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DictDataBase is a fast database for handling json or compressed json as the underlying storage mechanism.

  • Multi threading and multi processing safe. Multiple processes on the same machine can simultaneously read and write to dicts without losing data.
  • ACID compliant. Unlike TinyDB, it is suited for concurrent environments.
  • No database server required. Simply import DictDataBase in your project and use it.
  • Compression. Configure if the files should be stored as raw json or as json compressed with zlib.
  • Fast. Key-value pairs inside a json file can be accessed quickly and efficiently because the keys are indexed.
  • Tested with 99%+ coverage.

Why use DictDataBase

  • Your application concurrently reads and writes data from multiple processes or threads.
  • Using database server is a bit too much for your application.
    • But you need ACID guarantees.
  • Your use case requires reading key-value pairs from very large json files repeatedly. (For example, DictDataBase can handle about 2000 reads per second when reading single key-value pairs from a 2.5GB json file with 20000 key-value pairs.)
  • You need to repeatedly read and write many smaller json files.
  • Your use case is suited for working with json data, or you have to work with a lot of json data.

Why not DictDataBase

  • If your storage is slow.
  • Your use cases requires repeatedly modifying or writing data in a single very large json file
  • If a relational database is better suited for your use case.
  • If you need to read files that are larger than your system's RAM.

Install

pip install dictdatabase

Configuration

The following configuration parameters can be modified using DDB.config:

Storage directory

Set storage_directory to the path of the directory that will contain your json files:

DDB.config.storage_directory = "./ddb_storage" # Default value

Compression

If you want to use compressed files, set use_compression to True. This will make the db files significantly smaller and might improve performance if your disk is slow. However, the files will not be human readable.

DDB.config.use_compression = False # Default value

Indentation

Set the way how written json files should be indented. Behaves exactly like json.dumps(indent=...). It can be an int for the number of spaces, the tab character, or None if you don't want the files to be indented.

DDB.config.indent = "\t" # Default value

Notice: If DDB.config.use_orjson = True, then the value can only be 2 (spaces) or 0/None for no indentation.

Use orjson

You can use the orjson encoder and decoder if you need to. The standard library json module is sufficient most of the time. However, orjson is a lot more performant in virtually all cases.

DDB.config.use_orjson = True # Default value

Usage

Import

import dictdatabase as DDB

Create a file

This library is called DictDataBase, but you can actually use any json serializable object.

users_dict = {
   "u1": { "name" : "Ben", "age": 30, "job": "Software Engineer" },
   "u2": { "name" : "Sue", "age": 21, "job": "Architect" },
   "u3": { "name" : "Joe", "age": 50, "job": "Manager" },
}
DDB.at("users").create(user_data_dict)

There is now a file called users.json or users.ddb in your specified storage directory depending on if you use compression.

Check if file or sub-key exists

DDB.at("users").exists()
>>> True  # File exists
DDB.at("users", key="u10").exists() 
>>> False # Key "u10" not in users 
DDB.at("users", key="u2").exists() 
>>> True

Read dicts

d = DDB.at("users").read()
d == users_dict # True

# Only partially read Joe
joe = DDB.at("users", key="u3").read()
joe == users_dict["Joe"] # True

Note: Doing a partial read like with DDB.at("users", key="Joe").read() will return the value of the key at the outermost indentation level if the key appears in the file multiple times.

It is also possible to only read a subset of keys based on a filter callback:

DDB.at("numbers").create({"a", 1, "b", 2, "c": 3})

above_1 = DDB.at("numbers", where=lambda k, v: v > 1).read()
>>> above_1 == {"b", 2, "c": 3}

The where callback is a function that takes two parameters, the key and the value.

Write dicts

with DDB.at("users").session() as (session, users):
   users["u3"]["age"] = 99
print(DDB.at("users", key="u3").read()["age])
>>> 99

If you do not call session.write(), changes will not be written to disk!

Partial writing

Imagine you have a huge json file with many purchases. The json file looks like this: {<id>: <purchase>, <id>: <purchase>, ...}. Normally, you would have to read and parse the entire file to get a specific key. After modifying the purchase, you would also have to serialize and write the entire file again. With DDB, you can do it more efficiently:

with DDB.at("purchases", key="3244").session() as (session, purchase):
    purchase["status"] = "cancelled"
    session.write()

Afterwards, the status is updated in the json file. However, DDB did only efficiently gather the one purchase with id 134425, parsed its value, and serialized that value alone before writing again. This is several orders of magnitude faster than the naive approach when working with big files.

Folders

You can also read and write to folders of files. Consider the same example as before, but now we have a folder called purchases that contains many files <id>.json. If you want to open a session or read a specific one, you can do:

DDB.at("purchases/<id>").read()
# Or equivalently:
DDB.at("purchases", "<id>").read()

To open a session or read all, do the following:

DDB.at("purchases/*").read()
# Or equivalently:
DDB.at("purchases", "*").read()

Select from folder

If you have a folder containing many json files, you can read them selectively based on a function. The file is included if the provided function returns true when it get the file dict as input:

To open a session or read all, do the following:

for i in range(10):
    DDB.at("folder", i).create({"a": i})
# Now in the directory "folder", 10 files exist
res = DDB.at("folder/*", where=lambda x: x["a"] > 7).read() # .session() also possible
assert ress == {"8": {"a": 8}, "9": {"a": 9}} # True

Performance

In preliminary testing, DictDataBase showed promising performance.

SQLite vs DictDataBase

In each case, 16 parallel processes were spawned to perform 128 increments of a counter in 4 tables/files. SQLite achieves 2435 operations/s while DictDataBase managed to achieve 3143 operations/s.

More tests

It remains to be tested how DictDatabase performs in different scenarios, for example when multiple processes want to perform full writes to one big file.

Advanced

Sleep Timeout

DictDataBase uses a file locking protocol to coordinate concurrent file accesses. While waiting for a file where another thread or process currently has exclusive access rights, the status of the file lock is periodically checked. You can set the timout between the checks:

DDB.locking.SLEEP_TIMEOUT = 0.001 # 1ms, default value

A value of 1 millisecond is good and it is generally not recommended to change it, but you can still tune it to optimize performance in your use case.

Lock Timeout

When a lock file is older than the lock timeout, it is considered orphaned and will be removed. This could be the case when your operating terminates a thread or process while it holds a lock. The timeout can be adjusted:

DDB.locking.LOCK_TIMEOUT = 30.0 # 30s, default value

Chose a value that is long enough where you know that your database operations will less than it.

API Reference

at(path) -> DDBMethodChooser:

Select a file or folder to perform an operation on. If you want to select a specific key in a file, use the key parameter, e.g. DDB.at("file", key="subkey"). If the key appears multiple times in the file, the value of the key at the outermost indentation level will be returned.

If you want to select an entire folder, use the * wildcard, eg. DDB.at("folder", "*"), or DDB.at("folder/*"). You can also use the where callback to select a subset of the file or folder.

If the callback returns True, the item will be selected. The callback needs to accept a key and value as arguments.

Args:

  • path: The path to the file or folder. Can be a string, a comma-separated list of strings, or a list.
  • key: The key to select from the file.
  • where: A function that takes a key and value and returns True if the key should be selected.

Beware: If you select a folder with the * wildcard, you can't use the key parameter. Also, you cannot use the key and where parameters at the same time.

DDBMethodChooser

exists() -> bool:

Create a new file with the given data as the content. If the file already exists, a FileExistsError will be raised unless force_overwrite is set to True.

Args:

  • data: The data to write to the file. If not specified, an empty dict will be written.
  • force_overwrite: If True, will overwrite the file if it already exists, defaults to False (optional).

create(data=None, force_overwrite: bool = False):

It creates a database file at the given path, and writes the given database to it :param db: The database to create. If not specified, an empty database is created. :param force_overwrite: If True, will overwrite the database if it already exists, defaults to False (optional).

delete()

Delete the file at the selected path.

read(self, as_type: T = None) -> dict | T | None:

Reads a file or folder depending on previous .at(...) selection.

Args:

  • as_type: If provided, return the value as the given type. Eg. as_type=str will return str(value).

session(self, as_type: T = None) -> DDBSession[T]:

Opens a session to the selected file(s) or folder, depending on previous .at(...) selection. Inside the with block, you have exclusive access to the file(s) or folder. Call session.write() to write the data to the file(s) or folder.

Args:

  • as_type: If provided, cast the value to the given type. Eg. as_type=str will return str(value).