Async patterns

Same helper shape, real concurrency.

Every data function has an async counterpart. The async versions are the canonical implementation; sync helpers delegate to the same Rust worker pools.

xbbg v1 is async-first. Every data function has an async counterpart: bdp() → abdp(), bdh() → abdh(), bdib() → abdib(), and so on.

Architecture

Under the hood, xbbg runs a Rust async engine with pre-warmed worker pools. Each request worker holds an independent Bloomberg session, and concurrent calls are dispatched round-robin:

┌─────────────────────────────────────────────────┐
│                   xbbg Engine                   │
│                                                 │
│  ┌──────────────────────┐  ┌─────────────────┐  │
│  │  Request Worker Pool │  │ Subscription    │  │
│  │  (request_pool_size) │  │ Session Pool    │  │
│  │                      │  │ (sub_pool_size) │  │
│  │  Worker 1 ──session  │  │                 │  │
│  │  Worker 2 ──session  │  │  Session 1      │  │
│  │  ...                 │  │  ...            │  │
│  └──────────────────────┘  └─────────────────┘  │
│           │ round-robin          │ isolated      │
│           ▼                      ▼               │
│    bdp/bdh/bds/bdib       subscribe/stream       │
└─────────────────────────────────────────────────┘

With request_pool_size=2 (the default), two Bloomberg requests can run in parallel at the session level. Raising the pool size allows more simultaneous requests.

Sync/async function mapping

Sync	Async
bdp()	abdp()
bdh()	abdh()
bds()	abds()
bdib()	abdib()
bdtick()	abdtick()
request()	arequest()

All async functions accept identical parameters to their sync equivalents and return the same DataFrame types.

Basic usage in scripts

In a plain script or application (no existing event loop), use asyncio.run():

import asyncio
from xbbg import blp

async def get_data():
    df = await blp.abdp(tickers='AAPL US Equity', flds=['PX_LAST', 'VOLUME'])
    return df

data = asyncio.run(get_data())

Concurrent requests

asyncio.gather() runs multiple Bloomberg requests in parallel on a single thread. Requests are dispatched across the worker pool simultaneously rather than sequentially:

import asyncio
from xbbg import blp

async def get_multiple():
    results = await asyncio.gather(
        blp.abdp(tickers='AAPL US Equity', flds=['PX_LAST']),
        blp.abdp(tickers='MSFT US Equity', flds=['PX_LAST']),
        blp.abdh(tickers='GOOGL US Equity', flds=['PX_LAST'], start_date='2024-01-01'),
    )
    return results  # list of DataFrames, one per awaitable

results = asyncio.run(get_multiple())

The three requests above are dispatched at once. With the default request_pool_size=2, two run immediately and the third waits for a free worker. Increase the pool size to raise the concurrency ceiling:

from xbbg import configure

configure(request_pool_size=4)

Jupyter notebooks

Jupyter and VS Code Interactive run an IPykernel event loop. For one-shot request/response APIs, the sync wrappers keep the familiar Bloomberg syntax working by using a notebook-only background event-loop bridge:

from xbbg import blp

# Sync one-shot calls work in notebook cells
df = blp.bdp(tickers='AAPL US Equity', flds=['PX_LAST', 'VOLUME'])
hist = blp.bdh(tickers='AAPL US Equity', flds=['PX_LAST'], start_date='2024-01-01')

The bridge applies to bdp, bdh, bds, bdib, bdtick, and request. It preserves scoped xbbg.Engine(...) context across the background thread and propagates exceptions back to the cell.

If you are already writing async notebook code, keep using top-level await:

from xbbg import blp

df = await blp.abdp(tickers='AAPL US Equity', flds=['PX_LAST', 'VOLUME'])

WARNING

The notebook bridge is not a generic async escape hatch. FastAPI, ASGI services, task workers, and other async applications should use the a-prefixed APIs directly. Streaming and long-lived lifecycle APIs also remain explicitly async-aware.

GIL release

The Rust engine releases the Python GIL while waiting for Bloomberg I/O. This means Python threads can execute concurrently during a Bloomberg request — CPU-bound Python work is not blocked while requests are in flight. In practice:

• A asyncio.gather() over multiple Bloomberg calls does not serialize behind the GIL.
• Threads running independent Python work alongside Bloomberg requests are not stalled waiting for network responses.

This is a property of the Rust/PyO3 integration, not Python's asyncio scheduler.

Error handling

All xbbg exceptions inherit from BlpError, which lets you catch any Bloomberg-related failure with a single clause. The hierarchy:

Exception	When raised
BlpSessionError	Session start, connect, or service open failure
BlpRequestError	Request-level error returned by Bloomberg
BlpSecurityError	Invalid or inaccessible security identifier
BlpFieldError	Invalid or inaccessible field name
BlpTimeoutError	Request timed out
BlpValidationError	Field validation failure (strict mode)
BlpInternalError	Internal engine error

Single request

import asyncio
from xbbg import blp
from xbbg.exceptions import BlpSecurityError, BlpError

async def safe_fetch(ticker: str):
    try:
        return await blp.abdp(tickers=ticker, flds=['PX_LAST'])
    except BlpSecurityError as e:
        print(f"Unknown security: {ticker} — {e}")
        return None
    except BlpError as e:
        print(f"Bloomberg error: {e}")
        raise

asyncio.run(safe_fetch('INVALID US Equity'))

Concurrent requests with partial failures

By default, asyncio.gather() cancels all pending awaitables and re-raises the first exception. Pass return_exceptions=True to collect results and errors together instead:

import asyncio
from xbbg import blp
from xbbg.exceptions import BlpError

async def fetch_all(tickers: list[str]):
    results = await asyncio.gather(
        *[blp.abdp(tickers=t, flds=['PX_LAST']) for t in tickers],
        return_exceptions=True,
    )
    for ticker, result in zip(tickers, results):
        if isinstance(result, BlpError):
            print(f"Failed: {ticker} — {result}")
        else:
            print(f"OK: {ticker}, rows={len(result)}")

asyncio.run(fetch_all(['AAPL US Equity', 'INVALID', 'MSFT US Equity']))

WARNING

With return_exceptions=True, exceptions are returned as values in the results list — they are not raised. Check each result with isinstance(result, Exception) before using it.

Best practices

Batch tickers in a single call instead of many parallel calls.abdp() and abdh() accept a list of tickers. A single call with ['AAPL US Equity', 'MSFT US Equity', 'GOOGL US Equity'] is more efficient than three parallel calls — it sends one Bloomberg request and Bloomberg returns results for all tickers together.

# Preferred: one request, many tickers
df = await blp.abdp(
    tickers=['AAPL US Equity', 'MSFT US Equity', 'GOOGL US Equity'],
    flds=['PX_LAST', 'VOLUME'],
)

# Use gather when requests are genuinely different (different fields, dates, services)
results = await asyncio.gather(
    blp.abdp(tickers=['AAPL US Equity', 'MSFT US Equity'], flds=['PX_LAST']),
    blp.abdh(tickers='AAPL US Equity', flds=['PX_LAST'], start_date='2024-01-01'),
)

Use async when integrating with async frameworks. If you are building a FastAPI service, ASGI app, async task queue, or other async application, prefer the a-prefixed functions — they participate in the event loop without blocking it. Sync wrappers still raise in these generic async contexts and direct you to await abdp(...), await abdh(...), and the other async APIs.

Sync functions work in scripts and notebooks. If your script is sequential and you have no async requirements, the sync functions (bdp, bdh, bdib, etc.) are fully supported. In IPykernel notebooks and VS Code Interactive, one-shot sync calls use a background loop bridge; outside notebooks, sync wrappers use asyncio.run().

Match pool size to workload. The default request_pool_size=2 is appropriate for most workloads. If you are issuing many concurrent gather() calls with large fan-outs, raise it — but Bloomberg API servers also have per-connection limits, so test before committing to a large value.

For the full API reference, see Bloomberg Data API.