Metadata-Version: 2.1
Name: jeffy
Version: 0.1.2
Summary: Event and Contract Driven Serverless "Application" Framework
Home-page: https://github.com/serverless-operations/jeffy
Author: Serverless Operations
Author-email: info@serverless-operations.com
License: MIT License
Keywords: aws lambda serverless faas
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
Requires-Dist: jsonschema

Jeffy(Beta)
=======

# Description

Jeffy is Serverless **"Application"** Framework, which is
suite of Utilities for Lambda functions to make it easy to develop serverless applications.

Mainly, focusing on three things.

- Logging: Providing easy to see JSON format logging, auto logging as a decorator for capturing events and responses and errors, configurable to inject additional attributes what you want to see to logs.
- Tracing: Traceable events within related functions and AWS services with generating and passing `correlation_id`.
- Decorators: To save time to implement common things for Lambda functions, providing some useful decorators.

# TOC

- [Jeffy(Beta)](#jeffy-beta-)
- [Description](#description)
- [Install](#install)
- [Features](#features)
  * [Logger](#logger)
    + [Basic Usage](#basic-usage)
    + [Injecting additional attributes to CloudWatchLogs](#injecting-additional-attributes-to-cloudwatchlogs)
    + [Auto Logging](#auto-logging)
  * [Decorators](#decorators)
    + [json_scheme_validator](#json-scheme-validator)
    + [api_json_scheme_validator](#api-json-scheme-validator)
    + [api](#api)
    + [sqs](#sqs)
    + [sns](#sns)
    + [kinesis_stream](#kinesis-stream)
    + [dynamodb_stream](#dynamodb-stream)
    + [s3](#s3)
    + [schedule](#schedule)
  * [CorrelationIDs](#correlationids)
    + [Kinesis Clinent](#kinesis-clinent)
    + [SNS Client](#sns-client)
    + [SQS Client](#sqs-client)
    + [S3 Client](#s3-client)
- [Requirements](#requirements)
  * [Development](#development)
  * [Authors](#authors)
  * [License](#license)

# Install

```sh
$ pip install jeffy
```

# Features
## Logger
### Basic Usage
Jeffy logger automatically inject some Lambda contexts to CloudWatchLogs.
```python
from jeffy.framework import setup
app = setup()

def handler(event, context):
    app.logger.info({"foo":"bar"})
```

Output in CloudWatchLogs
```json
{
   "message": {
       "foo":"bar","item":"aa"
    },
   "aws_region":"us-east-1",
   "function_name":"jeffy-dev-hello",
   "function_version":"$LATEST",
   "function_memory_size":"1024",
   "log_group_name":"/aws/lambda/jeffy-dev-hello",
   "log_stream_name":"2020/01/21/[$LATEST]d7729c0ea59a4939abb51180cda859bf",
   "correlation_id":"f79759e3-0e37-4137-b536-ee9a94cd4f52"
}
```

### Injecting additional attributes to CloudWatchLogs
You can inject some additional attributes what you want to output with using `setup` method.

```python
from jeffy.framework import setup
app = setup()

app.logger.setup({
   "username":"user1",
   "email":"user1@example.com"
})

def handler(event, context):
    app.logger.info({"foo":"bar"})
```

Output in CloudWatchLogs
```json
{
   "message": {
       "foo":"bar","item":"aa"
    },
   "username":"user1",
   "email":"user1@example.com",
   "aws_region":"us-east-1",
   "function_name":"jeffy-dev-hello",
   "function_version":"$LATEST",
   "function_memory_size":"1024",
   "log_group_name":"/aws/lambda/jeffy-dev-hello",
   "log_stream_name":"2020/01/21/[$LATEST]d7729c0ea59a4939abb51180cda859bf",
   "correlation_id":"f79759e3-0e37-4137-b536-ee9a94cd4f52"
}
```

### Auto Logging
`auth_logging` decorator allows you to output `event`, `response` and `stacktrace` when you face Exceptions

```python
from jeffy.framework import setup
app = setup()

app.logger.setup({
   "username":"user1",
   "email":"user1@example.com"
})

@app.decorator.auto_logging
def handler(event, context):
    ...
```

Error output with auto_logging

```json
{
   "error_message": "JSONDecodeError('Expecting value: line 1 column 1 (char 0)')", 
   "stack_trace":"Traceback (most recent call last):
  File '/var/task/jeffy/decorators.py', line 41, in wrapper
    raise e
  File '/var/task/jeffy/decorators.py', line 36, in wrapper
    result = func(event, context)
  File '/var/task/handler.py', line 8, in hello
    json.loads('s')
  File '/var/lang/lib/python3.8/json/__init__.py', line 357, in loads
    return _default_decoder.decode(s)
  File '/var/lang/lib/python3.8/json/decoder.py', line 337, in decode
    obj, end = self.raw_decode(s, idx=_w(s, 0).end())
  File '/var/lang/lib/python3.8/json/decoder.py', line 355, in raw_decode
    raise JSONDecodeError('Expecting value', s, err.value) from None",
   "function_name":"jeffy-dev-hello",
   "function_version":"$LATEST",
   "function_memory_size":"1024",
   "log_group_name":"/aws/lambda/jeffy-dev-hello",
   "log_stream_name":"2020/01/21/[$LATEST]90e1f70f6e774e07b681e704646feec0"
}

```

## Decorators
Decorators make simple to implement common lamdba tasks, such as parsing array from Kinesis, SNS, SQS events etc.

Here are provided decorators

### json_scheme_validator
Decorator for Json scheme valiidator. Automatically validate `event.["body"]` with following json scheme you define. raise exception if the validation fails.

```python
from jeffy.framework import setup
app = setup()

@app.decorator.json_scheme_validator(
    json_scheme={
       "type":"object",
       "properties": {
           "message": {"type":"string"}
        }
    }
)
def handler(event, context):
    return event["body"]["foo"] 
```

### api_json_scheme_validator
Decorator for Json scheme valiidator for API Gateway. Automatically validate `event.["body"]` with following json scheme. Returns 400 error if the validation fails.

```python
from jeffy.framework import setup
app = setup()
@app.decorator.api_json_scheme_validator(
    json_scheme={
       "type":"object",
       "properties": {
           "message": {"type":"string"}
        }
    },
    response_headers={
       "Content-Type":"application/jsoset=utf-8"
    }
)
def handler(event, context):
    return event["body"]["foo"]
```

### api
Decorator for API Gateway event. Automatically parse string if `event["body"]` can be parsed as Dictionary and set correlation_id in `event["correlation_id"]` you should pass to next event, returns 500 error if unexpected error happens.

```python
from jeffy.framework import setup
app = setup()

@app.decorator.api
def handler(event, context):
    return event["body"]["foo"] # returns 500 error if unexpected error happens.
```

### sqs
Decorator for sqs event. Automaticlly parse `"event.Records"` list from SQS event source to each items for making it easy to treat it inside main process of Lambda.

```python
from jeffy.framework import setup
app = setup()

@app.decorator.sqs
def handler(event, context):
    return event["foo"]
    """
    "event.Records" list from SQS event source was parsed each items
    if event.Records value is the following,
     [
         {"foo": 1},
         {"foo": 2}
     ]

    event["foo"] value is 1 and 2, event["correlation_id"] is correlation_id you should pass to next event
    """
```

### sns
Decorator for sns event. Automaticlly parse `"event.Records"` list from SNS event source to each items for making it easy to treat it inside main process of Lambda.

```python
from jeffy.framework import setup
app = setup()

@app.decorator.sns
def handler(event, context):
    return event["foo"]
    """
    "event.Records" list from SNS event source was parsed each items
    if event.Records value is the following,
     [
         {"foo": 1},
         {"foo": 2}
     ]

    event["foo"] value is 1 and 2, event["correlation_id"] is correlation_id you should pass to next event
    """
```

### kinesis_stream
Decorator for kinesis stream event. Automaticlly parse `"event.Records"` list from Kinesis event source to each items and decode it with base64 for making it easy to treat it inside main process of Lambda.

```python
@app.decorator.kinesis_stream
def handler(event, context):
    return event["foo"]
    """
    "event.Records" list from Kinesis event source was parsed each items
    and decoded with base64 if event.Records value is the following,
     [
         <base64 encoded value>,
         <base64 encoded value>
     ]

    event["foo"] value is 1 and 2, event["correlation_id"] is correlation_id you should pass to next event
    """
```

### dynamodb_stream
Decorator for dynamodb stream event. Automaticlly parse `"event.Records"` list from Dynamodb event source to  items for making it easy to treat it inside main process of Lambda.

```python
from jeffy.framework import setup
app = setup()

@app.decorator.dynamodb_stream
def handler(event, context):
    return event["foo"]
    """
    "event.Records" list from Dynamodb event source was parsed each items
    if event.Records value is the following,
     [
         {"foo": 1},
         {"foo": 2}
     ]

    event["foo"] value is 1 and 2, event["correlation_id"] is correlation_id you should pass to next event
    """
```

### s3
Decorator for S3 event. Automatically parse body stream from triggered S3 object and S3 bucket and key name to Lambda.

```python
from jeffy.framework import setup
app = setup()

@app.decorator.s3
def handler(event, context):
    event["key"] # S3 bucket key
    event["bucket_name"] # S3 bucket name
    event["body"] # object stream from triggered S3 object
    event["correlation_id"] # correlation_id

```

### schedule
Decorator for schedule event. just captures correlation id before main Lambda process. do nothing other than that.

```python
from jeffy.framework import setup
app = setup()

@app.decorator.schedule
def handler(event, context):
    ...
```


## CorrelationIDs
CorrelationID is to trace subsequent Lambda functions and services. Jeffy automatically extract correlation IDs and caputure logs from the invocation event.

Also, Jeffy provide boto3 wrapper client to create and inject correlation IDs.

### Kinesis Clinent

```python
from jeffy.sdk.kinesis import Kinesis

def handler(event, context):
    Kinesis.put_record(
        stream_name=os.environ["STREAM_NAME"],
        data={"foo": "bar"},
        partition_key="uuid",
        correlation_id=event.get("correlation_id")
    )
```

### SNS Client

```python
from jeffy.sdk.sns import Sns

def handler(event, context):
    Sns.publish(
        topic_arn=os.environ["TOPIC_ARN"],
        message="message",
        subject="subject",
        correlation_id=event.get("correlation_id")
    )
```

### SQS Client

```python
from jeffy.sdk.sqs import Sqs

def handler(event, context):
    Sqs.send_message(
        queue_url=os.environ["QUEUE_URL"],
        message="message",
        correlation_id=event.get("correlation_id")
    )
```

### S3 Client

```python
from jeffy.sdk.s3 import S3

def handler(event, context):
    S3.upload_file(
        file_path="path/to/file", 
        bucket_name=os.environ["BUCKET_NAME"],
        object_name="path/to/object",
        correlation_id=event.get("correlation_id")
    )
```

# Requirements

- Python 3

Development
-----------

-   Source hosted at [GitHub](https://github.com/marcy-terui/jeffy)
-   Report issues/questions/feature requests on [GitHub
    Issues](https://github.com/marcy-terui/jeffy/issues)

Pull requests are very welcome! Make sure your patches are well tested.
Ideally create a topic branch for every separate change you make. For
example:

1.  Fork the repo
2.  Create your feature branch (`git checkout -b my-new-feature`)
3.  Commit your changes (`git commit -am"Added some feature"`)
4.  Push to the branch (`git push origin my-new-feature`)
5.  Create new Pull Request

Authors
-------

- Bought up initial idea by [Masashi Terui](https://github.com/marcy-terui) (<marcy9114@gmail.com>)
- Created and maintained by [Serverless Operations, Inc]()

License
-------

MIT License (see [LICENSE](https://github.com/marcy-terui/jeffy/blob/master/LICENSE))


