pysimplebase 0.0.5

High performance JSON-oriented database

Example Package

SimpleBase is a JSON based serverless database with improved performance on key operations

The strength of document-oriented NoSQL DBMS is their natural simplicity, but they are usually not very fast (unless they are serious server DBMS like MongoDB). SimpleBase fixes performance issues in critical areas

• Instant addition of new records to collections, regardless of the size of the collection due to a special storage architecture
• Faster work with operations, due to the fact that it is not required to encode / decode the entire collection (which can be very large)
• Collections are always stored in RAM with concurrency change tracking: data is re-read from disk only if it has been changed by another process
• ACID for multi-user and multi-threading
• Two types of indexes for key types of queries - a hash index and a special B-tree for full-text search
• Support for transactions (sessions)
• MongoDB-like syntax, incl. 100% similar query language
• Written in pure python, only about 1800 lines

Why SimpleBase?

It was written for situations where you need to organize a local database without a server with a JSON-oriented interface. But at the same time, increased performance requirements: for large collections (1000000+ documents in the collection), fast, almost instantaneous execution of some operations is required:

• Add a new document to the collection with 1000000 documents - 0.007 seconds, with 2000000 documents - also 0.0007 seconds - ie. operation time does not depend on the size of the table • Find an element by equality in a collection with 1000000+ entries in 1-2 microseconds • Organize real-time search by occurrence of a string across a large collection without friezes

Getting started sapmle

.. code-block:: Python

from simplebase import SimpleBase,DBSession

#creatig database db = SimpleBase("samples_db")

#inserting documents into collection id = db['goods'].insert({"name":"coffee", "price":15}) #insert one document print(db['goods'].get(id))

inserted = db['goods'].insert([{"name":"apple", "price":2},{"name":"apple", "price":3}]) #insert dataset

#insert or update (upsert) db['goods'].insert({"name":"coffee", "price":16,"_id":id},upsert=True) print(db['goods'].get(id))

#transaction with DBSession(db) as s: inserted = db['income'].insert({"product_id":id} , session=s) inserted = db['outgoing'].insert({"product_id":id} , session=s)

#updating db['goods'].update(inserted,{"updated":True}) print(db['goods'].all())

#simple search without index result = db['goods'].find({"name":"apple"})

#building complex queries result = db['goods'].find({"$and":[ {"price":{"$gt":1}}, {"price":{"$lte":10}} ]} ) print(result)

#hash indexes for unique values db['goods'].register_hash_index("hash_dynamic","name", dynamic=True) #there are dynamic and stored indexes db['goods'].reindex_hash("hash_dynamic") r = db['goods'].get_by_index(db["hash_dynamic"],"apple")

#text indexes db['goods'].register_text_index("fts","name", dynamic=True) #there are dynamic and stored indexes db['goods'].reindex_text("fts") r = db['goods'].search_text_index("appl")

#delete db['goods'].delete(id) db['goods'].delete(inserted)

#clear db['goods'].clear()