memstore 0.2.5.2

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MemStore

MemStore is a lightweight in-memory database written in Python. It supports key-value storage with integer IDs, single-field indexing, filtering by field values, and slicing records using integer-based positions. It uses dictionaries for data storage and retrieval.

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Installation

Since MemStore is a single-class implementation, you can simply include it in your project. No external package installation is required. Alternatively, if packaged:

pip install memstore

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Usage Examples

1. Initialize the Database

Create a database with optional indexes:

from memstore import MemStore

# Initialize with indexes on 'name' and 'age'
db = MemStore(indexes=['name', 'age'])

2. Insert Records

Add a single record and get its ID:

# Insert a single record
record_id = db.add({'name': 'Alice', 'age': 25, 'city': 'New York'})
print(f"Inserted record with ID: {record_id}")  # Output: Inserted record with ID: 0

3. Query Records

Retrieve records by ID or filter by field values:

# Get by ID
record = db.get(0)
print(record)  # Output: {'name': 'Alice', 'age': 25, 'city': 'New York'}

# Filter by indexed field
alice_records = db.filter({'name': 'Alice'})
print(alice_records)  # Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'})]

# Filter by non-indexed field
ny_records = db.filter({'city': 'New York'})
print(ny_records)  # Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'})]

# Filter with multiple conditions (mixed indexed and non-indexed)
alice_25_records = db.filter({'name': 'Alice', 'age': 25})
print(alice_25_records)  # Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'})]

4. List All Records

Retrieve all records in the database:

db.add({'name': 'Bob', 'age': 30, 'city': 'Boston'})
all_records = db.all()
for record_id, record in all_records:
    print(f"ID {record_id}: {record}")
# Output:
# ID 0: {'name': 'Alice', 'age': 25, 'city': 'New York'}
# ID 1: {'name': 'Bob', 'age': 30, 'city': 'Boston'}

5. Slice Records with islice

Retrieve a subset of records using integer-based slicing with islice:

# Add more records for slicing examples
db.add({'name': 'Charlie', 'age': 35, 'city': 'Chicago'})
db.add({'name': 'David', 'age': 40, 'city': 'Seattle'})

# Slice from start to position 2 (exclusive)
slice_1 = list(db.islice(stop=2))
print(slice_1)
# Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'}),
#          (1, {'name': 'Bob', 'age': 30, 'city': 'Boston'})]

# Slice from position 1 to 3
slice_2 = list(db.islice(1, 3))
print(slice_2)
# Output: [(1, {'name': 'Bob', 'age': 30, 'city': 'Boston'}),
#          (2, {'name': 'Charlie', 'age': 35, 'city': 'Chicago'})]

# Slice with step (every second record)
slice_3 = list(db.islice(0, None, 2))
print(slice_3)
# Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'}),
#          (2, {'name': 'Charlie', 'age': 35, 'city': 'Chicago'})]

# Slice with negative indexes (last two records)
slice_4 = list(db.islice(-2, None))
print(slice_4)
# Output: [(2, {'name': 'Charlie', 'age': 35, 'city': 'Chicago'}),
#          (3, {'name': 'David', 'age': 40, 'city': 'Seattle'})]

6. Access Records with iloc

Retrieve records by integer position using iloc, supporting both single index and slice operations:

# Get a single record by position
record = db.iloc[1]
print(record)  # Output: {'name': 'Bob', 'age': 30, 'city': 'Boston'}

# Get the last record using negative index
last_record = db.iloc[-1]
print(last_record)  # Output: {'name': 'David', 'age': 40, 'city': 'Seattle'}

# Slice records with iloc
slice_iloc = db.iloc[1:3]
print(slice_iloc)
# Output: [(1, {'name': 'Bob', 'age': 30, 'city': 'Boston'}),
#          (2, {'name': 'Charlie', 'age': 35, 'city': 'Chicago'})]

# Slice with step using iloc
slice_step = db.iloc[0::2]
print(slice_step)
# Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'}),
#          (2, {'name': 'Charlie', 'age': 35, 'city': 'Chicago'})]

7. Delete Records

Remove a record by ID:

success = db.delete(0)
print(f"Delete successful: {success}")  # Output: Delete successful: True
print(db.all())  # Output: [(1, {'name': 'Bob', 'age': 30, 'city': 'Boston'}),
#                           (2, {'name': 'Charlie', 'age': 35, 'city': 'Chicago'}),
#                           (3, {'name': 'David', 'age': 40, 'city': 'Seattle'})]

8. Manage Indexes

Add or remove indexes dynamically:

# Add a new index
db.add_index('city')
print(db.filter({'city': 'Boston'}))  # Output: [(1, {'name': 'Bob', 'age': 30, 'city': 'Boston'})]

# Drop an index
db.drop_index('name')
print('name' in db._indexes)  # Output: False

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Notes

• Data Structure: Records are stored as dictionaries with integer IDs assigned sequentially.
• Indexes: Only single-field indexes are supported (e.g., 'name'). Composite indexes are not available.
• Filtering: The filter method retrieves records matching all specified field-value pairs, using indexes when available for efficiency. It works with both indexed and non-indexed fields.
• Slicing with islice: The islice method allows positional slicing of records using integer indexes (positive or negative), supporting start, stop, and step parameters. It returns a list of (id, record) tuples.
• Positional Access with iloc: The iloc property provides a pandas-like interface for accessing records by integer position. It supports single integer indexing (returns a record dictionary) and slicing (returns a list of (id, record) tuples).
• Limitations: No field validation or update methods are provided. Deletion and retrieval are ID-based or filter-based only.
• Dependencies: Uses only Python standard library modules (collections, functools, itertools, operator, typing).