dhara

dhara is a modern continuation of Durus, a persistent object system for applications written in the Python programming language. It could be called a noSQL database. However, it does provide "ACID" properties (Atomicity, Consistency, Isolation, Durability).

The implementation of dhara is not multi-threaded but does provide concurrency via a client/server model. It is optimized for read heavy work loads and aggressively caches persistent objects in memory. For many applications, this design enables good performance with minimal effort from application programmers.

Bodai Ecosystem Role

Dhara is the curator of the Bodai ecosystem — the persistent object storage backend for adapter configs, service lifecycle state, and ecosystem events consumed by Mahavishnu, Akosha, Session-Buddy, Crackerjack, and Oneiric.

Standalone operation is a first-class design goal, not an afterthought — see Standalone use below. For how Dhara fits into the broader Bodai control plane, see the Bodai ecosystem notes.

Standalone Use

Dhara is a member of the Bodai ecosystem and serves there as the curator component — but it is fully usable on its own by any Python application. The Bodai control plane is one set of consumers; your service is not a special case, and you do not need to pull in any other Bodai component to use Dhara.

A standalone install has zero ecosystem dependencies:

uv pip install dhara
dhara db start --file ~/my_app.dhara

Three deployment shapes work without anything Bodai-specific:

• In-process. AsyncFileStorage + AsyncConnection open the database inside your Python process. No server, no socket, no extra runtime.
• Single-host server. dhara db start brings up the storage server on TCP :8685 by default (configurable via --port). Multiple processes on the same machine share one store.
• Distributed server. The same storage protocol across hosts. ACID transactions still serialize through the storage server; clients keep a persistent on-disk cache like ZEO clients do.

Serverless-friendly by design. With AsyncConnection and the asyncio-first API, Dhara fits cleanly into function-as-a-service contexts:

• AWS Lambda / Cloud Run / Vercel functions — use AsyncFileStorage for cold-fast access, or point all functions at a shared managed PostgreSQL.
• Cold-start mitigation — instantiate the storage inside the handler rather than at module scope. Per-connection caches reset to disk on every commit (the same persistent on-disk cache pattern ZEO pioneered, returned for asyncio), so cold starts are cheap.
• DuckDB analytical queries — the DuckDB backend reads from the same store and answers OLAP-shaped questions in one shot, useful for serverless "summarise and return" handlers.
• In-memory Storage backend — useful for unit tests and ephemeral pipelines that don't need to persist anything.

The MCP server (dhara mcp start, default port 8683) is itself optional — if your application does not need an AI/agent surface, skip it. The storage server and the MCP server are independent services.

Why Dhara, Not ZODB/ZEO?

A reasonable first question when you land here is: how does Dhara compare to ZODB and ZEO, the older and more widely-deployed Python object database with a similar design point?

The full feature-by-feature matrix — including a Mermaid diagram of both stacks, the lineage notes, and a "where each one still wins" section — lives in docs/ZODB_COMPARISON.md. The short version for the Bodai use case:

• The MCP layer. ZEO has nothing like this. Dhara exposes a FastMCP server on port 8683 so AI agents (and the rest of the Bodai stack) can read and write persistent state without a Python ZODB client in the loop. Most of the Bodai integration depends on this surface.
• The Oneiric adapter registry role. ZEO is a generic object store. Dhara is the canonical Oneiric adapter config store for the entire Bodai control plane — config for Mahavishnu adapters, Akosha embeddings, and Crackerjack quality gates all live here.
• Single-threaded by design. ZODB runs multi-threaded. Dhara explicitly does not. That is a deliberate trade — most Bodai-shaped workloads are read-heavy with short, infrequent writes that benefit from a simpler concurrency story.
• Modern Python stack. 3.14+ type hints throughout, msgspec for serialization alongside pickle, Oneiric layered config, asyncio-first AsyncConnection. ZODB 5.x is solid and production-proven, but is in maintenance rather than active development.

For non-Bodai workloads the comparison doc also covers the longer answer, including where ZODB/ZEO still wins — most notably ZODB's mature BTrees family of large-index containers (Dhara ships BTree since 0.10.0 but not the full family), and the _p_resolveConflict application-level merge hook for collaborative-edit patterns, which Dhara does not replicate.

A note on "asyncio-first": the AsyncConnection API fits asyncio handlers cleanly, but the storage server itself is single-writer — writes serialize through the server rather than running in parallel. "asyncio-first" here is about the client API shape, not server-side parallelism.

Origin

dhara was originally written by the MEMS Exchange software development team at the Corporation for National Research Initiatives (CNRI). dhara was designed to be the storage component for the Python-powered web sites operated by the MEMS Exchange. See the Acknowledgements section below for the full upstream lineage.

Overview

dhara offers an easy way to use and maintain a consistent collection of object instances used by one or more processes. Access and change of a persistent instances is managed through a cached Connection instance which includes commit() and abort() methods so that changes are transactional.

CLI Commands

Dhara ships a Typer-based unified CLI. All subcommands accept --help.

Top-level subcommands

MCP server lifecycle (dhara mcp ...)

dhara mcp start               # Start the FastMCP server (default :8683)
dhara mcp stop                # Stop it
dhara mcp status              # Is it running?
dhara mcp health              # Health probe
dhara mcp restart             # stop + start
dhara mcp config              # Show resolved MCP settings

Database operations (dhara db ...)

The db subcommand tree is the legacy-compatible interface carried over from the Durus 0.x CLI. Most new code should prefer dhara start --mode=... (see Modes below), but dhara db keeps existing scripts working.

dhara db start                # Start Dhara storage server
dhara db client               # Connect to a running server (interactive IPython)
dhara db pack                 # Reclaim storage space

Common options for database commands:

• --file PATH or -f PATH - Database file path
• --host HOST or -h HOST - Server host (default: 127.0.0.1)
• --port PORT or -p PORT - Server port (default: 8685 for the storage server, 8683 for the MCP server)
• --readonly - Open in read-only mode

Modes

Dhara's startup modes are the recommended way to bring the storage server up. They pre-wire host/port and the default backend so you do not have to memorize the right flags for each scenario.

dhara start --mode=lite         # Zero-config, local SQLite, port 8683
dhara start --mode=standard     # Full feature set, configurable storage, port 8685

The two modes are implemented in dhara/modes/lite.py and dhara/modes/standard.py. See dhara/modes/__init__.py for the mode detection and resolution rules.

Validation

The preferred local validation path is crackerjack (the CLI, not python -m crackerjack):

crackerjack run                # Full quality gate (format + lint + type + tests)
crackerjack run -p minor       # Bump + commit + tag + push + publish (when hooks pass)
crackerjack run -p patch       # Patch-level release
crackerjack doctor             # Diagnostic checks (pre-flight)
crackerjack health             # Health probe

For a single test file or a specific failure, drop down to pytest directly:

pytest tests/unit/test_storage_sqlite.py
pytest -k "test_cache_shrink" -x

When crackerjack run reports issues, fix them via Crackerjack's AI-assisted flow rather than re-running the gates by hand.

Configuration Surfaces

Dhara currently exposes two configuration layers:

• dhara.core.config.DharaSettings is the canonical runtime settings model for the CLI and MCP server
• dhara.config remains available for lightweight dataclass helpers and compatibility with older code

For service startup, operator configuration, and environment-variable overrides, use DharaSettings.

Quick Demo

Start a Dhara server (recommended):

dhara start --mode=lite

This starts the storage server in lite mode — a local SQLite-backed file, listening on 127.0.0.1:8683. Use --mode=standard for the production-shaped configuration (default port 8685).

If you have an existing Durus-style script that still calls dhara db start, that path is preserved under the dhara db ... subcommand tree for compatibility — see CLI Commands above.

Connect as a client:

dhara admin

This opens an interactive IPython shell connected to the running server. You have access to a dictionary-like persistent object, root. If you make changes to items of root and run connection.commit(), the changes are written to the file. If you make changes and then run connection.abort(), the attributes revert back to the values they had at the last commit.

Multiple clients: open a second terminal and run dhara admin again. Committed changes to root in one client are visible in other clients after the next connection.abort() or connection.commit().

Stop the server: Press Control-C in the server terminal.

Persistence example (using the legacy db commands):

# Start server with a persistent file
dhara db start --file test.dhara

# Connect, make changes, commit
dhara db client --file test.dhara
# In the shell:
# >>> root["hello"] = "world"
# >>> connection.commit()

# Stop and restart - data persists
dhara db start --file test.dhara
dhara db client --file test.dhara
# >>> root["hello"]
# 'world'

Direct file access (no server):

dhara db client --file test.dhara

All commands accept --help for more options.

Using dhara in a Program

To use dhara, a Python program needs to make a Storage instance and a Connection instance. For the Storage instance, you have two choices: AsyncFileStorage or ClientStorage. If your program is to be one of several processes accessing a shared collection of objects, then you want ClientStorage. If your program has no competition, then choose AsyncFileStorage. There is only one Connection class, and the constructor takes a storage instance as an argument.

Example using AsyncFileStorage to open an async Connection to a file:

import asyncio
from dhara.core.connection import AsyncConnection
from dhara.storage.async_file import AsyncFileStorage

async def main() -> None:
    storage = AsyncFileStorage("test.dhara")
    await storage.init()
    connection = await AsyncConnection.new(storage)

asyncio.run(main())

Example using ClientStorage to open a Connection to a dhara server:

from dhara.core.connection import Connection
from dhara.storage.client import ClientStorage

connection = Connection(ClientStorage())

Note that the ClientStorage constructor supports the address keyword that you can use to specify the address to use. The value must be either a (host, port) tuple or a string giving a path to use for a unix domain socket. If you provide the address you should be sure to start the storage server the same way. The dhara command line tool also supports options to specify the address.

The connection instance has a get_root() method that you can use to obtain the root object.

In your program, you can make changes to the root object attributes, and call connection.commit() or connection.abort() to lock in or revert changes made since the last commit. The root object is actually an instance of dhara.collections.dict.PersistentDict, which means that it can be used like a regular dict, except that changes will be managed by the Connection. There is a similar class, dhara.collections.list.PersistentList that provides list-like behavior, except managed by the Connection.

PersistentList and PersistentDict both inherit from dhara.core.persistent.Persistent, and this is the key to making your own classes participate in the dhara persistence system. Just add Persistent class A's list of bases, and your instances will know how to manage changes to their attributes through a Connection. To actually store an instance x of A in the storage, though, you need to commit a reference to x in some object that is already stored in the database. The root object is always there, for example, so you can do something like this:

# Assume mymodule defines A as a subclass of Persistent.
from mymodule import A
x = A()
root = connection.get_root() # connection set as shown above.
root["sample"] = x           # root is dict-like
connection.commit()          # Now x is stored.

Subsequent changes to x, or to new A instances put on attributes of X, and so on, will all be managed by the Connection just as for the root object. This management of the Persistent instance continues as long as the instance is in the storage. Sometimes, though, we wish to remove "garbage" Persistent instances from the storage so that the file can be smaller. This garbage collection can be done manually by calling the Connection's pack() method. If you are using a storage server to share a Storage, you can use the gcinterval argument to tell it to take care of garbage collection automatically.

Non-Persistent Containers

When you change an attribute of a Persistent instance, the fact that the instance has been changed is noted with the Connection, so that the Connection knows what instances need to be stored on the next commit(). The same change-tracking occurs automatically when you make dict-like changes to PersistentDict instances or list-like changes to PersistentList instances. If, however, you make changes to a non-persistent container, even if it is the value of an attribute of a Persistent instance, the changes are not automatically noted with the Connection. To make sure that your changes do get saved, you must call the _p_note_change() method of the Persistent instance that refers to the changed non-persistent container. You can see an example of this by looking at the source code of PersistentDict and PersistentList, both of which maintain a non-persistent container on a data attribute, shadow the methods of the underlying container, and add calls to self._p_note_change() in every method that makes changes.

Storage back-ends

Dhara ships several storage backends, all implemented under dhara/storage/. Pick the one that matches your durability and concurrency story.

Removed: FileStorage (the pre-async Durus path-style class) and SHELF-1 are gone. New and migrated code should use AsyncFileStorage (path-style) or AsyncSqliteStorage (URL-style) directly. AsyncFileStorage("test.dhara") and AsyncSqliteStorage("sqlite+aiosqlite:///test.dhara") point at the same database.

Acknowledgements

dhara is a modern fork and continuation of Durus, originally developed by the MEMS Exchange software development team at the Corporation for National Research Initiatives (CNRI). We are grateful for the foundational work done by the original Durus developers.

This modern version (dhara) includes:

• Modern Python 3.14+ type hints
• Enhanced serialization options (msgspec)
• Oneiric configuration and logging integration
• MCP server for modern AI/agent workflows
• Comprehensive security and performance improvements

The name dhara (ध्रुव) is Sanskrit for "immovable, eternal, constant," or "Pole Star" - complementing the original Latin name Durus, meaning "hard, sturdy, tough, enduring."

License

BSD 3-Clause License — see LICENSE in the project root for details.