllColumnsFromTable returns the correct fare_amount for the second mocked data row', async () => {
const result = await getAllColumnsFromTable(
client = mockDBSQLClientForSession,
tableSpec = 'mock-table-spec',
rowCount = 2);
expect(result[1].fare_amount).toEqual(3.5);
});
global.console.table = jest.fn();
test('printResult mock prints the correct fare_amount for the second mocked data row', () => {
printResult(data);
expect(console.table).toHaveBeenCalledWith(data);
expect(data[1].fare_amount).toBe(3.5);
});
For TypeScript, the preceding code looks similar. For Jest testing with TypeScript, use ts-jest.
Additional resources
The Databricks SQL Driver for Node.js repository on GitHub
Getting started with the Databricks SQL Driver for Node.js
Troubleshooting the Databricks SQL Driver for Node.js
API reference
Classes
DBSQLClient
class
Main entry point for interacting with a database.
Methods
connect
method
Opens a connection to the database.
Parameters |
---|
options Type: The set of options used to connect to the database. The Example: const client: DBSQLClient = new DBSQLClient();
client.connect(
{
host: serverHostname,
path: httpPath,
// ...
}
)
|
Returns:
Promise<IDBSQLClient>
openSession
method
Opens session between DBSQLClient and database.
Parameters |
---|
request Type: A set of optional parameters for specifying initial schema and initial catalog Example: const session = await client.openSession(
{initialCatalog: 'catalog'}
);
|
Returns:
Promise<IDBSQLSession>
getClient
method
Returns internal thrift TCLIService.Client object. Must be called after DBSQLClient has connected.
No parameters
Returns TCLIService.Client
close
method
Closes the connection to the database and releases all associated resources on the server. Any additional calls to this client will throw error.
No parameters.
No return value.
DBSQLSession
class
DBSQLSessions are primarily used for the execution of statements against the databbase as well as various metadata fetching operations.
Methods
executeStatement
method
Executes a statement with the options provided.
Parameters |
---|
statement Type: The statement to be executed. |
options Type: A set of optional parameters for determining query timeout, max
rows for direct results, and whether to run the query
asynchronously. By default Example: const session = await client.openSession(
{initialCatalog: 'catalog'}
);
queryOperation = await session.executeStatement(
'SELECT "Hello, World!"', { runAsync: true }
);
|
Returns:
Promise<IOperation>
getTypeInfo
method
Returns information about supported data types.
Parameters |
---|
request Type: Request parameters. |
Returns:
Promise<IOperation>
getCatalogs
method
Gets list of catalogs.
Parameters |
---|
request Type: Request parameters. |
Returns:
Promise<IOperation>
getSchemas
method
Gets list of schemas.
Parameters |
---|
request Type: Request parameters. Fields |
Returns:
Promise<IOperation>
getTables
method
Gets list of tables.
Parameters |
---|
request Type: Request parameters. Fields |
Returns:
Promise<IOperation>
getFunctions
method
Gets list of tables.
Parameters |
---|
request Type: Request parameters. Field |
Returns:
Promise<IOperation>
getPrimaryKeys
method
Gets list of primary keys.
Parameters |
---|
request Type: Request parameters. Fields |
Returns:
Promise<IOperation>
getCrossReference
method
Gets information about foreign keys between two tables.
Parameters |
---|
request Type: Request parameters. Schema, Parent, and Catalog name must be specified for both tables. |
Returns:
Promise<IOperation>
DBSQLOperation
class
DBSQLOperations are created by DBSQLSessions and can be used to fetch the results of statements and check up on their execution. Data is fetched through functions fetchChunk and fetchAll.
Methods
fetchAll
method
Waits for operation completion, then fetches all rows from operation.
Parameters: None
Returns:
Promise<Array<object>>
fetchChunk
method
Waits for operation completion, then fetches up to a specified number of rows from an operation.
Parameters |
---|
options Type: Options used to fetch. Currently, the only option is maxRows, which corresponds to the max number of data objects to be returned in any given array. |
Returns:
Promise<Array<object>>
close
method
Closes the operation and releases all associated resources. Must be done after no longer using operation.
No parameters.
No return value.