Retry Components on failure

Implement retry strategies in your Components to handle transient failures automatically.

tip

New to retry strategies? Start with the Retry Strategies concept guide to understand when and how to use retry configurations.

The following examples show how to configure retry strategies in different component types. For details on configuration levels and strategy parameters, see the Retry Strategies concept guide.

YAML Components

Basic retry with attempts

Simple retry configuration for HTTP Read Components:

read-component-retry.yaml

component:
  read:
    http:
      url: "http://api.example.com/data"
      parser: "json"
  retry_strategy:
    stop_after_attempt: 3

Combined retry limits

Robust retry configuration with both attempt and time limits:

combined-retry.yaml

component:
  read:
    http:
      url: "http://api.example.com/data"
      parser: "json"
  retry_strategy:
    stop_after_attempt: 5
    stop_after_delay: 600

SQL Components

Basic retry with attempt limit

This example retries up to 2 times before failing:

simple_retry.sql

{{ config(partitioned=True, retry_strategy=retry_strategy(stop_after_attempt=2)) }}

SELECT  *
FROM {{ ref("read_local") }}

Combined retry limits

Robust retry configuration with both attempt and time limits:

combined_retry.sql

{{ config(partitioned=True, retry_strategy=retry_strategy(stop_after_attempt=5, stop_after_delay=300)) }}

SELECT  *
FROM {{ ref("your_data") }}

Python Components

Python Components configure retry strategies using parameters inside their respective decorators (e.g. @read, @transform), providing the same flexibility as YAML configurations.

Python Read Component

This example shows retry configuration for a Python Read Component with Incremental processing:

retry.py

from datetime import datetime, timedelta

from ascend.application.context import IncrementalComponentExecutionContext
from ascend.resources import read, retry_strategy
from pandas import to_datetime


@read(strategy="incremental", incremental_strategy="append", retry_strategy=retry_strategy(stop_after_attempt=5))
def market_data_incremental(context: IncrementalComponentExecutionContext):
    import yfinance as yf

    if context.is_incremental:
        current_data = context.current_data()
        date_column = context.data_plane.normalize_identifier("Date")  # This normalizes the letter casing between DuckDB and Snowflake
        start_date = to_datetime(current_data[date_column].max().execute()) + timedelta(days=1)

Python Transform

Transforms can also benefit from retry strategies for handling temporary processing failures:

python-transform-retry.py

import ibis
from ascend.application.context import IncrementalComponentExecutionContext
from ascend.common.events import log
from ascend.models.shared.base import RetryStrategy
from ascend.resources import ref, transform


@transform(inputs=[ref("read_local")], retry_strategy=RetryStrategy(stop_after_attempt=2))
def test_py(read_local: ibis.Table, context: IncrementalComponentExecutionContext) -> ibis.Table:
    # Your transform logic here
    log.info("Reading data from source")
    return read_local

Project-level defaults

You can set default retry behavior for all Components in your Project, which individual Components can override as needed:

ascend_project.yaml

project:
  description: A collection of flows showcasing a variety of capabilities
  version: 0.0.1
  connections: ["connections/"]
  flows: ["flows/"]
  profiles: ["profiles/"]
  tests: ["tests/"]
  vaults: ["vaults/"]
  defaults:
    - kind: Component
      name:
        regex: .*
      spec:
        retry_strategy:
          stop_after_attempt: 3

Individual Components can override these defaults by specifying their own retry_strategy configuration.

Next steps

• Learn more about retry strategies
• Configure Project-level defaults for consistent retry behavior
• Use Profiles to manage environment-specific retry settings