How to use Deephaven in a local development environment (Python)

Set up your environment

This guide shows you how to create a Python project that uses Deephaven libraries for local development and testing. Because deephaven_enterprise.database resolves Java classes at import time, you must configure both Python packages and the Java classpath.

Prerequisites

• Python 3.10+: Required by the Core+ worker package.
• Java 17+: Set JAVA_HOME to your Java installation.
• Maven: Used to download Core+ JARs.
• Deephaven repository credentials: Required for downloading Core+ artifacts.

Install Python packages

Create a virtual environment and install the required packages:

Copy the requirements file and worker wheel from your Deephaven server, then install version-matched dependencies:

Download Core+ JARs

The deephaven_enterprise.database module requires Core+ Java classes. Use Maven to download the JARs from the Deephaven repository.

First, add your Deephaven credentials to ~/.m2/settings.xml:

Then create a pom.xml to download dependencies:

Then run:

Initialize the JVM

Before importing any deephaven_enterprise modules, initialize the JVM with the Core+ classpath. Create a conftest.py file for pytest:

Local unit testing

It may be helpful to have some local test data you can use to test your query's correctness. The steps below extract query logic into a testable function, then add pytest fixtures and tests for it.

The following query runs on a Deephaven worker and calculates the average and mid prices of stocks for a given day:

The query logic is extracted into helper functions in a separate module. These functions accept a Database parameter instead of importing db directly, making them testable with a mock:

We need some test data. Create the following CSV files:

tests/resources/StockTrades.csv — Sample trade data for the average price test:

tests/resources/StockQuotes.csv — Sample quote data for the mid price test:

1. Add a fixture to conftest.py for the execution context. Merely importing deephaven does not create a working execution context — the default context is only wired up once a Deephaven script session has started, which does not happen here. Instead, build one directly from the same TestExecutionContext the Groovy guide uses, which is self-contained and does not require a running server. The execution context is required for table operations like where and update_view.

2. Add a fixture for a mocked Database. Mocking the Database to read test data from CSV files is easier than creating a real Database instance.

3. Use the methods described in the Core Extract table values guide to test your queries:

The full conftest.py:

The full test file:

Connect to a remote DB

The steps above cover testing query logic locally, without a running Deephaven server. Client applications can also connect to Deephaven server installations to run queries on a remote database. See the Core+ Python Client for more information.

Related documentation

• Execution context
• Extract table values
• Database class