Flat file NoSQL solution [closed]

Question

Is there a built-in way in SQLite (or similar) to keep the best of both worlds SQL / NoSQL, for small projects, i.e.:

• stored in a (flat) file like SQLite (no client/server scheme, no server to install; more precisely : nothing else to install except pip install <package>)
• possibility to store rows as dict, without having a common structure for each row, like NoSQL databases
• support of simple queries

Example:

db = NoSQLite('test.db')
db.addrow({'name': 'john doe', 'balance': 1000, 'data': [1, 73.23, 18]})
db.addrow({'name': 'alice', 'balance': 2000, 'email': '[email protected]'})
for row in db.find('balance > 1500'):
    print(row)

# {'id': 'f565a9fd3a', 'name': 'alice', 'balance': 2000, 'email': '[email protected]'}   # id was auto-generated

Note: I have constantly been amazed along the years by how many interesting features are in fact possible with SQLite in a few lines of code, that's why I'm asking if what I describe here could maybe be available simply with SQLite by using only a few SQLite core features.

PS: shelve could look like a solution but in fact it's just a persistent key/value store, and it doesn't have query/find functions; also bsddb (BerkeleyDB for Python) looks deprecated and has no query feature with a similar API.

Answer 1

SQLite

• JSON1 extension and json_extract (see accepted answer). Example:

  import sqlite3, json  # tested with precompiled Windows binaries from https://www.sqlite.org/download.html (sqlite3.dll copied in C:\Python37\DLLs)
  
  class sqlitenosql:
      def __init__(self, f):
          self.db = sqlite3.connect(f)
          self.db.execute('CREATE TABLE test(data TEXT);')
  
      def close(self):
          self.db.commit()
          self.db.close()
  
      def addrow(self, d):
          self.db.execute("INSERT INTO test VALUES (?);", (json.dumps(d),))
  
      def find(self, query):
          for k, v in query.items():
              if isinstance(v, str):
                  query[k] = f"'{v}'"
          q = ' AND '.join(f" json_extract(data, '$.{k}') = {v}" for k, v in query.items())
          for r in self.db.execute(f"SELECT * FROM test WHERE {q}"):
              yield r[0]
  
  db = sqlitenosql(':memory:')
  db.addrow({'name': 'john', 'balance': 1000, 'data': [1, 73.23, 18], 'abc': 'hello'})
  db.addrow({'name': 'alice', 'balance': 2000, 'email': '[email protected]'})
  db.addrow({'name': 'bob', 'balance': 1000})
  db.addrow({'name': 'richard', 'balance': 1000, 'abc': 'hello'})
  for r in db.find({'balance': 1000, 'abc': 'hello'}):
      print(r)
  # {"name": "john", "balance": 1000, "data": [1, 73.23, 18], "abc": "hello"}
  # {"name": "richard", "balance": 1000, "abc": "hello"}    
  db.close()
  

• sqlitedict as mentioned in Key: value store in Python for possibly 100 GB of data, without client/server and Use SQLite as a key:value store with:

  key = an ID

  value = the dict we want to store, e.g. {'name': 'alice', 'balance': 2000, 'email': '[email protected]'}

• Further reading about use of SQLite with JSON: https://community.esri.com/groups/appstudio/blog/2018/08/21/working-with-json-in-sqlite-databases

TinyDB

TinyDB looks like a good solution:

>>> from tinydb import TinyDB, Query
>>> db = TinyDB('path/to/db.json')
>>> User = Query()
>>> db.insert({'name': 'John', 'age': 22})
>>> db.search(User.name == 'John')
[{'name': 'John', 'age': 22}]

However, the documentation mentions that it's not the right tool if we need:

• access from multiple processes or threads,
• creating indexes for tables,
• an HTTP server,
• managing relationships between tables or similar,
• ACID guarantees

So it's a half solution :)

Oher solutions

Seems interesting too : WhiteDB

Answer 2

It's possible via using the JSON1 extension to query JSON data stored in a column, yes:

sqlite> CREATE TABLE test(data TEXT);
sqlite> INSERT INTO test VALUES ('{"name":"john doe","balance":1000,"data":[1,73.23,18]}');
sqlite> INSERT INTO test VALUES ('{"name":"alice","balance":2000,"email":"[email protected]"}');
sqlite> SELECT * FROM test WHERE json_extract(data, '$.balance') > 1500;
data
--------------------------------------------------
{"name":"alice","balance":2000,"email":"[email protected]"}

---

If you're going to be querying the same field a lot, you can make it more efficient by adding an index on the expression:

CREATE INDEX test_idx_balance ON test(json_extract(data, '$.balance'));

will use that index on the above query instead of scanning every single row.