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Typed data structures for FoundationDB.

Project description

gateaux

Data structures and typing for FoundationDB.

"FoundationDB is a distributed database designed to handle large volumes of structured data across clusters of commodity servers. It organizes data as an ordered key-value store and employs ACID transactions for all operations." - taken from https://github.com/apple/foundationdb/

FoundationDB has, by design, a bare minimum of features. It presents an interface to applications which reads and writes binary data with a few basic helper layers. FoundationDB has no native support for rich data types (for example datetime objects) nor provides any extended data validation. FoundationDB is designed to have layers of abstraction built on top of it to provide additional features.

The premise of gateaux is that where you currently have, in fdb library terms, tr[(some, data)] = (other, arbitrary, data) of unstructured data is to enforce strict standardisation of data in these tuples while allowing more complex types (datetime, ipaddress etc.). In addition, the concept of structures allows for easier developer comprehension of what data is being stored in what FoundationDB keyspace. gateaux has a lot of code for not much of an interface, but it is designed to enforce structure and types and therefore is over-engineered and over-tested by design so you don't have to worry about your data structures in your upstream applications which use gateaux.

If you use gateaux your Python FoundationDB client code should look mostly the same, you just can't mistakenly pack the wrong type or make mistakes before writing data. Such additional validation is useful for larger codebases where you may be storing hundreds of key=value formats into FoundationDB and keeping track of them can be challenging.

gateaux is a pure Python 3 (>=3.6) library which provides rich data type handling and validation on top of the usual pack() and unpack() methods and extends the fdb.tuple built-in layer. It is loosely modelled from the interfaces to relational database object-relational mapper (RDBMS ORM) systems. Each gateaux structure (comparable to a "model" in the ORM world) effectively formats one single key/value pair at a time with more rigid validation than the fdb library provides out of the box.

gateaux does not handle FoundationDB connections for you, just the data parsing part of your application. Effectively gateaux is just a data schema enforcing fancy wrapper that sits on top of tuple packing and unpacking with some nice syntactic sugar. gateaux does not abstract away any of the useful existing fdb keyspace interface.

While there is overhead in checking data and converting it between types, gateaux is relatively performant as all it does is shuffle native Python data types about.

Installation

gateaux itself only depends on foundationdb and pytz. You first need to install the FoundationDB client libraries from:

https://www.foundationdb.org/download

and FoundationDB Python library from PyPI:

$ pip install foundationdb

Next, install gateaux from PyPI:

$ pip install gateaux

Python >= 3.6 is required due to the use of typing.

Examples

Enforced data format

gateaux enforces certain requirements. These are not suitable for every project so check carefully and verify the library is appropriate for your application before you use it:

  1. All structures are in their own FoundationDB subspace, but it is up to you what keyspace or subspace to use
  2. Key tuple members are variable, a key of 3 elements can contain 1, 2 or 3 values, this is to support prefixes and ranges for keys
  3. Value tuple members are fixed, a value of 3 elements must always contain 3 values
  4. Validation is strict, if you define a field as a StringField you cannot store bytes in it etc.
  5. While possible to support multiple data types, such as cast int(1) to str('1') if an integer is provided to a StringField, by design typing is enforced and this will raise an exception
  6. You should not use direct binary data with FoundationDB while using gateaux, always use tuples of other types, tr[b'key'] = b'value is incompatible with gateaux, but tr[(b'key',)] = (b'value',) is compatible.

gateaux conforms to the same idea of fdb.tuple such that packing then unpacking a tuple should always result in the same original tuple of data.

Structures

There is only one base structure which is inherited to create your own structures. Synopsis:

import fdb
import gateaux

fdb.api_version(620)
db = fdb.open()

class SomeUserStructure(gateaux.Structure):
    key = (gateaux.BinaryField(),)
    value = (gateaux.BinaryField(),)

some_keyspace = fdb.Subspace(('some', 'subspace'))
some_structure_instance = SomeUserStructure(some_keyspace)

Structures have one required argument, a FoundationDB subspace. Structure instances have the following interface for tuples:

  • structure.pack_key((...)) validates a tuple of data against the defined key fields and returns bytes. The bytes are a FoundationDB packed tuple in the defined directory.
  • structure.unpack_key(b'...') unpacks FoundationDB bytes into a tuple and then validates the data against the defined key fields returning the appropriate data type for the field.
  • structure.pack_value((...)) validates a tuple of data against the defined value fields and returns bytes. The bytes are a FoundationDB packed tuple in the defined directory.
  • structure.unpack_value(b'...') unpacks FoundationDB bytes into a tuple and then validates the data against the defined value fields returning the appropriate data type for the field.

And the following interface for dicts:

  • structure.pack_key_dict({...}) validates a dict of data against the defined key fields and returns bytes. The bytes are a FoundationDB packed tuple in the defined directory. To use key dicts you must have given all of your key fields a name.
  • structure.unpack_key_dict(b'...') unpacks FoundationDB bytes into a dict and then validates the data against the defined key fields returning the appropriate data type for the field. To use key dicts you must have given all of your key fields a name.
  • structure.pack_value_dict((...)) validates a dict of data against the defined value fields and returns bytes. The bytes are a FoundationDB packed tuple in the defined directory. To use value dicts you must have given all of your value fields a name.
  • structure.unpack_value_dict(b'...') unpacks FoundationDB bytes into a dict and then validates the data against the defined value fields returning the appropriate data type for the field. To use value dicts you must have given all of your value fields a name.

And the following properties:

  • structure.description a property which returns a dict describing the model, including the name of the structure and any doc string as well as lists of descriptions for each field in the key and value. You can use this to programmatically inspect a structure in the future and is useful if you have many structures.

Fields

All fields support the following arguments:

  • name=string If set it defines a name stored for the field.

  • help_text=string If set, help defines some optional help text to describe the data stored in the value.

  • null=boolean If set to True then the field can have a None value. Defaults to False.

  • default=value If set, defines a default for a field. The type must match the required type for the field. A default is only used if null=True and None is provided to the field.

Other field types may support more arguments.

BinaryField

Stores bytes. Optional arguments:

  • max_length=int If set defines the maximum number of bytes the field will store.

Accepted type: bytes

IntegerField

Stores integers. Optional arguments:

  • min_value=int If set defines the minimum number the field will accept
  • max_value=int If set defines the maximum number the field will accept

Accepted type: int

FloatField

Stores floats. Optional arguments:

  • min_value=float If set defines the minimum number the field will accept
  • max_value=float If set defines the maximum number the field will accept

Accepted type: float

BooleanField

Stores booleans.

Accepted type: bool

StringField

Stores strings. Optional arguments:

  • max_length=int If set defines the maximum length of the field will accept

Accepted type: str

DateTimeField

Stores datetime instances. Internally stored as a UNIX timestamp as floats in UTC.

Accepted type: datetime.datetime

Input datetime.datetime will be normalised to UTC and returned in UTC when read. You should account for this in your application. Storing a datetime.datetime in any other timezone will convert it to UTC when read. If the provided datetime.datetime instance has no timezone info it will be assumed to be UTC.

IPv4AddressField

Stores IPv4 addresses. Internally stored as 4 bytes.

Accepted type: ipaddress.IPv4Address

IPv4NetworkField

Stores IPv4 networks. Internally stored as 5 bytes (address + prefix length).

Accepted type: ipaddress.IPv4Network

IPv6AddressField

Stores IPv6 addresses. Internally stored as 16 bytes.

Accepted type: ipaddress.IPv6Address

IPv6NetworkField

Stores IPv4 networks. Internally stored as 17 bytes (address + prefix length).

Accepted type: ipaddress.IPv6Network

EnumField

Stores Enums. Internally stored as an integer that maps to a specified value. Required arguments:

  • members=tuple A mandatory tuple of ints to use as Enum members.

Example:

MEMBER_A = 0
MEMBER_B = 1
MEMBERS = (
    MEMBER_A,
    MEMBER_B
)
EnumField(members=MEMBERS)

Accepted type: int

UUIDField

Stores UUID instances. Internally stored as 16 bytes.

Accepted type: uuid.UUID

Tests

There is a pretty comprehensive test suite. As gateaux is designed to pack and unpack important data it has good coverage to make sure it's behaving as expected. You can run it by cloning this repository then executing:

$ ./run-tests.sh

The tests perform type checking and require "mypy" from http://mypy-lang.org/ to be globally installed. E.g. apt install mypy.

Contributing

All properly formatted and sensible pull requests, issues and comments are welcome.

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