Fast Python bindings to Sophia Database
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.

README.md

sophy, fast Python bindings for Sophia embedded database, v2.2.

About sophy

  • Written in Cython for speed and low-overhead
  • Clean, memorable APIs
  • Extensive support for Sophia's features
  • Python 2 and Python 3 support
  • No 3rd-party dependencies besides Cython
  • Documentation on readthedocs

About Sophia

  • Ordered key/value store
  • Keys and values can be composed of multiple fieldsdata-types
  • ACID transactions
  • MVCC, optimistic, non-blocking concurrency with multiple readers and writers.
  • Multiple databases per environment
  • Multiple- and single-statement transactions across databases
  • Prefix searches
  • Automatic garbage collection and key expiration
  • Hot backup
  • Compression
  • Multi-threaded compaction
  • mmap support, direct I/O support
  • APIs for variety of statistics on storage engine internals
  • BSD licensed

Some ideas of where Sophia might be a good fit

  • Running on application servers, low-latency / high-throughput
  • Time-series
  • Analytics / Events / Logging
  • Full-text search
  • Secondary-index for external data-store

Limitations

  • Not tested on Windoze.

If you encounter any bugs in the library, please open an issue, including a description of the bug and any related traceback.

Installation

The sophia sources are bundled with the sophy source code, so the only thing you need to install is Cython. You can install from GitHub or from PyPI.

Pip instructions:

$ pip install Cython
$ pip install sophy

Or to install the latest code from master:

$ pip install -e git+https://github.com/coleifer/sophy#egg=sophy

Git instructions:

$ pip install Cython
$ git clone https://github.com/coleifer/sophy
$ cd sophy
$ python setup.py build
$ python setup.py install

To run the tests:

$ python tests.py


Overview

Sophy is very simple to use. It acts like a Python dict object, but in addition to normal dictionary operations, you can read slices of data that are returned efficiently using cursors. Similarly, bulk writes using update() use an efficient, atomic batch operation.

Despite the simple APIs, Sophia has quite a few advanced features. There is too much to cover everything in this document, so be sure to check out the official Sophia storage engine documentation.

The next section will show how to perform common actions with sophy.

Using Sophy

Let's begin by import sophy and creating an environment. The environment can host multiple databases, each of which may have a different schema. In this example our database will store arbitrary binary data as the key and value. Finally we'll open the environment so we can start storing and retrieving data.

from sophy import Sophia, Schema, StringIndex

# Instantiate our environment by passing a directory path which will store the
# various data and metadata for our databases.
env = Sophia('/path/to/store/data')

# We'll define a very simple schema consisting of a single utf-8 string for the
# key, and a single utf-8 string for the associated value.
schema = Schema(key_parts=[StringIndex('key')],
                value_parts=[StringIndex('value')])

# Create a key/value database using the schema above.
db = env.add_database('example_db', schema)

if not env.open():
    raise Exception('Unable to open Sophia environment.')

CRUD operations

Sophy databases use the familiar dict APIs for CRUD operations:

db['name'] = 'Huey'
db['animal_type'] = 'cat'
print db['name'], 'is a', db['animal_type']  # Huey is a cat

'name' in db  # True
'color' in db  # False

db['temp_val'] = 'foo'
del db['temp_val']
print db['temp_val']  # raises a KeyError.

Use update() for bulk-insert, and multi_get() for bulk-fetch. Unlike __getitem__(), calling multi_get() with a non-existant key will not raise an exception and return None instead.

db.update(k1='v1', k2='v2', k3='v3')

for value in db.multi_get('k1', 'k3', 'kx'):
    print value
# v1
# v3
# None

result_dict = db.multi_get_dict(['k1', 'k3', 'kx'])
# {'k1': 'v1', 'k3': 'v3'}

Other dictionary methods

Sophy databases also provides efficient implementations for keys(), values() and items(). Unlike dictionaries, however, iterating directly over a Sophy database will return the equivalent of the items() (as opposed to the just the keys):

db.update(k1='v1', k2='v2', k3='v3')

list(db)
# [('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')]


db.items()
# same as above.


db.keys()
# ['k1', 'k2', 'k3']


db.values()
# ['v1', 'v2', 'v3']

There are two ways to get the count of items in a database. You can use the len() function, which is not very efficient since it must allocate a cursor and iterate through the full database. An alternative is the index_count property, which may not be exact as it includes transactional duplicates and not-yet-merged duplicates.

print(len(db))
# 4

print(db.index_count)
# 4

Fetching ranges

Because Sophia is an ordered data-store, performing ordered range scans is efficient. To retrieve a range of key-value pairs with Sophy, use the ordinary dictionary lookup with a slice instead.

db.update(k1='v1', k2='v2', k3='v3', k4='v4')


# Slice key-ranges are inclusive:
db['k1':'k3']
# [('k1', 'v1'), ('k2', 'v2'), ('k3', 'v3')]


# Inexact matches are fine, too:
db['k1.1':'k3.1']
# [('k2', 'v2'), ('k3', 'v3')]


# Leave the start or end empty to retrieve from the first/to the last key:
db[:'k2']
# [('k1', 'v1'), ('k2', 'v2')]

db['k3':]
# [('k3', 'v3'), ('k4', 'v4')]


# To retrieve a range in reverse order, use the higher key first:
db['k3':'k1']
# [('k3', 'v3'), ('k2', 'v2'), ('k1', 'v1')]

To retrieve a range in reverse order where the start or end is unspecified, you can pass in True as the step value of the slice to also indicate reverse:

db[:'k2':True]
# [('k2', 'k1'), ('k1', 'v1')]

db['k3'::True]
# [('k4', 'v4'), ('k3', 'v3')]

db[::True]
# [('k4', 'v4'), ('k3', 'v3'), ('k2', 'v2'), ('k1', 'v1')]

Cursors

For finer-grained control over iteration, or to do prefix-matching, Sophy provides a cursor interface.

The cursor() method accepts 5 parameters:

  • order (default=>=) -- semantics for matching the start key and ordering results.
  • key -- the start key
  • prefix -- search for prefix matches
  • keys -- (default=True) -- return keys while iterating
  • values -- (default=True) -- return values while iterating

Suppose we were storing events in a database and were using an ISO-8601-formatted date-time as the key. Since ISO-8601 sorts lexicographically, we could retrieve events in correct order simply by iterating. To retrieve a particular slice of time, a prefix could be specified:

# Iterate over events for July, 2017:
for timestamp, event_data in db.cursor(key='2017-07-01T00:00:00',
                                       prefix='2017-07-'):
    do_something()

Transactions

Sophia supports ACID transactions. Even better, a single transaction can cover operations to multiple databases in a given environment.

Example usage:

account_balance = env.add_database('balance', ...)
transaction_log = env.add_database('transaction_log', ...)

# ...

def transfer_funds(from_acct, to_acct, amount):
    with env.transaction() as txn:
        # To write to a database within a transaction, obtain a reference to
        # a wrapper object for the db:
        txn_acct_bal = txn[account_balance]
        txn_log = txn[transaction_log]

        # Transfer the asset by updating the respective balances. Note that we
        # are operating on the wrapper database, not the db instance.
        from_bal = txn_acct_bal[from_acct]
        txn_acct_bal[to_account] = from_bal + amount
        txn_acct_bal[from_account] = from_bal - amount

        # Log the transaction in the transaction_log database. Again, we use
        # the wrapper for the database:
        txn_log[from_account, to_account, get_timestamp()] = amount

Multiple transactions are allowed to be open at the same time, but if there are conflicting changes, an exception will be thrown when attempting to commit the offending transaction:

# Create a basic k/v store. Schema.key_value() is a convenience/factory-method.
kv = env.add_database('main', Schema.key_value())

# ...

# Instead of using the context manager, we'll call begin() explicitly so we
# can show the interaction of 2 open transactions.
txn = env.transaction().begin()

t_kv = txn[kv]
t_kv['k1'] = 'v1'

txn2 = env.transaction().begin()
t2_kv = txn2[kv]

t2_kv['k1'] = 'v1-x'

txn2.commit()  # ERROR !!
# SophiaError('txn is not finished, waiting for concurrent txn to finish.')

txn.commit()  # OK

# Try again?
txn2.commit()  # ERROR !!
# SophiaError('transasction rolled back by another concurrent transaction.')

Index types, multi-field keys and values

Sophia supports multi-field keys and values. Additionally, the individual fields can have different data-types. Sophy provides the following field types:

  • StringIndex - stores UTF8-encoded strings, e.g. text.
  • BytesIndex - stores bytestrings, e.g. binary data.
  • JsonIndex - stores arbitrary objects as UTF8-encoded JSON data.
  • MsgPackIndex - stores arbitrary objects using msgpack serialization.
  • PickleIndex - stores arbitrary objects using Python pickle library.
  • UUIDIndex - stores UUIDs.
  • U64Index and reversed, U64RevIndex
  • U32Index and reversed, U32RevIndex
  • U16Index and reversed, U16RevIndex
  • U8Index and reversed, U8RevIndex
  • SerializedIndex - which is basically a BytesIndex that accepts two functions: one for serializing the value to the db, and another for deserializing.

To store arbitrary data encoded using msgpack, you could use MsgPackIndex:

schema = Schema(StringIndex('key'), MsgPackIndex('value'))
db = sophia_env.add_database('main', schema)

To declare a database with a multi-field key or value, you will pass the individual fields as arguments when constructing the Schema object. To initialize a schema where the key is composed of two strings and a 64-bit unsigned integer, and the value is composed of a string, you would write:

key = [StringIndex('last_name'), StringIndex('first_name'), U64Index('area_code')]
value = [StringIndex('address_data')]
schema = Schema(key_parts=key, value_parts=value)

address_book = sophia_env.add_database('address_book', schema)

To store data, we use the same dictionary methods as usual, just passing tuples instead of individual values:

sophia_env.open()

address_book['kitty', 'huey', 66604] = '123 Meow St'
address_book['puppy', 'mickey', 66604] = '1337 Woof-woof Court'

To retrieve our data:

huey_address = address_book['kitty', 'huey', 66604]

To delete a row:

del address_book['puppy', 'mickey', 66604]

Indexing and slicing works as you would expect.

Note: when working with a multi-part value, a tuple containing the value components will be returned. When working with a scalar value, instead of returning a 1-item tuple, the value itself is returned.

Configuring and Administering Sophia

Sophia can be configured using special properties on the Sophia and Database objects. Refer to the configuration document for the details on the available options, including whether they are read-only, and the expected data-type.

For example, to query Sophia's status, you can use the status property, which is a readonly setting returning a string:

print(env.status)
"online"

Other properties can be changed by assigning a new value to the property. For example, to read and then increase the number of threads used by the scheduler:

nthreads = env.scheduler_threads
env.scheduler_threads = nthread + 2

Database-specific properties are available as well. For example to get the number of GET and SET operations performed on a database, you would write:

print(db.stat_get, 'get operations')
print(db.stat_set, 'set operations')

Refer to the documentation for complete lists of settings. Dotted-paths are translated into underscore-separated attributes.