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sodiray / dynofunc

Functional library for creating and sending dynamo requests on top of boto3
MIT License
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aws boto3 dynamo python

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Dynofunc

the library formerly known as Dynamof

Build Test Coverage PyPI - License

A small :fire: interface for more easily making calls to dynamo using boto. No bloated ORM - just functions that make creating the complex objects needed to pass to boto3 quick and easy.

Install

pip install dynofunc

Basic Features

If you've ever used boto3 directly before you know the pain that can exist trying to write a generic KeyCondition or ConditionExpression. dynofunc does these things for you. It provides simple functions that take common sense arguments and build the complex objects boto3 uses for you.

If you've ever used boto3 directly you know that handling errors is the absolute worst... how much time I've spent googling how to catch this error or that error.... and they're all different! dynofunc wraps the calls to boto3, catches all of its errors, inspects them to determine the specific error it represents, and then throws a concrete and documented exception you can catch with a standard try...except.

dynofunc is not a framework and its not opinionated. dynofunc is simply a collection of deterministic functions that take in arguments and output boto3 command objects. We also provide a small wrapper for executing those boto3 calls behind the scenes if thats not something you want to do yourself. The benefit, is that you can use raw boto3 calls and dynofunc calls right next to each other. dyanmof doesn't replace boto3, its a simple layer that sits on top to make things easier and more maintainable for you.

dynofunc wraps the boto3.client('dynamodb') (docs) functions exposing much easier to use api's. It's written in a functional style with the goal to be as useful to anyone in any way as possible. The wrappers around boto3 functions are split into two parts: operations and runners. A runner runs a specific operations. The operation contains all the necessary information for a dynamo action to be ran. This means, you don't have to use dynofunc to actually interact with dynamo if you don't want to but you could still use it as a utility to more easily generate the complex objects that are passed to boto3 functions.

Why Dynofunc?

If you're using python and dynamo you have 2 options: an ORM like PynamoDB or Boto3. Kudos to the people who made Pynamo, its great, but it really doesn't scale well. And your stuck with the ORM features even if you don't want them. Interacting with Boto3 directly is a pain. With things like KeyConditions and ConditionExpressions being so difficult to easily grasp you end up duplicating a lot of code in your database/repository/DAL layer. This was my experience. In my early day's I used Pynamo. Once I got tired of trying to bend it to my will at scale I started using Boto3 directly. But... this was still annoying. I wanted a non-opinonated library that could sit on top of Botot3 and do the repetitive, annoying to code work for me. dynofunc was born.

Whats Supported?

See the two lists below for what has been implemented and what hasn't. If your a developer and want to do something thats not done yet its super easy to implement a new operation. See the developer guide for directions.

Currently Supported Calls

Currently Unsupported Calls

Getting Started

Example: Create a table in dynamo

from boto3 import client
from dynofunc.executor import execute
from dynofunc.operations import create

client = client('dynamodb', endpoint_url='http://localstack:4569')

execute(client, create(table_name='users', hash_key='username'))

First thing to note... execute(client, some_operation(...)) isn't sexy... and as engineers sexy is important. Because dynofunc is a simple functional utility library its very easy to bend it into any api you would like.

Example: Customize the way you call dynofunc

Keep it functional

from functools import partial
from boto3 import client
from dynofunc.executor import execute
from dynofunc.operations import create
from dynofunc.attribute import attr

client = client('dynamodb', endpoint_url='http://localstack:4569')
db = partial(execute, client)

# Now calling looks like
db(create(table_name='users', hash_key='username'))
db(find(table_name='users', key={ 'username': 'sunshie '}))
db(update(
  table_name='users',
  key={ 'username': 'sunshie' },
  attributes={
      'roles': attr.append('admin'),
      'friends': attr.prepend('jake')
  }))

Make it a class

class DB:
  def __init__(self):
    self.client = client('dynamodb', endpoint_url='http://localstack:4569')
  def find(*args, **kwargs):
    return execute(self.client, find(*args, **kwargs))

db = DB()
db.find(table_name='users', key={ 'id': 21 })

Make it a table specific class

class Table:
  def __init__(self, table_name):
    client = client('dynamodb', endpoint_url='http://localstack:4569')
    self.table_name = table_name
    self.db = partial(execute, client)
  def find(*args, **kwargs):
    return self.db(find(self.table_name, *args, **kwargs))
  def update(*args, **kwargs):
    return self.db(update(self.table_name, *args, **kwargs))
  def delete(*args, **kwargs):
    return self.db(delete(self.table_name, *args, **kwargs))

users = Table('users')

users.find(key={ 'id': 21 })
users.update(key={ 'id': 21 }, attributes={ 'username': 'new_username_1993_bro' })
users.delete(key={'id': 21 })

Example: Catch errors from dynofunc

from dynofunc.exceptions import (
    UnknownDatabaseException,
    ConditionNotMetException,
    BadGatewayException,
    TableDoesNotExistException
)

try:
  db(update(
    table_name='users',
    key={ 'id': 43 },
    attributes={ 'username': 'sunshie' }))
except TableDoesNotExistException:
  # Handle case where table doesn't exist
except ConditionNotMetException:
  # Handle case where the condition wasn't met (the item you tried to update didn't exist)
except BadGatewayException:
  # Handle a network error
except UnknownDatabaseException:
  # Handle an unknown issue

Example: Use dynofunc to build boto3 arguments but still call boto3 yourself

from dynofunc import operations
from dynofunc.conditions import attr

query = operations.query(
  table_name='books',
  conditions=attr('title').equals('The Cost of Discipleship'))

result = client.query(**query.description)

API Documentation

dynofunc.operations
dynofunc.conditions

Operations

See the code See the test

Create Table

create(table_name, hash_key, allow_existing=False)

See boto3 docs

Parameter Required Data Type Description Example
table_name yes str The name to assign the table your creating 'users'
hash_key yes str The hash key (primary key) for your table 'user_id'
allow_existing no bool Creating a table that already exists will throw an error in boto3. Passing True here will ignore that error if its raised and ignore it. True

:orange_book: Limitations

Find Item

find(table_name, key)

See boto3 docs

Parameter Required Data Type Description Example
table_name yes str The name of the table to find an item in 'users'
key yes str|dict The key (primary key) of the item to find. If a string is passed it is associated with id by default. If an object is passed the first key and value are used to find the item 22 or { 'username': 'sunshie' }

:orange_book: Limitations

Add Item

add(table_name, item, auto_inc=False)

See boto3 docs

Parameter Required Data Type Description Example
table_name yes str The name of the table to add the item to 'users'
item yes dict The item to be added to the table in key value pairs. If auto_inc is not set to true then this dict must include a valid key value pair for the table's hash key { 'username': 'sunshie', 'user_status': 'unleashed' }

:orange_book: Limitations

:closed_book: Known Issues

Update Item

update(table_name, key, attributes)

See boto3 docs

Parameter Required Data Type Description Example
table_name yes str The name of the table to update the item on 'users'
key yes str|dict The key (primary key) of the item to find for updating. If a string is passed it is associated with id by default. If an object is passed the first key and value are used to find the item 22 or { 'username': 'sunshie' }
attributes yes dict The key values patch/set on the record { 'rank': 23 }

:orange_book: Limitations

Delete Item

delete(table_name, key)

See boto3 docs

Parameter Required Data Type Description Example
table_name yes str The name of the table to delete the item from 'users'
key yes str|dict The key (primary key) of the item to delete. If a string is passed it is associated with id by default. If an object is passed the first key and value are used to find the item 22 or { 'username': 'sunshie' }

:orange_book: Limitations

Query Table

query(table_name, conditions)

See boto3 docs

Parameter Required Data Type Description Example
table_name yes str The name of the table to execute the query on 'users'
conditions yes dynofunc.conditions.Condition This value should be built using the dynofunc.conditions module. See the docs on that module. attr('username').equals('sunshie') will build a proper Condition to pass.

:orange_book: Limitations

Conditions

The dynofunc.conditions module provides utility methods that make it simple to generate the complex data object boto3 needs when specifying conditions for querying, scanning, and other operations. Looking at the docs for the query function you'll see KeyConditionExpression. This is the parameter this module was created to build.

Example

from dynofunc.conditions import attr

cond = attr('username').equals('sunshie')

cond.expression
# 'username = :username'

cond.attr_values
# { ":username": { "S": "sunshie" } }

attr(name)

Parameter Required Data Type Description Example
name yes str The name of the attribute to begin using on a condition. Could be a column you want to match exactly or if its a number type then it could be a column you want to check for > or < on 'username'

The attr function returns a dynofunc.conditions.Attribute that contains three methods

Where value is always the value to use in the conditional comparison you build.

cand(*conditions)

Takes any number of condition expressions and combines them using the and rule.

Parameter Required Data Type Description Example
conditions yes *dynofunc.conditions.Condition Takes any number of Condition instances cand(attr('points').less_than(50)

Developer Guide

As a developer you can probably guide yourself, you just want to know in simple terms "how it works". Thats what I'll talk about here - the design, what calls what, what does what. After reading the developer guide you should feel comfortable making changes.

The Design

The goal when dynofunc was started was to create the complicated objects that boto3 takes in as arguments to its calls. It wasn't until later that a wrapper for executing those calls inside dynofunc was added so we could standardize wild boto3 exceptions for the client. This means, at the core of dynofunc is operations and builder - its that simple. The builder is (for the most part) a DTO (with some sugar) for containg all the arguments you specify for a given operation. If you look in any operation file you'll find the same pattern:

def operation_name(table_name, key, conditions=None):

    # Build is a function we can use to create boto3 arguments with the details
    # we pass to `ab.builder`. For example, `build(ab.TableName)` will return the
    # `table_name` we pass the builder here.
    build = ab.builder(
        table_name=table_name,
        key=key,
        conditions=conditions)

    # Description is the object that will get passed to the boto3 call. See `run` below.
    description = shake(
        TableName=build(ab.TableName),
        Key=build(ab.Key),
        ConditionExpression=build(ab.ConditionExpression),
        ExpressionAttributeValues=build(ab.ExpressionAttributeValues))

    return Operation(description, run)

def run(client, description):
    # This is where boto3 will be called - if so desired. In the executor this function gets wrapped
    # in error handling. Again, a pure function (ish - because were making a network call - but as
    # pure as pure can be for a network calling function).
    res = client.operation_name(**description)
    return response(res) # Returns a standard response object

You can see, this doesn't call dynamo or boto3, its a deterministic/pure function that takes in arguments and uses the builder to generate all the arguments for the boto3 call.

How it effects tests

Since the operation functions return an Operation we can write concise, full coverage tests by calling the operation function with different arguments and then looking in on the description it returned in side the Operation. Heres an example:

def test_operation_creates_description_with_table_name():
    res = operation_name(table_name='users', key={ 'username': 'sunshie '})
    assert res.description['TableName'] == 'users'

How to add an operation

Given the information above, here is a checklist you might use when addding a new operation. For example sake, lets say the operation name is deploy.

  1. Create a file for the new deploy operation at dynofunc/operations/deploy.py
  2. Define two functions inside this file deploy(...) and run(client, description)
  3. Add any arguments you might need to the deploy function
  4. Use the builder to generate all the arguments boto3 expects given the deploy function arguments.
  5. If needed, go to the builder, and add/modify the functions that create description attributes.
  6. Implement the run method. The executor expects all run functions to take a client and description and return a response. This should always be a stupid simple function that just calls boto3 and returns the result. You have a new operation you can use!