A simple CRUD wrapper around Amazon DynamoDB.
$ pip install cruddy
The first thing to do is to create a CRUD handler for your DynamoDB table. The constructor for the CRUD class takes a number of parameters to help configure the handler for your application. The full list of parameters are:
- table_name - name of the backing DynamoDB table (required)
- profile_name - name of the AWS credential profile to use when creating the boto3 Session
- region_name - name of the AWS region to use when creating the boto3 Session
- prototype - a dictionary that describes the prototypical object stored in your table (see below)
- supported_ops - a list of operations supported by the CRUD handler (choices are list, get, create, update, delete, search, increment_counter)
- encrypted_attributes - a list of lists or tuples where the first item is the name of the attribute that should be encrypted and the second item is the KMS master key ID to use for encrypting/decrypting the value.
- debug - if not False this will cause the raw_response to be left in the response dictionary
A prototype is a description of the prototypical item in your table. It's kind of like a template for the item. A prototype can be used to describe what attributes are in the item, which are required or optional, and the type of value that is associated with the attribute. In addition, there are special values you can use that allow a small range of calculated values in your item.
If you don't specify a prototype, cruddy will store whatever values are in the item with no validation or insertion of calculated values.
Let's look at a few examples using prototypes.
{
'id': '',
'created_at': 1,
'foo': 1
}
This prototype says that your item must have an id
attribute whose value is
of type str
, a created_at
attribute whose value is of type int
, and
a foo
attribute whose value is also an int
. Your item may contain
other items as well (this is not a schema) but it must contain these attribute
name/value pairs. If the item you pass into the create
method does not
contain these attributes cruddy will create the necessary attributes and will
initialize the value to what ever value you have specified.
The above example assumes that you are going to generate the id
and
created_at
values in your application code. You may, however, prefer to
have cruddy handle that for you. In that case, you can make use of cruddy's
calcuated value tokens.
{
'id': 'on-create:<uuid>',
'created_at': 'on-create:<timestamp>'
}
Now, when you create a new item you could supply one without an id
or
created_at
value and cruddy will calculate these values for you. If those
attributes already exist in the item, cruddy will not overwrite them. Note
that the calulated values are specified as on-create
. This is called a
trigger
and indicates when the calculation will be performed.
If you wanted to also have a timestamp to indicate when an item has been modified (i.e. created or updated) you could do this.
{
'id': 'on-create:<uuid>',
'created_at': 'on-create:<timestamp>',
'modified_at': 'on-update:<timestamp>'
}
The currently supported calculated value types are:
<uuid>
to generate a string representation of a Type4 UUID<timestamp>
to generate an integer timestamp generated byint(time.time()*1000)
The currently supported triggers for calculated values are:
- on-create will be applied when the item is created
- on-update will be applied when the item is created or updated
An easy way to configure your CRUD handler is to gather all of the parameters together in a dictionary and then pass that dictionary to the class constructor.
import cruddy
params = {
'profile_name': 'foobar',
'region_name': 'us-west-2',
'table_name': 'fiebaz',
'prototype': {'id': '<on-create:uuid>',
'created_at': '<on-create:timestamp>',
'modified_at': '<on-update:timestamp>'}
}
crud = cruddy.CRUD(**params)
Once you have your handler, you can start to use it.
item = {'name': 'the dude', 'email': 'the@dude.com', 'twitter': 'thedude'}
response = crud.create(item)
The response returned from all CRUD operations is a Python object with the following attributes.
- data is the actual data returned from the CRUD operation (if successful)
- status is the status of the response and is either
success
orerror
- metadata is metadata from the underlying DynamoDB API call
- error_type will be the type of error, if
status != 'success'
- error_code will be the code of error, if
status != 'success'
- error_type will be the full error message, if
status != 'success'
- raw_response will contain the full response from DynamoDB if the CRUD
handler is in
debug
mode. - is_successful a simple short-cut, equivalent to
status == 'success'
You can convert the CRUDResponse object into a standard Python dictionary using
the flatten
method
>>> response = crud.create(...)
>>> response.flatten()
{'data': {'created_at': 1452109758363,
'name': 'the dude',
'email': 'the@dude.com',
'twitter': 'thedude',
'id': 'a6ac0fd7-cdde-4170-a1a9-30e139c44897',
'modified_at': 1452109758363},
'error_code': None,
'error_message': None,
'error_type': None,
'metadata': {'HTTPStatusCode': 200,
'RequestId': 'LBBFLMIAVOKR8LOTK7SRGFO4Q3VV4KQNSO5AEMVJF66Q9ASUAAJG'},
'raw_response': None,
'status': 'success'}
>>>
The CRUD object supports the following operations. Note that depending on the
value of the supported_operations
parameter passed to the constructor, some
of these methods may return an UnsupportedOperation
error type.
Returns a list of items in the database. Encrypted attributes are not decrypted when listing items.
Returns the item corresponding to id
. If the decrypt
param is not
False (the default) any encrypted attributes in the item will be decrypted
before the item is returned. If not, the encrypted attributes will contain the
encrypted value.
Creates a new item. You pass in an item containing initial values. Any
attribute names defined in prototype
that are missing from the item will be
added using the default value defined in prototype
.
Updates the item based on the current values of the dictionary passed in. If
the encrypt
param is True (the default), encrypted attributes in item
are encrypted. To prevent double-encrypting when using list
or get
without decrypt=True
, you can specify encrypt=False
and the item will
be stored verbatim.
Deletes the item corresponding to id
.
The following operations extend beyond the basic CRUD functions but are included because of they are quite useful.
Cruddy provides a limited but useful interface to search GSI indexes in DynamoDB with the following limitations (hopefully some of these will be expanded or eliminated in the future.
- The GSI must be configured with a only HASH and not a RANGE.
- The only operation supported in the query is equality
To use the search
operation you must pass in a query string of this form:
<attribute_name>=<value>
As stated above, the only operation currently supported is equality (=) but
other operations will be added over time. Also, the attribute_name
must be
an attribute which is configured as the HASH
of a GSI in the DynamoDB
table. If all of the above conditions are met, the query
operation will
return a list (possibly empty) of all items matching the query and the
status
of the response will be success
. Otherwise, the status
will
be error
and the error_type
and error_message
will provide further
information about the error.
Atomically increments a counter attribute in the item identified by id
. You must specify the
name of the attribute as counter_name
and, optionally, the increment
which defaults to 1
.
In addition to the methods described above, cruddy also provides a generic handler interface. This is mainly useful when you want to wrap a cruddy handler in a Lambda function and then call that Lambda function to access the CRUD capabilities.
To call the handler, you simply put all necessary parameters into a Python dictionary and then call the handler with that dict.
params = {
'operation': 'create',
'item': {'foo': 'bar', 'fie': 'baz'}
}
response = crud.handler(**params)
So, you could define a Lambda function like this:
import logging
import json
import cruddy
LOG = logging.getLogger()
LOG.setLevel(logging.INFO)
config = json.load(open('config.json'))
crud = cruddy.CRUD(**config)
def handler(event, context):
LOG.info(event)
response = crud.handler(**event)
return response.flatten()
Where config.json
looks like this:
{
"region_name": "us-west-2",
"table_name": "foobar",
"prototype": {"id": "<on-create:uuid>",
"created_at": "<on-create:timestamp>",
"modified_at": "<on-update:timestamp>",
"foo": 1,
"bar": ""},
}
If you uploaded this function (and config file) to AWS Lambda you could then invoke the handler like this.
import json
import boto3
session = boto3.Session()
lambda_client = session.client('lambda')
params = {'operation': 'create', 'item': {'fie': 'baz'}}
response = lambda_client.invoke(
FunctionName='myfunction',
InvocationType='RequestResponse',
Payload=json.dumps(params))
cruddy_response = json.load(response['Payload'])
The variable cruddy_response
would now contain the response structure
returned by cruddy, flattened into a Python dictionary.
cruddy also offers a CLI that allows you to access your DynamoDB table or Lambda-based handler via a simple command line interface. It supports all operations supported by cruddy.
To use cruddy to directly interact with a DynamoDB table, you need to place the
configuration information for your cruddy handler in a JSON file. So, from our
previous example if we created a file called fiebaz.json
like this:
{
"profile_name": "foobar",
"region_name": "us-west-2",
"table_name": "fiebaz",
"prototype": {"id": "<on-create:uuid>",
"created_at": "<on-create:timestamp>",
"modified_at": "<on-update:timestamp>"}
}
We could then reference this when using the cruddy CLI:
$ cruddy --config fiebaz.json list
[
{<a listing of all items in fiebaz>}
...
]
Use the --help
for more information on how to use the cruddy CLI.
All of the operations of the CLI work exactly the same whether you are using it with a DynamoDB table directly or through a Lambda controller. The only difference is that rather than referencing a config file containing info about the table and other parameters needed to create the cruddy CRUD handler, you simply tell the CLI about the Lambda function.
$ cruddy --lambda-fn fiebaz list
[
{<a listing of all items in fiebaz>}
...
]
where fiebaz
is the name of your Lambda handler.