GeezerVison

* Automatically parse API arguments to function arguments

* Choose between strictly match a GraphQL field name or all of them to a function

* Integrates with [Data classes utilities](../../utilities/data_classes.md){target="_blank"} to access resolver and identity information

* Integrates with [Data classes utilities](../../utilities/data_classes.md){target="_blank" rel="nofollow"} to access resolver and identity information

* Works with both Direct Lambda Resolver and Amplify GraphQL Transformer `@function` directive

* Support async Python 3.8+ functions, and generators

**[Direct Lambda Resolver](https://docs.aws.amazon.com/appsync/latest/devguide/direct-lambda-reference.html){target="_blank"}**. A custom AppSync Resolver to bypass the use of Apache Velocity Template (VTL) and automatically map your function's response to a GraphQL field.

**[Direct Lambda Resolver](https://docs.aws.amazon.com/appsync/latest/devguide/direct-lambda-reference.html){target="_blank" rel="nofollow"}**. A custom AppSync Resolver to bypass the use of Apache Velocity Template (VTL) and automatically map your function's response to a GraphQL field.

**[Amplify GraphQL Transformer](https://docs.amplify.aws/cli/graphql-transformer/function){target="_blank"}**. Custom GraphQL directives to define your application's data model using Schema Definition Language (SDL). Amplify CLI uses these directives to convert GraphQL SDL into full descriptive AWS CloudFormation templates.

**[Amplify GraphQL Transformer](https://docs.amplify.aws/cli/graphql-transformer/function){target="_blank" rel="nofollow"}**. Custom GraphQL directives to define your application's data model using Schema Definition Language (SDL). Amplify CLI uses these directives to convert GraphQL SDL into full descriptive AWS CloudFormation templates.

This is the sample infrastructure we are using for the initial examples with a AppSync Direct Lambda Resolver.

???+ tip "Tip: Designing GraphQL Schemas for the first time?"

Visit [AWS AppSync schema documentation](https://docs.aws.amazon.com/appsync/latest/devguide/designing-your-schema.html){target="_blank"} for understanding how to define types, nesting, and pagination.

Visit [AWS AppSync schema documentation](https://docs.aws.amazon.com/appsync/latest/devguide/designing-your-schema.html){target="_blank" rel="nofollow"} for understanding how to define types, nesting, and pagination.

=== "getting_started_schema.graphql"

When working with [AWS AppSync Scalar types](https://docs.aws.amazon.com/appsync/latest/devguide/scalars.html){target="_blank"}, you might want to generate the same values for data validation purposes.

When working with [AWS AppSync Scalar types](https://docs.aws.amazon.com/appsync/latest/devguide/scalars.html){target="_blank" rel="nofollow"}, you might want to generate the same values for data validation purposes.

For convenience, the most commonly used values are available as functions within `scalar_types_utils` module.

Assuming you have [Amplify CLI installed](https://docs.amplify.aws/cli/start/install){target="_blank"}, create a new API using `amplify add api` and use the following GraphQL Schema.

Assuming you have [Amplify CLI installed](https://docs.amplify.aws/cli/start/install){target="_blank" rel="nofollow"}, create a new API using `amplify add api` and use the following GraphQL Schema.

```typescript hl_lines="7 15 20 22" title="Example GraphQL Schema"

--8<-- "examples/event_handler_graphql/src/amplify_graphql_transformer_schema.graphql"

[Create two new basic Python functions](https://docs.amplify.aws/cli/function#set-up-a-function){target="_blank"} via `amplify add function`.

[Create two new basic Python functions](https://docs.amplify.aws/cli/function#set-up-a-function){target="_blank" rel="nofollow"} via `amplify add function`.

???+ note

Amplify CLI generated functions use `Pipenv` as a dependency manager. Your function source code is located at **`amplify/backend/function/your-function-name`**.

You can subclass [AppSyncResolverEvent](../../utilities/data_classes.md#appsync-resolver){target="_blank"} to bring your own set of methods to handle incoming events, by using `data_model` param in the `resolve` method.

You can subclass [AppSyncResolverEvent](../../utilities/data_classes.md#appsync-resolver){target="_blank" rel="nofollow"} to bring your own set of methods to handle incoming events, by using `data_model` param in the `resolve` method.

=== "custom_models.py.py"

???+ tip

Read the **[considerations section for trade-offs between monolithic and micro functions](./api_gateway.md#considerations){target="_blank"}**, as it's also applicable here.

Read the **[considerations section for trade-offs between monolithic and micro functions](./api_gateway.md#considerations){target="_blank" rel="nofollow"}**, as it's also applicable here.

As you grow the number of related GraphQL operations a given Lambda function should handle, it is natural to split them into separate files to ease maintenance - That's when the `Router` feature comes handy.

???+ tip

All examples shared in this documentation are available within the [project repository](https://github.com/aws-powertools/powertools-lambda-python/tree/develop/examples){target="_blank"}.

All examples shared in this documentation are available within the [project repository](https://github.com/aws-powertools/powertools-lambda-python/tree/develop/examples){target="_blank" rel="nofollow"}.

Logger requires two settings:

| **message**: `Any` | `Collecting payment` | Unserializable JSON values are casted as `str` |

| **timestamp**: `str` | `2021-05-03 10:20:19,650+0200` | Timestamp with milliseconds, by default uses local timezone |

| **service**: `str` | `payment` | Service name defined, by default `service_undefined` |

| **xray_trace_id**: `str` | `1-5759e988-bd862e3fe1be46a994272793` | When [tracing is enabled](https://docs.aws.amazon.com/lambda/latest/dg/services-xray.html){target="_blank"}, it shows X-Ray Trace ID |

| **xray_trace_id**: `str` | `1-5759e988-bd862e3fe1be46a994272793` | When [tracing is enabled](https://docs.aws.amazon.com/lambda/latest/dg/services-xray.html){target="_blank" rel="nofollow"}, it shows X-Ray Trace ID |

| **sampling_rate**: `float` | `0.1` | When enabled, it shows sampling rate in percentage e.g. 10% |

| **exception_name**: `str` | `ValueError` | When `logger.exception` is used and there is an exception |

| **exception**: `str` | `Traceback (most recent call last)..` | When `logger.exception` is used and there is an exception |

You can set a Correlation ID using `correlation_id_path` param by passing a [JMESPath expression](https://jmespath.org/tutorial.html){target="_blank"}.

You can set a Correlation ID using `correlation_id_path` param by passing a [JMESPath expression](https://jmespath.org/tutorial.html){target="_blank" rel="nofollow"}.

???+ tip

You can retrieve correlation IDs via `get_correlation_id` method

You can also use `set_correlation_id` method to inject it anywhere else in your code. Example below uses [Event Source Data Classes utility](../utilities/data_classes.md){target="_blank"} to easily access events properties.

You can also use `set_correlation_id` method to inject it anywhere else in your code. Example below uses [Event Source Data Classes utility](../utilities/data_classes.md){target="_blank" rel="nofollow"} to easily access events properties.

=== "set_correlation_id_method.py"

???+ warning

`append_keys` is not thread-safe, please see [RFC](https://github.com/aws-powertools/powertools-lambda-python/issues/991){target="_blank"}.

`append_keys` is not thread-safe, please see [RFC](https://github.com/aws-powertools/powertools-lambda-python/issues/991){target="_blank" rel="nofollow"}.

You can append your own keys to your existing Logger via `append_keys(**additional_key_values)` method.

Logger is commonly initialized in the global scope. Due to [Lambda Execution Context reuse](https://docs.aws.amazon.com/lambda/latest/dg/runtimes-context.html){target="_blank"}, this means that custom keys can be persisted across invocations. If you want all custom keys to be deleted, you can use `clear_state=True` param in `inject_lambda_context` decorator.

Logger is commonly initialized in the global scope. Due to [Lambda Execution Context reuse](https://docs.aws.amazon.com/lambda/latest/dg/runtimes-context.html){target="_blank" rel="nofollow"}, this means that custom keys can be persisted across invocations. If you want all custom keys to be deleted, you can use `clear_state=True` param in `inject_lambda_context` decorator.

???+ tip "Tip: When is this useful?"

It is useful when you add multiple custom keys conditionally, instead of setting a default `None` value if not present. Any key with `None` value is automatically removed by Logger.

??? question "What are uncaught exceptions?"

It's any raised exception that wasn't handled by the [`except` statement](https://docs.python.org/3.9/tutorial/errors.html#handling-exceptions){target="_blank"}, leading a Python program to a non-successful exit.

It's any raised exception that wasn't handled by the [`except` statement](https://docs.python.org/3.9/tutorial/errors.html#handling-exceptions){target="_blank" rel="nofollow"}, leading a Python program to a non-successful exit.

They are typically raised intentionally to signal a problem (`raise ValueError`), or a propagated exception from elsewhere in your code that you didn't handle it willingly or not (`KeyError`, `jsonDecoderError`, etc.).

You can easily change the date format using one of the following parameters:

* **`datefmt`**. You can pass any [strftime format codes](https://strftime.org/){target="_blank"}. Use `%F` if you need milliseconds.

* **`datefmt`**. You can pass any [strftime format codes](https://strftime.org/){target="_blank" rel="nofollow"}. Use `%F` if you need milliseconds.

* **`use_rfc3339`**. This flag will use a format compliant with both RFC3339 and ISO8601: `2022-10-27T16:27:43.738+02:00`

???+ tip "Prefer using [datetime string formats](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes){target="_blank"}?"

???+ tip "Prefer using [datetime string formats](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes){target="_blank" rel="nofollow"}?"

Use `use_datetime_directive` flag along with `datefmt` to instruct Logger to use `datetime` instead of `time.strftime`.

=== "date_formatting.py"

Similar to [Tracer](./tracer.md#reusing-tracer-across-your-code){target="_blank"}, a new instance that uses the same `service` name - env var or explicit parameter - will reuse a previous Logger instance. Just like `logging.getLogger("logger_name")` would in the standard library if called with the same logger name.

Similar to [Tracer](./tracer.md#reusing-tracer-across-your-code){target="_blank" rel="nofollow"}, a new instance that uses the same `service` name - env var or explicit parameter - will reuse a previous Logger instance. Just like `logging.getLogger("logger_name")` would in the standard library if called with the same logger name.

Notice in the CloudWatch Logs output how `payment_id` appeared as expected when logging in `collect.py`.

Sampling decision happens at the Logger initialization. This means sampling may happen significantly more or less than depending on your traffic patterns, for example a steady low number of invocations and thus few cold starts.

???+ note

Open a [feature request](https://github.com/aws-powertools/powertools-lambda-python/issues/new?assignees=&labels=feature-request%2C+triage&template=feature_request.md&title=){target="_blank"} if you want Logger to calculate sampling for every invocation

Open a [feature request](https://github.com/aws-powertools/powertools-lambda-python/issues/new?assignees=&labels=feature-request%2C+triage&template=feature_request.md&title=){target="_blank" rel="nofollow"} if you want Logger to calculate sampling for every invocation

=== "sampling_debug_logs.py"

!!! note "In this context, an observability provider is an [AWS Lambda Partner](https://go.aws/3HtU6CZ){target="_blank"} offering a platform for logging, metrics, traces, etc."

!!! note "In this context, an observability provider is an [AWS Lambda Partner](https://go.aws/3HtU6CZ){target="_blank" rel="nofollow"} offering a platform for logging, metrics, traces, etc."

You can send logs to the observability provider of your choice via [Lambda Extensions](https://aws.amazon.com/blogs/compute/using-aws-lambda-extensions-to-send-logs-to-custom-destinations/){target="_blank"}. In most cases, you shouldn't need any custom Logger configuration, and logs will be shipped async without any performance impact.

You can send logs to the observability provider of your choice via [Lambda Extensions](https://aws.amazon.com/blogs/compute/using-aws-lambda-extensions-to-send-logs-to-custom-destinations/){target="_blank" rel="nofollow"}. In most cases, you shouldn't need any custom Logger configuration, and logs will be shipped async without any performance impact.

By default, Logger uses `json.dumps` and `json.loads` as serializer and deserializer respectively. There could be scenarios where you are making use of alternative JSON libraries like [orjson](https://github.com/ijl/orjson){target="_blank"}.

By default, Logger uses `json.dumps` and `json.loads` as serializer and deserializer respectively. There could be scenarios where you are making use of alternative JSON libraries like [orjson](https://github.com/ijl/orjson){target="_blank" rel="nofollow"}.

As parameters don't always translate well between them, you can pass any callable that receives a `dict` and return a `str`:

```

???+ tip

Check out the built-in [Pytest caplog fixture](https://docs.pytest.org/en/latest/how-to/logging.html){target="_blank"} to assert plain log messages

Check out the built-in [Pytest caplog fixture](https://docs.pytest.org/en/latest/how-to/logging.html){target="_blank" rel="nofollow"} to assert plain log messages

The Python standard library log records contains a [large set of attributes](https://docs.python.org/3/library/logging.html#logrecord-attributes){target="_blank"}, however only a few are included in Powertools for AWS Lambda (Python) Logger log record by default.

The Python standard library log records contains a [large set of attributes](https://docs.python.org/3/library/logging.html#logrecord-attributes){target="_blank" rel="nofollow"}, however only a few are included in Powertools for AWS Lambda (Python) Logger log record by default.

You can include any of these logging attributes as key value arguments (`kwargs`) when instantiating `Logger` or `LambdaPowertoolsFormatter`.

As of now, ElasticSearch (ELK) or 3rd party solutions are best suited to this task. Please refer to this [discussion for more details](https://github.com/aws-powertools/powertools-lambda-python/issues/460){target="_blank"}

As of now, ElasticSearch (ELK) or 3rd party solutions are best suited to this task. Please refer to this [discussion for more details](https://github.com/aws-powertools/powertools-lambda-python/issues/460){target="_blank" rel="nofollow"}