Udb.py

Udb is an in-memory database based on the Zope Foundation BTrees, the Rtree and on the native python's dict. Udb provides indexes support and limited MongoDB-like queries. Udb does not support any type of transactions for now.

Table of contents

• Requirements
• Installation
• Quick start
• Data schema for default values
  • Functional fields
• Indexes
  • Index declaration
  • Float precision
• Querying
  • Query validation
  • Comparison order
  • Getting plan
  • Scan operations
• Storages
• Select operation
• Delete operation
• Insert operation
• Update operation
• Limitations
• Running tests
• Benchmarks

Requirements

Python 2.7, Python 3.6

Installation

pip install udb_py

To enable BTree indexes support install Zope Foundation BTrees package:

pip install BTrees

To enable RTree indexes support install Rtree package (requires libspatialindex):

pip install Rtree

Quick start

Create the Udb instance with the indexes declaration:

from udb_py import Udb, UdbBtreeIndex

db = Udb({
    'a': UdbBtreeIndex(['a']),
    'b': UdbBtreeIndex(['b']),
    'cde': UdbBtreeIndex(['c', 'd', 'e']),
})

Insert records:

db.insert({'a': 1, 'b': 1, 'c': 3, 'd': 4, 'e': 5})
db.insert({'a': 2, 'b': 2, 'c': 3, 'd': 4, 'e': 5})
db.insert({'a': 3, 'b': 3, 'c': 3, 'd': 4, 'e': 5})
db.insert({'a': 4, 'b': 4, 'c': 3, 'd': 4, 'e': 6})
db.insert({'a': 5, 'b': 5, 'c': 3, 'd': 4, 'e': 7})

Select records:

a = list(db.select({'a': 1})

[{'a': 1, 'b': 1, 'c': 3, 'd': 4, 'e': 5}]

b = list(db.select({'b': 0})

[]  # no records with b=0

c = list(db.select({'c': 3, 'd': 4}, limit=2)

[{'a': 3, 'b': 3, 'c': 3, 'd': 4, 'e': 5}, {'a': 4, 'b': 4, 'c': 3, 'd': 4, 'e': 6}]

Data schema for default values

Data schema allows to fill the inserted record with default values. The default value can be defined as a primitive value or callable:

from udb_py import Udb

db = Udb(schema={
    'a': 'a',
    'b': 'b',
    'c': lambda key, record: 'b' if record['b'] == 'b' else 'c',
})

Functional fields

auto_id - generates unique id (uuid v1 by default)

from udb_py import Udb, auto_id

db = Udb(schema={
    'id': auto_id(),
})

current_timestamp - uses current timestamp (as int value)

from udb_py import Udb, current_timestamp

db = Udb(schema={
    'timestamp': current_timestamp(),
})

fn - calls custom function

from udb_py import Udb, fn

db = Udb(schema={
    'timestamp': fn(lambda record: record['a'] + record['b']),
})

optional - returns "None" value

from udb_py import Udb, optional

db = Udb(schema={
    'a': optional,
})

Indexes

To speed up the search for records, the necessary fields can be indexed. The Udb also includes a simple query optimiser that can select the most appropriate index.

BTree indexes:

• UdbBtreeMultivaluedIndex - btree based multivalued index supporting multiple records with the same index key.
• UdbBtreeMultivaluedEmbeddedIndex - same as the UdbBtreeMultivaluedIndex, but supports embedded list of values.
• UdbBtreeUniqIndex - btree based index operating with always single records, but the second record insertion with the same index key will raise IndexConstraintError.
• UdbBtreeIndex - btree based index operating with always single records, so that the second record insertion with the same index key will overwrite the old one. Can be used when the inserting record definitely generates a unique index key.

Hash indexes:

• UdbHashMultivaluedIndex - hash based multivalued index supporting multiple records with the same index key.
• UdbHashMultivaluedEmbeddedIndex - same as the UdbHashMultivaluedIndex, but supports embedded list of values.
• UdbHashUniqIndex - hash based index operating with always single records, but the second record insertion with the same index key will raise IndexConstraintError.
• UdbHashIndex - hash based index operating with always single records, so that the second record insertion with the same index key will overwrite the old one. Can be used when the inserting record definitely generates a unique index key.

Spatial indexes:

• UdbRtreeIndex - spatial index that supports "intersection with rectangle" and "near to point" search

Index declaration

As it was shown above, for the index declaration the Udb instance should be created with the indexes parameter that provides dict with the key as an index name and value as an index instance. The index instance should be created with the sequence of fields (1 at least) which will be fetched in the declared order from the indexed record. By this sequence of fields, the index key will be generated and will be associated with the indexed record.

from udb_py import Udb, UdbBtreeIndex

db = Udb(indexes={
    'abc': UdbBtreeIndex(['a', 'b', 'c'])  # "a", "b" and "c" fields will be fetched from the indexed record
})

record = {'a': 'A', 'b': 'B', 'c': 'C'}  # index key=ABC

In this case of declaration in order that the record to be indexed it must contain all of the fields declared in the sequence of index fields.

from udb_py import Udb, UdbBtreeIndex

db = Udb(indexes={
    'abc': UdbBtreeIndex(['a', 'b', 'c'])  # "a", "b" and "c" fields will be fetched from the indexed record
})

record = {'a': 'A', 'b': 'B'}  # won't be indexed, raises FieldRequiredError

Using dictionary in case of Python 3:

from udb_py import Udb, UdbBtreeIndex, required

db = Udb(indexes={
    'abc': UdbBtreeIndex({'a': required, 'b': required, 'c': required})  # "a", "b" and "c" fields will be fetched from the indexed record
})

record = {'a': 'A', 'b': 'B'}  # won't be indexed, raises FieldRequiredError

Using list of tuples in case of Python 2 (to keep key order):

from udb_py import Udb, UdbBtreeIndex, required

db = Udb(indexes={
    'abc': UdbBtreeIndex([('a', required), ('b', required), ('c', required)])  # "a", "b" and "c" fields will be fetched from the indexed record
})

record = {'a': 'A', 'b': 'B'}  # won't be indexed, raises FieldRequiredError

The default value for missing field can be defined as a primitive value or callable (functional index):

from udb_py import Udb, UdbBtreeIndex

db = Udb(indexes={
    'abc': UdbBtreeIndex({'a': 'a', 'b': 'b', 'c': 'c'})
})

record = {'a': 'A', 'c': 'C'}  # index key=AbC

from udb_py import Udb, UdbBtreeIndex

db = Udb(indexes={
    'abc': UdbBtreeIndex({'a': 'a', 'b': lambda key, values: 'b', 'c': 'c'})
})

record = {'a': 'A', 'c': 'C'}  # index key=AbC

Float precision

To be able to index float values enable the float mode with necessary precision (number of decimals):

from udb_py import Udb, UdbBtreeIndex

db = Udb(indexes={
    'abc': UdbBtreeIndex(['a']).set_float_precision(10)
})

db.insert({'a': 3.1415926525})

Querying

Udb supports limited MongoDB-like queries which can be used in the delete, select or update operations. The query generally is a python's dict with the key as a field and value as a primitive value or an equality condition over the field. The query dict is mutable, therefore it needs to be initialized every time anew.

Supported query operations:

• $eq - equal to a value

  udb.select({'a': {'$eq': 5}})

• $gt - greater then value

  udb.select({'a': {'$gt': 5}})

• $gte - greater or equal to a value

  udb.select({'a': {'$gte': 5}})

• $in - equal to an any value in the list of a values

  udb.select({'a': {'$in': 5}})

• $intersection - intersection with rectangle

  udb.select({'a': {'$intersection': {'minX': 5, 'minY': 5, 'maxX': 1, 'maxY': 5}}})

• $lt - less then value

  udb.select({'a': {'$lt': 5}})

• $lte - less or equal to a value

  udb.select({'a': {'$lte': 5}})

• $ne - not equal to a value

  udb.select({'a': {'$ne': 5}})

  • performs "seq" scan.

• $near - near to point with optional min and max distances

  udb.select({'a': {'$near': {'x': 5, 'y': 5, 'minDistance': 1, 'maxDistance': 5}}})

  • allocates sort buffer is case of "seq" scan
  • selects all records in case of unset maxDistance and set minDistance.

• $nin - not equal to an any value in the list of a values

  udb.select({'a': {'$nin': [1, 2, 3]}})

  • performs "seq" scan.

• primitive value - equal to a value

  udb.select({'a': 5})

Example:

records = list(udb.select({'a': 1}))
records = list(udb.select({'a': {'$gte': 1, '$lte': 3}}))
records = list(udb.select({'a': {'$in': [1, 2, 3], '$lte': 2}}))

Query validation

By default Udb does not check the query dict validity. To check its validity use validate_query method.

udb.validate_query({'a': {'$gte': [1, 2, 3]}})  # raises InvalidScanOperationValueError('a.$gte')

Comparison order

Due to the fact that the Udb database is not strictly typed for stored values, there is the following order of ascending comparisons for values ​​of different types:

• None
• boolean - false less then true
• int, float
• string

So, for example, the record containing int value always greater than the record containing boolean value for the same field. Also, it means, that the records having indexed field will be fetched in the provided order.

Getting plan

To get the query plan use select method with get_plan=True:

from udb_py import Udb, UdbBtreeIndex

db = Udb(indexes={
    'abc': UdbBtreeIndex({'a': 'a', 'b': lambda key, values: 'b', 'c': 'c'})
})

db.select({'a': 3}, sort='-a', get_plan=True)  # [(<udb.index.udb_btree_index.UdbBtreeIndex object at 0x104994080>, 'const', 1, 2), (None, 'sort', 0, 0, 'a', False)]

Scan operations

BTree index:

• const - an index covers only one record by the index key
• in - an index covers multiple records by the list of the index keys, each of which covers exactly one record
• range - an index covers multiple records by the index keys set by the minimum and maximum values
• prefix - an index covers range of records by the partial index key
• prefix_in - an index covers multiple records by the list of the partial index keys, each of which covers range of records

RTree index:

• intersection - an index covers records intersected by the rectangle
• near - an index covers records near to the point

No index:

• seq - scanning that is not covered by any index, all records will be scanned (worst case)

Storages

The storage allows keeping data persistent.

UdbJsonFileStorage stores data in the JSON file.

from udb_py import UdbJsonFileStorage

db = Udb(storage=UdbJsonFileStorage('db'))

db.load_db()

db.insert({'a': 'a'})

db.save_db()

UdbWalStorage stores data of delete, insert and update operations in the WAL (Write-Ahead-Logging) file chronologically.

from udb_py import UdbWalStorage

db = Udb(storage=UdbWalStorage('db'))

db.load_db()

db.insert({'a': 'a'})

db.save_db()  # does nothing; delete, insert and update data will be stored on the fly

Select operation

Selected records are mutable, so avoid to update them directly. Otherwise use copy on select mode:

udb.set_copy_on_select()

To limit the result subset to particular number of records use limit parameter:

records = list(udb.select({'a': 1}, limit=5)

To fetch the result subset from the particular offset use offset parameter:

records = list(udb.select({'a': 1}, offset=5)

Delete operation

udb.delete(q={'a': 1}, offset=5)

Insert operation

udb.insert({'a': 1})

Update operation

udb.update({'a': 2}, q={'a': 1}, offset=5)

Running tests with pytest

pytest . --ignore=virtualenv -v

Limitations

• Nested paths for indexing and querying are not supported, only the root level
• Transactions are not supported

Benchmarks

• Intel Core i7, 3.58 GHz, 4 cores, disabled HT
• 16GB 1600 MHz RAM
• PyPy3

INSERT (BTREE, 1ST INDEX COVERS 1 FIELD)

Total time: 2.9712460041046143 sec., per sample: 2.971246004104614e-06 sec., samples per second: 336559.1400437912, total samples: 1000000

SELECT (BTREE, 1ST INDEX COVERS 1 FIELD)

Total time: 1.7301840782165527 sec., per sample: 1.7301840782165527e-06 sec., samples per second: 577973.1836573046, total samples: 1000000

INSERT (BTREE, 1ST INDEX COVERS 1 FIELD, 2ND INDEX COVERS 1 FIELD, 3RD INDEX COVERS 2 FIELDS)

Total time: 6.8810200691223145 sec., per sample: 6.881020069122315e-06 sec., samples per second: 145327.29013353275, total samples: 1000000

SELECT (BTREE, 1ST INDEX COVERS 1 FIELD, 2ND INDEX COVERS 1 FIELD, 3RD INDEX COVERS 2 FIELDS)

Total time: 1.8345210552215576 sec., per sample: 1.8345210552215576e-06 sec., samples per second: 545101.4024361953, total samples: 1000000

INSERT (HASH, 1ST INDEX COVERS 1 FIELD)

Total time: 1.781458854675293 sec., per sample: 1.781458854675293e-06 sec., samples per second: 561337.6909467103, total samples: 1000000

SELECT (HASH, 1ST INDEX COVERS 1 FIELD)

Total time: 0.8209011554718018 sec., per sample: 8.209011554718018e-07 sec., samples per second: 1218173.458929125, total samples: 1000000

INSERT (HASH, 1ST INDEX COVERS 1 FIELD, 2ND INDEX COVERS 1 FIELD, 3RD INDEX COVERS 2 FIELDS)

Total time: 4.138401985168457 sec., per sample: 4.138401985168457e-06 sec., samples per second: 241639.16496847855, total samples: 1000000

SELECT (HASH, 1ST INDEX COVERS 1 FIELD, 2ND INDEX COVERS 1 FIELD, 3RD INDEX COVERS 2 FIELDS)

Total time: 1.001291036605835 sec., per sample: 1.001291036605835e-06 sec., samples per second: 998710.628020589, total samples: 1000000

INSERT (RTREE, 1ST INDEX COVERS 1 FIELD)

Total time: 9.943094968795776 sec., per sample: 9.943094968795777e-05 sec., samples per second: 10057.230702696503, total samples: 100000

SELECT (RTREE, 1ST INDEX COVERS 1 FIELD, LIMIT = 5)

Total time: 11.716284990310669 sec., per sample: 0.00011716284990310669 sec., samples per second: 8535.128676256994, total samples: 100000