| title | description |
|---|---|
Logger |
Core utility |
Logger provides an opinionated logger with output structured as JSON.
- Capture key fields from Lambda context, cold start and structures logging output as JSON
- Log Lambda event when instructed (disabled by default)
- Log sampling enables DEBUG log level for a percentage of requests (disabled by default)
- Append additional keys to structured log at any point in time
Logger requires two settings:
| Setting | Description | Environment variable | Constructor parameter |
|---|---|---|---|
| Logging level | Sets how verbose Logger should be (INFO, by default) | LOG_LEVEL |
level |
| Service | Sets service key that will be present across all log statements | POWERTOOLS_SERVICE_NAME |
service |
???+ example AWS Serverless Application Model (SAM)
=== "template.yaml"
```yaml hl_lines="9 10"
Resources:
HelloWorldFunction:
Type: AWS::Serverless::Function
Properties:
Runtime: python3.8
Environment:
Variables:
LOG_LEVEL: INFO
POWERTOOLS_SERVICE_NAME: example
```
=== "app.py"
```python hl_lines="2 4"
from aws_lambda_powertools import Logger
logger = Logger() # Sets service via env var
# OR logger = Logger(service="example")
```
Your Logger will include the following keys to your structured logging:
| Key | Example | Note |
|---|---|---|
level: str |
INFO |
Logging level |
location: str |
collect.handler:1 |
Source code location where statement was executed |
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{target="_blank"}, 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 enrich your structured logs with key Lambda context information via inject_lambda_context.
=== "collect.py"
```python hl_lines="5"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
@logger.inject_lambda_context
def handler(event, context):
logger.info("Collecting payment")
# You can log entire objects too
logger.info({
"operation": "collect_payment",
"charge_id": event['charge_id']
})
...
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="7-11 16-19"
{
"level": "INFO",
"location": "collect.handler:7",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"cold_start": true,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72"
},
{
"level": "INFO",
"location": "collect.handler:10",
"message": {
"operation": "collect_payment",
"charge_id": "ch_AZFlk2345C0"
},
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"cold_start": true,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72"
}
```
When used, this will include the following keys:
| Key | Example |
|---|---|
cold_start: bool |
false |
function_name str |
example-powertools-HelloWorldFunction-1P1Z6B39FLU73 |
function_memory_size: int |
128 |
function_arn: str |
arn:aws:lambda:eu-west-1:012345678910:function:example-powertools-HelloWorldFunction-1P1Z6B39FLU73 |
function_request_id: str |
899856cb-83d1-40d7-8611-9e78f15f32f4 |
When debugging in non-production environments, you can instruct Logger to log the incoming event with log_event param or via POWERTOOLS_LOGGER_LOG_EVENT env var.
???+ warning This is disabled by default to prevent sensitive info being logged
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
@logger.inject_lambda_context(log_event=True)
def handler(event, context):
...You can set a Correlation ID using correlation_id_path param by passing a JMESPath expression{target="_blank"}.
???+ tip
You can retrieve correlation IDs via get_correlation_id method
=== "collect.py"
```python hl_lines="5"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
@logger.inject_lambda_context(correlation_id_path="headers.my_request_id_header")
def handler(event, context):
logger.debug(f"Correlation ID => {logger.get_correlation_id()}")
logger.info("Collecting payment")
```
=== "Example Event"
```json hl_lines="3"
{
"headers": {
"my_request_id_header": "correlation_id_value"
}
}
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="12"
{
"level": "INFO",
"location": "collect.handler:7",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"cold_start": true,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72",
"correlation_id": "correlation_id_value"
}
```
We provide built-in JMESPath expressions for known event sources, where either a request ID or X-Ray Trace ID are present.
=== "collect.py"
```python hl_lines="2 6"
from aws_lambda_powertools import Logger
from aws_lambda_powertools.logging import correlation_paths
logger = Logger(service="payment")
@logger.inject_lambda_context(correlation_id_path=correlation_paths.API_GATEWAY_REST)
def handler(event, context):
logger.debug(f"Correlation ID => {logger.get_correlation_id()}")
logger.info("Collecting payment")
```
=== "Example Event"
```json hl_lines="3"
{
"requestContext": {
"requestId": "correlation_id_value"
}
}
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="12"
{
"level": "INFO",
"location": "collect.handler:8",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"cold_start": true,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72",
"correlation_id": "correlation_id_value"
}
```
???+ info "Info: Custom keys are persisted across warm invocations"
Always set additional keys as part of your handler to ensure they have the latest value, or explicitly clear them with clear_state=True.
You can append additional keys using either mechanism:
- Persist new keys across all future log messages via
append_keysmethod - Add additional keys on a per log message basis via
extraparameter
???+ note
append_keys replaces structure_logs(append=True, **kwargs) method. structure_logs will be removed in v2.
You can append your own keys to your existing Logger via append_keys(**additional_key_values) method.
=== "collect.py"
```python hl_lines="9"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
def handler(event, context):
order_id = event.get("order_id")
# this will ensure order_id key always has the latest value before logging
logger.append_keys(order_id=order_id)
logger.info("Collecting payment")
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="7"
{
"level": "INFO",
"location": "collect.handler:11",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"order_id": "order_id_value"
}
```
???+ tip "Tip: Logger will automatically reject any key with a None value" If you conditionally add keys depending on the payload, you can follow the example above.
This example will add `order_id` if its value is not empty, and in subsequent invocations where `order_id` might not be present it'll remove it from the Logger.
Extra parameter is available for all log levels' methods, as implemented in the standard logging library - e.g. logger.info, logger.warning.
It accepts any dictionary, and all keyword arguments will be added as part of the root structure of the logs for that log statement.
???+ info
Any keyword argument added using extra will not be persisted for subsequent messages.
=== "extra_parameter.py"
```python hl_lines="6"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
fields = { "request_id": "1123" }
logger.info("Collecting payment", extra=fields)
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="7"
{
"level": "INFO",
"location": "collect.handler:6",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"request_id": "1123"
}
```
You can set a correlation_id to your existing Logger via set_correlation_id(value) method by passing any string value.
=== "collect.py"
```python hl_lines="6"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
def handler(event, context):
logger.set_correlation_id(event["requestContext"]["requestId"])
logger.info("Collecting payment")
```
=== "Example Event"
```json hl_lines="3"
{
"requestContext": {
"requestId": "correlation_id_value"
}
}
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="7"
{
"level": "INFO",
"location": "collect.handler:7",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"correlation_id": "correlation_id_value"
}
```
Alternatively, you can combine Data Classes utility with Logger to use dot notation object:
=== "collect.py"
```python hl_lines="2 7-8"
from aws_lambda_powertools import Logger
from aws_lambda_powertools.utilities.data_classes import APIGatewayProxyEvent
logger = Logger(service="payment")
def handler(event, context):
event = APIGatewayProxyEvent(event)
logger.set_correlation_id(event.request_context.request_id)
logger.info("Collecting payment")
```
=== "Example Event"
```json hl_lines="3"
{
"requestContext": {
"requestId": "correlation_id_value"
}
}
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="7"
{
"timestamp": "2020-05-24 18:17:33,774",
"level": "INFO",
"location": "collect.handler:9",
"service": "payment",
"sampling_rate": 0.0,
"correlation_id": "correlation_id_value",
"message": "Collecting payment"
}
```
You can remove any additional key from Logger state using remove_keys.
=== "collect.py"
```python hl_lines="9"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
def handler(event, context):
logger.append_keys(sample_key="value")
logger.info("Collecting payment")
logger.remove_keys(["sample_key"])
logger.info("Collecting payment without sample key")
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="7"
{
"level": "INFO",
"location": "collect.handler:7",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"sample_key": "value"
},
{
"level": "INFO",
"location": "collect.handler:10",
"message": "Collecting payment without sample key",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment"
}
```
Logger is commonly initialized in the global scope. Due to Lambda Execution Context reuse, 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.
???+ danger "Danger: This can have unintended side effects if you use Layers" Lambda Layers code is imported before the Lambda handler.
This means that `clear_state=True` will instruct Logger to remove any keys previously added before Lambda handler execution proceeds.
You can either avoid running any code as part of Lambda Layers global scope, or override keys with their latest value as part of handler's execution.
=== "collect.py"
```python hl_lines="5 8"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
@logger.inject_lambda_context(clear_state=True)
def handler(event, context):
if event.get("special_key"):
# Should only be available in the first request log
# as the second request doesn't contain `special_key`
logger.append_keys(debugging_key="value")
logger.info("Collecting payment")
```
=== "#1 request"
```json hl_lines="7"
{
"level": "INFO",
"location": "collect.handler:10",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"special_key": "debug_key",
"cold_start": true,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72"
}
```
=== "#2 request"
```json hl_lines="7"
{
"level": "INFO",
"location": "collect.handler:10",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"cold_start": false,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72"
}
```
Use logger.exception method to log contextual information about exceptions. Logger will include exception_name and exception keys to aid troubleshooting and error enumeration.
???+ tip
You can use your preferred Log Analytics tool to enumerate and visualize exceptions across all your services using exception_name key.
=== "collect.py"
```python hl_lines="8"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
try:
raise ValueError("something went wrong")
except Exception:
logger.exception("Received an exception")
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="7-8"
{
"level": "ERROR",
"location": "collect.handler:5",
"message": "Received an exception",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"exception_name": "ValueError",
"exception": "Traceback (most recent call last):\n File \"<input>\", line 2, in <module>\nValueError: something went wrong"
}
```
You can use any of the following built-in JMESPath expressions as part of inject_lambda_context decorator.
???+ note "Note: Any object key named with - must be escaped"
For example, request.headers."x-amzn-trace-id".
| Name | Expression | Description |
|---|---|---|
| API_GATEWAY_REST | "requestContext.requestId" |
API Gateway REST API request ID |
| API_GATEWAY_HTTP | "requestContext.requestId" |
API Gateway HTTP API request ID |
| APPSYNC_RESOLVER | 'request.headers."x-amzn-trace-id"' |
AppSync X-Ray Trace ID |
| APPLICATION_LOAD_BALANCER | 'headers."x-amzn-trace-id"' |
ALB X-Ray Trace ID |
| EVENT_BRIDGE | "id" |
EventBridge Event ID |
Logger supports inheritance via child parameter. This allows you to create multiple Loggers across your code base, and propagate changes such as new keys to all Loggers.
=== "collect.py"
```python hl_lines="1 7"
import shared # Creates a child logger named "payment.shared"
from aws_lambda_powertools import Logger
logger = Logger() # POWERTOOLS_SERVICE_NAME: "payment"
def handler(event, context):
shared.inject_payment_id(event)
...
```
=== "shared.py"
```python hl_lines="6"
from aws_lambda_powertools import Logger
logger = Logger(child=True) # POWERTOOLS_SERVICE_NAME: "payment"
def inject_payment_id(event):
logger.structure_logs(append=True, payment_id=event.get("payment_id"))
```
In this example, Logger will create a parent logger named payment and a child logger named payment.shared. Changes in either parent or child logger will be propagated bi-directionally.
???+ info "Info: Child loggers will be named after the following convention {service}.{filename}"
If you forget to use child param but the service name is the same of the parent, we will return the existing parent Logger instead.
Use sampling when you want to dynamically change your log level to DEBUG based on a percentage of your concurrent/cold start invocations.
You can use values ranging from 0.0 to 1 (100%) when setting POWERTOOLS_LOGGER_SAMPLE_RATE env var or sample_rate parameter in Logger.
???+ tip "Tip: When is this useful?" Let's imagine a sudden spike increase in concurrency triggered a transient issue downstream. When looking into the logs you might not have enough information, and while you can adjust log levels it might not happen again.
This feature takes into account transient issues where additional debugging information can be useful.
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 if you want Logger to calculate sampling for every invocation
=== "collect.py"
```python hl_lines="4 7"
from aws_lambda_powertools import Logger
# Sample 10% of debug logs e.g. 0.1
logger = Logger(service="payment", sample_rate=0.1)
def handler(event, context):
logger.debug("Verifying whether order_id is present")
logger.info("Collecting payment")
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="2 4 12 15 25"
{
"level": "DEBUG",
"location": "collect.handler:7",
"message": "Verifying whether order_id is present",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"cold_start": true,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72",
"sampling_rate": 0.1
},
{
"level": "INFO",
"location": "collect.handler:7",
"message": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494+0200",
"service": "payment",
"cold_start": true,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72",
"sampling_rate": 0.1
}
```
Logger propagates a few formatting configurations to the built-in LambdaPowertoolsFormatter logging formatter.
If you prefer configuring it separately, or you'd want to bring this JSON Formatter to another application, these are the supported settings:
| Parameter | Description | Default |
|---|---|---|
json_serializer |
function to serialize obj to a JSON formatted str |
json.dumps |
json_deserializer |
function to deserialize str, bytes, bytearray containing a JSON document to a Python obj |
json.loads |
json_default |
function to coerce unserializable values, when no custom serializer/deserializer is set | str |
datefmt |
string directives (strftime) to format log timestamp | %Y-%m-%d %H:%M:%S,%F%z, where %F is a custom ms directive |
utc |
set logging timestamp to UTC | False |
log_record_order |
set order of log keys when logging | ["level", "location", "message", "timestamp"] |
kwargs |
key-value to be included in log messages | None |
from aws_lambda_powertools import Logger
from aws_lambda_powertools.logging.formatter import LambdaPowertoolsFormatter
formatter = LambdaPowertoolsFormatter(utc=True, log_record_order=["message"])
logger = Logger(service="example", logger_formatter=formatter)If you're migrating from other Loggers, there are few key points to be aware of: Service parameter, Inheriting Loggers, Overriding Log records, and Logging exceptions.
Service is what defines the Logger name, including what the Lambda function is responsible for, or part of (e.g payment service).
For Logger, the service is the logging key customers can use to search log operations for one or more functions - For example, search for all errors, or messages like X, where service is payment.
Python Logging hierarchy happens via the dot notation:
service,service.child,service.child_2
For inheritance, Logger uses a child=True parameter along with service being the same value across Loggers.
For child Loggers, we introspect the name of your module where Logger(child=True, service="name") is called, and we name your Logger as {service}.{filename}.
???+ danger
A common issue when migrating from other Loggers is that service might be defined in the parent Logger (no child param), and not defined in the child Logger:
=== "incorrect_logger_inheritance.py"
```python hl_lines="4 10"
import my_module
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
...
# my_module.py
from aws_lambda_powertools import Logger
logger = Logger(child=True)
```
=== "correct_logger_inheritance.py"
```python hl_lines="4 10"
import my_module
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
...
# my_module.py
from aws_lambda_powertools import Logger
logger = Logger(service="payment", child=True)
```
In this case, Logger will register a Logger named payment, and a Logger named service_undefined. The latter isn't inheriting from the parent, and will have no handler, resulting in no message being logged to standard output.
???+ tip
This can be fixed by either ensuring both has the service value as payment, or simply use the environment variable POWERTOOLS_SERVICE_NAME to ensure service value will be the same across all Loggers when not explicitly set.
You might want to continue to use the same date formatting style, or override location to display the package.function_name:line_number as you previously had.
Logger allows you to either change the format or suppress the following keys altogether at the initialization: location, timestamp, level, xray_trace_id.
=== "lambda_handler.py" > We honour standard logging library string formats{target="_blank"}.
```python hl_lines="7 10"
from aws_lambda_powertools import Logger
date_format = "%m/%d/%Y %I:%M:%S %p"
location_format = "[%(funcName)s] %(module)s"
# override location and timestamp format
logger = Logger(service="payment", location=location_format, datefmt=date_format)
# suppress the location key with a None value
logger_two = Logger(service="payment", location=None)
logger.info("Collecting payment")
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="3 5"
{
"level": "INFO",
"location": "[<module>] lambda_handler",
"message": "Collecting payment",
"timestamp": "02/09/2021 09:25:17 AM",
"service": "payment"
}
```
You can change the order of standard Logger keys or any keys that will be appended later at runtime via the log_record_order parameter.
=== "lambda_handler.py"
```python hl_lines="4 7"
from aws_lambda_powertools import Logger
# make message as the first key
logger = Logger(service="payment", log_record_order=["message"])
# make request_id that will be added later as the first key
# Logger(service="payment", log_record_order=["request_id"])
# Default key sorting order when omit
# Logger(service="payment", log_record_order=["level","location","message","timestamp"])
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="3 5"
{
"message": "hello world",
"level": "INFO",
"location": "[<module>]:6",
"timestamp": "2021-02-09 09:36:12,280",
"service": "service_undefined",
"sampling_rate": 0.0
}
```
By default, this Logger and standard logging library emits records using local time timestamp. You can override this behaviour via utc parameter:
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
logger.info("Local time")
logger_in_utc = Logger(service="payment", utc=True)
logger_in_utc.info("GMT time zone")By default, Logger uses str to handle values non-serializable by JSON. You can override this behaviour via json_default parameter by passing a Callable:
=== "collect.py"
```python hl_lines="3-4 9 12"
from aws_lambda_powertools import Logger
def custom_json_default(value):
return f"<non-serializable: {type(value).__name__}>"
class Unserializable:
pass
logger = Logger(service="payment", json_default=custom_json_default)
def handler(event, context):
logger.info(Unserializable())
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="4"
{
"level": "INFO",
"location": "collect.handler:8",
"message": ""<non-serializable: Unserializable>"",
"timestamp": "2021-05-03 15:17:23,632+0200",
"service": "payment"
}
```
By default, Logger uses StreamHandler and logs to standard output. You can override this behaviour via logger_handler parameter:
import logging
from pathlib import Path
from aws_lambda_powertools import Logger
log_file = Path("/tmp/log.json")
log_file_handler = logging.FileHandler(filename=log_file)
logger = Logger(service="payment", logger_handler=log_file_handler)
logger.info("Collecting payment")By default, Logger uses LambdaPowertoolsFormatter that persists its custom structure between non-cold start invocations. There could be scenarios where the existing feature set isn't sufficient to your formatting needs.
For minor changes like remapping keys after all log record processing has completed, you can override serialize method from LambdaPowertoolsFormatter:
=== "custom_formatter.py"
```python hl_lines="6-7 12"
from aws_lambda_powertools import Logger
from aws_lambda_powertools.logging.formatter import LambdaPowertoolsFormatter
from typing import Dict
class CustomFormatter(LambdaPowertoolsFormatter):
def serialize(self, log: Dict) -> str:
"""Serialize final structured log dict to JSON str"""
log["event"] = log.pop("message") # rename message key to event
return self.json_serializer(log) # use configured json serializer
logger = Logger(service="example", logger_formatter=CustomFormatter())
logger.info("hello")
```
=== "Example CloudWatch Logs excerpt"
json hl_lines="5" { "level": "INFO", "location": "<module>:16", "timestamp": "2021-12-30 13:41:53,413+0100", "event": "hello" }
For replacing the formatter entirely, you can subclass BasePowertoolsFormatter, implement append_keys method, and override format standard logging method. This ensures the current feature set of Logger like injecting Lambda context and sampling will continue to work.
???+ info
You might need to implement remove_keys method if you make use of the feature too.
=== "collect.py"
```python hl_lines="2 4 7 12 16 27"
from aws_lambda_powertools import Logger
from aws_lambda_powertools.logging.formatter import BasePowertoolsFormatter
class CustomFormatter(BasePowertoolsFormatter):
custom_format = {} # arbitrary dict to hold our structured keys
def append_keys(self, **additional_keys):
# also used by `inject_lambda_context` decorator
self.custom_format.update(additional_keys)
# Optional unless you make use of this Logger feature
def remove_keys(self, keys: Iterable[str]):
for key in keys:
self.custom_format.pop(key, None)
def format(self, record: logging.LogRecord) -> str: # noqa: A003
"""Format logging record as structured JSON str"""
return json.dumps(
{
"event": super().format(record),
"timestamp": self.formatTime(record),
"my_default_key": "test",
**self.custom_format,
}
)
logger = Logger(service="payment", logger_formatter=CustomFormatter())
@logger.inject_lambda_context
def handler(event, context):
logger.info("Collecting payment")
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="2-4"
{
"event": "Collecting payment",
"timestamp": "2021-05-03 11:47:12,494",
"my_default_key": "test",
"cold_start": true,
"lambda_function_name": "test",
"lambda_function_memory_size": 128,
"lambda_function_arn": "arn:aws:lambda:eu-west-1:12345678910:function:test",
"lambda_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72"
}
```
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{target="_blank"}.
As parameters don't always translate well between them, you can pass any callable that receives a Dict and return a str:
import orjson
from aws_lambda_powertools import Logger
custom_serializer = orjson.dumps
custom_deserializer = orjson.loads
logger = Logger(service="payment",
json_serializer=custom_serializer,
json_deserializer=custom_deserializer
)
# when using parameters, you can pass a partial
# custom_serializer=functools.partial(orjson.dumps, option=orjson.OPT_SERIALIZE_NUMPY)When unit testing your code that makes use of inject_lambda_context decorator, you need to pass a dummy Lambda Context, or else Logger will fail.
This is a Pytest sample that provides the minimum information necessary for Logger to succeed:
=== "fake_lambda_context_for_logger.py" Note that dataclasses are available in Python 3.7+ only.
```python
from dataclasses import dataclass
import pytest
@pytest.fixture
def lambda_context():
@dataclass
class LambdaContext:
function_name: str = "test"
memory_limit_in_mb: int = 128
invoked_function_arn: str = "arn:aws:lambda:eu-west-1:809313241:function:test"
aws_request_id: str = "52fdfc07-2182-154f-163f-5f0f9a621d72"
return LambdaContext()
def test_lambda_handler(lambda_context):
test_event = {'test': 'event'}
your_lambda_handler(test_event, lambda_context) # this will now have a Context object populated
```
=== "fake_lambda_context_for_logger_py36.py"
```python
from collections import namedtuple
import pytest
@pytest.fixture
def lambda_context():
lambda_context = {
"function_name": "test",
"memory_limit_in_mb": 128,
"invoked_function_arn": "arn:aws:lambda:eu-west-1:809313241:function:test",
"aws_request_id": "52fdfc07-2182-154f-163f-5f0f9a621d72",
}
return namedtuple("LambdaContext", lambda_context.keys())(*lambda_context.values())
def test_lambda_handler(lambda_context):
test_event = {'test': 'event'}
# this will now have a Context object populated
your_lambda_handler(test_event, lambda_context)
```
???+ tip Check out the built-in Pytest caplog fixture{target="_blank"} to assert plain log messages
Pytest Live Log feature duplicates emitted log messages in order to style log statements according to their levels, for this to work use POWERTOOLS_LOG_DEDUPLICATION_DISABLED env var.
POWERTOOLS_LOG_DEDUPLICATION_DISABLED="1" pytest -o log_cli=1???+ warning This feature should be used with care, as it explicitly disables our ability to filter propagated messages to the root logger (if configured).
How can I enable boto3 and botocore library logging?
You can enable the botocore and boto3 logs by using the set_stream_logger method, this method will add a stream handler
for the given name and level to the logging module. By default, this logs all boto3 messages to stdout.
from typing import Dict, List
from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools import Logger
import boto3
boto3.set_stream_logger()
boto3.set_stream_logger('botocore')
logger = Logger()
client = boto3.client('s3')
def handler(event: Dict, context: LambdaContext) -> List:
response = client.list_buckets()
return response.get("Buckets", [])What's the difference between append_keys and extra?
Keys added with append_keys will persist across multiple log messages while keys added via extra will only be available in a given log message operation.
Here's an example where we persist payment_id not request_id. Note that payment_id remains in both log messages while booking_id is only available in the first message.
=== "lambda_handler.py"
```python hl_lines="6 10"
from aws_lambda_powertools import Logger
logger = Logger(service="payment")
def handler(event, context):
logger.append_keys(payment_id="123456789")
try:
booking_id = book_flight()
logger.info("Flight booked successfully", extra={ "booking_id": booking_id})
except BookingReservationError:
...
logger.info("goodbye")
```
=== "Example CloudWatch Logs excerpt"
```json hl_lines="8-9 18"
{
"level": "INFO",
"location": "<module>:10",
"message": "Flight booked successfully",
"timestamp": "2021-01-12 14:09:10,859",
"service": "payment",
"sampling_rate": 0.0,
"payment_id": "123456789",
"booking_id": "75edbad0-0857-4fc9-b547-6180e2f7959b"
},
{
"level": "INFO",
"location": "<module>:14",
"message": "goodbye",
"timestamp": "2021-01-12 14:09:10,860",
"service": "payment",
"sampling_rate": 0.0,
"payment_id": "123456789"
}
```
How do I aggregate and search Powertools logs across accounts?
As of now, ElasticSearch (ELK) or 3rd party solutions are best suited to this task. Please refer to this discussion for more details